Handbook of Anatomical Models for Radiation Dosimetry (Series in Medical Physics and Biomedical Engineering)

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Handbook of Anatomical Models for Radiation Dosimetry

Series in Medical Physics and Biomedical Engineering Series Editors: John G Webster, E Russell Ritenour, Slavik Tabakov, and Kwan-Hoong Ng Other recent books in the series: Fundamentals of MRI: An Interactive Learning Approach Elizabeth Berry and Andrew J Bulpitt Handbook of Optical Sensing of Glucose in Biological Fluids and Tissues Valery V Tuchin (Ed) Intelligent and Adaptive Systems in Medicine Oliver C L Haas and Keith J Burnham A Introduction to Radiation Protection in Medicine Jamie V Trapp and Tomas Kron (Eds) A Practical Approach to Medical Image Processing Elizabeth Berry Biomolecular Action of Ionizing Radiation Shirley Lehnert An Introduction to Rehabilitation Engineering R A Cooper, H Ohnabe, and D A Hobson The Physics of Modern Brachytherapy for Oncology D Baltas, N Zamboglou, and L Sakelliou Electrical Impedance Tomography D Holder (Ed) Contemporary IMRT S Webb The Physical Measurement of Bone C M Langton and C F Njeh (Eds) Therapeutic Applications of Monte Carlo Calculations in Nuclear Medicine H Zaidi and G Sgouros (Eds) Minimally Invasive Medical Technology J G Webster (Ed) Intensity-Modulated Radiation Therapy S Webb

Physics for Diagnostic Radiology, Second Edition P Dendy and B Heaton

Series in Medical Physics and Biomedical Engineering


Edited by

Xie George Xu Rensselaer Polytechnic Institute Troy, New York, USA

Keith F. Eckerman Oak Ridge National Laboratory Tennessee, USA

Boca Raton London New York

CRC Press is an imprint of the Taylor & Francis Group, an informa business

A TA Y L O R & F R A N C I S B O O K

Taylor & Francis 6000 Broken Sound Parkway NW, Suite 300 Boca Raton, FL 33487-2742 © 2010 by Taylor and Francis Group, LLC Taylor & Francis is an Informa business No claim to original U.S. Government works Printed in the United States of America on acid-free paper 10 9 8 7 6 5 4 3 2 1 International Standard Book Number: 978-1-4200-5979-3 (Hardback) This book contains information obtained from authentic and highly regarded sources. Reasonable efforts have been made to publish reliable data and information, but the author and publisher cannot assume responsibility for the validity of all materials or the consequences of their use. The authors and publishers have attempted to trace the copyright holders of all material reproduced in this publication and apologize to copyright holders if permission to publish in this form has not been obtained. If any copyright material has not been acknowledged please write and let us know so we may rectify in any future reprint. Except as permitted under U.S. Copyright Law, no part of this book may be reprinted, reproduced, transmitted, or utilized in any form by any electronic, mechanical, or other means, now known or hereafter invented, including photocopying, microfilming, and recording, or in any information storage or retrieval system, without written permission from the publishers. For permission to photocopy or use material electronically from this work, please access www.copyright.com (http:// www.copyright.com/) or contact the Copyright Clearance Center, Inc. (CCC), 222 Rosewood Drive, Danvers, MA 01923, 978-750-8400. CCC is a not-for-profit organization that provides licenses and registration for a variety of users. For organizations that have been granted a photocopy license by the CCC, a separate system of payment has been arranged. Trademark Notice: Product or corporate names may be trademarks or registered trademarks, and are used only for identification and explanation without intent to infringe. Library of Congress Cataloging-in-Publication Data Handbook of anatomical models for radiation dosimetry / editors, Xie George Xu and Keith F. Eckerman. p. ; cm. -- (Series in medical physics and biomedical engineering) Includes bibliographical references and index. ISBN 978-1-4200-5979-3 (hardcover : alk. paper) 1. Radiation dosimetry--Mathematical models. I. Xu, Xie George. II. Eckerman, K. F. III. Series: Series in medical physics and biomedical engineering. [DNLM: 1. Phantoms, Imaging. 2. Computer Simulation. 3. Models, Anatomic. 4. Radiation Dosage. 5. Radiometry--instrumentation. WN 150 H236 2010] R905.H36 2010 612’.014480287--dc22 Visit the Taylor & Francis Web site at http://www.taylorandfrancis.com and the CRC Press Web site at http://www.crcpress.com

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Contents Series Preface ..................................................................................................................................ix Preface ..............................................................................................................................................xi Editors .............................................................................................................................................xv Contributors ................................................................................................................................ xvii

Part I

Phantoms

1. Computational Phantoms for Radiation Dosimetry: A 40-Year History of Evolution ............................................................................................3 X. George Xu 2. Stylized Computational Phantoms Developed at ORNL and Elsewhere .......................................................................................................................43 Keith F. Eckerman, John W. Poston, Sr., Wesley E. Bolch, and X. George Xu 3. The GSF Voxel Computational Phantom Family ...........................................................65 Maria Zankl 4. The ADELAIDE Teenage Female Voxel Computational Phantom ............................87 Martin Caon, Giovanni Bibbo, and John E. Pattison 5. The MCAT, NCAT, XCAT, and MOBY Computational Human and Mouse Phantoms ........................................................................................................105 W. Paul Segars and Benjamin M.W. Tsui 6. The 3D and 4D VIP-Man Computational Phantoms ..................................................135 X. George Xu, Tsi-Chian Ephraim Chao, Ahmet Bozkurt, Chengyu Shi, and Juying Zhang 7. The FAX06 and the MAX06 Computational Voxel Phantoms ...................................163 Richard Kramer, Helen Jamil Khoury, José Wilson Vieira, Vanildo Júnior de Melo Lima, Eduardo César de Miranda Loureiro, Gabriela Hoff, and Iwan Kawrakow 8. The University of Florida Pediatric Phantom Series ..................................................199 Choonsik Lee, Daniel L. Lodwick, Deanna Hasenauer Pafundi, Scott R. Whalen, Jonathan L. Williams, and Wesley E. Bolch 9. Japanese Computational Phantoms: Otoko, Onago, JM, JM2, JF, TARO, HANAKO, Pregnant Woman, and Deformable Child ..................................221 Kimiaki Saito, Kaoru Sato, Sakae Kinase, and Tomoaki Nagaoka

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10. Korean Computational Phantoms: KMIRD, KORMAN, KORWOMAN, KTMAN-1, KTMAN-2, and HDRK-Man .........................................255 Choonsik Lee and Chan Hyeong Kim 11. Chinese Voxel Computational Phantoms: CNMAN, VCH, and CVP ....................................................................................................................279 Binquan Zhang, Jizeng Ma, Guozhi Zhang, Qian Liu, Rui Qiu, and Junli Li 12. Pregnant Female/Fetus Computational Phantoms and the Latest RPI-P Series Representing 3, 6, and 9 Month Gestational Periods ......................................305 X. George Xu, Chengyu Shi, Michael G. Stabin, and Valery Taranenko 13. The Vanderbilt University Reference Adult and Pediatric Phantom Series ...................................................................................................................337 Michael G. Stabin, Mary Ann Emmons-Keenan, W. Paul Segars, and Michael J. Fernald 14. Mesh-Based and Anatomically Adjustable Adult Phantoms and a Case Study in Virtual Calibration of a Lung Counter for Female Workers ........347 Yong Hum Na, Juying Zhang, Aiping Ding, and X. George Xu 15. The ICRP Reference Computational Phantoms ...........................................................377 Maria Zankl, Keith F. Eckerman, and Wesley E. Bolch 16. Physical Phantoms for Experimental Radiation Dosimetry .....................................389 David E. Hintenlang, William E. Moloney, and James Winslow

Part II

Applications

17. Applications to Environmental Exposures ...................................................................413 Nina Petoussi-Henss and Kimiaki Saito 18. Applications to External Radiation Exposures in Nuclear Power Plants ...............425 Warren Dan Reece, Chan Hyeong Kim, and X. George Xu 19. Applications to Bioassay for Internal Radiation Contamination .............................449 Gary H. Kramer 20. Applications to Nuclear Medicine ..................................................................................471 Michael G. Stabin and Manuel Bardiès 21. Applications to Computed Tomography for Pediatric Patients ................................487 Wesley E. Bolch, Choonsik Lee, Choonik Lee, Jorge Hurtado, and Jonathan L. Williams 22. Applications to Computed Tomography for Adult Patients ......................................511 John J. DeMarco and Michael McNitt-Gray

Contents

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23. Applications to Optimization of X-Ray Radiographic Imaging ...............................525 Birsen Yazıcı, Il-Young Son, An Jin, and X. George Xu 24. Applications to Nuclear Medicine Imaging and Dosimetry Involving MCAT, NCAT, and MOBY Phantoms .........................................................549 Benjamin M.W. Tsui and W. Paul Segars 25. Applications to Secondary Radiation Dosimetry in External Beam Radiation Therapy ..................................................................................................567 Harald Paganetti 26. Applications to Image-Guided Radiation Treatment Planning ...............................591 Chengyu Shi, Martin Fuss, Niko Papanikolaou, and X. George Xu 27. Dose Calculations in Radiation Therapy Based on Patient Models Using the Geant4 Monte Carlo Code ..............................................................................607 Harald Paganetti 28. Applications to Patient-Specific Voxel Computational Phantoms in EGS Monte Carlo Codes for Radiation Treatment Involving Photons and Electrons .......................................................................................................633 C.-M. Charlie Ma 29. Applications to Nonionizing Radiation Protection ....................................................655 Ji Chen, Wolfgang Kainz, and Dagang Wu 30. Summary and Future Needs Related to Computational Phantoms.........................679 X. George Xu, Michael G. Stabin, Wesley E. Bolch, and W. Paul Segars About the Contributors ............................................................................................................685 Index .............................................................................................................................................705

Series Preface The International Organization for Medical Physics The International Organization for Medical Physics (IOMP), founded in 1963, is a scientific, educational, and professional organization of 76 national adhering organizations, more than 16,500 individual members, several corporate members, and four international regional organizations. The IOMP is administered by a council, which includes delegates from each of the adhering national organizations. Regular meetings of the council are held electronically as well as every three years at the World Congress on Medical Physics and Biomedical Engineering. The president and other officers form the executive committee, and there are also committees covering the main areas of activity, including education and training; scientific, professional relations; and publications. The objectives of the IOMP are • To contribute to the advancement of medical physics in all its aspects • To organize international cooperation in medical physics, especially in developing countries • To encourage and advise on the formation of national organizations of medical physics in those countries that lack such organizations Activities The official journals of the IOMP are Physics in Medicine and Biology, Medical Physics, and Physiological Measurement. The IOMP publishes a bulletin, Medical Physics World, twice a year, which is distributed to all members. A World Congress on Medical Physics and Biomedical Engineering is held every three years in cooperation with the International Federation for Medical and Biological Engineering (IFMBE) through the International Union for Physics and Engineering Sciences in Medicine. A regionally based International Conference on Medical Physics is held between the World Congresses. The IOMP also sponsors international conferences, workshops, and courses. IOMP representatives contribute to various international committees and working groups. The IOMP has several programs to assist medical physicists in developing countries. The joint IOMP Library program supports 69 active libraries in 42 developing countries and the Used Equipment Program coordinates equipment donations. The Travel Assistance Program provides a limited number of grants to enable physicists to attend the World Congresses. The IOMP Web site is being developed to include a scientific database of international standards in medical physics and a virtual education and resource center. Information on the activities of the IOMP can be found on its Web site at www.iomp.org.

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Preface Since the 1960s, the radiological science community has developed and applied more than 90 computational models of the human body—often referred to as “phantoms”—for ionizing radiation dosimetry studies. Each of the models not only defines the exterior features of the entire human body, but also includes extensive details on internal organs such as volume, mass, shape, and tissue composition. These computational phantoms are combined with Monte Carlo methods to precisely simulate complex radiation interactions and energy depositions in the human body involving various particles such as photons (x-rays and gamma rays), electrons, neutrons, and protons. Organ dose estimates, often normalized by measurable parameters, have been calculated for different irradiation scenarios found in occupational radiation protection, nuclear medicine, diagnostic imaging, and radiotherapy. Over the years, data derived from these computational phantoms have been adopted into the recommendations of the International Commission on Radiological Protection (ICRP) and other international and national bodies. Anthropomorphic phantoms of adults and children of various ages, as well as pregnant women, were depicted using increasingly sophisticated solid-geometry modeling techniques over the past 40 years. Early computational phantoms were based on simple quadric equations. Voxelized phantoms, which took advantage of medical imaging advances, started to emerge 20 years ago. These image-based phantoms brought an excitement to the research community because of their anatomical realism. In recent years, phantoms involving advanced geometries, such as the nonuniform rational B-splines (NURBS) and polygonal meshes, were reported with unprecedented capabilities such as deformable anatomy and real-time cardiac and respiratory motion simulations. In addition, a number of computational phantoms have been developed for studies involving nonionizing radiation—radio frequencies such as those emitted by electric power lines and wireless cellular phone technologies. These nonionizing radiation phantoms have similar anatomical features, as well as technical challenges, as those used for ionizing radiation dosimetry. Many such computational phantoms have been used for both fields of studies. For the first time, this book provides a comprehensive review of the historical development and application of a large number of important computational phantoms that have been widely reported in the literature. The history of computational phantoms is clearly in parallel with, and thus offers a unique perspective about, advances in computer technologies and medical imaging such as computed tomography and magnetic resonance imaging. By reading this book, the reader can obtain a unique sense of the scientific process in computational phantom development: the conception of an idea, the identification of original anatomical data, solutions of various computing problems, ownership and sharing of results, as well as the satisfaction and frustration associated with any scientific endeavor. This handbook contains 30 chapters and is the result of several years of planning and preparation involving ultimately 64 authors from 13 countries and regions. The idea of this book was first conceived during the Monte Carlo 2005 Topical Meeting in Chattanooga, Tennessee, April 17–21, 2005. A special session on “Tomographic Models for Radiation Protection Dosimetry” was attended by more than a dozen invited speakers worldwide. Recognizing the needs for research collaboration and dissemination, the session attendees strongly suggested two actions. The first was to form the Consortium of Computational Human Phantoms (CCHP) and a portal site for information related to xi

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Preface

computational human phantoms (www.virtualphantoms.org). The second was to initiate this collaborative book project to document a 40-year history of research and development. Previously, two workshops on “voxelized phantoms” were held: one at the National Board Radiological Protection, U.K., by Peter Dimbylow in 1995 that resulted in a compilation of presentations and the other at the Oak Ridge National Laboratory by Keith Eckerman in 2000. Despite the large number of phantoms that have been developed over the past four decades, information has been scattered and often lacking in detail, and certain data from the early work were difficult to locate. In the early 2000s, the ICRP decided to adopt voxelized computational phantoms as standards for radiation protection purposes. As a result, many colleagues had been actively involved for several years in activities associated with the ICRP Reference Computational Phantoms. These experiences accelerated our plan for this book, leading to the acceptance of the proposal by the publisher in 2007. It was clear to us that this book should possess the necessary depth and breadth by considering as broadly as possible phantoms that were historically important. At the same time, this book should also include applications of these phantoms in diverse radiological studies. We are extremely pleased that nearly all major phantom developers around the world accepted our invitation to contribute, except for a few colleagues whose busy schedules prevented them from participating—particularly Peter Dimbylow (for the NORMAN phantom) and George Zubal (for the “Zubal” phantom). It is obvious that we were unable to extend our invitation to many other researchers whose works are included in the tables of Chapter 1. Furthermore, there is a chance that we may have missed a few phantoms despite an exhaustive literature search. If your work is not mentioned anywhere in this book, please contact us so we can update our database that is maintained from the CCHP Web site. This book is divided into two main parts: the phantoms and their applications. Part I starts with Chapter 1, which provides a review of 40 years of research and development in computational phantoms. This chapter includes a discussion on the classification of phantoms and a comprehensive listing of computational and physical phantoms used for a variety of ionizing and nonionizing radiation applications. Several rare phantoms were included, such as the CAM phantom developed in 1973 for space radiation dosimetry. Chapters 2 through 15 provide detailed accounts for each of the well-known phantoms, such as the MIRD-5, GSF Voxel Family Phantoms, NCAT, the UF Hybrid Pediatric Phantoms, VIP-Man, as well as the latest ICRP Reference Phantoms. Chapter 11, which was contributed by three Chinese groups, details several phantoms, including one that, at the time of writing, has the smallest voxel size (0.2 mm), developed from the Chinese Visible Human Project. Chapter 16 is the final chapter in Part I, and it summarizes physical phantoms for experimental radiation dosimetry. In Part II, Chapters 17, 18, and 19 cover applications for radiation protection dosimetry involving environmental, nuclear power plant, and internal contamination exposures, respectively. These are followed by medical applications in Chapters 20 through 28, covering topics such as nuclear medicine therapy, CT examinations of pediatric and adult patients, x-ray radiological image optimization, nuclear medicine imaging, external photon and proton treatments, and management of respiration in modern image-guided radiation treatment. Chapters 27 and 28 deal with patient-specific phantoms used for radiation treatment planning involving two Monte Carlo code systems: GEANT4 and EGS, respectively. Applications for nonionizing radiation are described in Chapter 29. Finally, Chapter 30 discusses future needs for research and development. To supplement the information in this book, the following related data sets can be downloaded from http://files. virtualphantoms.org/public/FreeDownloads: (1) the VIP-Man image data; (2) Monte Carlo

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N-Particle (MCNP) input file for external photon beams source geometry using the VIPMan phantom; and (3) presentation slides. An ambitious project like this is impossible without the generous support from the leading contributors for each chapter. Their time, effort, and patience are greatly appreciated. The field of computational phantoms has a long and rich history, and the research community is deeply indebted to the visionary work by many pioneers. As editors, we would like to thank Binquan Zhang, Juying Zhang, Matthew Mille, and Paul Booth from Rensselaer Polytechnic Institute, who provided valuable assistance in handling, reviewing, and formatting the electronically submitted manuscripts. X. George Xu Keith F. Eckerman

Editors

Dr. X. George Xu is currently a professor of nuclear engineering and biomedical engineering at Rensselaer Polytechnic Institute (RPI), Troy, New York. He received his PhD in nuclear engineering/health physics from Texas A&M University in 1994. His current research interests include radiation dosimetry and anatomical modeling for various applications in health physics, diagnostic imaging, and radiotherapy. Dr. Xu directs the Rensselaer Radiation Measurements and Dosimetry Group (http://RRMDG.rpi. edu) and is also the founding director of the Center for Engineering-Based Patient Modeling (http://CEPM.rpi.edu) at RPI. In 2005, Dr. Xu cofounded the Consortium of Computational Human Phantoms (http://www.virtualphantoms.org) which aims to promote international research collaboration. He serves on several editorial boards and is involved in the technical committees of various associations such as the American Association of Physicists in Medicine, the American Nuclear Society, the Health Physics Society, the International Commission on Radiological Protection, and the National Council on Radiation & Measurements; he is also the past president of the Council on Ionizing Radiation Measurements and Standards. Dr. Keith F. Eckerman received his PhD in environmental health engineering from Northwestern University in 1972. He has been the leader of the Dosimetry Research Team, Environmental Science Division, Oak Ridge National Laboratory (ORNL) since 1979. He is a member of the International Commission on Radiological Protection and the National Council on Radiation Protection and Measurements. An internationally recognized authority on internal dosimetry and biokinetic modeling, Dr. Eckerman’s research covers radiation dosimetry; radiological assessments; and applications of mathematical models to radiation dosimetry, physiology, and metabolism. He has authored and coauthored more than 200 journal publications, book chapters, standards, and proceedings. Dr. Eckerman has received numerous awards, including the DOE Award-Operation Ivory Purpose in 1980, the Health Physics Society Distinguished Scientific Achievements Award in 1995, the NRC Special Achievement Award in 1997, and the Society of Nuclear Medicine Loevinger–Berman Award in 2001.

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Contributors

Manuel Bardiès Oncology Research Department INSERM Nantes, France

Aiping Ding Department of Nuclear Engineering Rensselaer Polytechnique Institute Troy, New York

Giovanni Bibbo Division of Medical Imaging Women’s and Children’s Hospital Adelaide, South Australia, Australia

Keith F. Eckerman Oak Ridge National Laboratory Oak Ridge, Tennessee

Wesley E. Bolch Department of Nuclear and Radiological Engineering University of Florida Gainesville, Florida

Mary Ann Emmons-Keenan Department of Radiology Vanderbilt University Nashville, Tennessee

Ahmet Bozkurt Department of Physics Harran University Sanliurfa, Turkey Martin Caon School of Nursing and Midwifery Flinders University Adelaide, South Australia, Australia Tsi-Chian Ephraim Chao Department of Medical Imaging and Radiological Sciences Chang Gung University Taipei, Taiwan Ji Chen Department of Electrical and Computer Engineering University of Houston Houston, Texas John J. DeMarco Department of Radiation Oncology University of California Los Angeles, California

Michael J. Fernald RADAR Inc. Nashville, Tennessee Martin Fuss Department of Radiation Medicine Oregon Health & Science University Portland, Oregon David E. Hintenlang Department of Nuclear and Radiological Engineering University of Florida Gainesville, Florida Gabriela Hoff Department of Physics Pontifical Catholic University of Rio Grande do Sul Porto Alegre, Brazil Jorge Hurtado Department of Nuclear and Radiological Engineering University of Florida Gainesville, Florida xvii

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An Jin Department of Biomedical Engineering Rensselaer Polytechnique Institute Troy, New York

Contributors

Choonsik Lee Department of Nuclear and Radiological Engineering University of Florida Gainesville, Florida

Wolfgang Kainz Center for Devices and Radiological Health Food and Drug Administration Silver Spring, Maryland

Junli Li Department of Engineering Physics Tsinghua University Beijing, China

Iwan Kawrakow Ionizing Radiation Standards Group National Research Council of Canada Ottawa, Ontario, Canada

Vanildo Júnior de Melo Lima Department of Anatomy Federal University of Pernambuco Recife, Brazil

Helen Jamil Khoury Department of Nuclear Energy Federal University of Pernambuco Recife, Brazil Chan Hyeong Kim Department of Nuclear Engineering Hanyang University Seoul, Korea Sakae Kinase Radiation Effect Analysis Group Japan Atomic Energy Agency Tokaimura, Japan Gary H. Kramer National Internal Radiation Assessment Section Health Canada’s Radiation Protection Bureau Ottawa, Ontario, Canada Richard Kramer Department of Nuclear Energy Federal University of Pernambuco Recife, Brazil Choonik Lee Department of Radiation Oncology Anderson Cancer Center Orlando, Florida

Qian Liu Britton Chance Center for Biomedical Photonics Huazhong University of Science and Technology Wuhan, China Daniel L. Lodwick Department of Nuclear and Radiological Engineering University of Florida Gainesville, Florida Eduardo César de Miranda Loureiro Department of Mechanical Engineering University of Pernambuco Recife, Brazil C.-M. Charlie Ma Department of Radiation Oncology Fox Chase Cancer Center Philadelphia, Pennsylvania Jizeng Ma China Institute of Atomic Energy Beijing, China Michael McNitt-Gray Department of Radiological Sciences David Geffen School of Medicine University of California Los Angeles, California

Contributors

William E. Moloney Petrone Associates, LLC New York, New York Yong Hum Na Department of Biomedical Engineering Rensselaer Polytechnic Institute Troy, New York Tomoaki Nagaoka Electromagnetic Compatability Group National Institute of Information and Communications Technology Tokyo, Japan Deanna Hasenauer Pafundi Department of Nuclear and Radiological Engineering University of Florida Gainesville, Florida Harald Paganetti Francis H. Burr Proton Therapy Center Massachusetts General Hospital Boston, Massachusetts

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Rui Qiu Department of Engineering Physics Tsinghua University Beijing, China Warren Dan Reece Department of Nuclear Engineering Texas A&M University College Station, Texas Kimiaki Saito Division of Environment and Radiation Japan Atomic Energy Agency Tokaimura, Japan Kaoru Sato Research Group for Radiation Protection Japan Atomic Energy Agency Tokaimura, Japan W. Paul Segars Duke Advanced Imaging Laboratories Duke University Medical Center Durham, North Carolina

Niko Papanikolaou Department of Radiation Oncology The University of Texas Health Science Center at San Antonio San Antonio, Texas

Chengyu Shi Department of Radiation Oncology The University of Texas Health Science Center at San Antonio San Antonio, Texas

John E. Pattison Department of Applied Physics University of South Australia Adelaide, South Australia, Australia

Il-Young Son Department of Electrical Engineering University of California San Diego, California

Nina Petoussi-Henss Institute of Radiation Protection German Research Center for Environmental Health Neuherberg, Germany

Michael G. Stabin Department of Radiology and Radiological Sciences Vanderbilt University Nashville, Tennessee

John W. Poston, Sr. Department of Nuclear Engineering Texas A&M University College Station, Texas

Valery Taranenko Department of Health Physics University of California San Francisco, California

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Benjamin M.W. Tsui Department of Radiology and Radiological Science Johns Hopkins University Baltimore, Maryland José Wilson Vieira Basic Department Polytechnic School of Pernambuco Recife, Brazil and Federal Institute of Education Science and Technology of Pernambuco Recife, Brazil Scott R. Whalen Department of Nuclear and Radiological Engineering University of Florida Gainesville, Florida Jonathan L. Williams Department of Radiology University of Florida Gainesville, Florida James Winslow Department of Nuclear and Radiological Engineering University of Florida Gainesville, Florida Dagang Wu Halliburton Houston, Texas

Contributors

X. George Xu Department of Mechanical, Aerospace and Nuclear Engineering and Department of Biomedical Engineering Rensselaer Polytechnic Institute Troy, New York Birsen Yazıcı Department of Electrical, Computer and Systems Engineering and Biomedical Engineering Rensselaer Polytechnic Institute Troy, New York Maria Zankl Institute for Radiation Protection German Research Center for Environmental Health Neuherberg, Germany Binquan Zhang China Institute for Radiation Protection Taiyuan, China Guozhi Zhang Wuhan National Laboratory for Optoelectronics Huazhong University of Science and Technology Wuhan, China Juying Zhang Department of Mechanical, Aerospace and Nuclear Engineering and Department of Biomedical Engineering Rensselaer Polytechnic Institute Troy, New York

Part I

Phantoms

1 Computational Phantoms for Radiation Dosimetry: A 40-Year History of Evolution X. George Xu

CONTENTS 1.1 Introduction ...................................................................................................................3 1.2 Solid-Geometry Modeling Techniques: Constructive Solid Geometry and Boundary Representation .................................................................................... 5 1.3 Historical Developments..............................................................................................7 1.3.1 Stylized Phantoms from 1960s to 2000s .......................................................7 1.3.2 Voxel Phantoms from 1980s to 2000s .......................................................... 12 1.3.3 BREP Phantoms from 2000s to Present ...................................................... 23 1.4 Physical Phantoms ...................................................................................................... 26 1.5 Monte Carlo Codes Used with Computational Phantoms.................................... 27 1.5.1 MCNP and MCNPX ...................................................................................... 31 1.5.2 EGS ................................................................................................................... 31 1.5.3 GEANT4 .......................................................................................................... 32 1.5.4 PENELOPE...................................................................................................... 32 1.5.5 FLUKA............................................................................................................. 32 1.6 Discussions .................................................................................................................. 32 1.7 Summary ......................................................................................................................34 Acknowledgments ................................................................................................................. 35 References ............................................................................................................................... 35

1.1 Introduction One of the most dynamic areas of research in radiation protection, radiological imaging, and radiotherapy is the modeling of human anatomy for Monte Carlo-based radiation transport and dose simulations. Radiation dosimetry aims to determine the amount and distribution pattern of energy deposited in various parts of the human body by internal or external radiation sources. To protect against occupational exposures, regulatory limits are set for radiation doses associated with radiosensitive organs. In both diagnostic radiology and nuclear medicine, internal and external photons traverse through the body to form an image, depositing radiation energy along the way. Radiotherapy, on the other hand, attempts to deliver a lethal dose to the target while sparing the adjacent healthy tissues from the adverse effects of radiation. Accurate radiation dosimetry is essential but also 3

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quite challenging for three reasons: (1) there are many diverse exposure scenarios resulting in unique spatial and temporal relationships between the source and human body; (2) an exposure can involve multiple radiation types which are governed by rather different radiation physics principles including photons (and gamma rays), electrons, positrons, alpha particles, neutrons, and protons; (3) the human body consists of three-dimensional (3D) inhomogeneous tissues of various geometric shapes and densities, leading to extremely complex radiation interaction patterns. It is not practical to make direct measurement of radiation doses using physical detectors inside the human body. Consequently, dose estimates for select organs of interest have always depended on physical or computational “anthropomorphic models” that mimic the interior and exterior anatomical features of the human body. Historically, the term “phantom” was used in most radiological science literature to mean a physical model of the human body. In the radiation protection community, however, the term has also been used to refer to a mathematically defined “anatomical” model that is distinctly different from a physiologically based computational model such as that related to respiration or blood flow. Throughout this book, we use the phrases “computational phantom” and “physical phantom” to avoid confusion, but the reader may occasionally encounter “phantom” or “model” in places where a computational model of the human or animal anatomy is discussed. A physical phantom is made of solid materials equivalent to bones and soft-tissues that can be molded to the shape of the human anatomy and then cut into slices containing cavities for tiny radiation dosimeters. The approach of using such physical phantoms was known to be expensive and time-consuming due to tedious experimental and radiation safety procedures. Luckily, the advent of the first-generation of computers and Monte Carlo simulation methods for nuclear weapons research in the 1940s made it gradually possible to calculate organ doses in a computational phantom. Each computational phantom defines not only the exterior features of the entire human body, but also includes details on internal organs such as their volume, mass, and shape. Coupled with information on tissue densities and chemical compositions, a computational phantom allows the Monte Carlo codes to simulate interactions and energy deposition in the body for various types of radiation. Although additional work is needed to specify a radiation source, the computational approach is, in general, quite advantageous in terms of its versatility, efficiency, precision, and radiation safety. In the case of internally distributed radionuclides, it is often necessary to obtain dose estimates via calculations involving a computational phantom rather than through experimental measurement. Therefore, since their advent in the 1960s, the use of computational human phantoms has become increasingly popular in the fields of radiation protection, imaging, and radiotherapy. Today, physical phantoms are used only in radiation protection dosimetry as benchmarks to computational results for external exposures. For nonionizing radiation, similar computational phantoms have been developed over the years to study the biological effects caused by radiofrequencies emitted by devices such as electric power lines and wireless cellular phones. Since the 1960s, approximately 121 computational phantoms,1–93 plus 27 physical phantoms,94–103 have been reported in the literature for studies involving ionizing and nonionizing radiation. A significant portion of the literature on radiation protection dosimetry is related to the development and application of these phantoms. The organs and body surfaces of computational phantoms have been defined in terms of a variety of solid geometry modeling techniques: quadric equations, voxels, and advanced primitives such as B-splines or nonuniform rational B-splines (NURBS) or polygon meshes. Each of these techniques was adopted at specific times in the last 40 years, exhibiting an interesting

Computational Phantoms for Radiation Dosimetry: A 40-Year History of Evolution

5

scientific journey that reflects the advances in computer and medical imaging technologies. Like other research fields, nontechnical considerations also had their share in shaping the world of computational phantoms that we know today. As the evolution continues, particularly at the accelerated speed witnessed in the past decade, it is vitally important to know where we are going. There are many interesting questions facing researchers today. Why did the computational phantoms evolve the way they did? What will be the future directions in this research field? The answers to these questions and many others require an understanding and evaluation of the rationales and processes responsible for some of the most widely used phantoms. This chapter serves as an introduction to the following chapters, which detail specific phantoms and their applications. The different modeling techniques are defined and a summary of historical milestones in the development of phantoms for ionizing and nonionizing radiation applications is provided.

1.2 Solid-Geometry Modeling Techniques: Constructive Solid Geometry and Boundary Representation Computational human phantoms are basically solid-geometry models that depict exterior and interior anatomical features of a human body. For radiation dosimetry, a phantom must define the surface of an organ in which radiation interactions and energy depositions are to be calculated by tracing individual particles. Clearly, the construction of such phantoms must consider multiple factors such as anatomy, radiosensitivity, computational efficiency, and geometrical compatibility with a Monte Carlo code. The computer graphics community has dealt extensively with solid-geometry modeling for computer-aided design (CAD). Two general methods of solid-geometry modeling have been widely developed: constructive solid geometry (CSG) and boundary representation (BREP).104–108 The topology—spatial location and relationship of the surfaces—is fundamentally different for these two methods. CSG allows a modeler to create a solid object using Boolean operators (or the equivalent) to combine very simple objects called primitives. Examples of these primitives include cuboids, cylinders, prisms, pyramids, spheres, cones, and ellipsoids—surfaces that are easily described by quadric equations. CSG representations are easy to adopt and can yield good results when the objects are relatively simple in shape. Modern CAD software systems, however, are based on the more powerful BREP methods. There are two types of information in the BREP: topological and geometric. Topological information provides the relationships among vertices, edges, and faces. In addition to connectivity, topological information also includes orientation of edges and faces. In advanced BREP-based CAD, the exterior of an object is defined as NURBS which afford very smooth surfaces. The faces can alternatively be represented as polygons whose vertices are defined by a set of coordinate values x, y, and z. A polygon mesh or unstructured grid is a collection of vertices and polygons that define the geometric shape of a polyhedral object in CAD. In principle, NURBS and polygonal meshes are interchangeable BREP data structures. Unlike the CSG representation, however, BREP is much more flexible because a richer set of operation tools are available (e.g., extrusion, chamfering, blending, drafting, shelling, and tweaking). These features allow BREP-based phantoms to include very complex anatomical features. Furthermore, the BREP technique is ideally suited for surface deformation—an operation necessary for the adjustment of organ size and for organ motion simulations as described later.


6

As an example, the left lung can be represented in the CSG method by “half an ellipsoid with a section removed.”12 The cut-out section, which is not specified by the original authors, can be defined by a Boolean operation of subtracting one ellipsoid (B) from the other (A) to create the left lung, as described in 2

2

2

2

2

2

⎛ X − 8.5 ⎞ ⎛ Y ⎞ ⎛ Z − 43.5 ⎞ A: ⎜ + ⎜ + ⎜ ≤ 1, Z ≥ 43.5 ⎝ 5 ⎟⎠ ⎝ 7.5 ⎟⎠ ⎝ 24 ⎟⎠ ⎛ X − 2.5 ⎞ ⎛ Y ⎞ ⎛ Z − 43.5 ⎞ B: ⎜ + ⎜ + ⎜ ≥ 1, if y < 0 ⎝ 5 ⎟⎠ ⎝ 7.5 ⎟⎠ ⎝ 24 ⎟⎠

(1.1)

In Figure 1.1a, the 3D shapes of the left lung before and after the Boolean operation are illustrated. These surface equations are computationally efficient and are accepted by nearly all Monte Carlo codes. However, even with complicated and carefully designed Boolean operations like this, phantoms based on quadric surfaces are not anatomically realistic in terms of their geometry. When using a Monte Carlo code, the geometry of the left lung is often further simplified by replacing the ellipsoid B with several planes. This type of phantoms is commonly referred to as “stylized” or “mathematical” phantoms. Using voxels as a CSG modeling technique, Figure 1.1b defines the left lung as an assembly of 3D cuboids. Medical image data can be converted to voxel geometry that provides a direct way to realistically describe the human anatomy. The geometry of a voxel is very simple for existing Monte Carlo codes to handle, although the large number of voxels may require the use of enhanced computer hardware or special Monte Carlo software preparation. On the other hand, however, each tomographic image slice needs to be treated by a “segmentation” process, which assigns each pixel to an organ or tissue of interest such as the lung, bone, or skin using a unique identification number. It can take a significant amount of time to prepare a voxel-based phantom because there is no automatic segmentation algorithm that works on all organs. Furthermore, a voxel phantom is based on images for one subject, thus lacking anatomical variability associated with organ size, shape, and

A

B

(a)

A

A

(b)

(c)

FIGURE 1.1 The left lung defined by different modeling techniques. (a) A stylized lung model using the CSG-type method before and after a Boolean operation is performed to remove a section of the ellipsoid B from A. (b) A CSG-type method involving a group of rigid voxels with an anatomical detail dependent on the voxel size. (c) A BREPtype of method involving a polygonal mesh that is easy to deform and anatomically accurate.


7

location that are important in the current paradigm for radiation protection dosimetry. Finally, the boundary of the lung in a voxel phantom is defined by uneven steps instead of a smooth surface, as show in Figure 1.1b. As a result, the anatomical fidelity depends on the voxel size, especially for thin and small tissues such as the skin, eye lens, ribs, and bone marrow. An adjustment to the organ shape will likely involve all underlying voxels, which is computationally inefficient. These types of computational human body models are commonly referred to as “voxel” or “tomographic” phantoms. The lung can also be defi ned by the advanced BREP modeling techniques involving NURBS or polygon mesh surfaces. The most common technique to create a BREP-based phantom involves the surface contour extraction of each organ from a tomographic image data set using a commercial software package, followed by the integration of individual organs into a whole body assembly. In essence, the contours convert the voxels into NURBS or mesh surfaces that are smooth and anatomically realistic. These phantoms are commonly referred to as “NURBS,” “mesh,” or “BREP” phantoms. Figure 1.1c shows the triangular meshes of a left lung, which was derived from high-resolution tomographic images.

1.3 Historical Developments Previously published reviews of the historical development of computational phantoms have focused on a certain time period or a particular phantom type.109–111 These reviews did not explicitly classify phantom modeling techniques and since the time of their publication a number of phantoms have been developed using the new BREP methods. An understanding of the modeling techniques is important because each one of them was predominantly adopted by the research community during a specific time of the development. Based on chronological and technical information in the literature, existing computational phantoms can be divided into three classes: (1) stylized phantoms (1960s to 2000s); (2) voxel phantoms (1980s to 2000s); and (3) BREP phantoms (2000s to present). 1.3.1 Stylized Phantoms from 1960s to 2000s Table 1.1 summarizes some of the most important and unique stylized phantoms developed from the 1960s to the 2000s.1–14 This generation of phantoms originated from work performed at Oak Ridge National Laboratory (ORNL) and are covered in detail in Chapter 2. The first attempts at developing a computational anthropomorphic phantom were reported by Fisher and Snyder at ORNL in the 1960s.7,8 Using CSG modeling techniques involving shapes such as elliptical cylinders and cones, they developed the so-called Fisher–Snyer adult phantom. Fisher and Snyder also developed the “similitude” children phantoms, which were scaled-down versions of the adult with added assumption that the entire body was a homogeneous tissue (i.e., the lungs and skeleton were ignored). In 1969, Snyder and his colleagues reported the first heterogeneous phantom that became known as the “MIRD-5 Phantom,” a named derived from the Medical Internal Radiation Dosimetry (MIRD) Committee of the Society of Nuclear Medicine which adopted the phantom.9 This phantom was composed of a skeleton, a pair of lungs, and the remainder (soft tissue). The representation of internal organs in this mathematical phantom was crude, as the simple equations captured only the most general description of the position and geometry of each

KMIRD

MCAT

Hanyang University, Korea

Johns Hopkins University, USA (formerly with the University of North Carolina) NASA, USA

CAM

ADAM and EVA

GSF, Germany

Developers

Phantom Names

5

10

3

Chapters in This Book

Quadric equations

Quadric equations

Quadric equations

Quadric equations

Data Types

A standing U.S. air force adult male representing 50th percentile height and weight. More than 1000 geometric surfaces and 2450 solid regions.

Gender-specific phantoms revised from the ORNL MIRD-5 phantom for external dose assessment. Several minor anatomical changes including the breast size. Outer body and internal organs of the ORNL adult male phantom modified according to Korean anthropometric data. 3D and 4D cardiac torso phantom with gated patient organ motion information for imaging applications.

Anatomical Features

Caucasian adult male


Korean adult male

Caucasian adult male and female

Human Subjects

I

I

I

I

Ionizing (I) or Nonionizing Radiation (N)

List of Developers, in Alphabetical Order, of Stylized Computational Phantoms Including Information on Phantom Names, Chapters in This Book, Phantom Data Types, Anatomical Features, Human Subjects, Ionizing versus Nonionizing Radiation, and References

TABLE 1.1

[6]

[3–5]

[2]

[1]

References

8 Handbook of Anatomical Models for Radiation Dosimetry

Radiation Protection Bureau, Canada

ORNL, USA

2

12

Cristy–Eckerman family phantoms

Pregnant women

Mathematical models of the embryo and fetus

2

Fisher–Snyder phantom (MIRD-5) and others

Quadric equations

Quadric equations

Quadric equations

Quadric equations

The first anthropomorphic phantom representing a hermaphrodite adult for internal dosimetry. Organ masses, body weight, and body height correspond to 50th percentile data recommended in ICRP 23. Later, age-specific phantoms were developed by others. Based on MIRD-5 phantom and others from ORNL. The age 15 phantom represents a 15 year old male and an adult female. Adult female in the family phantom was used to add uterine contents including the fetus, fetal skeleton, and placenta at three different gestational stages. Phantoms of the embryo and fetus representing additional gestational periods not included in the ORNL pregnant women phantoms for dosimetry studies involving commercial flights. Caucasian pregnant women at 8, 13, 26, 38 weeks of gestation

Caucasian pregnant women at 3, 6, and 9 months of gestation

Caucasian adult

Caucasian newborn, 1 year old, 5 years old, 10 years old, 15 years old, and the adult

I

I

I

[14]

[13]

[7–12]

Computational Phantoms for Radiation Dosimetry: A 40-Year History of Evolution 9

10


organ. The original model was intended to represent a healthy “average” adult male, the Reference Man, as defined by the International Commission on Radiological Protection (ICRP) from an extensive review of medical and other scientific literature on the European and North American populations.112 The Reference Man was a 20–30 year old Caucasian, 70 kg in weight and 170 cm in height (the height was later changed to 174 cm). In 1978, Snyder et al. published an elaborative set of specific absorbed fractions using an improved version of their heterogeneous phantom which contained more than 20 organs and more detailed anatomical features.10 The limitations associated with the approach of applying a set of scaling factors to the adult phantom to create age-dependent similitude phantoms were clear. Significant efforts were undertaken at ORNL during the mid-1970s by Poston and others to develop individualized pediatric phantoms of the newborn, 1 year old, 5 years old, 10 years old, and 15 years old.113–115 However, these phantoms were not widely adopted because the geometric shapes were very complex and, after Poston left ORNL, alternative approaches were developed. Building upon previous work, Cristy reported the development of a new series of stylized phantoms in 1980 and then with Eckerman in 1987 in ORNL/TM-8381.11,12 This series or “family” of phantoms consisted of an adult male, a newborn, and individuals of ages 1, 5, 10, and 15 (also representing an adult female with additional anatomical features). Each phantom is composed of three tissue types with distinct densities: bone, soft tissue, and lung. They were analytically defined in three principal geometric sections as illustrated in Figure 1.2a: an elliptical cylinder representing the arms, torso, and hips; a truncated elliptical cone representing the legs and feet; and an elliptical cylinder representing the head and neck. Figure 1.2b shows the skeleton and internal organs and tissues. A picture of the “family” is shown in Figure 1.2c. In 1995, Stabin and his colleagues at ORNL adapted the adult female phantom in this family to represent a pregnant woman at the end of each trimester of pregnancy.13 This set of three stylized pregnant female phantoms was used for various internal nuclear medicine applications. The 9 month pregnant female phantom is shown in Figure 1.2d. Since the 1980s, a number of revised MIRD-5 phantoms were reported, which incrementally improved upon the original Fisher–Snyder and Cristy–Eckerman phantoms using the same stylized modeling techniques; however, they are not explicitly listed in Table 1.1 (for example, Bouchet et al. on a revised head and brain model116). With the availability of general-purpose Monte Carlo codes and affordable computers in the 1980s, this latest series of phantoms, referred to as the “Cristy–Eckerman Phantoms,” were quickly adopted by many users for a wide variety of internal dosimetry applications. Later, this set of phantoms was also used for external dosimetry studies including the one at Texas A&M University discussed in Chapter 18. In parallel with the efforts at ORNL by Cristy and Eckerman to revise the MIRD-5 Phantom, Kramer et al. from the GSF, Germany used the anatomical descriptions of the hermaphrodite MIRD-5 phantom to develop a pair of gender-specific adult phantoms known as the ADAM and EVA for external dosimetry studies.1 The female EVA phantom was chosen to have a weight 83% of the MIRD-5 adult phantom. There are a number of minor anatomical differences, such as breast sizes, from those reported by Cristy and Eckerman.1,12 Table 1.1 also lists several additional efforts related to stylized phantoms. The stylized modeling technique was also adopted by one group for medical applications. The Mathematical Cardiac Torso (MCAT) phantom which includes the major thoracic structures and organs was developed by a research group led by Tsui at the University of North Carolina (currently with Johns Hopkins University) for use in nuclear medicine imaging research, specifically single-photon emission computed tomography (SPECT) and positron


(a)

11

(b)

ORNL–DWG 79–19955

0 year (c)

1 year

5 year

10 year

15 year

Adult (d)

FIGURE 1.2 Stylized phantoms. (a) Exterior view of the adult male. (b) Skeleton and internal organs. (c) “Family” phantoms representing individuals of both genders and various ages. (d) Cross-sectional view of the phantom for a 9 month pregnant female.

emission tomography (PET).3–5 The same group later developed the first NURBS-based motion-simulating phantom to be described in Chapter 5. The Computational Anatomical Man (CAM) phantom developed by Billings and Yucker in 1973 for the National Aeronautics and Space Administration (NASA) demonstrated a very different and aggressive approach in stylized modeling because the phantom reportedly consisted of 1100 unique geometric surfaces and 2450 solid regions.6 According to the authors, internal body geometries such as

12


organs, voids, bones, and bone marrow were explicitly modeled using the CSG modeling techniques. A computer program called CAMERA was also developed for performing analyses with the CAM phantom. The authors state that “extremely detailed geometrical model of the human anatomy, the most detailed yet prepared, has been developed for use in investigations dealing with exposure of astronauts to the natural space radiation environment. The model is equally applicable to investigations dealing with exposure of humans to radiation associated with nuclear weapon and nuclear power system environments as well as medical applications such as radiotherapy and radiography.”6 Indeed the surface geometry was so detailed that one may wonder how this was possible in the 1970s with much less capable computers. Unfortunately, the CAM phantom was never adopted for applications outside the aerospace industry and very little information about the work was accessible by the phantom research community until Jordan, a contracted phantom developer and user, recently released some of the images (http://cmpwg.ans.org/phantoms/ camera.pdf). In the early 1990s, it was clear that the research community no longer favored stylized phantom modeling methods. However, at least two groups continued to use these methods to develop computational phantoms of an embryo and fetus for space radiation dosimetry14 and an adult representing the Korean population.2 For 40 years since the first anthropomorphic phantom was reported, these anatomically simplified phantoms have been used as the de facto “standard” representations of the ICRP “Reference Man” methodology, which is based on “population-average” 50th percentile anatomical parameters.112,117 Applications of stylized phantoms have eventually included many aspects of radiation protection, radionuclide therapy, and medical imaging.118 In addition, national and international bodies have adopted organ dose estimates derived from these stylized phantoms in guidelines and regulations related to industrial and medical uses of ionizing radiation. Although stylized phantoms made it possible to carry out Monte Carlo computations during times when computers were much less powerful, the original developers, as discussed in Chapter 2, recognized the obvious shortcomings. Human anatomy is too complex to be realistically modeled with a limited set of surface equations. Many anatomical details in these models were compromised that sometimes led to inaccurate results. For example, when such phantoms were applied to nuclear medicine procedures where precise dosimetry is necessary, the calculated average organ and marrow doses did not produce strong correlations with observed marrow toxicity. Most nuclear medicine physicians consequently tend to administer lower-than-optimal amounts of radioactivity to avoid toxicity. For computed tomography (CT) dose reporting, all existing commercial software systems are based on the stylized patient models that are known to cause very large errors for low-energy x-rays.119 Similar stylized models have also been used to derive dose–response relationships for Japanese atomic bomb survivors and for medical patients in epidemiological studies. In the external-beam radiotherapy community, an early stylized homogenous phantom was used by the Radiation Epidemiology Branch of the National Cancer Institute (NCI) for nearly 30 years in studies related to organ doses of therapeutically irradiated patients.120 By the 1980s, a few groups of researchers began to seek new ways to develop anatomically realistic phantoms. 1.3.2 Voxel Phantoms from 1980s to 2000s The development of anatomically realistic models was desirable but impossible until early 1980s when powerful computer and tomographic imaging technologies became available. With the advent of CT and magnetic resonance (MR) imaging techniques, researchers could


13

for the first time visualize the internal structures of the body in three dimensions and store the images in versatile digital formats. These advantages brought about the exciting and prolific era of the so-called voxel or tomographic phantoms. Table 1.2 summarizes a total of 74 phantoms that were constructed from three types of tomographic images: CT and MR images from live subjects, as well as cross sectional photographs of cadavers.15–80 The ionizing and nonionizing radiation applications of these phantoms are clearly labeled in the table. In two previously published review articles, the number of voxel phantoms was reported to be 21 by Caon110 and 38 by Zaidi and Xu.111 The increase in phantom number is due to a more exhaustive literature search, recent developments, and the inclusion of phantoms developed for use solely in nonionizing radiation applications. In terms of solid-geometry modeling techniques, a voxel—one of the basic CSG primitives—is simply a 3D representation of a pixel. However, compared with the medical applications such as radiation treatment planning, the task of developing reference human phantoms presented some unique and intractable challenges: (1) to construct a whole-body phantom, image slices should ideally cover the entire body—a process not normally carried out in routine medical examinations because of x-ray exposures or the lengthy time required for MR procedures. (2) A large amount of internal organs/tissues must be identified and segmented for organ dose calculations, whereas, in radiotherapy, for example, only the tumor volume and adjacent regions are routinely outlined. (3) The image data size of a whole-body model, especially when high-resolution images are used, can be potentially too great for a computer to handle. (4) A standardized patient phantom is often used to study diverse radiation types such as photons, electrons, neutrons, and protons, thus requiring considerable Monte Carlo simulation capabilities. In terms of the developmental process, tomographic phantoms are fundamentally different from the stylized ones. A tomographic image data set is composed of many slices, each displaying a two-dimensional (2D) pixel map of the anatomy. The 3D volume of a voxel is measured by multiplying the pixel size by the thickness of an image slice. Unlike stylized phantoms which are based on quadric surface equations, a voxel phantom contains a huge number of tiny cubes grouped to represent various anatomical structures. However, both quadric surface equations and voxels (cuboids) belong to the same class of CGS geometries. The creation of a tomographic phantom involves four general steps: (1) acquire a set of tomographic images (e.g., CT, MR, or anatomical photography) that cover the entire volume of the body; (2) identify organs or tissues of interest (e.g., lungs, liver, skin, etc.) from the original image slice by assigning every pixel with an identification number; (3) specify the density (e.g., soft tissue, hard bone, air, etc.) and chemical composition of organs or tissues; and (4) register the segmented image slices into a 3D volume that can be used for 3D visualization (for checking anatomical structures) and for Monte Carlo calculations. Figure 1.3 illustrates these steps using the National Library of Medicine’s Visible Human image data set. The earliest effort to create image-based phantoms may have been reported in the work by Gibbs of Vanderbilt University published from 1982 to 1987.75–77 Forgotten by most latecomers, Gibbs and her coworkers explored the use of 2D x-ray images as the basis to form an anatomically realistic phantom. They then used this information in Monte Carlo calculations to assess the doses received by patients who underwent various radiological procedures. At nearly the same time, Zankl and her colleagues at GSF—National Research Center for Environment and Health in Germany decided to use CT imaging on healthy volunteers to develop what eventually became a family of 12 voxel phantoms: BABY, CHILD, DONNA, FRANK, HELGA, IRENE, GOLEM, GODWIN, VISIBLE HUMAN, LAURA, KLARA, and KATJA.27–33 As detailed in Chapter 3, the adult male phantoms were developed first.

Flinders University, Australia

Federal University of Pernambuco, Brazil

Brooks Air Force Base, USA China Institute for Radiation Protection, China Darmastadt University of Technology, Germany FCS Department, Italy

Developers

4

7

MAX06 and FAX06

ADELAIDE

7

CT

CT

CT

CT

MAX

FAX

MRI

DAM

7

Color photos

HUGO

Color photos

CNMAN

11

Color photos

Data Types

Visible Man

Phantom Names


Based VOXTISS8 phantom and adjusted to ICRP 89. Images of the trunk, the neck, and the lower part of the head were from CT scan of a 37 year old female. Images of the legs and feet were from CT scan of a 62 year old woman. The head and arms were from MAX phantom. Extension of MAX and FAX phantoms by adding more details in the skeleton that were further adjusted to match the values by ICRP 89. Torso phantom, without head and arms.

Dielectric anatomical phantom.

VHP. A total of 32 tissues were identified.

VHP. More than 40 tissues were identified. Chinese VHP.

Anatomical Features

Caucasian 14 year old female patient

Caucasian adult male and female patient

34 year old male volunteer Caucasian adult male patient Caucasian adult female patient

Caucasian 39 year old male cadaver

Caucasian 39 year old male cadaver Chinese adult male cadaver

Human Subjects

I

I

I

I

N

N

I

N

[23,24]

[22]

[21]

[20]

[19]

[18]

[17]

[15,16]

Ionizing (I) or Nonionizing Radiation (N) References

List of Developers, in Alphabetical Order, of Voxel Computational Phantoms Including Information on Phantom Names, Chapters in This Book, Phantom Data Types, Anatomical Features, Human Subjects, Ionizing versus Nonionizing Radiation, and References

TABLE 1.2


The weight and height are similar to those of ICRP 23 reference man. Modification of Golem phantom to agree with the ICRP 89 anatomical data. Head to knee. CT data from the VHP. 167 cm height and a weight of 59 kg.

CT CT CT CT CT

CT CT CT MRI

CT

FRANK

HELGA

IRENE

GOLEM

GODWIN

VISIBLE HUMAN LAURA

KLARA

KATJA

REGINA (ICRP Reference Phantom)

The modification of Laura to agree with ICRP 89 anatomical data. A woman in her 24th week of pregnancy. Based on the modified REGINA phantom and patient MRIs of the abdominal and pelvic regions. An adjusted LAURA phantom according to ICRP 89.

Whole body phantom (163 cm, 51 kg).

From mid thigh upwards.

Head and torso.

Whole body phantom (176 cm, 79 kg)

115 cm in height and weighed 21.7 kg.

CT

CT

DONNA

3

Trunk was based on MR images of a pregnant women and modified on CT images of a woman in the 30th week of pregnancy developed by RPI. The brain and spinal cord were from NORMAN and fitted into SILVY. 57 cm in height and weighed 4.2 kg.

CT

BABY

GSF, Germany

MRI, CT

CHILD

SILVY

Graz University of Technology, Austria

Caucasian 39 year old male cadaver Caucasian 43 year old female patient Caucasian 43 year old female patient Caucasian pregnant woman patient in her 24th week of pregnancy Caucasian 43 year old female patient

Caucasian 8 week old female cadaver Caucasian 7 year old female leukemia patient Caucasian 40 year old female patient Caucasian 48 year old male patient Caucasian 26 year old female patient Caucasian 32 year old female patient Caucasian 38 year old male patient Caucasian 38 year old male patient

Caucasian 30th week pregnant woman patient

I

I

I

I

I

I

I

I

I

I

I

(continued)

[34,35]

[33]

[32]

[32]

[31]

[32]

[31]

[29,31]

[29,30]

[30]

[29,30]

[27,28]

[27,28]

I I

[25,26]

N


Color photos

HDRK-Man

Pregnant female, hybrid phantoms (four phantoms)

NORMAN-05

High-definition reference Korean male phantom from the visible Korean human data. MRI Normalized man. Only 10 ribs. MRI Weight and height were scaled to the values by ICRP 89. MRI Based on NORMAN with new tissues recommended by ICRP. Quadric Based on NAOMI and Chen’s stylized equations and fetal phantoms. MRI

PET and CT

KTMAN-2

MRI

MRI

KORWOMAN

10

Korean male of average height and weight. Korean female of average height and weigh. Legs were modeled from the VHP data. Korean Typical Man (172 cm, 65 kg, without arms). Korean Typical Man-2 (172 cm, 68 kg).

MRI

KTMAN-1

An adjusted GOLEM phantom according ICRP 89

Anatomical Features

CT

Data Types

REX (ICRP Reference Phantom) KORMAN

HPA, U.K. (formerly NORMAN NRPB) NAOMI

Hanyang University, Korea

Developers

Chapters in This Phantom Names Book

Pregnant woman at 8, 13, 26, 38 weeks of gestation

Caucasian adult male Caucasian healthy adult female volunteer Caucasian adult male

Korean 25 year old male volunteer Korean 35 year old male volunteer Korean 33 year old adult male cadaver

Korean 30 year old healthy male Korean 35 year old female

Caucasian 38 year old male leukemia patient

Human Subjects

N

I

N, I N

I

I

I

I

I

I

[47]

[46]

[41–43] [44,45]

[39,40]

[38]

[38]

[37]

[36]

[34,35]



TABLE 1.2 (continued)


NIICT, Japan

Duke: 34 year old male (174 cm, 70 kg) Ella: 26 year old female (160 cm, 58 kg) Billie: 11 year old female (148 cm, 34 kg) Thelonious: 6 year old male (107 cm, 17 kg). Japanese adult male voxel phantom (170 cm, 65 kg). Japanese adult male voxel phantom: CT scan in supine posture. The male subject recruited for the construction of JM was selected to obtain CT scan in upright posture. Japanese adult female phantom (162 cm, 57 kg). Japanese adult female phantom (152 cm, 44 kg). Adult male phantom (171.4 cm, 65.0 kg) representing average anatomical values of Japanese 18 year old male. Adult female phantom (159.1 cm, 52.6 kg) representing average anatomical values of Japanese 30 year old female. Based on the HANAKO phantom and the abdominal phantom of a 26 week pregnant woman. Transformed from the TARO phantom into children models using the FFD algorithm.

MRI

CT CT MRI

ONAGO

JF

TARO

MRI

MRI/FFD

Pregnant woman

Deformed Children

MRI

CT

JM2

HANAKO

CT

CT

Visible Chinese human project.

Color photos

JM

9

9

JAEA, Japan

OTOKO

11

Huazhong VCH University of Science and Technology, China IT’IS, Switzerland The virtual family (four phantoms)

Japanese 26 week pregnant woman volunteer Japanese 3, 5, and 7 year children

Japanese 22 year old female volunteer

Japanese adult female volunteer Japanese adult female volunteer Japanese 22 year old male volunteer

N

N

N, I

N, I

I

I

I

I

I

N

Caucasian volunteers of different gender and ages

Japanese adult male volunteer Japanese 54 year old male volunteer Japanese 54 year old male volunteer

I

Chinese adult male cadaver

(continued)

[61]

[59,60]

[57,58]

[57]

[56]

[55]

[54]

[53]

[52]

[51]

[48–50]


University of Florida, USA

Tsinghua University, China University Hospitals of Leuven, Belgium

RPI, USA

ORNL, USA

Developers

CT

CT

UF 2 months

UF newborn

CT

Phantom 2

MRI

CT

CT

MRI

8

Anatomical Features

A voxel phantom equivalent to a 2 month old male newborn, representing a critically ill child. A normal 6 day female newborn phantom; the lungs were created using CT images of a 1 month old patient and the adrenal glands created using CT images of a 2 month old male patient.

Baby phantom (30.4 cm, 0.59 kg).

Baby phantom (50 cm, 1.91 kg).

A pregnant woman phantom covering the abdominal region. A whole-body model of the RANDO physical phantom. Chinese Voxel phantom (170 cm, 70 kg).

CT and quadric Voxelized head and torso phantom equations with stylized arms and legs. Color photos High resolution images from VHP.

Data Types

Phantom 1

11

12

6

3D VIP-Man

Pregnant woman RANDO CT phantom CVP

21

VOXMAT


Caucasian 6 day old female newborn cadaver

22 week old stillborn male baby cadavers Caucasian 6 month old male cadaver

Chinese adult male volunteer 33 week old stillborn male baby cadaver

Caucasian 39 year old male cadaver 30 week pregnant woman patient Adult male


Human Subjects

I

I

I

I

I

I

I

I

I

[69]

[69]

[68]

[68]

[66,67]

[65]

[64]

[63]

[62]





VOXTISS8

CT

MANTISSUE3–6

Yale University, USA

CT

CT

Radiography

MRI

Anatomically based model Gibbs phantoms Zubal

University of Utah, USA Vanderbilt University, USA

Color photos

CT

MEET Man

21

CT

University of Karlsruhe, Germany

UF series B (five phantoms)

UF series A (five phantoms)

Arms and legs from the VHP were attached to the Zubal phantom. Arms and legs were attached to the Zubal phantom and the arms were straightened along the phantom side.

Based on the UF series A phantoms with arms and legs from CT images of a healthy Korean adult attached. The organ masses were adjusted to ICRP 89 reference data. Models for simulation of electromagnetic, elastomechanic and thermic behavior of man, developed from the VHP. Anatomic phantom. The outer parts of the arms are missing. Head, trunk and proximal extremities from x-ray images. Head and torso.

UF pediatric phantom series without arms and legs.

Caucasian adult male volunteer Caucasian representative female cadaver Caucasian adult male patient Caucasian adult male patient Caucasian adult male patient

Caucasian 38 year old adult male cadaver

9 month, 11 and 14 year old males; 4 and 8 year old females patients 9 month, 11 and 14 year males; 4 and 8 year female patients

I

N

I

I

N

N, I

I

I

[80]

[79]

[78]

[75–77]

[74]

[72,73]

[71]

[70]



20

Identification of organs in each slice of a 2D pixel map

Registration of all slices

Finished 3D voxel phantom

FIGURE 1.3 (See color insert following page 524.) Steps to create a voxel phantom illustrated using the Visible Human cadaver image data set.

These were soon followed by the adult female, pediatric, and pregnant woman phantoms. The GOLEM and LAURA phantoms have recently undergone significant revision, as described in Chapter 15, to yield the REGINA and REX phantoms which are being released to the public as the ICRP Reference Computational Phantoms.34,35 Several processes were considered at the time when this pair of reference phantoms was developed: (1) CT image data sets of individuals close to the Reference Man and Reference Woman (height and weight) were needed; (2) the data sets were segmented; (3) the body heights were adjusted to reference values by scaling the voxels; (4) the skeletal masses were adjusted to the reference values; (5) individual organs were adjusted to reference values by adding and subtracting voxels. In 1994, Zubal et al. from Yale University published a head-torso model named VoxelMan which was developed from CT images.78 The original phantom was used for optimizing nuclear medicine imaging. Improvements to the original phantom were made with a magnetic resonance imaging (MRI) scan data of a human brain. This phantom is commonly referred to as the “Zubal Phantom” by users who are allowed to freely download the original data through the Internet. Two early users later revised the original data to report what are known as the MANTISSUE3-6 and VOXTISS8 phantoms by attaching arms and legs in two different positions to the original torso phantom.79,80 Adopting this publically available data, Kramer et al. from Brazil developed an adult male phantom named MAX (Male Adult voXel) in 200320 and later an adult female phantom named FAX in 200421 both adjusted according to ICRP-89 reference body heights and organ masses. As described in Chapter 7, Kramer et al. revised the skeletons (cortical bone, spongiosa, medullary yellow bone marrow, and cartilage) of MAX and FAX in 2006 to improve their compatibility with the latest ICRP-103 recommendations. These revised phantoms are known as MAX06 and FAX06. The work by Kramer et al. is one of the earliest efforts to create ICRP-89 compatible voxel phantoms. In 1996, Dimbylow from the National Radiological Protection Board (NRPB) (which has been recently renamed to Health Protection Agency) in the United Kingdom reported the development of an adult male phantom known as NORMAN from MR images.41 NORMAN, which has a body height similar to the ICRP Reference Man, was first used by Dimbylow in a finite-element simulation code to determine the specific energy absorption


21

rate from exposures to nonionizing electromagnetic fields.42 In 1997, his colleague Jones adopted NORMAN to estimate organ doses from external and internal photon sources.43 In 2005, Dimbylow developed an adult female phantom, NAOMI, also from MRI scans.44 The phantom was rescaled to a height of 1.63 m and a mass of 60 kg, the dimensions of the ICRP Reference Woman. However, to date, the NAOMI phantom has been used only in nonionizing radiation calculations. In 2005, a revised version of the NORMAN phantom, called NORMAN-5, was created by Ferrari & Gualdrini from ENEA-ION Istituto di Radioprotezione in Italy to derive external photon dose data.46 On year later, Dimbylow merged the NAOMI with the stylized fetal phantoms developed by Chen to create a series of hybrid phantoms of pregnant women.47 The process of adjusting two types of geometrical information was reported to be cumbersome. In 1999, Caon et al. from Flinders University, Australia, reported a torso phantom named ADELAIDE created from CT images of a 14 year old girl.23,24 As described in more detail in Chapter 4, this phantom was interesting because, for some time, it was the only set of data for a teenage girl and their studies provided CT dose estimates for this patient group. Chapter 4 also includes discussion on how to define the voxel geometry for Monte Carlo calculations. Caon later summarized his and other researchers’ experience on voxel phantoms.110 Chapter 6 provides a detailed review of the work related to the VIP-Man voxel phantom reported in 2000 by Xu and two of his students at Rensselaer Polytechnic Institute (RPI) in the United States.63 VIP-Man was the first phantom that was based on cross-sectional color photographic images of a cadaver. The original photographs were of a 39 year old male, which were made available through the National Library of Medicine’s famous Visible Human Project (VHP). VIP-Man is unique because the digitally captured color transversal photos had the best resolutions at that time: the pixel resolution is 0.33 mm × 0.33 mm and each photograph was taken after removal (by shaving) of each successive 1 mm layer by a cryomacrotome.63 The VIP-Man phantom consists of more than 3.7 billion voxels, and the original images were segmented to yield more than 1400 organs and tissues, although only approximately 80 were ultimately adopted for radiation dosimetry purposes. With ultrafine and color images, attempts were made to segment and label a number of small and radiosensitive tissues: stomach mucosa, skin, and red bone marrow. The finalized VIP-Man phantom had a heavy body mass of 103 kg, which served as an interesting variation from the ICRP reference value. The VIP-Man was used for a large number of studies in health and medical physics, as described in Chapter 6. Later, this group would extend the 3D phantom into 4D using the NURBS technique to be discussed in the section below. It should be noted here that several other groups in Table 1.2 also used the VHP images, but they primarily considered the CT data set without the arms. In 2004, Shi and Xu from RPI also reported the Pregnant Woman phantom developed from rare partial-body CT images of an 8 month pregnant patient.64 Chapter 12 is dedicated to phantoms for pregnant women and fetuses, including the earlier stylized phantoms by Stabin et al. as well as a series of BREP phantoms. Using CT scans of a physical phantom called RANDO, the RPI group created a voxel RANDO phantom for studies involving external beam treatment. Realizing the need for additional phantoms representing children of various ages, Bolch and colleagues from the University of Florida (UF) developed a series of pediatric voxel phantoms that appeared between 2002 and 2006 which represented children with ages ranging from newborn to 15 years old.69–71 Chapter 8 describes an early procedure they used for developing the newborn voxel phantoms. This approach was later extended to two groups (Groups A and B) of phantoms. Group A is composed of male and female

22


voxel phantoms of a newborn, a 1 year old, a 5 years old, a 10 years old, and a 15 years old for whom the phantom stature, total weight, and individual organ masses are targeted to within 1% of ICRP Publication 89 reference values. Group B phantoms are constructed by scaling the Group A phantoms up and down to yield phantom at each 1 year age interval, from newborn to 15 years old. The intent of the UF pediatric series was to provide a reference library of phantoms that could be matched to individual patients for age-specific organ dose assessment. Chapter 8 also describes their more recent efforts to develop BREP phantoms using the NURBS techniques that will be reviewed in the section below. Two Japanese groups were noted in Table 1.2 for their independent efforts to develop voxel phantoms since 2001.52–61 As described in Chapter 9, Saito et al. from the Japanese Atomic Energy Research Institute (JAERI) developed an adult male model named Otoko (the first Asian phantom) and an adult female phantom named Onago. More recently, Saito et al. has developed the JM, JM2, and JF phantoms which have a refined vertical slice thickness.52–56 These phantoms were used mainly for radiation dosimetry applications in Japan. The work of Saito et al. was influenced by earlier projects at the GSF, Germany. The other group, Nagaoka et al., from the National Institute of Information and Communications Technology (NIICT), Japan reported an adult male model, named TARO, and an adult female model, named HANAKO, developed from MR images for radiofrequency electromagneticfield studies.57–60 Later Nagaoka et al. would use a free-form deformation (FFD) to change the exterior features of the adult male phantom to develop Deformed Children phantoms of 3 years old, 5 years old, and 7 years old.61 The authors reported that it was difficult to develop these phantoms with the FFD algorithm and the internal organs are not adjusted to age-dependent values. As described in Chapter 10 by Lee and Kim, since 2004, several Korean phantoms have been developed by researchers at Hanyang University in Korea from various image sources: Korean Man (KORMAN), Korean Typical MAN-1 (KTMAN-1), Korean Typical Man-2 (KTMAN-2), and High-Definition Reference Korean (HDRK), and Korean WOMAN (KORWOMAN). The HDRK phantom was based on sectioned color photographs of an adult male cadaver that has high image resolution.39,40 The early work on these phantoms was carried out by the two Lee brothers who moved in the early 2000s to the University of Florida, where they gradually published work on the Korean phantoms and also made important contributions to the NURBS-based phantoms later. Kim spent several years in the United States to complete his PhD from Texas A&M University and then to serve as a research professor at RPI before returning to Hanyang University as a faculty member in the early 2000s. Their separate involvements in the area of voxel phantom development clearly originated from their experiences in the United States. Three voxel phantoms representing an adult Chinese male have been reported since 2007: CNMAN produced from color photographs of a cadaver by the China Institute for Radiation Protection,17 VCH produced from a different set of cadaver color photographs by the Huazhong University of Science and Technology,48–50 and CVP produced from MR images by Tsinghua University.66,67 The Chinese government undertook the Chinese version of the VHP that resulted in multiple cadaver image data sets, some with slice thickness as fine as 0.2 mm. These phantoms and their applications in ionizing radiation dosimetry are described and compared in Chapter 11. The lead developer of the CNMAN phantom, B Zhang, served as a research associate at RPI in 2007–2008. ORNL has not been actively involved in the development of voxel phantoms, although Eckerman was instrumental in the work at GSF related to the ICRP Reference Computational Phantoms and a number of voxel phantom projects at several universities in the United States. The only reported effort was that of Akkurt et al. in 2008 that involves a hybrid of voxel and stylized geometries.62


23

People who have been interested in nonionizing radiation applications form a different group of voxel phantom developers listed in Table 1.2. Most of this work was neglected in the previous review articles by Caon110 and by Zaidi and Xu.111 Interestingly, the phantoms used for studies of temperature rise in the human body from the interactions of radiofrequency energy were constructed through nearly identical steps and some of these phantoms, such as the NORMAN phantom, have been used for both ionizing and nonionizing radiation applications. However, the majority of voxel phantoms were developed with only one application in mind. Table 1.2 clearly labels voxel phantoms that have been used for nonionizing applications: the Visible Man from the VHP color photographs by the Brooks Air Force,15,16 the DAM adult male phantom from MR images by a group in Italy,19 the SILVY 30 week pregnant woman phantom from hybrid CT (originally obtained by RPI) and MR images by the Graz University of Technology, Austria,25,26 the Virtual Family for two adults and two children from MR images by IT’IS, Switzerland,51 the MEET Man from VHP color photographs by the University of Karsrule, Germany,72,73 and the Anatomically Based Model from MR images by the University of Utah.74 The redundancy in developing voxel phantoms from similar image sets such as the VHP is obvious. 1.3.3 BREP Phantoms from 2000s to Present Five groups reported a total of 28 BREP-based phantoms, which are summarized in Table 1.3. Segars’s PhD thesis at the University of North Carolina was the first publication that systematically described the NURBS-based modeling techniques.81 As detailed in Chapter 5, the Cardiac-Torso (NCAT) phantom was developed from the Visible Human CT image data set and the 3D anatomy was later extended into the fourth dimension to model cardiac and respiratory motions. The beating heart model of the 4D NCAT was based on 4D tagged MRI data from a real patient. The 4D NCAT phantom offers a vast improvement over the stylized MCAT phantom, with more realistic models of the anatomy and the cardiac system, and the respiratory motions. The 4D NCAT has gained a widespread use particularly in nuclear medicine imaging research for evaluating and improving myocardial SPECT imaging. The conceptual design of the NCAT phantom also served as basis for the development of a 4D digital mouse phantom named MOBY.83,84 The 4D Extended Cardiac-torso (XCAT) phantom was recently developed as the next version of the 4D NCAT. It includes more detailed and realistic anatomy and physiology, suitable for use in higher-resolution imaging applications. The XCAT phantom includes whole-body male and female anatomies based on the high-resolution Visible Male and Female anatomical data sets.82 In addition to the basic anatomy, the cardiac and respiratory motions were also updated in the XCAT phantom. Anatomically variable XCAT phantoms are currently under development. In 2005, the research group led by Xu at RPI used the 3D VIP-Man phantom to simulate respiratory motions by adopting the gated respiratory motion data of the NCAT phantom.85 The 4D VIP-Man Chest phantom was used to study external-beam treatment planning for a lung cancer patient.86 The group later decided to apply the BREP techniques to a more challenging problem and, in 2007, reported the development of a series of phantoms representing a pregnant woman and her fetus at the end of 3, 6, and 9 month gestations.87 These phantoms, referred to as the RPI Pregnant Females, were defined by polygonal meshes which were derived from separate anatomical information of a nonpregnant female, a 7 month pregnant woman CT data set, and a mesh model of the fetus. The organ volumes were adjusted in the mesh format using a commercial software package.87 The paper by Xu et al.87 was rated one of the 10 best papers in 2007 by Physics in Medicine and Biology. Chapter 12 presents details on this set of phantoms. Continuing their triangular mesh approach,

RPI, USA

Duke University, USA (formerly with the University of North Carolina)

Developers

RPI-Pregnant females (3, 6, and 9 month)

12

Polygon meshes

NURBS

4D VIP-Man chest

6

NURBS

MOBY

NURBS

NURBS

5

Data Types

XCAT

NCAT


Organ surfaces were extracted from the 3D VIP-Man phantom and then extended to 4D by adding the respiration of the NCAT phantom. Based on a mixture of anatomical data. Organs of the mother and fetus were adjusted to match ICRP-89 references.

NURBS-based cardiac torso phantom including organs from the VHP ct data of the male and female. Gated MRI data set of a normal patient and 3D angiogram data are used for motion modeling. 4D eXtended cardiac torso phantom based on the NCAT, including more detailed and realistic anatomy and physiology. Mouse phantom from MR images.

Anatomical Features

3, 6, and 9 month pregnant female

16 week old male C57bl/ 6 mouse Caucasian 39 year old male cadaver

Caucasian 39 year old male and 59 year old female

Caucasian 39 year old male and 59 year old female

Human Subjects

I

I

I

I

I

[87]

[85,86]

[83,84]

[82]

[81]


List of Developers, in Alphabetical Order, of BREP Computational Phantoms Including Information on Phantom Names, Chapters in This Book, Phantom Data Types, Anatomical Features, Human Subjects, Ionizing versus Nonionizing Radiation, and References

TABLE 1.3


UFH-NURBS phantoms

Pregnant female (nine phantoms)

Adult and pediatric phantom series (seven phantoms)


University of Houston, USA

Vanderbilt University, USA

Adult Male and Female

13

29

8

14

Based on mesh anatomical models that Adult male and female are adjusted to match with ICRP-89 references. Software supports deformation and posture change. NURBS UF Hybrid NURBS based on previous Caucasian 6 day old female voxel phantoms. newborn cadaver, 14 year male patient and two 14 year female patients. BREP/stl Nine phantoms of limited organs A pregnant woman in the formatted covering 1–9 month pregnant females 34th gestational week and a CAD from MRI of a nonpregnant female and nonpregnant female pregnant woman. NURBS Derived from the NCAT phantom Caucasian adult male and with organ and body masses adjusted to female, newborn, 1 year old, match ICRP-89 references. 5 years old, 10 years old, and 15 years old

Polygon meshes

I

[93]

[92]

[90,91]

I

N

[88,89]

I


26


this group reported in 2008 the development of a pair of adult male and female phantoms, the so-called RPI Adult Male and Female.88,89 As described in Chapter 14, this pair of adult phantoms was carefully adjusted to match the ICRP-89 reference values for more than 70 organs and 45 bones (including cortical bone, spongiosa, and cavities) as well as muscles. Several software algorithms were systematically developed to automate the deformation and organ overlap detection that were based entirely on about 126 sets of triangle meshes. The RPI Adult Male and Female phantoms are mesh-based BREP phantoms. As an application, the female phantom was recently used to create phantoms of female workers with different breast size for the purpose of studying the effect of this parameter on the lung counting of internally deposited radionuclides.89 The mesh models had to be converted to voxels to work with Monte Carlo codes that only handle CSG shapes. In 2007 and 2008, the UF group led by Bolch reported their work on “hybrid” male and female phantoms of newborn and 15 year old patients.90,91 They created the BREP phantom series, called UFH-NURBS phantoms using the following steps. First, they segmented patient-specific CT image data from which they then generated polygonal meshes. These meshes were then converted to the NURBS format using commercial software. In this last process, they extracted several contours from the polygonal meshes, and then generated the NURBS surfaces by a software tool called “lofting.” It was then in the NURBS geometrical domain they carried out organ adjustment to match the ICRP89 reference. Therefore, their phantoms are in fact NURBS-based BREP phantoms, like those developed by Segars et al.81 In the fi nal step, the NURBS-based phantoms were voxelized so that they could be implemented in Monte Carlo calculations. However, in order to voxelize the smooth NURBS models, they transferred the NURBS surfaces back the polygonal meshes. More details are available in Chapter 8. The paper by Lee et al.90 was also rated one of the 10 best papers in 2007 by Physics in Medicine and Biology. In 2008, the Vanderbilt group led by Stabin, in collaboration with Segars from Duke University, reported a “family” of adult and pediatric phantoms by adapting the NURBSbased NCAT adult male and female phantoms.93 ICRP-89 reference body and organ values were used to adjust NURBS surfaces. The authors state in Chapter 13 several advantages of this approach: (1) NURBS-based phantoms can be developed much more quickly than working with voxels and manually segmenting individual patient image data sets; (2) the phantoms have a higher level of internal consistency; and (3) the phantoms are complete from head to toe, thus avoiding the problem of missing organs in some of the medical images. It is noted that the groups at RPI, UF, and Vanderbilt (and Duke) developed these BREP phantoms as part of the joint Virtual Patients Project funded by the NCI as well as other individual projects. The last item in Table 1.3 is a series of nine phantoms representing a pregnant female in each gestational month developed by a group from the University of Houston and the U.S. Food Drug Administration (FDA) for studying the effects of radiofrequencies emitted from various electronic devices.92 These phantoms only include a limited number of organs such as the body, placenta, embryonic fluid, bladder, bone, fetus, and the uterus. They used patient-specific MR images and CAD software to model the organ shapes. The applications of these phantoms for nonionizing radiation are covered in detail in Chapter 29.

1.4 Physical Phantoms Table 1.4 summarizes selected physical phantoms that are often used to benchmark calculations performed on computational phantoms.94–103 These phantoms are typically used


27

for three different applications: external radiation dosimetry, internal radiation dosimetry, and imaging quality assurance. For external radiation dosimetry, a physical phantom is designed so that small radiation dosimeters can be inserted in different locations of the phantom to measure doses from external irradiation. Examples of this type of phantom include the RANDO phantom by the Phantom Laboratory and the ATOM phantom by the CIRS, which contain tissue equivalent slices that have anatomical maps and cavities for organ dose measurements.94–96 Internal dosimetry phantoms are designed to either contain removable organs that are doped with long-lived radioactive materials or hollow body regions that are filled with short-lived radioactive liquids to mimic an internally exposed individual for the purposes of calibrating bio-assay devices (e.g., a lung counting or nuclear medicine imaging devices). The Physical Torso Phantom by Lawrence Livermore National Laboratory (LLNL) and the Bottle Manikin Absorption (BOMAB) phantom family by the Radiation Protection Bureau, Canada are examples of bioassay calibration phantoms. There are many phantoms that are used for image quality assurance purposes. Most of these phantoms cover only partial body and some are anatomically very simple. Table 1.4 lists examples of several such phantoms by the CIRS and Kyoto Kagaku Co. that are used for image analysis. With anatomically realistic computational phantoms discussed earlier, the UF group led by Hintenlang has fabricated several physical phantoms representing a newborn, 1 year old, and adult male. Details about this effort are provided in Chapter 16. Rapid prototyping processes offer great promise to quickly produce physical phantoms from patient-specific data.

1.5 Monte Carlo Codes Used with Computational Phantoms Computational phantoms are integrated with Monte Carlo codes that simulate radiation transport inside the human body for the purposes of determining the energy and pattern of radiation interactions. Most medical physics applications employ photons and electrons with energies up to 20 MeV and protons up to 250 MeV. But health physics dosimetry often also considers neutron sources found in nuclear reactors. For radiation protection in a high-energy physics accelerator facility or space radiation environment additional particles with energies in the TeV range must be taken into account. Each type of radiation interacts with matter differently. For example, photons (such as x-rays or gamma-rays) interact primarily via the photoelectric effect, Compton scattering, and pair production processes.121 The probability of an interaction occurring within an organ or tissue is determined by “nuclear cross sections” that are associated with the specific radiation energy, the tissue electron density, and the tissue chemical composition. Mathematically, the differential cross section per electron for a photon undergoing the Compton scattering at angle ϕ per unit solid angle Ω is analytically determined by 2

r 2 ⎛ h ν′ ⎞ ⎛ h ν h ν′ dσ ⎞ = 0 ⎜ + − sin 2ϕ⎟ ⎟ ⎜ ⎠ dΩϕ 2 ⎝ h ν ⎠ ⎝ h ν′ h ν

(1.2)

where r0 is the classical electron radius, hν and hν¢ are the photon energies before and after the scattering, respectively. Extensive photon cross-section libraries have been developed.122,123

RANDO

Alderson Research Laboratories (acquired by the Phantom Laboratory), USA

LLNL, USA

CTU-41

Kyoto Kagaku, co. LTD, Japan

Chest phantom N1 “lungman” LLNL

3D sectional torso phantom PBU-50

CIRS, USA

ATOM

Phantom Names

Developers

16

16

16


Bone, lung, and soft tissue are included. Standard phantom includes head, torso, upper femur, and genitalia. Legs and arms are included with the newborn and 1 year pediatric phantoms. Breasts can be added. Removable lungs, heart, liver, pancreas, kidney, and spleen are included. The skeleton, lungs, liver, mediastinum, and kidney models are embedded in soft tissue substitute. One-piece anthropomorphic torso phantom with anatomical structures. The inner components consists of mediastinum, pulmonary vasculature, and an abdomen block. Removable organs such as the lungs, heart, liver, kidneys, spleen, tracheobronchial lymph nodes are included. Chest plates simulate different chest wall thicknesses. The first generation of LLNL phantom contains a real human male rib cage.

Lungs, soft tissue, and breasts are included; natural human skeletons were used.

Anatomical Features


Japanese adult male Japanese

Caucasian adult male torso Japanese adult male

Caucasian newborn, 1, 5, and 10 year old children, adult male and female

Caucasian adult male and female

Human Subjects

I

I

[98]

[97]

[97]

[97]

I

I

[96]

[96]

[94,95]

I

I

I


List of Developers, in Alphabetical Order, of Physical Phantoms Including Information on Phantom Names, Chapters in This Book, Anatomical Features, Human Subjects, Ionizing versus Nonionizing Radiation, and References

TABLE 1.4



Radiation Health Research Institute of Korea Hydro and Nuclear Power, Korea Radiation Protection Bureau, Canada

UF newborn physical phantom

16

20

BOMAB phantom family

MIRD stylized newborn UF 1 year old UF adult male

16

Typical Korean male

Based on UF newborn voxel phantom including soft tissue, bone, and lungs.

CT images. CT images. Newborn cadaver

1 year old child Adult male

Based on CT images of a human subject. The Korean adult male phantom contains bone, lungs, and soft tissue without arms and legs. The rapid prototyping and manufacturing technique were used. Each phantom is composed of 10 elliptical Canadian 5 and containers representing head, neck, chest, 10 years old, gut, arm, thigh, and calf. reference man, reference woman, 5 and 95 percentile men. Based on stylized computational phantom. Newborn

I

I

[102,103]

[101] [101]

[101]

[100]

I

I

[99]

I


30


In general, Boltzmann radiation transport problems described by various differential, integral, and integro-differential equations can be solved by numerical computational methods including finite difference, finite element, discrete ordinates, and Monte Carlo. However, only the Monte Carlo methods are currently able to account for all aspects of particle interactions within 3D heterogeneous media such as the human body. Monte Carlo methods, which are based on statistical simulations, have a long history, but the real application to radiation transport simulations and the associated software development came from the need for nuclear weapons research at Los Alamos National Laboratory during the World War II.124 In a Monte Carlo code, random numbers are used to determine the distance and fate of a particle by comparing interaction probabilities for every geometrical region of interest. This rather tedious process is repeated for an extremely large number of particles, and each particle is tracked in the 3D anatomical model until all its energy is absorbed. The inherent statistical uncertainty can be controlled to be less than 1%, which is often more precise than an experimental result performed in a physical phantom using a dosimeter (for quantities such as the absorbed dose). Computers have significantly improved in affordability and computing power in the last 20 years. Public-domain Monte Carlo code packages are well supported by scientists at national labs and are updated constantly with the help from a very large pool of users. As a result, there has been widespread interest in recent decades in the use of Monte Carlo techniques in all aspects of nuclear engineering, health physics, and medical physics. First of all, simulations involving a computational phantom need to defi ne the anatomical geometries in terms of shapes that are accepted by a Monte Carlo code. In addition, information on the density and chemical composition for each of the identified organs and tissues of interest must be specified. Then, the radiation source terms are modeled according to a specific source-to-human-body irradiation condition. In radiation protection dosimetry involving external radiation, the most common irradiation geometries are external parallel beams impinging on the entire phantom that stands vertically in vacuum.125 The following standard irradiation geometries are often used: anterior–posterior (AP), posterior–anterior (PA), left lateral (LLAT), right lateral (RLAT), rotational (ROT), and isotropic (ISO), as discussed in detail in Chapter 18. The organ doses are often normalized by the particle fluence or air-kerma that can be measured using a radiation detector. For internal dosimetry, a quantity called the specific absorption fraction is calculated for each of the source-to-target organ pairs, as discussed in Chapter 20. A medical procedure, on the other hand, often involves partial-body irradiation and more complex source terms, such as in the case of x-ray CT examination discussed in Chapters 21 and 22. The benchmarking of these Monte Carlo calculations is often performed using a physical phantom and a radiation-delivering device in identical irradiation conditions. Most Monte Carlo codes were originally developed for nuclear engineering and high energy physics research. Although these codes have been vigorously validated for radiation physics, the software packages are often difficult to use without extensive experience. Some of the codes are not optimized for dealing with large numbers of voxels in the anatomical models. Nearly all existing Monte Carlo codes use the CSG-type of shapes. There are a few comprehensive reviews or introductory articles about the Monte Carlo methods for applications in health physics and medical physics.128–130 In the section below, several public-domain and popular Monte Carlo code systems are briefly summarized:


31

Monte Carlo N-Particle (MCNP), Monte Carlo N-Particle eXtended (MCNPX), Electron Gamma Shower (EGS), GEANT4, PENELOPE, and FLUKA. 1.5.1 MCNP and MCNPX The MCNP transport code system is a general-purpose Monte Carlo code that deals with neutron, photon, electron, or coupled neutron/photon/electron transport.131 MCNPX combines MCNP Version 5 with the LAHET intranuclear cascade code.132 MCNPX extends MCNP by supporting additional particle types, new cross-section libraries, and the ability to use physics models for energies where tabular data are not available. For years, both MCNP and MCNPX have been actively maintained by support individuals located at the Los Alamos National Laboratory. Both codes treat an arbitrary 3D configuration of materials in the CSG-type of geometric cells. Pointwise cross-section data typically are used, although group-wise data also are available. Important standard source defi nition features that make these codes very versatile and easy to use include: a powerful general source, criticality source, and surface source; both geometry and output tally plotters; a rich collection of variance reduction techniques; a flexible tally structure; and an extensive collection of cross-section data. The codes work very efficiently with the stylized and voxelized computational phantoms. However, there is a limitation on the maximum number of voxels in a computational phantom and this problem led to the increase in voxel size from 0.33 × 0.33 × 1 mm3 to 4 × 4 × 4 mm3 for the VIP-Man phantom that has been implemented in both codes (see Chapter 6). Recently, there has been an increased interest by the nuclear engineering community in developing algorithms to automatically convert CAD-based geometries to those accepted by MCNP (for examples, see an MS thesis which summarized the status126). Among several commercial software packages, the Monte Carlo automatic modeling system (MCAM) has many interesting features.127 However, none of them are able to run the BREP geometries directly. MCNP and MCNPX are widely used in the nuclear engineering and health physics communities, as well as medical physics. The future of these codes depend on the ability to meet various application needs such as those associated with advanced computational phantoms. The latest versions, MCNP 5.1.40 and MCNPX 2.6.0, can be requested from the Radiation Safety Information Computational Center (RSICC) (http:// www-rsicc.ornl.gov/). 1.5.2 EGS The EGS code system is a general-purpose package for the Monte Carlo simulations of the coupled transport of electrons and photons in an arbitrary geometry for particles with energies from a few keV up to several TeV. Some have referred to the EGS code as the de facto gold standard for medical physics radiation dosimetry. The EGSnrc system, developed and maintained by the National Research Council (NRC) of Canada, is an extended and improved version of the EGS4 package originally developed at Stanford Linear Accelerator (SLAC).133 Its current energy range of applicability is considered to be 1 keV to 10 GeV. Both NRC and SLAC have rights associated with EGS4 and EGSnrc. The VIP-Man phantom was also implemented in the EGS4 version without a problem in terms of the number of voxels. In Chapter 28, detailed information is given on the use of EGS code system for radiation treatment dosimetry using patient-specific phantoms.

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1.5.3 GEANT4 GEANT4 is a toolkit for Monte Carlo simulations of electromagnetic, hadronic and optical processes, and a large set of long-lived particles, materials, and elements.107,134 The energy range covers from 250 eV to TeV. Its functionality, modeling capabilities, and performance are continuously extended and enhanced.135 The object-oriented design of GEANT4 allows the user to understand, customize, or extend the toolkit in all domains. At the same time, its modular architecture allows the user to load and use only the components needed. In terms of geometry definition, like other codes, GEANT4 cannot directly process BREP geometries such as NURBS and meshes. The software was originally developed and supported by RD44, a worldwide consortium of more than 100 scientists from different countries in Europe and elsewhere.136 GEANT4 is widely used in Europe and the high-energy physics accelerator community, and its open-source developmental approach will likely attract more users in the future. This code was used to implement several adult and pediatric voxel phantoms to study second cancer effects in patient treated with external protons (Chapter 25). The use of GEANT4 code for treatment planning with patient specific phantoms is described in Chapter 27. This chapter also includes a discussion of an effort to model organ motions. 1.5.4 PENELOPE PENELOPE is a Monte Carlo code system for photon–electron transport simulations, which was developed and supported in Spain.137,138 The code covers a wide energy range from a few hundred eV to about 1 GeV. A mixed procedure is used for the simulation of electron and positron interactions. Photon interactions (Rayleigh scattering, Compton scattering, photoelectric effect, and electron–positron pair production) and positron annihilation are simulated in a detailed way. The cross-section data in the very low-energy region of the code allows the calculation of radiation interactions at the cellular levels. Recently, there have been several attempts to develop direct simulations with BREP-type of geometries. The latest version is PENELOPE2006.139 1.5.5 FLUKA FLUKA is a general-purpose Monte Carlo code system for an extended range of 60 different particles: photons and electrons from 1 keV to thousands of TeV, neutrinos, muons of any energy, hadrons of energies up to 20 TeV and all the corresponding antiparticles, neutrons, and heavy ions.140–142 FLUKA can handle CSG geometries. The latest version of this package is FLUKA 2008.3.5. This software was developed and supported by an Italian group. A number of stylized and voxel phantoms were implemented in the FLUKA code but, despite its excellent features, the application of this code has been relatively limited.

1.6 Discussions The shift from stylized phantoms to voxel phantoms in the late 1980s was motivated by the desire to improve upon anatomical realism. The advent of modern computers and medical imaging fueled the research efforts by many researchers whose work is listed in Table 1.2. For a long period of time in the 1990s and early 2000s, however, it was unclear to the research community what roles voxel phantoms would play. If voxel phantoms were to replace stylized phantoms, how much improvement in dose estimates should be expected? There were strong


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indications already that the approach of developing and applying the voxel phantoms was not perfect, as reviewed by Caon110 and more recently by Zaidi and Xu.111 For example, the segmentation of original images into organs and tissues required a very laborious and tedious manual process (there is only a limited number of organs such as the skeleton which can be processed automatically and semiautomatically), often taking months or even years to complete. The earlier phantoms, such as GOLEM, BABY, and CHILD, developed at GSF had relatively poor image slice thickness (from 4 to 8 mm), thus inevitably compromising the anatomical accuracy.27,28,31 Even today, there is no consensus as to what constitutes a true segmentation because the process often involves some level of user-specific assumption about the anatomy during the image analysis. Certain organs such as the GI tract have poor image contrast, and the segmentation is nearly impossible in CT without enhancement. A typical image data set at more than 2 mm × 2 mm pixel resolution is not fine enough to delineate many small radiosensitive organs. As a result, the skin of most existing voxel phantoms is defined artificially as the outermost layer of voxels. The segmentation of the red bone marrow is also challenging. Consequently, its dose is nearly always calculated empirically because it is not easy to model it directly in the phantom. When the developers of the VIP-Man phantoms reported that the red bone marrow was segmented from color pixels of 0.33 mm × 0.33 mm resolution, the work was scrutinized by others partially due to the lack of consensus about the segmentation process. The lack of standardized procedures contributed to the current situation that although many phantoms and dosimetry data are reported, the accuracy may be impossible to evaluate. Original voxel phantoms were realistic in depicting the anatomy, but they are personspecific. The anatomical differences between two equally realistic voxel phantoms surprised many developers who were used to the idea that a radiation protection phantom must represent the average population. Realizing that there would be likely only one set of such “reference” phantoms, many developers later rushed to revise the original voxel phantoms by adjusting the organ sizes in the original image data to match with the ICRPrecommended anatomical data. Others mixed anatomical sources from different subjects. In doing so, these phantoms lost the anatomical realism—which was the original motivation to abandon the stylized phantoms. In the history of voxel phantom development, Zubal was one of the first who shared the original image data with other users based on a mutual agreement. Heated debate continues regarding the intellectual property associated with the developed phantoms. It is often a technical necessity for a researcher to name a phantom that is associated with his or her research contribution. However, it is not clear who should own such a right because the four steps of developing a phantom discussed above can be carried out by different groups. One scenario is when the original images were acquired and segmented by one individual and then a different individual performed additional image processing and modification before implementing the data into a specific Monte Carlo code. Such changes produce a practically unique phantom, and proper naming is often required for research purposes. But the ownership of the product is not always agreed upon by involved individuals. Some have chosen not to share or to use phantoms partially due to this concern. Others are afraid that sharing may cost an advantage in research in a time when too many voxel phantoms exist. Since 1998, the ICRP’s Task Group on Dose Calculations (DOCAL) and the SNM’s MIRD Committee have been evaluating new dosimetry data from these tomographic phantoms. In particular, the DOCAL, which is administered by the ICRP Committee 2, has been developing guidelines to facilitate the shift from stylized phantoms. However, it is likely that other voxel phantoms will continue to be used in various applications. Recently, the ICRP has decided to establish the ICRP Reference Computational Phantoms, to be described in Chapter 15. The history of computational phantom development has shown that it is the need for application, not the need for police-making, that will determine the course of technological

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advancement. The need for simulating organ motions for cardiac imaging resulted in the developments of MCAT (a CSG type) by Tsui et al.4 using quadric and superquadric surface equations and the NCAT models (a BREP type) by Segars81 using the NURBS technology. Xu and Shi (2005) adopted the method described by Segars to develop a respiration-simulating 4D VIP-Man model for the need to understand the effects of respiration on radiation treatment.83 Using the same approach, Lee et al.90 developed the size-adjustable pediatric models.90 More recently, the BREP-based pregnant females by Xu et al.87 and those by Stabin et al.93 are also examples of application-driven research that will likely continue to dominate the research horizon in the future. There are several issues that can be addressed in the very near future regarding the BREP-based methods. The NURBS geometries are flexible and computationally efficient, but fine details may be lost on certain organs that have a complex topology. On the other hand, polygonal models can be used to create very smooth surfaces with impressive anatomical detail by paying a price of having too many vertices. The human body is a particular challenge in modeling for that it consists of organ surfaces of complex and unique shapes. For cardiac and respiratory motions at the frequency range of 10–100 cycles per second, the mesh models may still be acceptable. However, previous work has also shown that the NURBS primitives were very easy to adopt for both real-time and non real-time applications. Therefore, the specific strategy should be based on the specific applications and user preference. Regardless of the specific BREP data structure, there is currently an urgent need for application-based software that can streamline the process described in this paper. Finally, it would be a breakthrough if the Monte Carlo calculations can be performed directly and accurately in the BREP geometry. To date, the history of phantom development has been centered on the “Reference Man” paradigm which mandates a computational phantom to match approximately the 50th percentile values in terms of body height and weight for a specific gender and age group. Given the anatomical specificity in any voxel phantom, the Reference Man concept works against the original wish to improve the dose estimate in a population of workers who are obviously different from the anatomy depicted by the one voxel phantom. In contrast, the BREP phantoms may have demonstrated the feasibility to develop new-generation phantoms that represent a much broader range of individuals in terms of body height and weight, as well as organ topology. These features were impossible even 10 years ago, but the technology and collective experience of the research community seems to support that idea that we should and can move beyond the “Reference Man” paradigm. In one attempt to collaborate on research, a special conference session was organized in 2005.143 During the conference, attendees proposed to form the Consortium on Computational Human Phantoms (CCHP; www.virtualphantoms.org) to facilitate data sharing, dissemination, and intercomparison. It is expected that, in the future, the concerted efforts, such as the CCHP, will allow a consensus to be developed involving as many individuals as possible in the field of human radiation dosimetry modeling. The last chapter of this book, Chapter 30, contains some thoughts on what are needed for near and long-term research.

1.7 Summary It is clear from the more than 40 years history of anatomical modeling that in the early days, between the 1960s and the 1980s, the research community relied on the first-generation of


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CSG-type MIRD phantoms which required the least computational power. The attempt at developing realistic phantoms was first explored in the development of the CAM phantom which was little known outside of the space radiation dosimetry community. From the 1980s, the advent of medical imaging and more powerful computers made it possible to create voxel phantoms which were geometrically simple but anatomically realistic. Since the 2000s, however, several groups have demonstrated the feasibility of creating phantoms which are deformable using the BREP modeling techniques, thus opening the door for more advanced applications. For the first time, leading phantom developers and application experts collaborated to document the details of their historical research. In the subsequent chapters, you will witness a history of computational phantoms that is clearly in parallel with, and thus offers a unique perspective about, advances in computer technologies and medical imaging. By reading this book, you can obtain a unique sense of the computational phantom development process: the conception of an idea, the identification of original anatomical data, the solution of various computing problems, the ownership and sharing of results, as well as the satisfaction and frustration associated with any scientific endeavor. It is still an open question whether or not the recently developed ICRP Reference Computational Voxel Phantoms will actually improve radiation protection dosimetry. The long-time paradigm in representing a population of individuals with a limited number of “reference” phantoms seems to work against the power of image-based phantoms that are anatomical realistic, but person-specific. Is it necessary or feasible to bring about a paradigm change in this concept? If so, how should the research community participate? There is no doubt that we are at crossroads now, perhaps as we were 20 years ago. The history to be unfolded in the following chapters may help us arrive at some answers and hints about the future.

Acknowledgments Since 2003, I have been in close collaboration on projects related to computational phantoms with Drs. Michael Stabin, Randy Brill, Wesley Bolch, Harald Paganetti, and Paul Segars. Some of their ideas are reflected in this chapter. During the preparation of this chapter, Dr. Binquan Zhang, a visiting scholar, and Mr. Juying Zhang, a doctoral student, both from Rensselaer, helped me compile the phantom tables. My research in computational phantoms would have not been possible without support at different times by the following governmental agencies: National Science Foundation/CAREER Program (BES-9875532), National Library of Medicine (R03LM007964), National Cancer Institute (R42CA115122 via Vanderbilt), National Cancer Institute (R01CA116743), and National Library of Medicine (R01LM009362).

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94. Anderson, S.W. et al. An instrumented phantom system for analog computation of treatment plans, Am J Roentgenol, 87, 185, 1962. 95. The Phantom Library, http://www.phantomlab.com/rando.html. 96. Computerized Imaging Reference Systems (CIRS), http://www.cirsinc.com. 97. Kyoto Kagaku Co., LTD, http://www.kyotokagaku.com/. 98. Griffith, R.V. et al. Tissue equivalent torso phantom for intercalibration of in vivo transuranic nuclide counting facilities, in Advances in Radiation Protection Monitoring, STI/PUB/494 Proceedings of the IAEA Conference, IAEA-SM-229/56, Vienna: IAEA, 1978. 99. Kim, J.I. et al. Physical phantom of typical Korean male for radiation protection purpose, Radiat Prot Dosim, 118, 131, 2006. 100. Kramer, G.H., Burns, L., and Noel, L. The BRMD BOMAB phantom family, Health Phys, 61, 895, 1991. 101. Tresser, M.A. and Hintenlang, D.E. Construction of a newborn dosimetry phantom for measurement of effective dose, Health Phys, 76, S190, 1999. 102. Jones, A.K. et al. Tomographic physical phantom of the newborn child with real-time dosimetry I. Methods and techniques for construction, Med Phys, 33, 3274, 2006. 103. Staton, R.J. et al. A tomographic physical phantom of the newborn child with real-time dosimetry. II. Scaling factors for calculation of mean organ dose in pediatric radiography, Med Phys, 33, 3283, 2006. 104. Leyton, M. A Generative Theory of Shape, Berlin: Springer-Verlag, 2001. 105. Agostinelli, S. et al. Geant4 a simulation toolkit, Nucl Instrum Methods Phys Res Sec A, 506, 250, 2003. 106. Stroud, I. Boundary Representation Modeling Techniques, London: Springer-Verlag, 2006, ISBN 978-1-84628-312-3. 107. Geant4 Team 2007 Geant4 User’s Guide for Application Developers, http://geant4.web.cern.ch/geant4/ G4UsersDocuments/UsersGuides/ForApplicationDeveloper/html, Last accessed August 2007. 108. Wikipedia 2007, http://en.wikipedia.org/wiki/Wikipedia, a website maintained by Wikipedia, the free encyclopedia. Last accessed August 2007. 109. Zaidi, H. and Sgouros, G. Therapeutic Applications of Monte Carlo Calculations in Nuclear Medicine, Bristol: IoP, 2003. 110. Caon, M. Voxel-based computational models of real human anatomy: A review, Radiat Environ Biophys, 42, 229, 2004. 111. Zaidi, H. and Xu, X.G. Computational anthropomorphic models of the human anatomy: The path to realistic Monte Carlo modeling in radiological sciences, Annu Rev Biomed Eng, 9, 471, 2007. 112. International Commission on Radiological Protection. Report of the Task Group on Reference Man, ICRP Publication 23, 1975. 113. Hwang, J.M.L., Shoup, R.L., and Poston, J.W. Mathematical description of a newborn human for use in dosimetry calculations, ORNL/TM-5453, Oak Ridge, TN: Oak Ridge National Laboratory, 1976. 114. Jones, R.M. et al. The development and use of a fifteen-year-old equivalent mathematical phantom for internal dose calculations ORNL/TM-5278, Oak Ridge, TN: Oak Ridge National Laboratory, 1976. 115. Deus, S.F. and Poston, J.W. The development of a mathematical phantom representing a 10-yearold for use in internal dose calculations, in Proceedings of the Symposium on Radiopharmaceutical Dosimetry, Oak Ridge, TN: HEW Publication (FDA) 76–8044, 1976. 116. Bouchet, L.G. et al. MIRD pamphlet no. 15: Radionuclide S values in a revised dosimetric model of the adult head and brain, J Nuc Med, 40, 62, 1999. 117. International Commission on Radiological Protection. Basic Anatomical and Physiological Data for Use in Radiological Protection: Reference Values, ICRP Publication 89, Oxford: Elsevier, 2002. 118. ICRU. Phantoms and computational models in therapy, diagnosis and protection. ICRU Report 48, Bethesda, MD: International Commission on Radiation Units and Measurements, 1992.


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119. Gu, J.W., Dorgu, A., and Xu, X.G. Comparison of main software packages for CT dose reporting, Health Phys, 95, s1, 50, 2008. 120. Stovall, M., Smith, S.A., and Rosenstein, M. Tissue doses from radiotherapy of cancer of the uterine cervix, Med Phys, 16, 726, 1989. 121. Attix, F.H. Introduction to Radiological Physics and Radiation Dosimetry, New York: Wiley, 1986. 122. Hubbell, J.H. Photon cross sections, attenuation coefficients and energy absorption coefficients from 10 keV to 100 GeV, NSRDS-NBS 29, 1969. 123. Storm, E. and Israel, H.I. Photon cross sections from 1 keV to 100 MeV for elements Z = 1 to Z = 100, Nuclear Data Tables A, 7, 565, 1970. 124. Hammersley, J.M. and Handscomb, D.C. Monte Carlo Methods, London, New York: Methuen; Wiley, 1964. 125. Zhang, J.Y., Na, Y.H., and Xu, X.G. Development of whole-body phantoms representing an average adult male and female using surface-geometry methods, Med Phys, 35, 2875, 2008. 126. Furler, M. Methods of converting geometry in CAD to MCNP code. MS thesis, Rensselaer Polytechnic Institute, May 2007. 127. Liu, X et al. Development & application of MCNP auto-modeling tool: MCAM 3.0, in Proceedings of the 23rd Symposium on Fusion Technology, Venice, Italy, 2004. 128. Raeside, D.E. Monte Carlo principles and applications, Phys Med Biol, 21, 181, 1976. 129. Turner, J.E., Wright, H.A., and Hamm, R.N. A Monte Carlo primer for health physicists, Health Phys, 48, 717, 1985. 130. Andreo, P. Monte Carlo techniques in medical radiation physics, Phys Med Biol, 36, 861, 1991. 131. X-5 Monte Carlo team, 2003 X-5 Monte Carlo team. MCNP—A general Monte Carlo N-particle transport code, Version 5, volume I: Overview and theory, LA-UR-03-1987, Los Alamos National Laboratory, 2003. 132. Pelowitz, D.B. MCNPX User’s Manual Version 2.5.0, Los Alamos National Laboratory, Report LA-CP-05-0369, 2005. 133. http://www.irs.inms.nrc.ca/EGSnrc/EGSnrc.html 134. Agostinelli, S. et al. Geant4—A simulation toolkit. Nucl Instrum Methods Phys Res A, 506, 250, 2003. 135. Allison, J. et al. Geant4 developments and applications, IEEE Trans Nucl Sci, 53, 270, 2006. 136. http://geant4.web.cern.ch/geant4/collaboration/working_groups.shtml#wg.Run 137. http://www.nea.fr/html/dbprog/peneloperef.html 138. Salvat, F., Fernandez-Varea, J.M., and Sempau J. PENELOPE, a Code System for Monte Carlo Simulation of Electron and Photon Transport, France: OECD Publications, 2003, ISBN 92-64-02145-0. 139. Salvat, F., Fernández-Varea, J.M., and Sempau, J. PENELOPE, a code system for Monte Carlo simulation of electron and photon transport. Workshop Proceedings, Barcelona, Spain, 2006. 140. http://www.fluka.org/fluka.php 141. Fasso, A. et al. The FLUKA code: Description and benchmarking, in CERN-2005-10, INFN/ TC_05/11, SLAC-R-773, 2005. 142. Battistoni, G. et al. FLUKA: A multi-particle transport code, in Proceedings of the Hadronic Shower Simulation Workshop. September. Fermilab 6–8: AIP Conference Proceeding, Batavia, IL, 2006. 143. Tomographic models for radiation protection dosimetry session. Monte Carlo 2005 Topical Meeting: The Monte Carlo Method: Versatility Unbounded In A Dynamic Computing World, Chattanooga, TN, April 17–21, 2005.

2 Stylized Computational Phantoms Developed at ORNL and Elsewhere Keith F. Eckerman, John W. Poston, Sr., Wesley E. Bolch, and X. George Xu

CONTENTS 2.1 Introduction .................................................................................................................43 2.2 Historical Developments of Stylized Anthropomorphic Phantoms ................... 45 2.2.1 Phantoms Developed in Early 1960s by Reddy, Callahan, and Brownell ................................................................................................. 45 2.2.2 The MIRD-5 Phantom Developed in 1960s by Fisher and Snyder ........ 45 2.2.3 The Early Pediatric Phantoms Developed by Poston and Coworkers .............................................................................................. 48 2.2.4 The Stylized “Family” Phantom Series Developed in 1980s by Cristy and Eckerman .............................................................................. 49 2.2.5 Works Associated the MIRD Committee ..................................................54 2.2.6 Stylized Models of the Lower Abdomen .................................................. 56 2.2.7 Stylized Phantoms Representing Pregnancy Women ............................ 57 2.2.8 Other Stylized Models of the Human Anatomy ...................................... 58 2.2.9 GSF Gender-Specific Phantoms, ADAM and EVA .................................. 58 2.2.10 The CAM Phantom Developed by NASA for Space Radiation Dosimetry .................................................................................... 59 2.3 Summary ...................................................................................................................... 60 References ............................................................................................................................... 61

2.1 Introduction In internal radiation dosimetry, a phantom is a mathematical representation of the human body which, when coupled with a Monte Carlo radiation transport computer code, can be used to estimate the absorbed dose to tissue and organs of the body from radionuclides distributed in the body—either uniformly or located in specific organs. The phantom description includes information on the elemental compositions and densities of the body or specific organs to allow the Monte Carlo code to track the radiation interactions and energy deposition in the body. The first-generation computational phantoms were developed to better assess organ doses from internally deposited radioactive materials in workers and patients. Some of

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the earliest dose assessment techniques were developed in the first third of the twentieth century primarily for use with interstitial radiation sources such as radium. According to Loevinger,1 the dosimetry of radioactive materials distributed in the body had been under consideration as early as the 1920s. Quimby has provided an excellent historical review of the early development of radiation dosimetry in nuclear medicine.2 The early techniques were adaptations of techniques used for external dose assessment with assumptions and corrections applied to account for the different types of radiation used.3 However, rather than being able to measure the exposure or the absorbed dose, an internal dose assessment required a calculation. Internal dose calculations were performed during early days using the formulation presented by Marinelli and his colleagues in the 1940s.4,5 These equations considered only the absorbed dose from beta-emitting radionuclides (classified as nonpenetrating radiation) and from gamma rays (penetrating radiation) emitted in the decay of these radiation sources. For beta radiation, the simple equation was Dβ = 73.8E βCT where Dβ is the absorbed dose (rad) Eβ is the average beta particle energy (MeV) C is the radionuclide concentration (μCi/g) T is the effective half-life of the radionuclide (days) This simple formula only required that the average beta energy, the concentration, and the effective half-life of the radionuclide be known. This equation assumes that the emitted beta-radiation energy was totally absorbed in the tissue or volume of interest. For a radionuclide that also emits gamma radiation, the equation was D γ = 0.0341CT Γg where Dγ is the absorbed dose (rad) C is the radionuclide concentration (μCi/g) T is the effective half-life of the radionuclide (days) Γ is the specific gamma-ray constant for the radionuclide (R/μCi–h) g is a geometry factor For gamma radiation, the geometry factor was applied to account for the radiation energy escaping the volume of interest (the organ) without contributing to the absorbed dose. Later, Loevinger and his colleagues elaborated on these formulations and provided dose calculations for simple shapes such as right circular cylinders as well as specific organs of interest, e.g., the thyroid gland.6,7 In 1959, the International Commission on Radiological Protection (ICRP) used very simple models for the internal dosimetry calculations associated with the Report of ICRP Committee II.8 In these calculations, each organ of the body was represented as a sphere with an “effective radius.” The radionuclide of interest was assumed to be located at the center of the sphere and the “effective absorbed energy” was calculated for each organ. Corrections were made for the photon energy lost from the sphere (similar to the Marinelli approach). In this approach, even the total body was represented as a 30 cm radius sphere. It is also interesting to note that the 30 cm radius sphere

Stylized Computational Phantoms Developed at ORNL and Elsewhere

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was used for an organ designated as “muscle” as well as for the small intestine and the entire gastrointestinal tract. At the time, these approaches provided reasonably accurate estimates of the dose from a distributed radionuclide. However, most dosimetrists and researchers hoped for improved techniques and more accurate dosimetry estimates as technology developed. There was also a need for dose calculations for a number of new radionuclides introduced into nuclear medicine and more was known regarding the distribution and retention of these radionuclides in specific organs. Of course, the next step was to attempt to model individual organs of the body and ultimately the entire human body in a realistic manner. With the increase in the size and speed of computers, some progress occurred during the late 1950s and through the 1960s and eventually the efforts led to stylized anthropomorphic phantoms—those resemble the human anatomy.

2.2 Historical Developments of Stylized Anthropomorphic Phantoms 2.2.1 Phantoms Developed in Early 1960s by Reddy, Callahan, and Brownell Early phantoms were simple shapes: spheres, disks, and cylinders. To simplify the calculations, many early phantoms were assumed to be composed of water and no attempt was made to simulate the elemental composition of tissue.9,10 From 1964 through 1967, Reddy, Callahan, and Brownell published the results of their Monte Carlo calculations for photon dosimetry. They considered point and volume-distributed photon sources in organ phantoms represented by spheres, thick ellipsoids, flat ellipsoids, and elliptical cylinders. These authors focused on the concept of the absorbed fraction (AF) of energy, which is simply the fraction of the emitted photon energy absorbed in the region of interest. The concepts of the absorbed fraction of energy and the specific absorbed fraction, i.e., the absorbed fraction per gram of absorbing material were introduced by Loevinger and Berman in Medical Internal Radiation Dose (MIRD) Pamphlet No. 1.14 In 1968, Brownell et al. published an extensive compilation of these results for photon radiations as MIRD Pamphlet No. 3.15 In 1971, Ellett and Humes continued the calculation of absorbed fractions for small volumes containing photon-emitting radioactivity.16 The next step in development of phantoms was to construct models representing the entire trunk of an adult human. Initially, the trunk was simulated by a right circular cylinder. A typical phantom was a right cylinder 30 cm in diameter and 60 cm in height. This phantom was used for both external radiation sources as well as internal radiation sources. In some cases the phantom was subdivided into smaller regions, which could provide dose estimates for organs or tissues located in the vicinity of the target volume.17 At about the same time, models of specific, small organs (e.g., thyroid gland, gonads) were being placed inside the cylindrical phantom and again both external and internal radiation sources were being investigated. 2.2.2 The MIRD-5 Phantom Developed in 1960s by Fisher and Snyder A number of laboratories developed computational phantoms for use in their research during the late 1960s and early 1970s. To track all these developments would require an extensive discussion so the activities at the Oak Ridge National Laboratory (ORNL),

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under the direction of Walter S. Snyder, will be discussed to illustrate the rapid development of realistic phantoms. In 1966, Fisher and Snyder described the development of an adult phantom of use in dose calculations.18 The adult phantom was assumed to be standing erect with the arms at the sides of the body. Three specific regions were defi ned; the head and neck, the trunk including the arms, and the legs. The head and neck were represented by a 14 cm × 20 cm elliptical cylinder with a height of 24 cm. The trunk and arms were modeled as a larger elliptical cylinder, 20 cm × 40 cm with a height of 70 cm. The legs below the buttocks were modeled as a truncated elliptical cone with a height of 80 cm. Regions of little dosimetric importance were not included, e.g., the hands, feet, ears, nose, etc. The composition of the phantom was assumed to be tissue distributed homogeneously throughout. No attempt was made to model the lungs or skeleton or to defi ne the locations of specific organs in the phantom. Approximately 120 subregions were defi ned in the phantom, which were used to assign approximate values of the absorbed doses to organs located within specific regions. In some cases, absorbed dose estimates for large organs required the evaluation of the doses deposited in several of these regions. In 1967, Fisher and Snyder reported on the development of an adult phantom with 22 internal organs and more than 100 subregions.19 This phantom represented the next step in the development of anthropomorphic phantoms for use in dose calculations. Although the skeleton and lung regions were represented in the phantom the phantom was homogeneous as these regions were not assigned specific densities and elemental compositions. This point was very misleading in that many of the early drawings indicated such regions. However, a careful reading of the research reports from the ORNL group will confirm the homogeneous composition. Estimates of the absorbed dose to the skeleton were obtained by summing all the energy deposited in the entire skeleton and dividing by the mass associated with the skeletal volume of unit density. No special treatments were applied to obtain absorbed dose estimates for the lungs. Calculations using the adult phantom agreed well with those of Ellett et al.11–13 but were about 50% lower than those obtained using the ICRP spherical organ methods.8 Even though the original phantom was designed for use with internally deposited radionuclides, Snyder saw many other applications. In addition, in 1967, he used the phantom to study the distribution of dose in the body from external, point sources of gamma rays.20 He studied four photon energies (0.07, 0.15, 0.5, and 1.0 MeV) and four different source locations at distances of 1 and 2 m from the center of the phantom. A heterogeneous phantom was the next logical step in the development of anthropomorphic phantoms. Snyder and his colleagues21 published an extensive compilation of data on the absorbed fraction of energy for monoenergetic photons sources, uniformly distributed in selected organs in this phantom. This phantom was composed of three regions: skeleton, lungs, and the remainder (soft tissue). The densities of these regions were about 1.5, 0.3, and 1.0 g/cm3, respectively. The organ masses were selected to follow as closely as possible the data of Reference Man.22 This phantom ultimately became known in the nuclear medicine community as the “MIRD phantom.” The MIRD phantom was developed by Snyder’s research group at ORNL and, even though Snyder chose to call the phantom a “standard man approximation,” it was based on information being compiled for ICRP Publication 2322 on Reference Man. In reality, there was a parallel effort at the time to provide improved estimates of absorbed dose and dose equivalent for the ICRP. These estimates were later published as the ICRP Publication 30 series.23


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The heterogeneous phantom and its application contained three principal idealizations: 1. Simple geometrical shapes were used to approximate the shape of the body and its organs. Twenty-two internal organs were included in the design but other unimportant structures (the nose, hands, feet, etc.) were ignored. It was assumed that each organ or tissue was homogeneous in terms of composition and density. However, different compositions and densities were used for the skeletal region, the lungs, and the remainder of the body (soft tissue). 2. The monoenergetic radiation source was assumed to be uniformly distributed in one or more of the organs (i.e., the source organs). 3. Monte Carlo methods were used to obtain a set of photon histories upon which estimates of the energy deposited in the organs and tissues (i.e., target organs) of the phantom could be derived. Only photon transport was considered and energy transferred to electrons by photon interactions was assumed to be deposited locally. Fifteen source organs and 25 target organs or regions were included in the set of calculations. Twelve photon energies were considered ranging from 0.01 to 4.0 MeV. A limited number of histories were followed in each calculation (25,000–50,000 histories) and, for low-energy photons; the absorbed fractions of energy for many organs were unreliable and were not included in the tabulations. In 1978, Snyder et al. published the results of an extensive set of calculations of specific absorbed fractions of energy using an improved heterogeneous phantom.8 As with previous calculations, these results were for monoenergetic photon sources uniformly distributed in organs of the heterogeneous phantom. However, significant improvements had been made in the phantom. These improvements included 1. The head section was represented by a right elliptical cylinder topped by half and ellipsoid. 2. The leg regions consisted of the frustums of two circular cones. 3. The male genitalia were moved to a position outside the trunk and on the front of the revised model of the legs. 4. Detailed descriptions of the scapulae and the clavicles were included. 5. The stomach and upper and lower large intestine were modeled as organs with walls and contents. 6. A source and target region representing skin was added to the phantom. Twenty source organs and 20 target organs or regions were included in the set of calculations. Twelve photon energies were considered ranging from 0.01 to 4.0 MeV. The number of histories followed was increased to 60,000 in hopes of increasing the reliability of the results. However, as before, calculations for low-energy photons were very unreliable and other methods were used to provide an estimate of the specific absorbed fractions in these cases. Over the years, a number of changes (improvements) to the heterogeneous phantom have been made. However, the fundamental use of the phantom for internal dose calculations has remained essentially the same since its inception. Initially, Monte Carlo transport codes used in internal dose assessment were capable only of transporting photons. Perhaps one of the most widely used computer codes was

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the ALGAM code developed at ORNL.25 This code was used with the original Snyder– Fisher phantom and continued to be the basic code used for calculations by the ORNL group as the complexity of the phantom increased. In these calculations, it was assumed that electrons, created by photon interactions, deposited their energy at the point of creation. That is, the ALGAM code was not capable of transporting electrons. Computer codes, developed more recently, now have the capability of transporting both photons and electrons, including the production of bremsstrahlung, and considering other types of photon and electron interactions.26,27 Subsequently investigations using a coupled electron/photon transport code indicated that for most situations, this apparent deficiency was not significant.28,29 2.2.3 The Early Pediatric Phantoms Developed by Poston and Coworkers Development of the adult human phantom by Snyder and his colleagues was paralleled by the development of phantoms representing humans of other ages.18 These phantoms represented children with ages of 0 (newborn), 1, 5, 10, and 15. These early designs were assumed to have outer dimensions that represented the average height, surface area, and body mass of a child of the particular age. All “pediatric phantoms” were obtained by applying a set of simple transformations to the axes of the Cartesian coordinate system in which the adult phantom was defi ned. These phantoms became known as the “similitude phantoms” because of their resemblance to children. This approach had its limitations because children are generally not just “little adults.” However, these phantoms were the first developed to answer a real need in the nuclear medicine community.30 Improvements in the pediatric models were closely linked with the development of the heterogeneous adult phantom. Even though these new phantoms were heterogeneous, the pediatric phantoms were obtained through the same transformation method. The outside dimensions were obtained by applying a series of transformations to the coordinate system and no consideration was given to the actual organ sizes or shapes of the “transformed” organs. Although the masses of these “transformed organs” had to be known for the calculation of absorbed dose, these masses were never published.31 The limitations associated with transforming the major axes of the adult phantom should be clear. Children are not simply small adults and their organs are not necessarily “smaller adult organs.” Some organs are larger in a child than in the adult and get smaller as the individual approaches adulthood, e.g., the thymus. In addition, it was important that the actual size, shape, location, and mass of each organ be known for accurate dose calculations. For these reasons, and others, a significant effort was undertaken at ORNL during the mid-1970s to develop individual pediatric phantoms based upon a careful review of the existing literature for each particular age. This effort produced the next generation of mathematical phantoms that, although they appeared to be modeled after the adult, were designed independently. Three “individual phantoms” were designed by Hwang et al.32,33 These were the newborn, the 1- and 5-year-old models. A separate effort was undertaken by Jones et al.34 for the 15 year old, and Deus and Poston35 undertook the design of a 10 year old after the other four designs were complete. The development of the 10 year old was significantly different from those for the other four ages. In fact, this design was intended to point the way to the next generation of more realistic phantoms. Even though the design was completed and used for a limited number of dose calculations, it was not popular because of the very complex geometry and other approaches to the development of phantoms were followed.36,37


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2.2.4 The Stylized “Family” Phantom Series Developed in 1980s by Cristy and Eckerman Cristy of ORNL developed a new series of stylized models of various ages in 1980.36 The series included an adult, a newborn, and individuals of ages 1, 5, 10, and 15 developed from anthropological data (legs, trunk, and head) and from age-specific organ masses published in ICRP Publication 23.22 Although some of the organ shapes and centroids were still obtained using the similitude rule from the Snyder–Fisher adult model, these phantoms represented a great improvement for pediatric dosimetry over the similitude pediatric phantoms. These hermaphrodite phantoms presented new regions and improvements such as a new stylized breast tissue region for all ages, the inclusion of the new model of the heart developed by Coffey,38,39 and a new model of the thyroid. While the ORNL pediatric model series was initially published in 1980 by Cristy,36 these models were not readily utilized until 1987 with the publication of ORNL/TM-8381 authored by Cristy and Eckerman.37 The mathematical phantoms were designed by Cristy36 after the adult phantom of Snyder et al.43 but has different densities and chemical compositions for lung, skeletal, and soft tissues. (The term “soft tissues” will be used herein for all near-unit-density tissues, i.e., density, 1 g/cm3.) These phantoms have been described by Cristy,36 but several changes were introduced since the 1980 report and are summarized in ORNL/TM-8381 authored by Cristy and Eckerman.37 One major change was that the age-15 phantom was modified to represent both a 15-year-old male and an adult female, following the observation that the body weight and dimensions of a reference adult female are approximately the same as those in the age-15 phantom.22 The breasts, the ovaries, and the uterus in the age-15 phantom were modified to be appropriate for an adult female. Also, the size of the liver was changed slightly, and the position of the gallbladder was changed so as not to overlap the new liver. These changes are noted in the description of these organs. This phantom is labeled “15-AF” in subsequent publications. These changes coincided with efforts at GSF, Germany to develop gender-specific phantoms, such as the so-called ADAM and EVA—a revised version of the MIRD-5 phantoms reported by Kramer et al.40 The latest phantoms were used with the ETRAN Monte Carlo photon transport code41,42 to calculate specific absorbed fractions of energy in all five pediatric phantoms, as well as in the adult male, for 12 photon energies (0.01–4.0 MeV). Electron transport was not considered in these simulations and the electron energy was assumed to be locally deposited. The phantom labeled “Adult male,” although a hermaphrodite, in the descriptions below is the Snyder adult phantom,43 with certain organs modified as described by Cristy.36 In brief, these modifications were the following: female breast tissue was added to the trunk (this phantom, like all the others, is hermaphroditic and could represent a larger than average adult female), and the improved heart model of Coffey39 was fitted into the trunk. The lungs had to be redesigned to accommodate the new heart; the difference in size between right and left lungs—not represented in the Snyder phantom— was incorporated into the new design. The head was redesigned to incorporate the ideas of Hwang, Shoup, and Poston, 32 including a change in position of the thyroid. The gallbladder of Hwang et al.33 was added. A modification of the descending colon was made to eliminate a small overlap with the pelvic skeleton and to make the wall thickness uniform. Other minor changes were made so that the “Adult male” phantom would be consistent with the manner in which certain organs were fitted into the pediatric phantoms: the position of the adrenals, the position of the gallbladder, the size


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of the pancreas, and the shape and position of the thymus were all changed for this reason. Two additional modifications to the “Adult male” phantom were made by Cristy and Eckerman.37 The volumes of the breasts and the uterus have been changed slightly to be consistent with the “15-AF” phantom. Eckerman and Ryman44 revised the head region to include a neck, added the esophagus, and consideration of the extra thoracic airways. Another noteworthy change was the chemical composition and density of each type of tissue in the phantoms (skeletal, lung, and soft tissues). Also, compositions of the skeletal and soft tissues of the newborn became different from those at other ages. These changes also affected organ masses and whole body masses which were tabulated by Cristy and Eckerman.37 The same phantom descriptions and diagrams were purposely followed in a series of ORNL technical reports by Snyder et al.,21 Cristy, 36 and Cristy and Eckerman.37 As shown in Figure 2.1, each phantom consists of three major sections: (1) an elliptical cylinder representing the trunk and arms; (2) two truncated circular cones representing the legs and feet; and (3) a circular cylinder on which sets an elliptical cylinder capped by half an ellipsoid representing the neck and head. Attached to the legs section is a small region with a planar front surface to contain the testes. Attached to the trunk are portions of two ellipsoids representing the female breasts (not shown in Figure 2.1). The

2A1 2B1

ORNL-DWG. 74-9373

H2 2A2

H1

Phantom dimensions and dose regions Age (year)

Weight (kg)

H1 (cm)

H2 (cm)

H3 (cm)

A1 (cm)

B1 (cm)

A2 (cm)

0 1 5 10 15 Adult

3.148 9.112 18.12 30.57 53.95 69.88

23 33 45 54 65 70

13 16 20 22 23 24

16 28.8 46 64 78 80

5.5 8 11 14 18 20

5 7 7.5 8 9 10

4.5 6.5 6.5 6.5 7 7

H3

The adult human phantom FIGURE 2.1 The adult male phantom and associated dimensions. The same descriptions and diagrams like this were purposely followed in a series of ORNL technical reports by Snyder et al., 21 Cristy, 36 and Cristy and Eckerman. 37


51

ORNL–DWG 79–19955

0 year

1 year

5 year

10 year

15 year

Adult

FIGURE 2.2 External views of the age-specific phantom, phantoms representing an adult and children at 15 (adult female), 10, 5, 1, and 0 (newborn) years of age. When used for an adult female, the 15 year phantom has breasts appropriate for a reference adult female, which are not shown. (From Cristy, M. and Eckerman, K.F. Specific absorbed fractions of energy at various ages from internal photon sources, ORNL/TM-8381, Oak Ridge, TN: Oak Ridge National Laboratory, 1987.)

arms are embedded in the trunk, and minor appendages such as fingers, feet, chin, and nose are omitted. Drawings depicting the external features of all the family phantoms are shown in Figure 2.2. The pediatric phantoms were designed to form a developmentally consistent family with the existing Snyder adult phantom. The exterior of each phantom has approximately the form of the human body; but, as in their adult phantom, there has been no attempt to introduce small variations which would be presumed to have only a small effect on the scattering of photons. Similarly, the description of the interior organs, while approximately correct as to size, shape, position, composition, and density, are simplified to provide formulas which are readily calculated on a digital computer. The exact specifications of the phantom and the internal organs are given below. Figure 2.3 shows a schematic view of the principal organs. In these phantoms, the body is represented as erect with the position z-axis directed upward toward the head. The x-axis is directed to the phantom’s left, and the y-axis is directed toward the posterior side of the phantom. The origin is taken at the center of the base of the trunk section of the phantom. In general, the dimensions (cm) are given to two decimal places. The use of two decimal places does not imply that the average dimensions in some human population are known to such precision. This use is for convenience in designing the organs with correct volumes and spatial relationships. The trunk section includes the arms and the pelvic region to the crotch. The female breasts are appended to the outside of the trunk section. The volumes and masses for the trunk given above do not include the breasts. The head section includes a neck, represented by a right circular cylinder, and the head, consisting of a right elliptical cylinder topped by half an ellipsoid. The trunk, exclusive of the female breasts, is represented by a solid elliptical cylinder. The legs region of each phantom consists of the frustrums of two circular cones.


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Brain Skull Spine

Arm bone

Ribs

ORNL-DWG 56-B212AR2 Organs not shown Adrenals Stomach Marrow Pancreas Skin Spleen Ovaries Testes Thymus Thyroid Lungs Leg bones Heart

Liver Upper large intestine Uterus Bladder

Gall bladder Kidneys Small intestine Lower large intestine

Pelvis

0

5

10

Centimeters FIGURE 2.3 Anterior view of the principal organs in the head and trunk of the adult phantom developed by Snyder et al.43 Although the heart and head have been modified, this schematic illustrates the simplicity of the geometries of the organs. (From Cristy, M. and Eckerman, K.F. Specific absorbed fractions of energy at various ages from internal photon sources, ORNL/TM-8381, Oak Ridge, TN: Oak Ridge National Laboratory, 1987.)

The phantom developers clearly understood the simplification of the stylized modeling techniques in the phantom development. Figure 2.4 compares the idealized model of the skeleton (on the left) and a more realistic representation (on the right).37 The regional distributions of the active (hematopoietic) bone marrow and the inactive (fatty) marrow vary greatly with age. The approximate weights of the total (active plus inactive) marrow, the active marrow, and the inactive marrow as a function of age are also provided in Figure 2.4.37 Data from Hudson,45 Custer,46 ICRP, 22 and Woodard and Holodny47 were used to estimate the weight of the total marrow. The weights of active and inactive marrow were calculated from the total marrow values by the method of Cristy.48 The marrow, active or inactive, was assumed to be distributed uniformly in the bone regions defined. In calculating an absorbed fraction for active and for inactive marrow in these regions by the Monte Carlo computer program, it was assumed that the marrow absorbs energy per gram as efficiently as did bone. This assumption was not grossly in


Skeletal region defined in phantom Skull Spine Ribs Scapulae Arm bones—upper portion Clavicles Leg bones—upper portion Pelvis Total amount of active bone marrow:

53

ORNL–DWG 70-4810R2A Distribution of active marrow in adult phantom – of this report – of Snyder et al. (1974) 13.1% 28.4 10.2 4.8 1.9

8.3% 29.9 19.2 2.9 2.3

1.6 3.8

0.8 3.4

36.2

33.3

1500 g

1120 g

Active bone marrow FIGURE 2.4 The idealized model of the skeleton is shown on the left and a more realistic representation on the right. The shaded areas indicate where the active marrow is located in the adult. The amount of active marrow in given bones, expressed as the percentage of the active marrow in the body, is also given for the adult. (From Cristy, M. and Eckerman, K.F. Specific absorbed fractions of energy at various ages from internal photon sources, ORNL/ TM-8381, Oak Ridge, TN: Oak Ridge National Laboratory, 1987.)

error at energies of 200 keV or more; but it is increasingly inaccurate at energies below 100 keV, where the photoelectric effect dominates the photon interaction process. The effect is to overestimate the dose to marrow and to underestimate the dose to the bone mineral component of the mixture. The researchers noted that it was impossible to define in terms of conventional geometry the intricate microscopic intermixture of bone and marrow spaces in a more realistic fashion in the macroscopic characterization used in photon transport. As a consequence, another method of calculating this absorbed fraction was developed.37 The breasts in the age-15 male/adult female phantom have been changed from those given by Cristy36 for the age-15 phantom. The latter were designed to represent adolescent breasts. Note also that the breasts in the “Adult male” phantom as described in Cristy36 are modified slightly here to be consistent with the age-15 male/adult female phantom. The authors stated that there had been some disagreement between Kramer and coworkers49,51 and Cristy36,50 on the appropriate size of the breast for a reference adult female. Cristy36 recommends a volume of 190–200 mL for the size of a single breast, in accord with the 180 g mass recommended by the ICRP.22 Kramer and coworkers first recommended a volume of about 365 mL49 and later changed their recommendation to 260–270 mL.50 The difference in recommended representative breast sizes (~195 mL vs. ~265 mL) by Cristy and Eckerman37 is similar to the difference between the median (193 mL) and the mean (238 mL) in one study50,52 (52, 50), and the standard deviation of the mean is large (50%).

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Cristy53 argues that this difference in breast size does not yield important differences in estimates of dose to the breast from either internal or external sources of photons, except at energies well below 0.025 MeV. At such low energies the phantoms may be too simple in design to give meaningful estimates of dose to the breasts for either internal or external sources—e.g., the distribution of the radiosensitive glandular tissue within the breast could become important here. Thus, this disagreement may be academic.53 The lobes of the thyroid lie between two concentric cylinders and are formed by a cutting surface. It was stated in Cristy36 that, when compared with the adult phantom of Snyder et al.,43 the “thyroid has been moved closer to the front surface of the body, after Hwang, Shoup, and Poston.33 The thyroid had been located too deeply within the neck-and-head region for external dose calculations.54 The new position is better for external sources anterior to the body, but it will remain unsuitable for external sources from the back or sides until a separate neck region is added to the phantom design. This difficulty is unimportant for internal emitters.” The uterus is an ellipsoid cut by a plane. The uterus in the age-15 male/adult female phantom has been changed from that in the age-15 phantom of Cristy,36 to represent an adult female rather than an adolescent. The volume was calculated from the data given in ICRP Publication 23.22 The uterus in the “Adult male” phantom was also modified to be consistent with the change in the “15-AF” phantom. The shapes are slightly different because of differences in trunk shape in the two phantoms. Generally organ shapes were allowed to change according to change in trunk shape, unless there was information to the contrary.37 2.2.5 Works Associated the MIRD Committee Since the publication of the stylized dosimetric model of Snyder et al. in MIRD Pamphlet 5 Revised,24 the MIRD committee has refined several internal organs to support the development of radioimaging tracers and therapeutic nuclear medicine. Modifications to the MIRD stylized model have been published as MIRD pamphlets, which include equations of the new geometries, tabulated absorbed fractions of energy for monoenergetic photons and electrons, and tabulated radionuclide S-values. In 1999, the MIRD committee adopted six new age-specific models of the head and brain55 representing average reference brain and head for a newborn, 1, 5, 10, and 15 years old (also representing the average adult female), and adult male. These phantoms were intended to provide better dosimetric tools in response to the increased number of neuroimaging radiopharmaceuticals.56 Due to the regional uptake of these new agents within the brain, accurate absorbed dose calculations required the use of a detailed model of the subregions of the brain not available with previous models. Similar to previous stylized models, simplistic geometrical shapes (intersection of ellipsoids, elliptical cylinders, tori, and planes) were used to represent the different regions of the head and brain, with volumes derived from published reference masses57 and shapes from analysis of MRI images. Twenty-one subregions were modeled within the head and neck, including five regions representing bony structures (simulated as an homogenized mixture of bone and red marrow with a density of 1.4 g/cm3) and 16 tissue regions (density of 1.04 g/cm3). Within the brain, eight subregions were delineated: the caudate nuclei, cerebellum, cerebral cortex, lateral ventricles, lentiform nuclei (a composite region of both the putamen and the globus pallidus), thalami, the third ventricle, and the white matter. Other regions considered within the head included the cranial cerebrospinal fluid (CSF), cranium, eyes, mandible, spinal cord, spinal cerebrospinal fluid, spinal skeleton, teeth, thyroid, upper face region, and the skin.


55

In both the ORNL phantoms of Cristy and Eckerman and in the MIRD adult model developed by Snyder and his colleagues, the kidneys were represented as two symmetric ellipsoids cut by a plane with no differentiation of their internal structure. However, because of their unique physiology, the kidneys are seen to concentrate radioactivity nonuniformly.58 Snyder and Ford59 designed a dosimetric model of the human kidney to study the administration of the mecurial diuretic chlormerodrin (neohydrin) labeled with 197Hg and 203Hg. Besides the kidneys, other internal organs of interest included the ovaries and the bladder. The kidneys were assumed to be of equal size (with a total volume of 305.8 cm3) and to be composed of three separate regions, the cortex, the medulla, and the papillary region. Dose estimates were provided for the cortex, medulla, ovaries, and bladder per millicurie-hour of residence of 197Hg and 203Hg in the cortex, in the medulla, in the bladder, and in the total body (blood). MacAfee published in 1970 a multiregion kidney model of an adult60 representing the renal cortex and medulla as two concentric elliptical shells, and the renal pelvis as a wedgeshaped hollow structure at the center of each kidney. In 1975, the MIRD committee used this model in MIRD Dose Estimate Report No. 6 to calculate the dose to the renal cortex and renal medulla from 197Hg- and 203Hg-labeled-Clormerodrin.61 Patel described a multicompartment kidney model in 1988.62 This model, which was similar to the model used by Snyder and Ford,59 consisted of three regions (the cortex, medulla, and papillae) that served as both the source and target regions. The geometry of the kidney was the same as in the original Snyder–Fisher phantom, i.e., the kidney was assumed to be an ellipsoid cut by a plane parallel to the z-axis of the phantom. This model was incorporated into the Snyder–Fisher heterogeneous phantom in a modified version of the ALGAM transport code25 and absorbed fractions and specific absorbed fractions of energy were calculated for 12 monoenergetic photon sources in the range 0.01–4.0 MeV. These results were used to obtain S-values for selected radionuclides for the three regions of the kidney as the sources. The radionuclides considered were 32P, 51Cr, 57Co, 67Ga, 99mTc, 111In, 123I, 131I, 127Xe, 133Xe, and 201Tl. A new kidney model has been adopted by the MIRD committee and was published as MIRD Pamphlet No. 19.63 Following the increased use of radiopharmaceuticals in therapeutic nuclear medicine and recent associated kidney toxicity,64 the MIRD committee developed six advanced stylised models of the kidney (newborn, 1, 5, 10, 15, and adult male). The outer dimensions of these models conformed to those used in the ORNL single-region kidney models while 12 interior structures were defi ned for each kidney: five medullary pyramids, five papillae, the renal pelvis, and the renal cortex. Although the number of medullary pyramids in these models was less than that seen in the real anatomy (6–18 pyramids), it represented a compromise between the mathematical simplicity needed for Monte Carlo transport calculations, and the need for an improved anatomic representation over the concentric ellipsoid-shell model of McAfee.60 Each region was derived from dimensions and volumes given in ICRP Publication 2322 for both the newborn and the adult, and assumed constant volume ratios between the different kidney subregions for the other ages. In these models, each medullary pyramid was modeled by half-ellipsoids (two vertical and three horizontal) with the papillae at its tip, the pelvis by a portion of an ellipsoid within the whole kidney, and the cortex was the remainder of the kidney. In both MIRD Pamphlet Nos. 15 and 19, the EGS4 Monte Carlo transport code26,65 was used for photon and electron transport. In these two pamphlets, absorbed fractions of energy were tabulated for selected source and target combinations (12 energies were simulated between 10 keV and 4 MeV). Following the MIRD method of internal dose calculation,66

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mean absorbed doses to the target regions per unit cumulated activity in the source region (S-values) were tabulated for specific radionuclides. 2.2.6 Stylized Models of the Lower Abdomen The development of nuclear medicine imaging and therapy over the past decade has resulted in the need for more accurate dosimetry in regions that either were not represented or poorly represented in the MIRD and ORNL stylized phantoms. The lower abdomen is a particular region of the anatomy that is difficult to model properly due to the intricate geometry of its organs. Many assumptions were made in modeling these organs leading to either the organ not being represented, or being oversimplified. Development of new radioagents with specific uptake in the prostate or in the wall of the gastrointestinal tract has led to a need to modify the dosimetric model of the lower abdomen. In 1994, Stabin developed a mathematical model of the prostate gland and included it in the ORNL stylized model.67 This new organ was modeled as a single sphere located just below the urinary bladder, with a volume consistent with Reference Man of ICRP Publication 23.22 This model was used to calculate absorbed fractions of energy and S-values for selected radionuclides. In 1999, Mardirossian et al. recognized that the relative spatial position of the urinary bladder, rectum, and prostate were poorly represented in the ORNL phantom series. They developed a new model of the lower part of the colon,68 and separated the rectum from the lower large intestine. This new model included an explicitly defined rectum, anatomically correct sigmoid and descending colons, and a prostatic urethra and seminal duct. These modifications were implemented in the ORNL phantom series, after changing the position of the bladder and prostate gland to properly model the relative positions of these organs. These models were developed not only for the adult male model, but also for the other phantoms in the ORNL series using physiological and anatomical descriptions published in ICRP Publication 23.22 Because the intestinal tract and curvatures cannot be modeled with simple geometrical shapes that can be easily coupled to a Monte Carlo transport code, all models of this region have relied on thickening the wall region to preserve anatomical wall and content mass. The critical cells for these organs have been identified as the cells within the mucosa layer. In 1994, Poston et al. developed a revision of the GI tract of the MIRD adult phantom to better represent these sensitive targets.28,29 The actual wall of the GI tract was divided in its thickness into four regions of varying radiosensitivities; these layers were very difficult to model because the thickness of each layer varied from one section to another along the different regions of the GI tract. Poston et al. developed two methods to model this wall. The first method divided the tissue wall into 10 small, concentric layers (100 μm thick for the adult) and the dose to each layer was recorded separately. Then, the determination of the mucosa layer thickness for each section would give directly the dose to the mucosa. However, since it is not possible to determine directly the mucosa layer thickness for a specific patient, the subdivision into 10 regions has not been used for medical dose calculation. In a second method, Poston et al. measured the average thickness of the mucosa layer along the GI tract from anatomic slides of cross sections of a human GI tract. Different mucosa thicknesses for the stomach, the small intestine and the large intestine were obtained. This layer was included in the GI wall of the adult mathematical phantom and coupled to the EGS4 Monte Carlo transport code.26 Stubbs et al.69 presented calculations of the radiation-absorbed dose to the walls of hollow organs. These authors studied all four sections of the gastrointestinal tract but only


57

for four radionuclides important to nuclear medicine. They presented S-values (in units of Gy/Bq s) for 90Y, 99mTc, 123I, and 131I and concluded, as did Poston et al., that the ICRP “onehalf” assumption was overly conservative for nonpenetrating radiation. More recently, an improved (but very simple) geometric model for the small intestine has been developed and used in a study of electron energy deposition in the wall of the tract.70 Results were obtained using this model for monoenergetic electrons in the range 10–500 keV. Depth dose profiles were developed so that dose to the more sensitive cells could be assessed. Electron transport calculations using the computer code MCNP4A27 and the new model showed that only a small fraction of the available energy reaches the critical cells in the crypts of the wall. For electron energies below 330 keV, the only contribution to absorbed dose to the stem cells came from bremsstrahlung. For higher electron energies (i.e., >330 keV), the dose distribution curve was dominated by monoenergetic electrons. 2.2.7 Stylized Phantoms Representing Pregnancy Women In 1995, Stabin et al.71 added the fetus and placenta to the stylized adult female phantom and created phantoms of the pregnant female at each trimester (3-, 6-, and 9-months). The greatest changes to the adult female phantom involved the growth of the uterus and the existence of a compartment representing uterine contents. Stabin et al. represented the uterus in a manner similar to that described by Cloutier et al.72 Figure 2.5 shows a drawing of the cross-sectional view of the uterine region at 9-months in the Stabin et al. pregnant female phantom series.

Body surface

z΄

Placenta Uterine wall Other uterine tissue Fetal skeleton Fetal soft tissue y΄

50° Body surface z

x =0

y

FIGURE 2.5 Images of the 9-month uterine model in the Stabin et al. pregnant female phantom series. (From Stabin, M.G. et al., Mathematical models and specific absorbed fractions of photon energy in the nonpregnant adult female and at the end of each trimester of pregnancy, ORNL/TM-12907, Oak Ridge, TN: Oak Ridge National Laboratory, 1995.)

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At the end of the first trimester (3 months) the uterus was represented by a right circular cone with a hemispherical cap. The axis of the cone was oriented in the Y-direction, formed at a 33° incline to the horizontal. After the third month of pregnancy, and until about the seventh month the main area of growth in the uterus occurred along its long axis with a small increase in breadth. In the 6-month phantom, the uterus is modeled as a cylinder capped at both end by hemispheres. The long axis of the cylinder ran in the Y-direction, tilted upward at an angle of 40° from the horizontal. In the 9-month phantom, the uterus was modeled as a cylinder capped at both ends by hemispheres, as in the 6-month phantom. The uterus was modeled to have extended in length as well as breadth, and the upper hemisphere was considerably larger than the lower hemisphere, and larger than in the 6-month phantom. The two hemispherical sections were connected by a section of a cone whose long axis of the cylinder ran in the Y-direction, and was tilted upward at an angle of 40° from the horizontal. The uterine contents of the 3-month phantom were modeled as a homogeneous mixture of soft tissue. No attempt was made at that time to model the presence of skeletal material. The uterine contents were thus used as a target to represent the fetus in this phantom. In the 6- and 9-month phantoms, the fetus, the placenta, and the amniotic fluid were explicitly modeled as separate regions. The fetal skeleton was also explicitly modeled in these phantoms and given the composition of the skeleton of the newborn phantom of Cristy and Eckerman.37 The placenta was also modeled as a hemispherical shell, in the 6- and 9-month phantoms. The whole body trunk was extended in the 6- and 9-month phantoms to accommodate the enlarged uterus, and a few of the abdominal organs were redesigned or moved. Urinary bladder and small intestine were significantly remodeled to model their significant displacement and, in the case of bladder, decreased in volume. In 2004, Chen extended the stylized pregnant female phantoms into four pregnancy periods, 8 weeks, 3, 6, and 9-months, for external ionizing radiation dosimetry.73 2.2.8 Other Stylized Models of the Human Anatomy Other modifications and additions to the ORNL and MIRD stylized models include a peritoneal cavity,74 a new model of the nasal cavity and major airway,75 and a new model of the long bones.76 The peritoneal cavity model was developed in 1989 by Watson et al.74 They modified the MIRD phantom to include a region representing the peritoneal cavity to support the dosimetry associated with several therapeutic and diagnostic techniques involving injection of radioactive material into the peritoneal cavity. Similarly, in 1997, Deloar et al. developed a model of the nasal cavity and major airway to support the dosimetry associated with 15O-labeled gases as positron emission tomography (PET) imaging agents.75 In 2000, Clairand et al. modified the model of the long bones of the ORNL phantom series to properly differentiate the cortical bone, trabecular bone, and medullary cavity.76 This effort was done to support bone dosimetry for photon sources and to allow more accurate marrow dose calculations for children. Because in children, the active marrow is not only found in the trabecular bone but also in the medullary cavities, the stylized models of the long bones of the legs and arms (truncated circular cones) of the ORNL phantom series did not allow for accurate bone dosimetry calculations. 2.2.9 GSF Gender-Specific Phantoms, ADAM and EVA A revised version of the MIRD-5 phantom24 was adopted by the GSF, Germany in 1975 and afterward used by Kramer and his colleagues in a series of studies related to external photon dosimetry. 77,78


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The researchers noted that the MIRD-5 phantom represented a partially hermaphrodite adult only—the phantom has the dimensions of the male reference man including testes, ovaries, and uterus but no female breasts. Since the GSF researchers were interested in applying the phantoms to external dosimetry where the female breasts were important, it was believed that the MIRD-5 phantom could lead to inaccurate result for the female workers using the newly released tissue weighting factor.79 The GSF researchers decided to revise the MIRD-5 phantom by developing two separate sex-specific phantoms. In what became known as the ADAM and EVA phantoms, a number of changes in anatomical details were made.40 The EVA phantom was derived by shrinking all relevant volumes of the MIRD-5 phantom with the total whole body mass ratio of 0.83 that was revealed from the analysis of ICRP reference organ masses. Then, the female organ masses were modified to create space for neighboring organs. Finally, sex-specific organ such as testes, ovaries, uterus, and breasts were introduced into the appropriate phantom to yield ADAM and EVA, respectively. The chin was introduced by removing a section of the neck to created a more realistic external irradiation geometry for the thyroid. The female breasts were represented by two ellipsoid sections attached to the trunk of EVA. At the same time, at the birthplace of the MIRD-5 phantom, Cristy was developing agedependent hermaphrodite phantoms for internal dosimetry applications that were less sensitive to the shape and size of the female breasts.36 A debate about the differences of breast sizes of the reference female went on between the groups at GSF and ORNL.49–51 These two sets of stylized phantoms were later used by a large number of users worldwide. 2.2.10 The CAM Phantom Developed by NASA for Space Radiation Dosimetry The computational anatomical male (CAM) phantom was documented by Billings and Yucker in 1973 in a technical report to the National Aeronautics and Space Administration (NASA).80 The computational anatomical female (CAF) phantom was also developed.81 The approach used to develop this pair of phantoms appeared to be very different and aggressive and the CAM reportedly consisted of 1100 unique geometric surfaces and 2450 solid regions. Internal body geometry such as organs, voids, bones, and bone marrow were explicitly modeled using constructive solid geometry (CSG) techniques, according to the authors. A computer program called CAMERA was also developed for performing analyses with the CAM phantom. The authors state that “extremely detailed geometrical model of the human anatomy, the most detailed yet prepared, has been developed for use in investigations dealing with exposure of astronauts to the natural space radiation environment. The model is equally applicable to investigations dealing with exposure of humans to radiation associated with nuclear weapon and nuclear power system environments as well as medical applications such as radiotherapy and radiography.” Indeed the surface geometry was so detailed that one may wonder how this was possible in the 1970s with much less capable computers. Unfortunately, CAM and CAF phantoms were adopted exclusively for studies involving space radiation environments of interest to the aerospace industry.81 Very little information about the original phantom development was available to the research community outside the centers associated with the NASA until recently. Jordan, a long-time NASA-contracted phantom user, recently released some of the images (http://cmpwg.ans.org/phantoms/camera.pdf). It is interesting to note one unique exterior anatomical feature of the CAM phantom: the arms are separated from the trunk, unlike the MIRD-5 phantom and its successors. Two images of the CAM phantom are shown in Figure 2.6.


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(a)

(b)

FIGURE 2.6 (See color insert following page 524.) Surface plots of the CAM phantom. (a) The whole body view showing arms separated from the trunk (http://cmpwg.ans.org/phantoms/camera.pdf). (b) The close-up view of the facial details.

2.3 Summary Mathematical formulations of the organs and tissues of the body used in the dosimetry of internally distributed radionuclides existed as early as the 1940s, although the first anthropomorphic phantom was not reported until in the 1960s. In the 1970s and 1980s, the sophistication of these stylized phantoms increased significantly. This evolution began with the specification of a single organ mass, followed by the use of simple shapes to simulate organs or the entire body of an adult human. More sophisticated models were later developed which used simple shapes to model the geometry of select organs or the entire human body of an adult human. The desire to model the entire body of a “Reference Man” and to specify the location, shape, volume, and mass of organs in the body as realistically as possible has remained the same to this day. The climax for stylized phantoms was reached in the 1980s when the gender- and age-specific family phantoms were systematically documented and widely adopted for various studies in internal and external radiation dosimetry, as well as in medical imaging and radiotherapy. By that time, Monte Carlo codes and personal computers were accessible to a large number of researchers.


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The research on stylized human models which was carried out at ORNL through the 1980s played an essential role in the history of computational phantoms. The sex-specific adult phantoms at GSF, Germany in the early 1980s were revisions of the MIRD-5 phantom that was originally developed at ORNL. Major extensions in the 1990s, for examples, on the pregnant women and brain/head models, were also closely tied to the earlier work at ORNL. The direct involvement of ORNL’s scientists in the Society of Nuclear Medicine’s (SNM’s) MIRD committee facilitated the necessary standardization process. It is clear that close collaborations between leading developers were a key factor contributing to the success of these first-generation computational phantoms. Not all phantoms of this generation enjoyed the same recognition in the history. In fact, a few phantoms such as the CAM have been practically unknown by the mainstream radiation protection dosimetry community. As described in Chapter 1 of this book, the late 1980s would go into the history also as the beginning of an exciting new era of voxel phantoms. Collaboration in the information age required new approaches and no single developer would dominate the new research agenda as ORNL once did.

References 1. Loevinger, R. Distributed radionuclide sources. In Radiation Dosimetry Volume III, 2nd edn., Attix, F.H. and Tochilin, E., eds. (New York: Academic Press), p. 51, 1969. 2. Quimby, E.H. The development of radiation dosimetry in nuclear medicine. In Medical Radionuclides: Radiation Dose and Effects, Cloutier, R.J., Edwards, C.L., and Snyder, W.S., eds., AEC Symposium Series 20 (CONF-691212) ), (Washington, DC: U.S. Atomic Energy Commission) p. 7, 1970. 3. NCRP. The experimental basis for absorbed-dose calculations in medical uses of radionuclides. NCRP Report No 83 (Bethesda, MD: National Council on Radiation Protections and Measurements), 1985. 4. Marinelli, L.D. Dosage determination with radioactive isotopes, Am. J. Roentgenol. Rad. Ther., 47, 210, 1942. 5. Marinelli, L.D., Quimby, E.H., and Hine, G.J. Dosage determination with radioactive isotopes. II. Practical considerations in therapy and protection, Am. J. Roentgenol. Rad. Ther., 59, 260, 1948. 6. Loevinger, R., Japha, E.M., and Brownell, G.L. Discrete radioisotope sources. In Radiation Dosimetry, Hine, G.J. and Brownell, G.L., eds. (New York: Academic Press), p. 693, 1965. 7. Loevinger, R., Holt, J.G., and Hine, G.J. Internally administered radionuclides. In Radiation Dosimetry, Hine, G.J. and Brownell, G.L., eds. (New York: Academic Press), p. 801, 1956. 8. ICRP. Report of Committee II on Permissible Dose for Internal Radiation International Commission on Radiological Protection (Oxford: Pergamon Press), 1959. 9. Berger, M.J. Mird Pamphlet No. 2: Energy Deposition in Water by Photons from Point Isotropic Sources (New York: Society of Nuclear Medicine), 1968. 10. Berger, M.J. Mird Pamphlet No. 2: Energy Deposition in Water by Photons from Point Isotropic Sources (New York: Society of Nuclear Medicine), 1971. 11. Ellett, W.H., Callahan, A.B., and Brownell, G.L. Gamma-ray dosimetry of internal emitters, I. Monte Carlo calculations of absorbed doses from point sources, Brit. J. Radiol., 37, 45, 1964. 12. Ellett, W.H., Callahan, A.B., and Brownell, G.L. Gamma-ray dosimetry of internal emitters, II. Monte Carlo calculations of absorbed doses from uniform sources, Brit. J. Radiol., 38, 541, 1965. 13. Reddy, A.R., Ellett, W.H., and Brownell, G.L. Gamma-ray dosimetry of internal emitters, I. Monte Carlo calculations of absorbed doses for low-energy gamma-rays, Brit. J. Radiol., 42, 512, 1967.

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14. Loevinger, R. and Berman, M. MIRD Pamphlet No. 1 A Schema for Absorbed-Dose Calculations for Biologically Distributed Radionuclides (New York: Society of Nuclear Medicine), 1968. 15. Brownell, G.L., Ellett, W.H., and Reddy, A.R. MIRD Pamphlet No. 3 Absorbed Fractions for Photon Dosimetry (New York: Society of Nuclear Medicine), 1968. 16. Ellett, W.H. and Humes, R.M. MIRD Pamphlet No. 8 Absorbed Fractions for Small Volumes Containing Photon-Emitting Radioactivity (New York: Society of Nuclear Medicine), 1971. 17. Auxier, J.A., Snyder, W.S., and Jones, T.D. Neutron interactions and penetration in tissue. In Radiation Dosimetry Volume I, 2nd edn., Attix, F.H. and Tochilin., eds. (New York: Academic Press), p. 275, 1968. 18. Fisher, H.L.J. and Snyder, W.S. Variation of dose delivered by 137Cs as a function of body size from infancy to adulthood, ORNL-4007 (Oak Ridge, TN: Oak Ridge National Laboratory), P. 221, 1966. 19. Fisher, H.L.J. and Snyder, W.S. Distribution of dose delivered in the body size from a source of gamma rays distributed uniformly in an organ, ORNL-4168 (Oak Ridge, TN: Oak Ridge National Laboratory), p. 245, 1967. 20. Snyder, W.S. The variation of dose in man from exposure to a point source of gamma rays, ORNL-4168 (Oak Ridge, TN: Oak Ridge National Laboratory), p. 257, 1967. 21. Snyder, W.S. et al. MIRD Pamphlet No. 5 Estimates of Absorbed Fractions for Monoenergetic Photon Sources Uniformly Distributed in Various Organs of a Heterogeneous Phantom (New York: Society of Nuclear Medicine), 1969. 22. ICRP. Report on the Task Group on Reference Man, ICRP Publication 23 (Oxford: International Commission on Radiological Protection), 1975. 23. ICRP. Limits for Intakes of Radionuclides by Workers, ICRP Publication 30 (Oxford: International Commission on Radiological Protection), 1979. 24. Snyder, W.S., Ford, M.R., and Warner, G.G. Mird Pamphlet No. 5, Revised Estimates of Specific Absorbed Fractions for Monoenergetic Photon Sources Uniformly Distributed in Various Organs of a Heterogeneous Phantom (New York: Society of Nuclear Medicine), 1978. 25. Warner, G.G. and Craig, A.N.J. ALGAM: A computer program for estimating internal dose in a man phantom, ORNL-TM-2250 (Oak Ridge, TN: Oak Ridge National Laboratory), 1968. 26. Nelson, W.R., Hirayama, R.H., and Rogers, D.W.O. The EGS4 code system SLAC Report 265 (Stanford, CA: Stanford Linear Accelerator Center), 1985. 27. Briesmeister, J.F. MCNP—A general Monte Carlo N-particle transport code, version 4a LA-12625 (Los Alamos, NM: Los Alamos National Laboratory), 1993. 28. Poston, J.W. Jr. et al. Calculation of absorbed energy in the gastrointestinal tract, Health Phys., 71, 300, 1996. 29. Poston, J.W. Jr. et al. A revised model for the calculation of absorbed energy in the gastrointestinal tract, Health Phys., 71, 307, 1996. 30. Kereiakes, J.G. et al. Doses to infants and children: A plea for a standard child, Health Phys., 11, 999, 1965. 31. Poston, J.W. The development of early pediatric models and their application to radiation absorbed dose calculations. In Dosimetry of Administered Radionuclides, Adelstein, S.J., Kassis, A.I., and Burt, R.W., eds. (Washington, DC: American College of Nuclear Physicians), p. 105, 1990. 32. Hwang, J.M.L. et al. Mathematical description of a one- and five-year-old child for use in dosimetry calculations, ORNL/TM-5293 (Oak Ridge, TN: Oak Ridge National Laboratory), 1976. 33. Hwang, J.M.L., Shoup, R.L., and Poston, J.W. Mathematical description of a newborn human for use in dosimetry calculations, ORNL/TM-5453 (Oak Ridge, TN: Oak Ridge National Laboratory), 1976. 34. Jones, R.M. et al. The development and use of a fifteen-year-old equivalent mathematical phantom for internal dose calculations, ORNL/TM-5278 (Oak Ridge, TN: Oak Ridge National Laboratory), 1976. 35. Deus, S.F. and Poston, J.W. The development of a mathematical phantom representing a 10-year-old for use in internal dose calculations. In Proceedings of the Symposium on Radiopharmaceutical Dosimetry, HEW Publication (FDA) 76-8044 (Oak Ridge, TN: Oak Ridge National Laboratory), 1976.


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36. Cristy, M. Mathematical phantoms representing children of various ages for use in estimates of internal dose, ORNL/NUREG/TM-367 (Oak Ridge, TN: Oak Ridge National Laboratory), 1980. 37. Cristy, M. and Eckerman, K.F. Specific absorbed fractions of energy at various ages from internal photon sources, ORNL/TM-8381 (Oak Ridge, TN: Oak Ridge National Laboratory), 1987. 38. Coffey, J.L. and Watson, E.E. S-values for selected radionuclides and organs with the heart wall and heart contents as source organs. In Third International Radiopharmaceutical Dosimetry Symposium (Rockville, MD: U.S. Department of Health and Human Services), 1981. 39. Coffey, J.L. A revised mathematical model of the heart for use in radiation absorbed dose calculation, MS Thesis University of Tennessee, (Knoxville, TN), 1978. 40. Kramer, R. et al. The calculation of dose from external photon exposures using reference human phantoms and Monte Carlo methods: Part I. The male (ADAM) and female (EVA) adult mathematical phantoms, GSF-Report S-885 (Neuherberg-Muenchen: Institut fuer Strahlenschutz, GSF-Forschungszentrum fuer Umwelt und Gesundheit), 1982. 41. Seltzer, S.M. An overview of ETRAN Monte Carlo methods. In Monte Carlo Transport of Electrons and Photons (New York: Plenum Press), 1988. 42. Seltzer, S.M. Electron-photon Monte Carlo calculations: The ETRAN code, Appl. Radiat. Isot., 42, 917, 1991. 43. Snyder, W.S. et al. A tabulation of dose equivalent per microcurie-day for source and target organs of an adult for various radionuclides: Part 1. ORNL-5000 (Oak Ridge, TN: Oak Ridge National Laboratory), 1974. 44. Eckerman, K.F. and Ryman, J.C. External exposure to radionuclides in air, water, and soil. Federal Guidance Report 12, PB94-114451, (Washington, DC) 1993. 45. Hudson, G. Bone-marrow volume in the human foetus and newborn, Brit. J. Haemat., 11, 446, 1965. 46. Custer, R.P. An Atlas of the Blood and Bone Marrow, 2nd edn. (Philadelphia: W. B. Saunders), 1974. 47. Woodard, H.Q. and Holodny, E. A summary of the data of Mechanik on the distribution of human bone marrow, Phys. Med. Biol., 5, 57, 1960. 48. Cristy, M. Active bone marrow distribution as a function of age in humans, Phys. Med. Biol., 26, 389, 1981. 49. Kramer, R. and Drexler, G. Representative breast size of reference female, Health Phys., 40, 913, 1981. 50. Cristy, M. Representative breast size of reference female, Health Phys., 43, 930, 1982. 51. Kramer, R., Williams, G., and Drexler, G. Reply to M. Cristy, Health Phys., 43, 932, 1982. 52. Katch, V.L. et al. Contribution of breast volume and weight to body fat distribution in females, Am. J. Phys. Anthropol., 53, 93–100, 1980. 53. Cristy, M. Calculation of annual limits of intake of radionuclides by workers: Significance of breast as an explicitly represented tissue, Health Phys., 46, 283, 1984. 54. Kerr, G.D. Organ dose estimates for the Japanese atomic-bomb survivors, Health Phys., 37, 487, 1979. 55. Bouchet, L.G. et al. MIRD Pamphlet No. 15: Radionuclide S values in a revised dosimetric model of the adult head and brain, J. Nucl. Med., 40, 3, 62S, 1999. 56. London, E.D. Imaging Drug Action in the Brain (Boca Raton, FL: CRC), 1993. 57. Blinkov, S.M. and Glezer, I.I. The Human Brain in Figures and Tables (New York: Plenum), 1968. 58. Petegnief, Y. et al. Quantitative autoradiography using a radioimager based on a multiwire proportional chamber, Phys. Med. Biol., 43, 3629, 1998. 59. Snyder, W.S. and Ford, M.R. A dosimetric study for the administration of neohydrin labeled with 203Hg and 197Hg, ORNL-4168 (Oak Ridge, TN: Oak Ridge National Laboratory), p. 267, 1967. 60. McAfee, J.G. Problems in evaluating the radiation dose for radionuclides excreted by the kidneys. In Medical Radionuclides: Radiation Dose and Effects, Cloutier, R.J., Edwards, C.L., and Snyder, W.S., eds. (Oak Ridge, TN: U.S. Atomic Energy Commission), p. 271, 1969. 61. Blau, M. et al. MIRD dose estimate No. 6: Hg-197 and Hg-203-labeled chlormerodrin, J. Nucl. Med., 16, 1214, 1975.

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62. Patel, J.S. A Revised Model of Kidney for Medical Internal Radiation Dose (College Station, TX: Texas A&M University), 1988. 63. Bouchet, L.G. et al. MIRD Pamphlet No. 19: Absorbed fractions and radionuclide s values for six age-dependent multiregion models of the kidney, J. Nucl. Med., 44(7), 1113, 2002. 64. Boerman, O.C., Oyen, W.J., and Corstens, F.H. Between the Scylla and Charybdis of peptide radionuclide therapy: Hitting the tumor and saving the kidney, Eur. J. Nucl. Med., 28, 1447, 2001. 65. Ford, R.L. and Nelson, W.R. The EGS code system: Computer Programs for Monte Carlo Simulation of Electromagnetic Cascade Shower Report 210 (Stanford: Stanford Linear Accelerator Center), 1978. 66. Loevinger, R., Budinger, T.F., and Watson, E.E. MIRD Primer for Absorbed Dose Calculations (New York: The Society of Nuclear Medicine), p. 128, 1991. 67. Stabin, M. A model of the prostate gland for use in internal dosimetry, J. Nucl. Med., 35(3), 516, 1994. 68. Mardirossian, G. et al. A new rectal model for dosimetry applications, J. Nucl. Med., 40(9), 1524, 1999. 69. Stubbs, J.B., Evans, J.F., and Stabin, M.G. Radiation absorbed doses to the walls of hollow organs, J. Nucl. Med., 39, 11, 1989–1995. 70. Bhuiyan, N.U. A Revised Dosimetric Model for Calculation of Electron Dose in the Small Intestine (College Station, TX: Texas A&M University), 2000. 71. Stabin, M.G. et al. Mathematical models and specific absorbed fractions of photon energy in the nonpregnant adult female and at the end of each trimester of pregnancy, ORNL/TM-12907 (Oak Ridge, TN: Oak Ridge National Laboratory), 1995. 72. Cloutier, R.J. et al. Dose to fetus from radionuclides in bladder, Health Phys., 25, 147, 1973. 73. Chen, J. Mathematical models of the embryo and fetus for use in radiological protection, Health Phys., 86, 285, 2004. 74. Watson, E.E. et al. A model of the peritoneal cavity for use in internal dosimetry, J. Nucl. Med., 30, 2002, 1989. 75. Deloar, H.M. et al. Internal dose estimation including the nasal cavity and major airway for continuous inhalation of C15O2, 15O2, and C15O using the thermoluminescent dosimeter method, J. Nucl. Med., 38, 1603, 1997. 76. Clairand, I., Bouchet, L.G., and Bolch, W.E. A new macroscopic model of the long bones for skeletal dosimetry, J. Nucl. Med., 41, 1062, 2000. 77. Kramer, R. and Drexler, G. The dose equivalent index (DEI) as a function of angular distribution of photons, Health Phys., 38, 426, 1980. 78. Kramer, R. and Drexler G. Dose equivalent conversion factors for external photon irradiation, in Radiation Protection: A Systematic Approach to Safety. Proceedings of the 5th Congress of the International Radiation Protection Society, Jerusalem (Oxford: Pergamon Press), p. 311, 1980. 79. Kramer, R. and Drexler, G. On the calculation of the effective dose equivalent, Rad. Prot. Dosim., 3, 13, 1982. 80. Billings, M.P. and Yucker, W.R. The Computerized Anatomical Man CAM Model, NASA CR-134043 (Washington, DC: Government Printing Office), 1973. 81. Atwell, W. Anatomical models for space radiation applications: An overview, Adv. Space Res., Oct; 14(10), 415, 1994.

3 The GSF* Voxel Computational Phantom Family Maria Zankl

CONTENTS 3.1 Introduction .................................................................................................................65 3.2 Construction of the GSF Voxel Phantoms ............................................................... 66 3.2.1 Region Growing ............................................................................................. 67 3.2.2 Threshold and Morphological Operations................................................. 67 3.2.3 Interactive Drawing of the Borderline ........................................................ 68 3.2.4 Manual Drawing of Organs ......................................................................... 68 3.2.5 Assignment of Organ Identification Numbers .......................................... 68 3.2.6 Estimation of Bone Marrow Distribution .................................................. 68 3.3 Description of the GSF Voxel Phantoms .................................................................. 69 3.4 Applications of the GSF Voxel Phantoms in Radiation Dosimetry ..................... 76 3.4.1 Monte Carlo Codes ........................................................................................ 76 3.4.2 Bone Dosimetry.............................................................................................. 76 3.4.3 Idealized Geometries (External) .................................................................. 76 3.4.4 Environmental Dosimetry............................................................................ 78 3.4.5 Dosimetry for Medical Imaging .................................................................. 78 3.4.6 Specific Absorbed Fraction Calculations for Organ Self-Absorption (Original Source Masses) .................................................80 3.4.7 SAF Calculations for Organ Cross-Fire ......................................................80 3.4.8 Applications Outside the Helmholtz Zentrum München .......................80 3.5 Conclusions .................................................................................................................. 82 References ............................................................................................................................... 82

3.1 Introduction To protect against ionizing radiation (occupational, environmental, and medical), researchers must determine the radiation dose to specific body organs and tissues for the scope of risk assessment. For this purpose, a series of computational phantoms (hereafter “phantoms”) of the human body were designed in the past that have been used together with

* On January 1, 2008, the “GSF—National Research Center for Environment and Health” has changed its name to “Helmholtz Zentrum München—German Research Center for Environmental Health.” However, since the voxel models have been known as “GSF voxel phantoms” for many years, this name is at present retained for our phantom family.

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computer codes, to simulate the radiation transport and energy deposition in the body. The first generation of computational body phantoms were the so-called mathematical computational phantoms: mathematical expressions representing simple geometrical bodies that were used to describe idealized arrangements of body organs.1–5 This type of computational phantom has also been called the “MIRD-type,” due to the fact that its most famous representatives have been published in documents of the Medical Internal Radiation Dose (MIRD) Committee.1,2 During the last two decades, various groups have developed a new generation of body computational phantoms, “tomographic” or “voxel computational phantoms” (from voxel = volume element), as an extension and improvement to these earlier computational phantoms. These computational phantoms are segmented from computed or magnetic resonance tomographic data of real persons and, thus, offer a more realistic replication of human anatomy.6–13 This is only a small selection, and is not exhaustive, since the most important of these phantoms are described explicitly in other chapters of this book. The first voxel phantoms were developed at Vanderbilt University and at the GSF— National Research Center for Environment and Health, independently and more or less simultaneously. The phantom from Vanderbilt University was an adult female,14,15 and those from GSF were pediatric phantoms—an 8-week-old baby and a 7-year-old child.6,16–18 At the GSF, the pediatric phantoms, Baby and Child, were followed first by Golem, the voxel phantom of an adult male,11 and then by nine individual voxel phantoms: three male (Frank, Godwin, and the Visible Human), and six female (Donna, Helga, Irene, Katja, Laura, and Klara), where one of them—Katja—was pregnant.12,19–22

3.2 Construction of the GSF Voxel Phantoms All voxel phantoms constructed so far by our working group were based on computed tomographic (CT) image data of living patients, with the exception of the 8-week-old baby that was constructed from the image data of a dead body. All patients were scanned with a large number of contiguous axial slices. Each slice consists of a matrix of typically 256 × 256 or 512 × 512 pixels (picture elements, in a planar image). The volume elements are the pixels multiplied by the thickness of the slice. The single slice images are stacked, resulting in a three-dimensional (3D) array of voxels. In the primary image data, each pixel has a value that is characteristic of a certain physical property of the respective volume element; in the case of CT images this property is attenuation of x-rays of a specific radiation quality. The pixel values are the “Hounsfield numbers” or “CT numbers” that relate the attenuation property of a pixel to that of water (having the value 0) and range typically from −1000 (for air) to approximately between 500 and 1000 (for bone). Many image-processing software tools convert these Hounsfield numbers to nonnegative integer “gray values” by adding a constant value to the Hounsfield number of each pixel. Today, typically 12 bits are used to store these values, thus permitting a data range between 0 and 4095. Formerly, when storage needed to be used sparingly, the gray values were often limited to 255, thus reducing the storage requirement to 1 byte per pixel. Clearly, this renormalization was accompanied by a loss of image contrast. The image property coded by the gray values—namely the attenuation of the x-ray beam of the CT equipment—is related to the tissue electron density, not to the anatomical site. That means the anatomical boundaries between organs and tissues have to be determined

The GSF Voxel Computational Phantom Family

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on the basis of the gray values and gray-value contrasts, and then have to be combined suitably with anatomical knowledge by the user. Each pixel is then assigned to an organ or tissue to which the corresponding voxel belongs. This process of assigning each voxel to a specific organ is called segmentation. Although image-processing methods are steadily improving, and researchers have been aiming for automatic segmentation for a long time, thorough segmentation of a large body section into many organs still involves a large amount of manual work. The reason is that there are only a small number of tissues that separate well from their neighborhood by gray-value contrast. For CT, these are mainly the bones and the lungs. The separation of muscle and adipose tissue can also be made by gray-value thresholds. The gray values of many individual organs, however, are inhomogeneous, and the gray-value ranges of various organs largely overlap. Therefore, organs that are in close contact to each other cannot be separated automatically on the basis of their gray values. Consequently, segmentation is still rather tedious and time-consuming. In principle, the organs and tissues to be segmented could be divided into three types: (1) organs and tissues that can be separated from the surrounding tissue or the neighboring organs by their gray value, i.e., they can be segmented using a threshold and/or region growing procedure; (2) organs and tissues that can be distinguished visually, i.e., an interactive method is applied and the user “draws” the borderline; and (3) organs and tissues that cannot be seen on the CT images, in which case the user has to draw the organ, consulting, if necessary, an anatomical atlas. Since the segmentation of the GSF voxel computational phantom family is a process extending over many years and is still ongoing, a variety of software tools and computer hardware have been involved in its construction. Therefore, the segmentation technique is described in general terms here. The following segmentation procedures were applied. 3.2.1 Region Growing For the segmentation of organs with good contrast and a small number of elements in each slice, for example the external body contour or the lungs, a “region growing” procedure was used. For this procedure, a seed point and a range of gray values are selected. A borderline is generated that encompasses all pixels around the seed point that have gray values in the selected range. At the next slice, the seed point and the range of gray values are taken again and the borderline is adjusted. Subsequently the borderline can be filled by a morphological method, i.e., all pixels inside the borderline are set to 1. The result is saved as a binary 3D data set in which those pixels inside the borderline have the value 1, and all the others have the value 0. 3.2.2 Threshold and Morphological Operations For organs with good contrast and several elements in each slice, a threshold method was applied to avoid using too many seed points. The threshold method sets all voxels between a minimum and maximum gray value to 1, and all other voxels to 0. This results in a 3D binary file. Subsequently, this was followed in most cases by a manual image editing, i.e., an erasing of the “wrong” pixels slice-by-slice, or a mathematical morphological operation. Some functions of morphological methods used in this work are the following: the “erode” operation peels off a layer from an organ using a specified structuring element; in our case mostly the smallest available element was used, which leads to a reduction of one pixel row within a single slice. The “dilate” operation expands the organ by adding a specified structuring element layer. The “close” operation is a dilation followed by an erosion,

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both of which use the same structuring element. A closing will generally fill in thin gulfs and small holes in the binary volume. The “open” operation is an erosion followed by a dilation again using the same structuring element. This operation will generally suppress sharp capes and eliminate small objects. A “connect” operation identifies, counts and—if that option is selected—numbers the separate objects in an image. If a value greater than 1 is selected for the minimum allowable component size, all components smaller than this size are set to zero. This is another method to eliminate small objects. The final result, after all these possible manipulations, is again a 3D binary file in which all pixels belonging to the segmented tissue have the value 1 and all the others have the value 0. 3.2.3 Interactive Drawing of the Borderline As mentioned earlier, most of the organs to be segmented cannot be separated easily from the surrounding tissue by a threshold. Nevertheless, the user might be able to see or guess where each organ is located—if necessary, by consulting an anatomical atlas or a medical expert. In these cases, B-spline functions are useful, which allow drawing a borderline using a Bezier spline curve that is defined by a number of base points chosen by the user. The first advantage is that—in contrast to freehand drawn lines—the borderline curve is much smoother. The second advantage is that the line can usually be transferred into the next slice and adapted by slightly moving the base points to the actual contours of the organ. Typical organs segmented with this method are the kidneys, the liver, and the spleen. The resulting binary contour of the segmented organ can then be filled as already described. 3.2.4 Manual Drawing of Organs In some cases it is easier to “draw” the organ directly into the already segmented data set, because its location is known exactly, and its shape in the two-dimensional (2D) slice images can be represented by a simple geometric shape, such as a circle or an ellipse. Typical examples for these tissues are the eye lenses and the spinal cord. Another reason to draw organs freehand is that that their location is fixed only in general terms and the exact position is variable. Examples for this situation are the lymphatic nodes, a tissue type that was introduced in the voxel phantoms of the ICRP adult Reference Male and Reference Female (see Chapter 14). 3.2.5 Assignment of Organ Identification Numbers After an organ or tissue was segmented using one of the methods described above, it was stored as a binary file on hard disk. Subsequently, the binary files were assigned their specific organ identification number and compiled into one volume. Each organ or tissue is thus represented by those volume elements (voxels) identified as belonging to it from the CT slice images, having been assigned a common organ identification number instead of their original gray values. 3.2.6 Estimation of Bone Marrow Distribution The original gray values of the skeleton were stored in addition to the organ identification number, since they were used to estimate the bone marrow distribution. The proportion of bone marrow in each voxel was approximated using the original gray value of this voxel


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and by linearly interpolating between a gray value corresponding to pure bone marrow and a gray value corresponding to pure hard bone. Here, different voxel densities are assumed to represent different mixtures of hard bone and bone marrow. This method, described in detail elsewhere,11 does not allow modeling of the complicated trabecular bone structure or the bone endosteum (also called “bone surface” which is the radiosensitive tissue adjacent to the mineral bone structures). The percentage of hard bone and bone marrow in each voxel in the skeleton, however, can be estimated from the CT pictures and, thus, the distribution of bone marrow in the whole skeleton can be reconstructed with a resolution corresponding to that of the CT scan. The method as it has so far been applied to the GSF voxel phantom family has three shortcomings. First, it has been assumed in the past that red and yellow bone marrow are equally distributed in all bones, except the long bones, where in the adult phantoms all bone marrow below mid of humeri and femurs is assumed inactive (i.e., yellow) following the literature data.23,24 In all other bones, constant (age-dependent) ratios of red and yellow bone marrow were assumed, disregarding the more detailed information on marrow cellularity in individual bones also available from the literature.23,24 This neglect was shown to have only a small dosimetric effect, 25 and it can be easily corrected in future studies. The second issue is due to the so-called partial volume effect. Voxels that are located at the boundaries of bones and surrounding soft tissue contain parts of both tissues and have, thus, attenuation properties that correspond to a mixture of these tissues. Applying the interpolation procedure assumes that these voxels contain bone marrow, whereas in reality the bones are enclosed by an outer shell of cortical bone. This leads to an overestimation of the red bone marrow (RBM) dose for radiation of low penetrability. The third shortcoming concerns the composition of the skeleton: so far, the skeleton of the GSF voxel phantoms was considered to consist of only mineral bone and bone marrow; the not-directly segmented proportion of cartilage and the so-called miscellaneous component have not been considered for the elemental composition of the skeleton. This results in an average density of the skeleton that is slightly higher than the “reference” density that arises when all skeletal constituents are accounted for. Golem’s average bone density, e.g., is 1.45 g∙cm−3, compared to 1.35 g∙cm−3 for the reference skeleton.26

3.3 Description of the GSF Voxel Phantoms Table 3.1 shows the GSF voxel phantoms available at the moment, given also the age, height, and weight of the individual from whose data the phantoms were constructed. The first voxel phantom constructed at the former GSF was the phantom “Baby,” segmented from the CT images of the dead body of an 8-week-old baby. The phantom “Child” was segmented next, modeled from the image data of a 7-year-old girl who underwent a whole body irradiation for leukaemia treatment, and who was also relatively small for her age. Figure 3.1 shows the two pediatric phantoms. The figure shows some of the main organs or tissues in a 3D representation and demonstrates the anatomical realism of this type of phantom. The fi rst adult voxel computational phantom constructed by our working group— “Golem”—was segmented from the image data of a patient who was 176 cm in height and had a weight of 68.9 kg. These data are in good agreement with the older ICRP data on the adult male Reference Man, 27 whose height and weight were 170 cm and 70 kg.

f 7 115 21.7 1.1

8

1.54

19.0 256 256 144 64 Whole body

f 8w 57 4.2 1.4

4

0.85

2.9 256 256 142 54 Whole body

Gender Age Height (cm) Weight (kg) Number of (nonzero) voxels (million) Slice thickness (voxel height, mm) Voxel in-plane resolution (mm) Voxel volume (mm3) Number of columns Number of rows Number of slices Number of organs Coverage

Child

Baby

Property

35.2 256 256 179 62 Whole body

1.875

10

f 40 176 79 2.2

Donna

2.7 512 512 193 62 Head and trunk

0.74

5

m 48 174 95 23.7

Frank

34.6 256 256 220 121 Whole body

2.08

8

m 38 176 69 1.9

Golem

Main Characteristics of the Members of the GSF Voxel Phantom Family

TABLE 3.1

9.6 512 512 114 62 Head to thigh, no arms

0.98

10

f 26 170 81 8.3

Helga

17.6 262 132 348 62 Whole body

1.875

5

f 32 163 51 3.0

Irene

17.6 240 132 346 88 Whole body

1.875

5

f 43 168 59 3.5

Laura

4.84

f 43 163 62.3 4.0

Katja

4.3 512 512 250 133 Head to thigh

15.2 299 146 346 136+19 Whole body plus fetus

0.91/0.94 1.775

5

m 38 180 103 20.1

Visible Human

34.8 256 256 220 88 Whole body

2.085

8.0

m 38 176 73.0 2.0

Godwin

15.1 256 256 346 88 Whole body

1.765

4.84

f 43 163 60.0 3.9

Klara



FIGURE 3.1 GSF pediatric voxel phantoms; baby (left) and child (right).

71

Then Donna, Frank, Helga, and Irene followed. Donna and Irene20 are whole-body phantoms of a tall person and a person of slight build, respectively. Both Donna and Irene have intestines that were segmented from a high-resolution CT data set of the intestinal region of another female patient and were then fitted into the pelvic regions of these computational phantoms.20 Helga is the head-to-thigh computational phantom of a large and heavy adult female, and Frank12 is the headand-trunk computational phantom of a heavily built male patient whose arms and part of the body contour are not contained in the slice images, since they were outside the field of view. The “Visible Human” was constructed at GSF from CT data from the Visible Human Project (VHP) of the American National Library of Medicine. Another voxel phantom of the same individual exists, called VIP-Man, segmented from the color photographic images.10,28 The differences between these two VHP image data sets are that the CT images have larger pixel sizes and the arms are missing in comparison with the color photographic images. Figure 3.2 shows the male voxel phantoms

FIGURE 3.2 (See color insert following page 524.) GSF male voxel phantoms; Golem (left), Frank (middle), and Visible Human (right).

72


FIGURE 3.3 (See color insert following page 524.) GSF female voxel phantoms: Donna, Helga, Irene, and Laura (from left to right).

Golem, Frank, and the Visible Human; the female GSF voxel phantoms Donna, Helga, Irene, and Laura are presented in Figure 3.3. When it became clear that our working group would undertake the task of constructing voxel computational phantoms that represented the ICRP adult Reference Male and Reference Female, we decided to modify the segmented voxel computational phantoms that should already conform as far as possible to the required external dimensions to limit the degree of anatomical distortion that had to be applied. Among the male phantoms, Golem was considered suitable, but none of the existing female voxel computational phantoms were close enough to the reference values of height and total body mass.29 Therefore, “Laura” was segmented specifically for this purpose. “Godwin” and “Klara”21 are the results of our first attempt to modify Golem and Laura such that the respective derivatives agree with the ICRP reference anatomical data.29 The final results of these endeavors—voxel computational phantoms that represent the ICRP adult Reference Male and Reference Female and that have been adopted by the ICRP—are presented separately in Chapter 14. For “Katja” a fetus was segmented from an magnetic resonance imaging (MRI) data set of the abdominal and pelvic regions of a female patient in the 24th week of gestation. The abdomen and pelvis of the reference female voxel phantom were then modified such that enough space was created to accommodate the segmented fetus and placenta. We did this by reducing the volume of the bladder contents, by packing the intestines more closely, by shifting them backwards, and by extending the body circumference appropriately. Katja is shown in Figure 3.4. All voxel computational phantoms except the Baby and the Visible Human were based on image data of live patients who had to undergo whole body or head-and-trunk


73

CT examinations for various reasons. The GSF voxel phantoms contain a large number of organs and tissues, including most of the ICRP critical organs, except for the bone endosteum (or bone surfaces), the fi ne soft tissue layer lining the surfaces of the trabeculae and of the long bones’ medullary cavities, and a detailed representation of the RBM located in the small marrow cavities in the spongiosa regions. Most of the adult phantoms have also some tissues relevant to inhalation, like extrathoracic airways (anterior nasal passages, larynx, pharynx), the trachea, and the main branches of bronchi. The latter tissues are thin layers and are represented in the voxel computational phantoms by a single pixel row. Because the pixels’ side length is larger than the thickness of these tissue layers, however, the masses of these tissues are larger in the voxel phantoms than in reality. Obviously, it is not possible to segment structures below voxel resolution and this problem is common to all existing voxel computational phantoms. For the mucous membranes of anterior and posterior nasal passage, which are only a few micrometers thick in reality, this FIGURE 3.4 mass difference is even larger. Despite these limitations, the GSF voxel phantom Katja of presence of these target tissues at their appropriate anatomia woman in the 24th week cal location is a clear advantage compared to the MIRD-type of pregnancy. phantoms, where the doses to these tissues have to be approximated by the doses to “surrogate” organs such as thyroid or lungs. The accuracy of the masses of the eye lenses and skin is also restricted by the voxel size. Concerning the latter tissue, one layer of voxels at the surface of the body represents skin. Consequently, the pixel side length defi nes the skin thickness. This is the reason that the skin masses of the voxel phantoms are somewhat higher than the ICRP reference value, and also differ between the individual voxel computational phantoms. The number and type of organs that were segmented in the individual voxel phantoms evolved in the course of time, and therefore the list of organ identification numbers is also different among the phantoms, depending on when they were segmented. The Baby and Child have 54 and 64 segmented objects, respectively. In Golem, 121 structures were segmented, 67 of them skeletal components, which is the largest number of individual bones among all our phantoms. Sixty-two organs and tissues were identified for Donna, Helga, Irene, and Frank, and 133 for the Visible Human, who has the same number of skeletal components as Golem. Laura, Godwin, and Klara have 88 segmented organs and tissues, respectively. Recently, Baby was complemented with several objects, and the resulting “Babynew” has now 66 segmented organs and tissues. Katja has 136 segmented objects, plus the placenta, the amniotic fluid, the umbilical cord, and 16 tissues that have been segmented in the fetus. Table 3.2 shows the masses of the main organs of the individual voxel phantoms. Multiplying the respective volumes with appropriate tissue densities derived the organ masses. The organ masses of the individual computational phantoms deviate from those of the ICRP reference values,29 since the voxel computational phantoms were constructed from data of real individuals, whereas the reference data represent values averaged over whole populations. However, if one considers those organs that are known to be radiosensitive,30,31 a majority of the organ masses agree within approximately 30% with the ICRP reference values.

Adipose tissue Adrenals Bladder wall Bladder contents Brain Breast Colon wall Colon contents Eye lens Gallbladder Heart Kidneys Liver Lungs Muscle Oesophagus Ovaries Pancreas

2,759. 4.21 2.55 11.5 376. 0.12c 25.8 16.5 0.23 1.03 43.7 30.3 182. 35.5 2,759.a 1.26 0.35 2.96

a

Baby-New

13,580 3.94 24.5 169. 1,316. – 84.6 49.5 0.56 – 206. 188. 733. 153. 13,580a – 2.67 30.1

a

Child 34,820 21.7 61.0 45.0 1,208. 43.9c 322. 309. 1.9 6.6 446.d 281. 1,585. 631. 25,420 27.7 12.1 41.2

Donna 30,590 13.6 56.6 218. 1,827. 1.8c 379. 666. 0.7 9.0 381.d 494. 2,072. 1,338. 13,820b 62.6 – 60.0

b

Frank 19,970 22.8 68.4 272. 1,218. – 297. 237. 0.9 8.3 716. 316. 1,592. 729. 26,970 30.1 – 71.9

Golem 39,800 6.6 60.8 22.1 1,279. 134.c 426. 609. 1.6 5.7 531.d 390. 1,757. 463. 21,340b 28.0 11.9 43.3

b

Helga

Masses (g) of Main Organs of the Members of the GSF Voxel Phantom Family

TABLE 3.2

11,630 12.4 39.0 25.5 1,255. 57.0c 271. 273. 1.6 19.3 472.d 212. 1,225. 685. 21,100 24.3 11.9 61.9

Irene 22,670 12.6 31.9 92.7 1,127. 84.4c 310. 310. 0.66 9.95 280.d 280. 1,334. 1,108. 18,680 21.8 11.3 93.5

Laura

26,040 7.2 51.9 41.2 1,429. – 790. 2,186. 0.5 3.1 637. 383. 2,037. 1,026. 40,970b 86.2 – 62.5

b

Visible Human

23,690 13.0 43.8 156.2 1,300 200c 360. 320 0.4 10.2 250.d 275. 1,400. 950. 17,500 35.0 11.0 120.

Katja

20,050 14.0 50.0 200. 1,450. 3.36c 370. 300. 1.3 13.8 330.d 310. 1,800. 1,200. 29,000 40.0 – 140.

Godwin

23,720 13.0 40.0 200. 1,300. 200.c 360. 320. 0.4 10.2 250.d 275. 1,400. 950. 17,500 35.0 11.0 120.

Klara


e

d

c

b

a

416. 211. 31.7 50.2

Skeleton Skin Small intestine Small intestine contents Spleen Stomach wall Stomach contents Testes Thymus Thyroid Uterus

151. 52.1 150. 1.91 30.3 4.96 14.6

2,048. 1,180. 490.e

– 1,228.

306. 195. 305. – 19.0 18.7 71.7

7,484. 4,351. 435. 363.

– 1,012.

174. 233. 140. 21.1 10.7 25.8 –

10,450. 4,703. 959.e

7,250.b 737.b 664. 382. 339. 127. 177. – 3.3 22.3 –

54.7 1,177.

23.3 1,363.b

Adipose tissue and muscle not separated (in these early phantoms). Corresponding mass of part of the arms and legs is missing. Glandular tissue only. Wall (muscle) only. Not separated.

14.7 6.55 12.7 1.28 13.5 1.35 1.35

– 50.8

Prostate RBM

298. 62.8 10.3 – 7.7 31.5 79.8

6,503.b 1,653.b 443. 637.

– 1,043.

203. 163. 205. – 25.3 20.0 25.0

8,201. 3,620. 396. 311.

– 916.

257. 125. 209. – 19.4 24.8 82.5

8,501. 3,012. 843. 424.

– 1,058.

266. 258. 166. 25.5 14.0 31.8 –

8,841.b 1,950.b 521. 767.

37.0 1,399.

130. 140. 230. – 20.0 17.0 484.

7,148. 2,740. 600. 280.

– 900.

150. 150. 250. 35.0 25.0 20.0 –

10,585. 4,404. 650. 350.

17.0 1,170.

130. 140. 230. – 20.0 17.0 80.0

7,148. 2,708. 600. 280.

– 900.

The GSF Voxel Computational Phantom Family 75

76


3.4 Applications of the GSF Voxel Phantoms in Radiation Dosimetry 3.4.1 Monte Carlo Codes For applications in radiation dosimetry, the voxel computational phantoms were combined with various Monte Carlo codes that simulated the radiation transport in material. In our working group, the main focus was on photon dosimetry. Two codes are used predominantly for our computations. The first is a program sometimes called the “GSF code” that was further developed at the GSF5,18,32 from the ALGAM photon transport code stemming from the Oak Ridge National Laboratory.33 This code assumes that the energy transferred at a point of inelastic photon interaction is deposited at that point; secondary electrons are not pursued further (“kerma approximation”). The main advantage of this technique is its high calculation speed, since the pursuit of secondary particles is rather time-consuming, especially in the high-energy domain, where the ranges of these particles are long. The kerma approximation is valid as long as there is approximate secondary particle equilibrium, which can be supposed for all points located well within the body. However, for superficial organs, such as skin and testes, the kerma approximation leads to overestimations of up to a factor of two at photon energy 10 MeV; for these organs, it is valid only below approximately 1 MeV.34,35 The second code used frequently for our computations is the EGSnrc code package.36 Furthermore, the phantoms Godwin and Klara were also combined with MCNPX37,38 and PENELOPE,39 and some preliminary calculations have been performed with these latter code packages.40,41 3.4.2 Bone Dosimetry We used approximations to evaluate the doses to the radiation sensitive tissues in bone: the bone endosteum and the RBM. The mean dose to the skeleton (including hard bone, and red and yellow bone marrow) was taken as a conservative estimate of the dose to the bone endosteum. For the RBM dosimetry, a method initially developed by Kramer32 and later adapted for the use with voxel phantoms19 is employed: the physical transport property of a bone voxel is determined by the mixture medium, from which the total energy deposited in that voxel can be deduced by the Monte Carlo transport code. This amount of energy is then partitioned to the individual bone components according to their mass proportions and mass energy–absorption coefficients. For active bone marrow an additional correction factor is applied which accounts for the extra photoelectrons produced in the bone trabeculae that enter the marrow cavities.42 3.4.3 Idealized Geometries (External) Organ dose conversion coefficients for seven adult voxel phantoms were calculated for those idealized beam geometries commonly assumed to represent occupational exposures, i.e., irradiation by broad parallel beams of monoenergetic photons. The directions of photon incidence were anterior–posterior (AP), posterior–anterior (PA), left lateral (LLAT), right lateral (RLAT), and a full 360° rotation of the photon beam around the longitudinal axis of the body (ROT). The photon energies ranged from 10 keV to 10 MeV. The voxel phantoms employed were Donna, Frank, Golem, Helga, Irene, and the Visible Human from the GSF voxel phantom family, and “Voxelman.”7,43 These calculations were used to get an idea of the range of dose values to be expected for individual phantoms, and to study systematic dosimetric differences between voxel and mathematical phantoms.19 Figure 3.5 shows the


77

Organ equivalent dose/air kerma (Sv Gy–1)

1.6 1.4 1.2 1.0 0.8 0.6

Donna Helga Irene Golem Frank Voxelman Visible Human

0.4 0.2 0.0 0.01

0.1

1

10

Photon energy (MeV) FIGURE 3.5 Equivalent dose conversion coefficients (organ equivalent dose normalized to air kerma free in air, in Sv Gy -1) for the stomach for seven individual voxel phantoms. The geometry is a whole body irradiation with a broad parallel beam of monoenergetic photons impinging in AP direction.


1.6 1.4 1.2 1.0 0.8 Donna Helga Irene Golem Frank Voxelman Visible Human

0.6 0.4 0.2 0.0 0.01

0.1

1

10

Photon energy (MeV) FIGURE 3.6 Equivalent dose conversion coefficients (organ equivalent dose normalized to air kerma free in air, in Sv Gy -1) for the kidneys for seven individual voxel phantoms. The geometry is a whole body irradiation with a broad parallel beam of monoenergetic photons impinging in PA direction.

stomach equivalent dose conversion coefficients for the seven voxel phantoms for an AP irradiation with monoenergetic photons. The individual variability is only moderate. A further example is given in Figure 3.6 where the respective conversion coefficients for a PA irradiation and the kidneys are shown. In this case, the individual differences are much larger.


78

3.4.4 Environmental Dosimetry For environmental sources, we calculated organ dose conversion coefficients for the Baby and the Child,44,45 and more recently also for various adult voxel phantoms. The geometries considered were a semi-infinite volume source in air that mimics irradiation from a radioactive cloud, an infinite planar source at depth 0.5 g ∙ cm−2 in the ground simulating the deposition of radionuclides in the ground and allowing for surface roughness and initial migration with precipitation, and a semi-infinite volume source in the ground that reflects the homogeneous distribution of natural radioactivity in the ground. Figure 3.7 shows, as an example, liver equivalent doses normalized to the air kerma free in air at 1 m above the ground for the volume source in air and the phantoms Baby, Child, Golem, Visible Human, Voxelman, Donna, and Helga. The conversion coefficients increase substantially with decreasing body size due to a reduced self-shielding capability of smaller bodies. For the adult voxel phantoms, the individual differences—although following the same trend—are only moderate, as can be expected for environmental geometries, since these lead to a rather homogeneous dose distribution in the body with only limited influence of the exact location of an individual organ. A more detailed discussion of dosimetry for environmental exposures is presented in Chapter 15. 3.4.5 Dosimetry for Medical Imaging The evaluation of patient organ and effective dose is important, since the dose due to medical exposures is by far the larger contribution to the total dose received on average by an individual. For a study on the individual variability of organ doses from x-ray examinations, the voxel computational phantoms Donna, Irene, Golem, and Visible Human were employed.46 The following examinations (performed in supine position) were simulated:


1.0

0.8

0.6

0.4

Baby Child Golem Visible Human Voxelman Donna Helga

0.2

0.0 0

0.1

1

10

Photon energy (MeV) FIGURE 3.7 Equivalent dose conversion coefficients (organ equivalent dose normalized to air kerma free in air 1 m above the ground, in Sv Gy -1) for the liver for seven individual voxel phantoms. The geometry is irradiation by monoenergetic photons from a volume source in air.


79

thorax AP (the most frequent examination in intensive care situations); lumbar spine AP and LAT; pelvis AP; ribs AP; abdomen AP; thoracic spine AP. The exposure conditions were chosen to be optimal for the examination (conventional radiography) and were taken from the European Guidelines on Quality Criteria for diagnostic radiographic images47 and respective German recommendations. The organ doses were found to depend highly on the exposure conditions; and the position of the projection field is critical, particularly for those organs that are partly in the field or near the edge of the field. For some examinations individual dose differences occur, attributed to the different amount of shielding of an organ, depending on the individual topology of these organs. In Table 3.3, organ doses

TABLE 3.3 Organ Equivalent Doses per Entrance Dose Free-in-Air (mSv ⋅ mGy −1) for an Abdomen AP Planar X-Ray Examination of the Voxel Phantoms Irene, Golem, Donna, and Visible Human and for Tube Voltages 75 and 90 kV 75 kV

Bladder (wall) Breast (glandular tissue) Colon (wall) Gonads Liver Lungs Oesophagus RBM Skeleton Skin Stomach (wall) Thyroid Adrenals Brain Kidneys Muscle Pancreas Uterus Small intestine (wall) Spleen Thymus Upper large intestine Lower large intestine Entrance skin surface Exit skin surface

90 kV

Irene

Golem

Donna

Visible Human

Irene

Golem

Donna

0.689 0.007

0.410 –

0.269 0.011

0.175 –

0.784 0.011

0.487 –

0.325 0.015

0.223 –

0.598 0.274 0.390 0.013 0.008 0.071 0.156 0.094 0.325 0.001 0.143 0.000 0.166 0.112 0.266 0.312 0.697

0.469 0.004 0.097 0.005 0.006 0.056 0.112 0.091 0.195 0.000 0.049 0.000 0.091 0.071 0.240 – 0.423

0.373 0.091 0.069 0.007 0.007 0.033 0.084 0.084 0.253 0.001 0.119 0.000 0.072 0.059 0.202 0.134 0.442

0.282 0.014 0.032 0.003 0.004 0.021 0.065 0.103 0.092 0.000 0.040 0.000 0.053 0.046 0.111 – 0.349

0.691 0.356 0.463 0.018 0.013 0.097 0.197 0.101 0.391 0.001 0.195 0.000 0.220 0.131 0.343 0.389 0.798

0.555 0.006 0.124 0.007 0.009 0.078 0.145 0.097 0.236 0.001 0.069 0.000 0.126 0.084 0.311 – 0.510

0.455 0.124 0.090 0.011 0.011 0.048 0.112 0.090 0.306 0.001 0.162 0.000 0.102 0.072 0.264 0.176 0.532

0.342 0.020 0.045 0.005 0.006 0.031 0.090 0.108 0.115 0.000 0.059 0.000 0.078 0.056 0.149 – 0.429

0.130 0.006 0.740

0.023 0.001 0.535

0.095 0.003 0.447

0.020 0.000 0.374

0.169 0.009 0.841

0.033 0.002 0.631

0.127 0.005 0.537

0.030 0.001 0.380

0.402

0.359

0.278

0.180

0.483

0.427

0.349

0.398

1.276

1.267

1.190

1.358

1.312

1.315

1.240

1.402

0.024

0.014

0.017

0.005

0.038

0.023

0.025

0.008

Note: The total filtration is 2.5 mm Al; the focus-to-image receptor distance 115 cm.

Visible Human

80


per entrance dose (free in air) are given for an abdomen AP examination of the four voxel computational phantoms at two different settings of the tube voltage. If the entrance dose is taken as the normalization quantity, the organ dose conversion coefficients increase with increasing tube voltage. However, this effect is more than compensated by the higher amount of radiation transmitted for higher tube voltages and thus reaching the image receptor. This is also indicated by the increasing dose conversion coefficients for the exit skin surface. In those cases where the image receptor demands a constant dose, therefore, the entrance dose required per image receptor dose decreases with increasing tube voltage, and the absolute organ equivalent doses decrease as well. Concerning patient size, the conversion coefficients decrease with increasing patient diameter for organs located in the beam, such as the colon (upper and lower large intestine), pancreas, and kidneys. Also, the seeming increase in conversion coefficients is more than compensated by the higher amount of radiation transmitted through a thinner body. For organs located at the edge of the beam, such as the stomach and the adrenals, there is no obvious dependence of the conversion coefficients on patient diameter, since the proportion of the organ that is inside the beam or the organ distance from the edge of the beam are dominated by the individual organ topology rather than by the patient thickness. 3.4.6 Specific Absorbed Fraction Calculations for Organ Self-Absorption (Original Source Masses) The calculation of absorbed dose to organs due to incorporated radionuclides is based on the quantity absorbed fraction (AF) that specifies the fraction of energy emitted by radioactivity in a “source” organ that is absorbed in a “target” organ. Dividing the AF by the target organ mass leads to the so-called specific absorbed fraction (SAF). Since photons have only a moderate ability to penetrate, especially at lower energies, the larger amount of energy is absorbed in the source organ itself. Consequently, the SAFs for organ self-absorption (target = source) depend strongly on source organ mass that can be very different among the individual voxel computational phantoms. An example is given in Figure 3.8 for self-irradiation of the thyroid.48 The large differences of the SAF values for the individual computational phantoms reflect the large range of thyroid masses that are between 6.2 g (for Voxelman) and 31.5 g (for Visible Human). The statement of the MIRD commission that the photon self-dose to an organ scales as (1/mass2/3)49 is exactly reflected by these data.50 3.4.7 SAF Calculations for Organ Cross-Fire The situation is different when irradiation of a target organ by photons released in a different source organ is considered. For this organ “cross-fire,” the source or target organ masses have no significant influence, and the variations are assumed to be attributable to individual differences in the organ topology, such as the distance of source and target organ. At the former GSF, SAF have been calculated for approximately 40 source organs and seven adult voxel phantoms.48 As an example, Figure 3.9 shows the SAF values for liver as source and stomach as target organ. 3.4.8 Applications Outside the Helmholtz Zentrum München Further to the studies performed by our working group, some members of the GSF voxel phantom family have been used by other researchers for a variety of applications,


81

Frank Golem Visible Human Voxelman Donna Helga Irene

SAF (kg–1)

100

10

1

0.01

0.1 1 Photon energy (MeV)

10

FIGURE 3.8 SAFs (kg−1) for self-irradiation of the thyroid with monoenergetic photons and for seven adult voxel phantoms.

SAF (kg–1)

0.1

0.01 Frank Golem Visible Human Voxelman Donna Helga Irene 0.001 0.01

0.1

1

10

Photon energy (MeV) FIGURE 3.9 SAFs (kg−1) for irradiation of the stomach with monoenergetic photons that originate in the liver, and for seven adult voxel phantoms.

such as dosimetry for multidetector CT,51 and dose calculations for exposure to space radiation.52,53 In principle, one could assign other types of properties to the segmented voxel array, such as resilience, stability, or other physical, chemical or biological properties. This might further broaden the applicability of this family of voxel phantoms.


82

3.5 Conclusions Voxel computational phantoms are the most developed, complete, and realistic computational phantoms of the human anatomy. Thus, they offer a clear improvement compared to the older mathematical phantoms whose organs are described by relatively simple geometrical bodies. Generally, voxel phantoms are a breakthrough in the realistic modeling of the human body and enable a better accuracy for many radiation protection dosimetry applications. There is, however, an obvious variability among the dose data for the individual voxel phantoms, due to differences in stature and individual anatomical features. Despite the obvious advantages of the existing voxel phantoms, compared to the stylized mathematical computational phantoms, it is also clear that a voxel phantom—being constructed from the image data of an individual—does not necessarily represent a whole population, or an average person. Especially in view of the significant influence of organ masses on the resulting organ doses in internal dosimetry, it is obvious that even a large variety of individual voxel phantoms cannot meet the demand for computational phantoms of standard persons. The emergence of such reference computational phantoms does not, however, mean that the other voxel phantoms are becoming obsolete. To a certain extent, they could be used as tools toward a more personalized dosimetry. Since the currently existing voxel phantoms range from slim persons to heavy persons, the dose values published so far give a dose range in which an individual dose may be expected to lie, together with an indication of the magnitude of dose differences to be expected between individual persons. Furthermore, it is believed that they can be used to roughly estimate the doses to an individual by selecting those for the voxel computational phantom fitting best to the person under consideration. It should be clear, however, that existing voxel computational phantoms—both individual and reference—cannot represent any real individual, and that organ dose conversion coefficients from literature cannot be directly applied to an individual person. Especially in those situations where a reliable dose assessment for an individual is required, this approach is not possible, e.g., for radiation treatment planning purposes.

References 1. Snyder, W.S. et al. Estimates of absorbed fractions for monoenergetic photon sources uniformly distributed in various organs of a heterogeneous phantom, J. Nucl. Med., 10, Suppl 3, 5, 1969. 2. Snyder, W.S., Ford, M.R., and Warner, G.G. Estimates of specific absorbed fractions for photon sources uniformly distributed in various organs of a heterogeneous phantom, MIRD Pamphlet No. 5, revised, Society of Nuclear Medicine, New York, 1978. 3. Cristy, M. Mathematical phantoms representing children of various ages for use in estimates of internal dose, U.S. Nuclear Regulatory Commission Report NUREG/CR-1159 (also Oak Ridge National Laboratory Report ORNL/NUREG/TM-367), 1980. 4. Cristy, M. and Eckerman, K.F. Specific absorbed fractions of energy at various ages from internal photon sources, Part I: Methods, TM-8381/V1, Oak Ridge National Laboratory, Oak Ridge, TN, 1987. 5. Kramer, R. et al. The calculation of dose from external photon exposures using reference human phantoms and Monte Carlo methods: Part I. The male (ADAM) and female (EVA) adult mathematical phantoms, GSF-Report S-885, Institut fuer Strahlenschutz, GSF-Forschungszentrum fuer Umwelt und Gesundheit, Neuherberg-Muenchen, 1982.


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6. Zankl, M. et al. The construction of computer tomographic phantoms and their application in radiology and radiation protection, Radiat. Environ. Biophys., 27, 153, 1988. 7. Zubal, I.G. et al. Computerized three-dimensional segmented human anatomy, Med. Phys., 21, 299, 1994. 8. Dimbylow, P.J. The development of realistic voxel phantoms for electromagnetic field dosimetry, Workshop on Voxel Phantom Development, Chilton, U.K., 1996. 9. Caon, M., Bibbo, G., and Pattison, J. An EGS4-ready tomographic computational model of a 14-year-old female torso for calculating organ doses from CT examinations, Phys. Med. Biol., 44, 2213, 1999. 10. Xu, X.G., Chao, T.C., and Bozkurt, A. VIP-MAN: An image-based whole-body adult male model constructed from color photographs of the Visible Human Project for multi-particle Monte Carlo calculations, Health Phys., 78, 476, 2000. 11. Zankl, M. and Wittmann, A. The adult male voxel model “Golem” segmented from whole body CT patient data, Radiat. Environ. Biophys., 40, 153, 2001. 12. Petoussi-Henss, N. et al. The GSF family of voxel phantoms, Phys. Med. Biol., 47, 89, 2002. 13. Kramer, R. et al. MAX06 and FAX06: Update of two adult human phantoms for radiation protection dosimetry, Phys. Med. Biol., 51, 3331, 2006. 14. Gibbs, S.J. et al. Patient risk from interproximal radiography, Oral Surg. Oral Med. Oral Pathol. Oral Radiol. Endod., 58, 347, 1984. 15. Gibbs, S. et al. Radiation doses to sensitive organs from intraoral dental radiography Dentomaxillofac Radiol., 16, 67, 1987. 16. Williams, G. et al. The calculation of dose from external photon exposures using reference and realistic human phantoms and Monte Carlo methods, Phys. Med. Biol, 31, 449, 1986. 17. Williams, G. et al. The construction of 3D whole body images from CT data and the use of image processing methods to produce files for Monte Carlo dose calculations, CAR 87, Springer Verlag, Berlin, Germany, 1987, p. 148. 18. Veit, R. et al. Tomographic anthropomorphic models, Part I: Construction technique and description of models of an 8 week old baby and a 7 year old child, GSF-Report 3/89, GSF—National Research Center for Environment and Health, Neuherberg, Germany, 1989. 19. Zankl, M. et al. Organ dose conversion coefficients for external photon irradiation of male and female voxel models, Phys. Med. Biol., 47, 2367, 2002. 20. Fill, U.A. et al. Adult female voxel models of different stature and photon conversion coefficients for radiation protection, Health Phys., 86, 253, 2004. 21. Zankl, M. et al. GSF male and female adult voxel models representing ICRP Reference Man— the present status, The Monte Carlo Method: Versatility Unbounded in a Dynamic Computing World, Chattanooga, TN, 2005. 22. Becker, J. et al. About Katja, a virtual human phantom of a 24-week pregnant woman, Proceedings of the 7th International Scientific Conference SATERRA “Human and Environment,” Mittweida, Germany, 2007. 23. Cristy, M. Active bone marrow distribution as a function of age in humans, Phys. Med. Biol., 26, 389, 1981. 24. ICRP. Basic Anatomical and Physiological Data for Use in Radiological Protection: The Skeleton, ICRP Publication 70, Pergamon Press, Oxford, U.K., 1995. 25. Schlattl, H., Zankl, M., and Petoussi-Henss, N. Organ dose conversion coefficients for voxel models of the reference male and female from idealized photon exposures, Phys. Med. Biol., 52, 2123, 2007. 26. Zankl, M., Eckerman, K.F., and Bolch, W.E. Voxel-based models representing the male and female ICRP reference adult—the skeleton, Radiat. Prot. Dosim., 127, 174, 2007. 27. ICRP. Report of the Task Group on Reference Man, ICRP Publication 23, Pergamon Press, Oxford, 1975. 28. Spitzer, V.M. and Whitlock, D.G. Atlas of the Visible Human Male, Jones and Bartlett Publishers, Sudbury, MA, 1998. 29. ICRP. Basic Anatomical and Physiological Data for Use in Radiological Protection: Reference Values, ICRP Publication 89, Pergamon Press, Oxford, U.K., 2003.

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30. ICRP. 1990 Recommendations of the International Commission on Radiological Protection, ICRP Publication 60, Pergamon Press, Oxford, U.K., 1991. 31. ICRP. The 2007 Recommendations of the International Commission on Radiological Protection, ICRP Publication 103, International Commission on Radiological Protection, Elsevier, Oxford, U.K., 2007. 32. Kramer, R. Ermittlung von Konversionsfaktoren zwischen Körperdosen und relevanten Strahlungskenngrößen bei externer Röntgen- und Gamma-Bestrahlung, S-556, GSF—National Research Center for Environment and Health, Neuherberg, Germany, 1979. 33. Warner, G.G. and Craig, A.M. ALGAM, a computer program for estimating internal dose from gamma-ray sources in a man phantom, TM-2250, Oak Ridge National Laboratory, Oak Ridge, TN, 1968. 34. Chao, T.C., Bozkurt, A., and Xu, X.G. Conversion coefficients based on the VIP-Man anatomical model and EGS4-VLSI code for external monoenergetic photons from 10 keV to 10 MeV, Health Phys., 81, 163, 2001. 35. Saito, K. et al. Construction of a computed tomographic phantom for a Japanese male adult and dose calculation system, Radiat. Environ. Biophys., 40, 69, 2001. 36. Kawrakow, I. and Rogers, D.W.O. The EGSnrc code system: Monte Carlo simulation of electron and photon transport, PIRS Report 701, National Research Council of Canada (NRCC), Ottawa, 2003. 37. Waters, L. MCNPX user’s manual, version 2.3.0, Los Alamos National Laboratory Report, LA-UR-02-2607, April 2002. 38. Hendricks, J.S. et al. MCNPX extensions, version 2.5.0, LA-UR-05-2675, LANL, Los Alamos, NM, 2005. 39. Salvat, F., Fernandez-Varea, J.M., and Sempau, J. PENELOPE, a code system for Monte Carlo simulation of electron and photon transport, OECD, 2003. 40. Taranenko, V. and Zankl, M. Photon and electron transport simulation in voxel geometry with PENELOPE, Biomed. Tech. (Berl), 50, 271, 2005. 41. Taranenko, V., Zankl, M., and Schlattl, H. Voxel phantom setup in MCNPX, in Proc. The Monte Carlo Method: Versatility Unbounded in a Dynamic Computing World, LaGrange Park, USA: American Nuclear Society, Chattanooga, TN, 2005. 42. King, S.D. and Spiers, F.W. Photoelectron enhancement of the absorbed dose from x-rays to human bone marrow: Experimental and theoretical studies, Br. J. Radiol., 58, 345, 1985. 43. Zubal, I.G. et al. Two dedicated software, voxel-based, anthropomorphic (torso and head) phantoms, in Proc. Workshop on Voxel Phantom Development, Dimbylow, P.J., Ed. National Radiological Protection Board, Chilton, U.K., 105, 1996. 44. Saito, K. et al. Organ doses as a function of body weight for environmental gamma rays, J. Nucl. Sci. Technol., 28, 627, 1991. 45. Saito, K. et al. The calculation of organ doses from environmental gamma rays using human phantoms and Monte Carlo methods, Part I: Monoenergetic sources and natural radionuclides in the ground, GSF—Report 2/90, GSF—National Research Center for Environment and Health, Neuherberg, Germany, 1990. 46. Petoussi-Henss, N., Zankl, M., and Panzer, W. Estimation of organ doses in radiology using voxel models describing different patients, Biomed. Tech. (Berl.), 50, 664, 2005. 47. European Commission. Quality criteria for diagnostic radiographic images, EUR 16260, Office for Official Publications of the European Communities, Luxembourg, 1996. 48. Zankl, M. et al. The application of voxel phantoms to the internal dosimetry of radionuclides, Radiat. Prot. Dosim., 105, 539, 2003. 49. Snyder, W.S. et al. “S” absorbed dose per unit cumulated activity for selected radionuclides and organs, MIRD Pamphlet 11, Revised, Society of Nuclear Medicine, New York, 1975. 50. Petoussi-Henss, N. et al. Patient-specific scaling of reference S-values for cross-organ radionuclide S-values: What is appropriate?, Radiat. Prot. Dosim., 127, 192, 2007.


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51. DeMarco, J.J. et al. Estimating radiation doses from multidetector CT using Monte Carlo simulations: Effects of different size voxelized patient models on magnitudes of organ and effective dose, Phys. Med. Biol., 52, 2583, 2007. 52. Ballarini, F. et al. GCR and SPE organ doses in deep space with different shielding: Monte Carlo simulations based on the FLUKA code coupled to anthropomorphic phantoms, Advances in Space Research, 37, 1791, 2006. 53. Trovati, S. et al. Human exposure to space radiation: Role of primary and secondary particles, Radiat. Prot. Dosimetry, 122, 362, 2006.

4 The ADELAIDE Teenage Female Voxel Computational Phantom Martin Caon, Giovanni Bibbo, and John E. Pattison

CONTENTS 4.1 Introduction ................................................................................................................. 87 4.2 The ADELAIDE Phantom .......................................................................................... 88 4.2.1 The Constraints Imposed by the Available Images .................................. 88 4.2.2 Segmentation Method: Identifying the Organ Boundaries ..................... 89 4.2.3 ADELAIDE Body Size Compared to Mean Body Sizes for Age ............. 92 4.2.4 The Size of ADELAIDE’s Organs ................................................................ 93 4.2.5 The Shape and Locations of Organs in the Body ...................................... 97 4.2.6 The Location of Organs within the Body ................................................... 99 4.2.7 Conversion of Segmented Anatomy Images to an Input File for the EGS4 Monte Carlo Code ........................................................... 99 4.3 Organ Dose Calculations with ADELAIDE and EGS4........................................ 101 4.4 Limitations of the Computational Phantom ......................................................... 102 References ............................................................................................................................. 102

4.1 Introduction The motivation for producing a pediatric computational phantom was to determine the absorbed organ doses from computed tomography (CT) examinations using the public domain EGS4 Monte Carlo code.1 The project was conceived in about 1992 in response to the dearth of available absorbed dose data for children who underwent CT procedures. Inspiration was derived from the work of Cristy, who extrapolated the mathematical medical internal radiation dose (MIRD) computational phantoms to children2 and from the work of Zankl et al. who produced voxel computational phantoms of two children.3 The Monte Carlo dose calculations for CT procedures using “adult” MIRD computational phantoms by Shrimpton et al.4 and later for plain radiography in children with MIRD computational phantoms5 also inspired the ADELAIDE work. In the early 1990s, desktop PCs were not capable of performing Monte Carlo calculations; picture archiving and communication systems (PACS) were not yet available; the Digital Imaging and Communications in Medicine (DICOM) standard for distributing and viewing medical image from any origin was not operating; computers were not networked; the electronic

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transfer of data was limited by the capacity of the “floppy disk”; and the Internet had not yet developed into the information distributing system that it currently is. Consequently, working with medical images on computers was not straightforward.6 The situation in 2007 is much better and the number of voxel computational phantoms has increased dramatically.7 Despite the improvements in computer hardware and software, segmenting the large number of medical images required for a voxel computational phantom is a timeconsuming task. Even though steps have been taken to semiautomate the task,8–10 segmentation is still a lengthy operation. This chapter describes the segmentation procedure used for ADELAIDE’s construction and the anatomy of the computational phantom.

4.2 The ADELAIDE Phantom 4.2.1 The Constraints Imposed by the Available Images Obtaining the medical images that are required to produce a voxel computational phantom was not an easy task in the 1990s. Medical images are confidential patient medical records and were not readily shared by medical centers. Furthermore, extensive CT examinations that spanned a large proportion of a patient’s anatomy were not common and in any case usually did not include the arms and legs. Consequently, prior to the construction of ADELAIDE11 only two voxel computational phantoms of children existed and they were modeled from the cadaver of an 8 week old baby and from a 7 year old patient who was undergoing treatment for leukemia.3 The purpose for constructing ADELAIDE was to calculate torso organ doses from CT. Thus, even though significant anatomy (the head, arms, and legs) were excluded from the computational phantom (see Figure 4.1), that anatomy is not of interest in many CT examinations so is not irradiated. However, the CT scanner patient table was also modeled so that the attenuation it produces was also simulated. Hence, the computational phantom suited its purpose. Indeed, the presence of arms alongside the torso would be detrimental to the accurate calculation of CT dose, as arms are routinely held out of the beam. Their inclusion would shield the underlying tissue from the x-ray beam. At the time of ADELAIDE’s construction (commencing in 1996), the DICOM standard had not been widely deployed to medical imaging equipment, so getting access to the GE format image files stored in the scanner’s computer was not straightforward. A software program that was able to interrogate the CT scanner and download the GE proprietary format files and convert them to be saved as.tif files was obtained from GE and used for the purpose. Fifty-four CT images at an interval of 10 mm of a female torso captured by a FIGURE 4.1 The span of anatomy included in the General Electric HiSpeed Advantage CT scanner were ADELAIDE voxel model. (From Caon, available for modeling. The image files were 512 × 512 M., Bibbo, G., and Pattison, J., Phys. Med. pixels in size and the field of view for the images was a Biol., 44, 2213, 1999. With permission.)

The ADELAIDE Teenage Female Voxel Computational Phantom

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FIGURE 4.2 Left: Original 512 × 512.tif image. Right: Image showing extrapolated tissue. Bottom: Image resized to 128 × 128 and segmented.

circle of diameter 29 cm. As the patient’s width at the shoulders and hips was greater than 29 cm, the original images showed truncated anatomy in these regions (see for example Figure 4.2a, a slice through the chest superior to the heart). This problem was overcome by estimating the external contour of the skin and using image processing software to manually draw in the skin boundary for the missing anatomy in each image (Figure 4.2b). Some additional extrapolation was done on the bitmap data after exporting it to “MS Excel.” Each added pixel was then identified as containing either muscle, bone, subcutaneous fat or skin and its gray scale value assigned accordingly. 4.2.2 Segmentation Method: Identifying the Organ Boundaries Segmentation was done completely manually using Paint Shop Pro v4.14 (JASC Inc.) and Image-Pro Plus v3.0 (Media Cybernetics). Images were resized to 128 × 128 pixels which resulted in voxels of size 2.53 × 2.53 ×10.0 mm. As the slices were 10 mm apart in the z-direction, the loss of resolution in the x- and y-directions that resulted from the resizing was not seen as significant. Resizing also had the advantage of making the computational phantom manageable on the computer that was available. A disadvantage of the low (by modern standards) resolution was that the skin surface and the boundary between organs were “stepped” (Figure 4.2c). The image processing software allowed the images to be magnified so that individual pixels could be seen, their gray scale value to be viewed and the value altered as desired. In this way, a boundary between organs or tissue could be drawn and the gray scale values of the contained pixels altered to the value chosen for that tissue. Where necessary this was done pixel by pixel.


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An underlying assumption used in preparing the voxel computational phantom is that the individual tissues or organs are homogeneous and so are assigned the same gray scale value. That is, we assume that all of the voxels assigned to a particular organ or tissue have the same composition and density. This, of course, is not the case as for example the pixels in subcutaneous fat tissue had gray scale values that typically ranged from 65 to 90, for skin a typical range is from 85 to 125, muscle pixels ranged from 138 to 173, liver from 153 to 204, spleen from 169 to 200 (where 0 is black for air/lung and 255 is white for cortical bone). The assigned gray scale was chosen to be typical of the pixels of that tissue while being sufficiently different from the chosen gray scale of neighboring organs to be distinguishable by the naked eye when viewed on the computer screen. For the purposes of identifying the different media in EGS4, each organ/tissue was in addition to a gray scale value, assigned an EGS4 medium index (Table 4.1). TABLE 4.1 Assigned Gray Scale Values to Organ/Tissue and EGS4 Medium Indices EGS4 Medium Index 1 2 3 4 5 6 7 8 10 11 13 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30

Tissue

Assigned Gray Scale

Air surrounding model Cortical bone, internal bone, and marrow Heart (and great blood vessels) Spinal cord Skin Subcutaneous fat Breasts Lungs Esophagus Muscle and soft tissue Kidney Liver Spleen Stomach and contents Blood (aorta and IVC) Gas (in digestive system) Bowel and contents Pancreas Gall bladder (contents) Uterus Ovaries Bladder (empty) Thymus Trachea Thyroid Colon and contents Bone surface

0 255 3 4 110 70 40 1 10 66 80 15 16 17 30 60 90 50 250 22 33 44 26 27 39 77 254

Note: Indices 9 and 12 are assigned to the CT scanner’s patient table. Index 14, initially assigned to spongy (cancellous) bone, is not used as it could not be reliably distinguished from cortical bone.


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The process of segmentation is tedious due to the large number of organs to delineate in each CT slice and the large number of slices. The majority of organs such as the lungs, liver, spleen, kidneys, bone, muscle, and subcutaneous fat were easily distinguishable by eye on a computer screen and their edges were straightforward to identify. However, on a magnified version of the image, selecting the individual voxels that lie on the boundary of a tissue was often a matter of judgment. For example while skin, subcutaneous fat and muscle were distinguishable from each other, the gray scale values of the pixels at their common borders were intermediate in value between the gray scale values of pixels located in the center of fat and muscle tissue. Voxels inside the skin were usually assigned to fat if they were sufficiently dark to distinguish them from the higher gray scale value of muscle tissue. The precise boundary between breast tissue and fat was difficult to discern. Where there was significant doubt, voxels were assigned to breast to avoid underestimating their size. Consecutive CT slices were examined to ensure that the size and shape of the breasts were sequentially consistent. The subject had very little discernable visceral fat so tissue not included in an organ and deep to subcutaneous fat was assigned to muscle and soft tissue. In the case of the esophagus, pancreas, thymus, and ovaries, it was difficult to distinguish the organ from the surrounding tissue of similar gray scale. In this case, a radiologist and a CT anatomy atlas were used to identify the organs. The adrenals and kidneys were not separated and were given the same gray scale and medium index. The digestive system consists of the stomach, the small intestine, the large intestine, and their contents—which may be gas, semiliquid, or a semisolid paste. Gas in the system was easily distinguishable, as gas voxels had gray scale 0. Gas was also discernable in the trachea and esophagus. Where gas occurred within an organ, voxels containing gas were distinguished from voxels containing solid media and energy imparted to the gas was not included in the calculation of absorbed dose to the organ. Sometimes, the contents of the gut were also apparent. For example in the stomach, the horizontal boundary between liquid chyme (stomach contents) and gas indicated that below the boundary, chyme was present rather than stomach wall (see Figure 4.3). The ascending and descending colon were usually imaged in the CT slices as transverse sections so a reasonable attempt at distinguishing the contents from the wall could be made. However, the boundary between large intestine and small intestine was not clear. Nor was the difference between small intestine wall and its contents apparent due to the complicated pattern of folding of the intestine. Hence, contents and wall were given the same gray scale number. The stomach and descending colon were distinguished from each other and from the small intestine and ascending and transverse colon. These latter three organs of the gut (and their contents) were collectively called bowel and given the one gray scale and EGS4 medium index. Bone tissue stood out clearly in the CT images as the white pixels (gray scale 255) contrasted with the surrounding tissue. The gray scale value gradually decreases at the edges of bones so bone tissue was taken to occupy all pixels with gray scale greater than 225. The outermost bone voxels were further distinguished as being “surface bone voxels.” This was done to facilitate the calculation of absorbed dose to ICRP 60’s “bone surface.” Note that the thickness of this tissue, like the skin, is determined by the voxel FIGURE 4.3 size. Because of this limitation, the calculated Note the gas in the stomach and bowel.

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dose obtained from ADELAIDE is to the surface voxels rather than to the “bone surface” referred to by ICRP 60. Dose to the 10 μm layer of tissue at the bone surface is enhanced (over dose to soft tissue not adjacent to a soft tissue/bone interface) due to the absorption of the energy of photoelectrons that are produced in bone. The dose to bone surface is in practice replaced by the mean dose in bone.12 It is argued that since the dose to bone is higher than to tissue, using mean dose to bone is a conservative estimate for the enhanced dose to the soft tissue layer adjacent to bone. Replacing it instead with mean dose to surface bone voxels (which are composed of cortical bone), as we have done, may overestimate the dose to bone surface even more as the attenuation coefficient for cortical bone is greater than for less dense bone. The less dense cancellous bone (containing marrow) was, in some images, distinct from the more dense cortical bone. However, it was decided not to distinguish cancellous from cortical bone as we were not confident that all the cancellous bone was able to be identified. The position of some slices and the images of bones displayed at times made it difficult to distinguish between pixels with gray scale less than 255 due to containing cancellous bone, from those due to being the edge of a bone imaged at a glancing angle. Hence cancellous bone and cortical bone (but not the surface voxels) were given the same gray scale and called collectively “internal bone.” Internal bone was considered to be a homogeneous mixture consisting of cortical bone, red marrow, and yellow marrow in the proportions 40%, 39%, and 21% by mass respectively. These percentages were chosen so that when the masses of voxels containing bone surface and internal bone were added to give total skeleton mass, the proportions of this mass due to the three components of the skeleton are 69% cortical bone, 20% red marrow, and 11% yellow marrow. These proportions (69%, 20%, and 11%) are the same as those Cristy2 used in his 15 year old computational phantom when only the bones that the Cristy computational phantom has in common with our torso are considered. Some tissues that are not specified by ICRP 60 as needed to determine effective dose (either as a tissue assigned a weighting factor or as one of the remainder tissues) are included in ADELAIDE because they were visible on the images. These were subcutaneous fat, the heart and adjacent great blood vessels, the descending aorta and inferior vena cava (and the blood they contain), the spinal cord, the trachea, that proportion of internal bone that is yellow marrow or cortical bone, and the contents of the gall bladder. The heart includes the heart wall and the enclosed blood and a portion of the great blood vessels where these were not able to be distinguished from the heart. When the images clearly showed the aortic arch, the descending aorta, and the inferior vena cava (and other smaller blood vessels), these tissues were identified as blood. Voxels assigned to “muscle and soft tissue” included the readily identified skeletal muscle, lymph tissue, any soft tissue adjacent to organs that was clearly not part of the organ and visceral fat. There was very little discernable visceral fat surrounding the organs. Hence, it was common for one organ to share a boundary with another organ without any intervening soft tissue being evident. If any soft tissue was actually present between such organs (without being discernable), it has been included within the organs. Consequently, it is unlikely that the size of organs has been underestimated in the process of segmentation and it is more likely that their size has been overestimated to a small extent. 4.2.3 ADELAIDE Body Size Compared to Mean Body Sizes for Age Hitchcock et al.13 in a survey of the body size of young Australians living in Perth found the mean and standard deviation of the mass of a sample of 175 girls aged 13 years (average age 13.5 years) to be 48.7 ± 8.0 kg. The figures for 145 girls of 14 years (average age 14.5 years)


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were 52.1 ± 9.2 kg. The standard deviations indicate that it is common for the mass of girls of these ages to vary by up to 17% from the mean. The ADELAIDE torso was modeled from a patient of age 13 years and 11 months and mass about 48 kg. Hence, the mass of ADELAIDE is slightly less (by 1.5%) than the average mass of Australian 13 year old girls, but well within the normal range. The heights of the 13 and 14 year old girls in Hitchcock et al.’s survey were 158.1 ± 6.9 cm and 161.3 ± 6.3 cm, respectively. For the case of height, it can be expected that a 4% deviation from the mean height-for-age is not uncommon in girls in their early teens. ADELAIDE was constructed from a patient whose height was about 157 cm, hence is slightly less (by 0.7%) than, but very close to the average height of Australian 13 year old girls. Hitchcock et al. compared the median weights and heights from their survey with data from the United States (published by the National Center for Health Statistics in 1977) and found small differences in weight between the girls and no significant height differences. Hence the Australian averages are very close to the averages for girls in the United States. A single voxel computational phantom cannot be representative of all 13–14 year old girls, however ADELAIDE is close to the average stature for her age. 4.2.4 The Size of ADELAIDE’s Organs The Cristy 15 year old MIRD computational phantom 2 was designed to represent an average-sized teenager and is intermediate in size between a boy and girl of that age. On the other hand, ADELAIDE is representative of the average height and weight of an Australian 13 year old girl. Differences between the two computational phantoms are to be expected due to the different “ages” of the computational phantoms and also because ADELAIDE is female while the Cristy computational phantom is hermaphrodite. The anatomies of ADELAIDE and a MIRD style phantom differ markedly in the disposition of internal space to organs. The organs of a MIRD computational phantom, being described by mathematical equations, are constrained in shape to that which can be represented easily by equations. While the volumes of organs are based on anatomical data, the resulting shapes resemble the actual shape of organs only very approximately. They are further constrained in position by the need to occupy a space that does not overlap that of another organ. This results in some “free space” between organs—filled with soft tissue—that is not present in real anatomy. On the other hand, the organs of ADELAIDE are realistic in shape, fit snugly against each other and are separated (if at all) by the actual amount of soft tissue present in a real patient. A major difference is in the allocation of soft tissue (see Table 4.2). In ADELAIDE, subcutaneous fat (medium 6) was visible beneath the skin so is identified and excluded from the category “muscle and soft tissue” (medium 11). Similarly, 1023 cm3 of gas was visible in the digestive system and esophagus and this also is excluded. In the Cristy computational phantom, all the torso volume outside of the identified organs is deemed soft tissue. Hence 18,364 g (67%) of the 27,284 g torso is soft tissue. When all the soft tissues of ADELAIDE are totaled (media with indices: 4, 6, 10, 11, 18, and 27), the result is that 12,245 g (52%) of the 23,636 g torso is soft tissue. Another major difference between the Cristy computational phantom and ADELAIDE is the absence of arms in the latter. The Cristy computational phantom includes the soft tissue and bone of each arm within the elliptical cylinder of the torso envelope. To arrive at a mass for a Cristy “torso,” we have taken the two arms to constitute 10% of the total Cristy body mass (i.e., 5564 g of the 55,644 g mass) and in Table 4.2 have subtracted this mass from the mass (32,800 g) of the Cristy torso. Organ mass in the ADELAIDE computational phantom is determined by the formula: No.of voxels assigned to organ × volume of a voxel × density of voxel.


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TABLE 4.2 Organ Masses of the ADELAIDE 14 Year Old Torso Compared with Cristy’s 15 Year Old Trunk and Data for 14 Year Old Japanese Girls Medium Index

Tissue

ADELAIDE Mass (g)

Cristy 15 Year Old Mass (g)

Red marrow (39% of “internal bone”) Yellow marrow (21% of int bone) Cortical bone (40% of int bone) Heart (ADELAIDE Inc., blood and great blood vessels) Spinal cord Skin Subcutaneous fat Breasts Lungs

866

845a

466

404

888

1703b

705

238 (+330 blood)

10 11 13

Esophagus Muscle and soft tissue Kidney

15 16 17

2

3 4 5 6 7 8

69 1018 2577 300 1085

Ogiu et al. Mass (g)g

237 ± 15

1059c 44 650

729 ± 63

31 9337 363 (inc. adrenals)

(31.4d) 18,420e 235 (+10 for adrenals)

232 ± 21 (+8 for adrenals)

Liver

1543

1220

1285 ± 157

Spleen

204

117

127 ± 35

128 (+208 cont)

112 (+185 cont)

221 1023 cm3 620 (+646 cont)

549 (+573 cont)

21

Stomach wall (38% of ADELAIDE stomach and contents) Blood (in aorta and IVC) Gas (in digestive system) Bowel walls (small and large) (49% of wall and contents) Pancreas

58

62

79 ± 11

22 23 24 25 26

Gall bladder (contents) Uterus Ovaries Bladder Thymus

8 35 11 19 (empty) 15

47 25 5 34 (+152 cont) 27

35 ± 15

27 28

Trachea Thyroid

10 18

12

12 ± 2

29

Colon wall (54% of ADELAIDE wall and contents) “Bone surface” Total

243 (+207 cont)

120 (+104 cont)

1736 23,636

27,280f

18 19 20

30 a

b

c d e f

g

Includes red marrow of clavicles, scapulae, ribs, pelvis (includes 50% of upper half of femur), spine, one-third of upper portion of arm bones. Mass of Cristy bone (clavicles, scapulae, ribs, pelvis, spine, one-third of upper portion of arm bones, excludes marrow). Volume of skin on trunk × skin specific gravity (1.105). Yamaguchi’s value.21 Cristy named tissues = 8860 g (unnamed soft tissue = 27,280 – 8860 = 18,420 g). Mass of Cristy 15 year old trunk = 32,800 g (add breasts (+44 g), minus arms each assumed to be 5% of total body weight (−5564 g for both arms) = 27,280 g). Mass values ± standard deviation.


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Table 4.2 presents the organ masses determined in this way as well as the organ masses in Cristy’s 15 year old computational phantom and average organ masses of between 3 and 6 Japanese 14 year old girls.14 One would expect that the organs in the Cristy 15 years old, because it represents a larger-sized teenager than ADELAIDE, and considering the effect that the larger organs of boys would have on its organ sizes, would be larger than the organs in ADELAIDE. This is not the case. The contents of the gall bladder, the urinary bladder walls, and the thymus do have a smaller mass in ADELAIDE. In addition, the masses of six organs or tissues (red marrow, yellow marrow, skin, stomach wall, small and upper large intestine walls, and the pancreas) are within 15% of each other in the two computational phantoms. However, the other organs and tissues (except for muscle and soft tissue) are significantly greater in mass in ADELAIDE (see Table 4.2). We will consider muscle and soft tissue first. The Cristy computational phantom has 67.5% of its mass allocated to muscle and (unnamed) soft tissue, while ADELAIDE has 51.8% of its mass as muscle and soft tissue (including subcutaneous fat, spinal cord, esophagus, blood in blood vessels, and the trachea). Given that the Cristy computational phantom represents an average 15 years old and ADELAIDE is close to the average size of an Australian 13 years old, it could be expected that the proportion of body mass that is muscle and soft tissue would be more similar than it is. It is possible that the Cristy value is too high or the ADELAIDE value is too low (when compared to the “real” proportion) or that both computational phantoms fail to estimate the true proportion accurately. In support of the first contention, the MIRD computational phantom has significant gaps between its organs that contain soft tissue. In ADELAIDE, almost all of the volume within the thoracic and abdominopelvic cavities is assigned to identified organs. That is, there is no identifiable visceral fat and very little soft tissue separating the organs. The external profile of the MIRD computational phantom, by not narrowing at the waist, allows more soft tissue to be present between the digestive organs and the skin than is the case in real teenagers. In support of the second contention, the organs in ADELAIDE are likely to incorporate some of the connective tissue (mesenteries) adjacent to their borders and between them and the neighboring organ. If anything, this overestimates the size of the organs in ADELAIDE. For example, the diaphragm was not separately distinguished so the diaphragm tissue is included in the liver, stomach, or heart. Heart tissue includes the pericardium. Part of the omentum (tissue layer overlying the intestine) will have been assigned to the intestinal tissue. Hence, in ADELAIDE, the assigned mass of an organ is likely to overestimate the actual mass. However, in the cases where this has occurred, the overestimation is likely to be small, since if the amount of tissue between organs was not small, it would have been discernable in the CT images and identified. Thus, the third contention (that the proportion of body mass that is muscle and soft tissue in the Cristy computational phantom is too high, and in the ADELAIDE computational phantom is too low) is the most likely to be true, but the actual typical proportion of body mass that is muscle and soft tissue is probably closer to the proportion in ADELAIDE. To highlight the difference in the size of organs between the two torsos, the organ mass as a percentage of their respective torso mass is presented in Table 4.3 (for the organs used to calculate effective dose). Except for the bladder, the percentages for ADELAIDE’s organs are all greater than for Cristy’s 15 year old torso. The proportional organ mass for ADELAIDE is 50% greater than the Cristy torso for six organs and 100% greater or more for four of these. By noting the effect of reducing the size of ADELAIDE’s organs to approximately the size of Cristy’s and assigning the extra tissue to soft tissue, it is possible


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TABLE 4.3 Mass of Organs in the ADELAIDE Torso as a Percentage of the Torso’s Mass, Compared to the Values for the Cristy Torso

Ovaries Red marrow Colon wall Lung Stomach wall Bladder Breast Liver Esophagus Thyroid Skin Bone surface Remainder Totals

ADELAIDE

Cristy 15 Years Old

ADELAIDE/Cristy

0.046 3.7 1.03 4.59 0.54 0.08 1.27 6.53 0.13 0.076 4.30 7.33 (surface voxels) 45.0 74.6a

0.018 3.1 0.44 2.38 0.41 0.12 0.16 4.47 0.099 (thymus) 0.044 3.88 10.82 (skeleton) 71.3 97.2b

2.6 1.2 2.3 1.9 1.3 0.7 7.9 1.5 1.3 1.7 1.1 0.7 0.6

Note: Cristy torso mass corrected to exclude mass of arms. a Remaining 23.3% is heart, gall bladder contents, subcutaneous fat, trachea, spinal cord, contents of digestive system, remaining “internal bone,” and blood. b Remaining 2.7% is heart and gall bladder contents.

to quantify any possible overestimate. Hence if the marrow and internal bone is decreased by 1000 g, the size of the heart by 200 g, the lungs by 400 g, kidney by 100 g, liver by 300 g, spleen by 80 g, bowel by 100 g, colon by 200 g, a sum of 2380 g of extra soft tissue is “produced” (and the organs of ADELAIDE and the Cristy 15 years old have approximately equal mass). The revised total soft tissue in ADELAIDE is 2380 + 12,245 g = 14,625 g of the 23,663 g torso, or 62% of the torso mass. This is closer to the Cristy 15 year old trunk’s value of 67.5%. However, it is unlikely that the size of ADELAIDE’s organs have been overestimated by this amount during the segmentation process. The masses of the organs in the Cristy computational phantom were obtained using ICRP 23,15 which in turn uses data from the first half of the twentieth century. The Cristy organ masses are almost identical to those of the preceding 15 year old computational phantom of Jones et al.,16 which used organ mass data from the same sources. The major differences are that Cristy used masses that were about 8% larger for the components of the digestive system, 10% larger for the pancreas and masses that were 10% and 17% smaller for the liver and spleen, respectively. The body size of children in most Western countries has been increasing during the twentieth century. In Australia, NSW school children have increased in height by 8–9 cm between 1908 and 1971.13 A further height gain of about 1.5 cm has occurred in Perth school children (aged from 5 to 16 years) when compared to NSW children in the period 1970–1972 to 1984.13 When this increase in children’s stature is considered along with the period from which the MIRD computational phantoms draw their anatomical data, it seems likely that organ masses would have increased along with the increase in stature of children. Consequently, it is likely that the organ masses in the MIRD computational phantoms underestimate the typical organ masses of children living in the 1990s.


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Some support for this likelihood is provided by the organ mass data of Ogiu et al. for Japanese people.14 Their autopsy data include the masses of eight different organs from six 14 year old females. While it is not known whether the mass of an organ taken from a cadaver is the same as the organ mass in the live individual, the average masses of six of the eight organs is greater than the masses used by Cristy (the other two are about the same mass). That this is the case even though the Cristy computational phantom is a year “older” and includes organs that are the average of male and female organ masses supports the argument that the Cristy organ masses underestimate the organ masses of contemporary 15 years old. A clear example of an organ important for radiological protection whose size, in proportion to body size, seems to be underestimated is the female breast (see Table 4.3). The Cristy computational phantom includes breasts each of size 22 cm3. The 14 year old patient from who ADELAIDE was constructed had breasts each of about 150 cm3. ADELAIDE’s breasts, as a proportion of body size, are much larger than those of the Cristy 15 years old. Another is the lungs. ADELAIDE has lungs that are 4.6% of the torso mass while the Cristy 15 year old lungs are 2.4% of the trunk’s mass (Table 4.3). The Cristy 15 year old computational phantom’s lungs are assigned a mass of 650 g, volume 2200 mL and density 0.295 g cm−3. The average lung mass of 16 individuals of 14 years of age in the Ogiu autopsies is 800 g, which is significantly larger. The ADELAIDE torso has a lung mass—determined from lung volume, 4174 mL, and the ICRU density of 0.26 g cm−3 for an adult inflated lung—of 1085 g. The lung volume represents a maximum value achieved after inspiration and while breath-holding during the CT examination. Hence, the lung mass value depends on the density used for lung tissue. We will consider an alternatively derived value for lung density. The Cristy adult lungs have a mass of 999 g, volume 3400 mL, and density 3.4 g cm−3. An alternative value for mass, taken from an Anatomy and Physiology text,17 is 1180 g for males. The same book gives total lung capacity (which includes the amount of air that can be forcefully inspired after inspiration of the normal tidal volume) as 5800 mL. Hence, a maximally inflated lung due to a breath-holding procedure during a CT examination may have a density of 0.2 g cm−3. Using this density with ADELAIDE’s lung volume results in a lung mass of 835 g. This mass is similar to the Ogiu et al. value (800 g). Organ mass alone may not have a great influence on average absorbed dose to organs as dose is measured in joules per kilogram. If absorbed dose is reasonably evenly distributed within adjacent tissues of similar densities, tissue surrounding an organ that is inadvertently assigned to that organ will not greatly alter the joules per kilogram imparted to the organ. A larger organ will occupy a larger volume and this may alter the pattern of shielding and exposure to x-rays experienced by nearby organs and tissues. Hence it is the disposition of organs within the trunk and the shielding afforded by surrounding tissues that is more likely to affect absorbed dose than mass alone. 4.2.5 The Shape and Locations of Organs in the Body To some extent, the internal organs change their shape and position relative to each other as a result of the movements associated with breathing, heartbeat, food digestion, and body posture. For example, using images from a patient lying supine on the patient table of a CT scanner to construct ADELAIDE, has some consequences for the resulting representation of anatomy when compared to that of a person standing. One is that the organs are acted upon differently by gravity and they recline toward the dorsal surface of the body. That this is the case is apparent from the images of the stomach and duodenum, where

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the horizontal surface of the liquid chyme (stomach contents) is clearly visible below the gas it also contains. If ADELAIDE had been modeled from a person standing erect, the organs would be shifted anteriorly and inferiorly in their position. Another consequence of using a living subject is that gas is present in the digestive system, esophagus, and trachea. ADELAIDE has about 1 L of gas in these organs. Radiation will not suffer the attenuation in gas that it does in tissue. Hence, the tissues adjacent to pockets of gas will be exposed differently to radiation than if surrounded by tissue. NORMAN, the adult male voxel computational phantom of the NRPB,18 had regions on the dorsal surface of the back that had been flattened by lying supine during MRI acquisition and which had to be “corrected” during segmentation. In the case of ADELAIDE, the torso is required for the calculation of organ doses from CT examinations which are conducted on patients lying supine. Hence the flattening of the buttocks and back is a desirable realistic feature. Using mathematical equations to approximate complex volumes, as the MIRD computational phantoms do, results in compromises that cause the shape of mathematical organs to depart from their real shape. In real anatomy, the upper surface of the liver and stomach are domed and project superiorly so that for several centimeters they are surrounded by the lower lobes of the lungs. Mini et al., in a survey of the CT scans of five men and five women, found that the section of the trunk where lungs, stomach, and liver overlapped contained 30% of the volume of the lungs, and about 50% of the volumes of the stomach and liver.19 This means that a significant part of the liver and stomach may be in the x-ray beam during a lung examination and a significant part of the lungs will be in the x-ray beam during a liver examination. MIRD computational phantoms represent the lower surface of the lungs and upper surface of the liver as horizontal planes so that liver and lungs do not share the same transverse slice. Consequently, using a MIRD computational phantom for a calculation of absorbed dose during a chest examination to image the lungs means that the liver and stomach will be totally out of the direct beam. As a consequence, the dose to these organs will be underestimated by using a MIRD computational phantom. In real anatomy, the external boundary of the lungs and the intestines follow the curve of the body wall and fill all the space within it. In MIRD computational phantoms, the lungs and intestines do not extend outward to the body wall. This results in the MIRD small and large intestine being too deep with respect to the abdominal wall. These organs in a MIRD computational phantom will experience an attenuated x-ray beam when compared to a voxel computational phantom. In the human rib cage, the fi rst seven ribs are joined to the sternum by their costal cartilage, ribs 8–10 are joined to a common cartilage that is, in turn, attached to the sternum. Ribs 11 and 12 do not extend across the front of the abdomen. Consequently, the lungs and liver are not completely enclosed by the rib cage at the front of the body. MIRD computational phantoms do not include a sternum and represent all 12 ribs as horizontal bands encircling the trunk, which results in the lungs and diaphragm being partly obscured by bone. Staniszewska has noted these deficiencies and that an underestimation of absorbed dose to the lungs should result.20 She modified the Cristy computational phantom and compared absorbed doses to organs (in mGy) to the Cristy computational phantom and her modified computational phantom resulting from PA chest radiography. Her modified 10 years old received 35% less dose to the ribs, 160% more dose to the lungs, and 5% more dose to the liver. A large proportion of ADELAIDE’s skeleton is assigned to “surface bone voxels.” This is the result of some bones, such as the scapulae and vertebrae, being irregular in shape. In addition, the scapulae are very thin. This thinness and their spine and coracoid and


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acromion processes (bony projections) result in a large part of the scapulae being classified as “surface bone voxels.” Similarly, vertebrae have transverse and spinous processes and consequently a large surface area. 4.2.6 The Location of Organs within the Body For the purposes of calculating average absorbed dose to organs, there are some considerations that are more important than the size and shape of organs. One is the position of the organs relative to the x-ray beam. That is, whether the distribution of organs within a computational phantom places the organs wholly within the beam or not for a particular examination. A second consideration is the effect that the shielding provided by surrounding tissue has on the photon flux and spectrum incident on an organ. An organ directly irradiated by an x-ray beam will be imparted more energy than an organ outside the beam and receiving only scattered radiation. Hence, as discussed above, a real patient undergoing a CT lung examination will have part of their liver directly irradiated and one undergoing a liver examination will have the lower parts of their lungs irradiated. This is not necessarily the case with a MIRD computational phantom. Consequently, average absorbed dose to liver from a lung CT examination may be actually greater than is estimated by a simulation using a MIRD computational phantom. Similarly, average absorbed dose to lungs from a liver CT examination may be actually greater than is estimated by a MIRD simulation. The intestines of a MIRD computational phantom are quite compact and do not overlap into the transverse slices that contain the liver and stomach. Nor do they extend downward far enough to surround the bladder and uterus. In ADELAIDE, the intestines extend further in all directions than do the MIRD representation of intestines. Consequently, in the voxel computational phantom, the x-ray beam is more likely to be intercepted by the intestines. The ADELAIDE computational phantom has intestines overlain by about 2 cm of muscle and soft tissue whereas the MIRD computational phantom may have 5 cm of overlying tissue. Hence, the intestines of the latter are shielded to a greater extent than they should be. This shielding effect has been noticed by Yamaguchi in his report on the age dependence of effective doses for external photons incident on MIRD computational phantoms. He reached the following conclusion: “Since the thickness of the shielding tissues increases generally with body size, organs in older bodies receive correspondingly less equivalent dose.”21 and this conclusion is reiterated on page 44 of ICRP 74.12 The presently observed age dependence of effective doses may alter as the greater availability of pediatric voxel computational phantoms with more realistic amounts of overlying tissue is used to calculate effective dose. The presence of arms in the MIRD computational phantom and the construction of its rib cage further shield the MIRD organs in comparison to the anatomy represented by ADELAIDE. It is possible to remove the arms from the MIRD computational phantom as has been done for some radiography examinations considered in NRPB R-186.22 The MIRD computational phantom modified in this way would have been a more suitable one for CT examinations where imaging of the arms is not required. 4.2.7 Conversion of Segmented Anatomy Images to an Input File for the EGS4 Monte Carlo Code In order for the computational phantom ADELAIDE to be “EGS4 ready,” it must be included in an input file which has a transparent standard form and is accessible to users of EGS4. Our EGS4 input file (Adele.inp) is of similar format to those for the user-codes XYZDOS.

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MOR and INHOMP.MOR which are included in the EGS4 code system distribution. In particular, the anatomy data for each pixel of an image is input row by row in the form: IL, IU, JL, JU, KL, KU, MEDIUM, DENSITY where IL and IU are the lower and upper I indices for a group of adjacent voxels all containing the same medium JL and JU are the J indices for those voxels and KL and KU are the K indices for the same voxels MEDIUM is the EGS4 medium index for the medium contained in the voxels (see Table 4.2) and DENSITY is the density of the medium (a value of 0.0 for density means that the default density as recorded in the PEGS4 data file is used). Each time the medium changes in adjacent voxels, a new line in the input file is required to define the medium in the next group of voxels. Hence, the number of data lines in the input file that define the anatomy is very large. It is essential that the medium index that appears in each line be the true value for the organ or tissue of interest. Ensuring that this is so is a time-consuming task due to the range of gray scale values that can occur within the one organ and to the overlapping ranges of gray scale values in different organs. How this was done is described below. Using the image processing software Image-Pro Plus, each segmented CT slice may be viewed as an image or as a bitmap (a two-dimensional array of pixels with their assigned gray scale values). The bitmap array of each image was exported to Microsoft Excel as a bitmap using the “dynamic data exchange” facility in Image-Pro Plus and saved as an Excel file. The two-dimensional arrays of assigned gray scale numbers in the Excel worksheets were extended if the array came from an image that had truncated shoulders and hips. This was done by adding extra columns to the worksheet and filling the cells in the extra columns with the appropriate EGS4 medium index for skin, subcutaneous fat, soft tissue, or bone as required. The gray scale values of all organs were checked to ensure that all pixels assigned to that organ had the same value. This was necessary as it is possible for a liver pixel (say) to have a gray scale value that differs from its assigned (after segmentation) value of 15 by an amount small enough to be indistinguishable by the naked eye from a pixel with value 15 when the image is viewed on a computer screen. The gray scale values of pixels in muscle or soft tissue located outside of the organs were searched to ensure that their value was not the same as that assigned to one of the organs. The Excel files after checking were saved as comma-delimited (.CSV) files. These 54.CSV files (one per K index), in turn, became the input for a FORTRAN program, “READDATA. MOR”—written by the first author (MC) for the purpose—which converted the organspecific gray scale numbers into the media indices (consecutive numbers as required by EGS4 ) and formatted the numbers one J index at a time into the form described above, that is IL, IU, JL, JU, KL, KU, MEDIUM, DENSITY This form is suitable for inclusion as a line in the input file for our EGS4 user-code. For example, nine lines from the input file “adele.inp” appear below.


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39, 41, 93, 93, 56, 56, 5,0.0 42, 44, 93, 93, 56, 56, 6,0.0 45, 76, 93, 93, 56, 56, 11,0.0 77, 83, 93, 93, 56, 56, 6,0.0 84, 117, 93, 93, 56, 56, 5,0.0 118, 135, 93, 93, 56, 56, 6,0.0 136, 156, 93, 93, 56, 56, 11,0.0 157, 159, 93, 93, 56, 56, 6,0.0 160, 161, 93, 93, 56, 56, 5,0.0 They describe the voxels from slice 56 (KL = KU = 56), J index 93 (JL = JU = 93) and voxels with I indices between 39 and 161. The first line assigns three voxels (JL = 39, JU = 41) to skin (medium index = 5), the next line defines three voxels (JL = 42, JU = 44) to contain subcutaneous fat (medium index = 6), the third line defines the next 32 voxels (JL = 45, JU = 76) to contain muscle/soft tissue (medium index = 11), and so on. Voxels with I indices from 1 to 38 and from 162 to 200 are outside the computational phantom and so contain air. Each line ends with 0.0 indicating that the density of the medium is the same as that in the PEGS data file, that is, the default value. The resulting 54 fi les of formatted numbers were concatenated to form the single input fi le (of size 1.8 MB), which characterizes the anatomy of our computational phantom ADELAIDE.

4.3 Organ Dose Calculations with ADELAIDE and EGS4 The effective dose for CT examinations of the ADELAIDE computational phantom has been reported.11,23 The actual dose was calculated for a GE HiSpeed Advantage helical scanner and is specific to that scanner. Furthermore, the actual dose depends on the amount of anatomy imaged, the exposure factors used and to a lesser extent on the computational phantom used to calculate the scanner’s x-ray spectrum.24 Using tissue weighting factors from ICRP 60,25 the effective dose from a CT examination of the chest spanning 26 cm was 2.32 ± 0.05 mSv/100 mAs and for an abdomen examination spanning 24 cm was 2.16 ± 0.05 mSv/100 mAs. ADELAIDE is close to the average stature of a 13 year old female. By increasing (or decreasing) the size of the voxels by 5% (in each dimension), it is possible to extrapolate ADELAIDE to the average stature for a 16 (or 11–12) years old. When this is done, the effective dose to the 95% scaled ADELAIDE for CT examinations that spanned the equivalent anatomy was 8% higher for the chest examination and 4% higher for the abdomen examination.23 For the 105% scaled ADELAIDE, the dose for the chest examination was the same while the dose due to the abdomen examination was 5% less.

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4.4 Limitations of the Computational Phantom The major limitation of ADELAIDE is that the anatomy modeled is not the complete anatomy of an individual—the arms, legs, and head are not present. Nevertheless, the computational phantom is suited to CT dose calculations but less suited to other applications. Another limitation is introduced by the size of the voxels (2.53 × 2.53 × 10 mm). For example, thin tissues such as the scapula and the skin may be modeled as larger (thicker) than they actually are. The effect on absorbed dose is probably small as the unit of absorbed dose is J kg−1 rather than joules. This supposition is reasonable provided that the shielding effect of (and on) surrounding tissues is not greatly affected. Bone marrow was not identified as active or inactive and was not distinguished from bone. That is, “bone” was modeled as a uniform mixture of 40% (by mass) of cortical bone, 39% red marrow, and 21% yellow marrow with a consequent density of 1.34 g cm−3. The adrenal glands are included in the volume of each kidney. While gas within the gut was segmented, the contents of the gut were not distinguished from the walls. The stomach was estimated to be 38% of the total stomach and contents (see Table 4.2), the bowel wall was 49% of the mass identified as bowel and contents, while the colon was estimated at 54% of the total combined colon wall and contents.

References 1. Nelson, W.R., Hirayama, H., and Rogers, D.W.O. The EGS4 Code System, SLAC-265, Stanford, CA: Stanford Linear Accelerator Centre, Stanford University, 1985. 2. Cristy, M. Mathematical Phantoms Representing Children of Various Ages for Use in Estimates of Internal Dose, U.S. Nuclear Regulatory Commission Rep. NUREG/CR-1159, Oak Ridge, TN: Oak Ridge National Laboratory Rep. ORNL/NUREG/TM-367, 1980. 3. Zankl, M. et al. The construction of computer tomographic phantoms and their application in radiology and radiation protection, Radiation and Environmental Biophysics, 27, 153, 1988. 4. Shrimpton, P.C., Jones, D.G.F., Hillier, M.C., Wall, B.F., Le Heron J.C., and Faulkner K. Survey of CT Practice in the UK Part 2: Dosimetric Aspects, NRPB-R249, Chilton, Didcot: National Radiological Protection Board, 1991. 5. Hart, D., Jones, D.G., and Wall, B.F. Coefficients for Estimating Effective Doses from Paediatric X-Ray Examinations, NRPB-R279, Chilton, Didcot: National Radiological Protection Board, 1996. 6. Caon, M., Bibbo, G., and Pattison, J. Running the EGS4 Monte Carlo code with Fortran 90 on a Pentium computer, Australasian Physical and Engineering Sciences in Medicine, 19, 201, 1996. 7. Caon, M. Voxel-based computational models of real human anatomy: A review, Radiation and Environmental Biophysics, 42, 229, 2003. 8. Caon, M. and Mohyla, J. Automating the segmentation of medical images for the production of voxel tomographic computational models, Australasian Physical and Engineering Sciences in Medicine, 24, 166, 2001. 9. Lee, C. et al. Hybrid computational phantoms of the male and female newborn patient: NURBSbased whole-body models, Physics in Medicine and Biology, 52, 3309, 2007. 10. Zankl, M. and Wittmann, A. The adult male voxel model “Golem” segmented from wholebody CT patient data, Radiation and Environmental Biophysics, 40, 153, 2001. 11. Caon, M., Bibbo, G., and Pattison, J. An EGS4-ready tomographic computational model of a 14-year-old female torso for calculating organ doses from CT examinations, Physics in Medicine and Biology, 44, 2213, 1999.


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12. ICRP. Conversion Coefficients for Use in Radiation Protection against External Radiation. Publication 74, Annals of the ICRP, 26, 28, 1996. 13. Hitchcock, N.E., Maller, R.A., and Gilmour, A.I. Body size of young Australians aged five to 16 years, The Medical Journal of Australia, 145, 368, 1986. 14. Ogiu, N., Nakamura, Y., Ijiri, I., Hiraiwa, K., and Ogiu, T. A statistical analysis of the internal organ weights of normal Japanese people, Health Physics, 72, 368, 1997. 15. ICRP. Report of the Task Group on Reference Man, ICRP Publication 23, Oxford: Pergamon Press, 1975. 16. Jones, R.M., Poston, J.W., Hwang, J.M.L., Jones, T.D., and Warner, G.G. The Development and Use of Fifteen Year-Old Equivalent Mathematical Phantom for Internal Dose Calculations, ORNL/ TM-5278, Oak Ridge Tennessee: Oak Ridge National Laboratory, 1976. 17. Seely, R., Stephens, T., and Tate, P. Anatomy and Physiology, 4th edn., Boston: WCB/McGrawHill, 1998. 18. Dimbylow, P.J. FDTD calculations of the whole-body averaged SAR in an anatomically realistic voxel model of the human body from 1 MHz to 1 GHz, Physics in Medicine and Biology, 42, 479, 1997. 19. Mini, R., Vock, P., Mury, R., and Schneeberger, P. Radiation exposure of patients who undergo CT of the trunk, Radiology, 195, 557, 1995. 20. Staniszewska, M.A. A modification to Cristy’s mathematical human phantoms for Monte Carlo simulations, Journal of Radiological Protection, 12, 85, 1992. 21. Yamaguchi, Y. Age dependent effective doses for external photons, Radiation Protection Dosimetry, 55, 123, 1994. 22. Jones, D.G. and Wall, B.F. Organ Doses from Medical X-Ray Examinations Calculated Using Monte Carlo Techniques, Report NRPB-R186, Chilton, Didcot: National Radiological Protection Board, 1985. 23. Caon, M., Bibbo, G., and Pattison, J. Monte Carlo calculated effective dose to teenage girls from computed tomography examinations, Radiation Protection Dosimetry, 90, 445, 2000. 24. Caon, M., Bibbo, G., Pattison, J., and Bhat, M. The effect on dose to computed tomography phantoms of varying the theoretical x-ray spectrum: A comparison of four diagnostic x-ray spectrum calculating codes, Medical Physics, 25, 1021, 1998. 25. ICRP. 1990 Recommendations of the International Commission on Radiological Protection, ICRP Publication 60, Oxford: Pergamon Press, 1991.

5 The MCAT, NCAT, XCAT, and MOBY Computational Human and Mouse Phantoms W. Paul Segars and Benjamin M.W. Tsui

CONTENTS 5.1 Introduction ............................................................................................................... 105 5.2 The 4D Mathematical Cardiac-Torso Phantom ..................................................... 107 5.3 The 4D NURBS-Based Cardiac-Torso Phantom ................................................... 114 5.4 The 4D Extended Cardiac-Torso Phantom ............................................................ 121 5.5 The 4D Mouse Whole-Body Phantom.................................................................... 126 5.6 Summary .................................................................................................................... 132 References ............................................................................................................................. 132

5.1 Introduction In addition to the field of radiation dosimetry, computerized phantoms are also used in simulation studies to evaluate and improve medical imaging devices and techniques. Medical imaging simulation consists of virtual experiments that can be carried out entirely on the computer. Computer-generated phantoms serve as the subjects, while researchers use computer-based models of the imaging process to simulate predictive imaging data from them, as seen in Figure 5.1. A major advantage to using computer-generated phantoms in simulation studies is that the exact anatomy and physiological functions of the phantom are known, thus providing a standard form from which to quantitatively evaluate and improve medical imaging devices, image processing, and reconstruction techniques. Another advantage of using phantoms is that they can be easily altered to model different anatomies and medical situations, providing a large population of subjects with which to perform research. A vital aspect of a simulation is to have a realistic computational phantom of the subject’s anatomy. Without this, the results of the simulation may not be indicative of what would occur in actual patients or animal subjects, and would, therefore, have limited practical value. Previous chapters have highlighted the two main categories of phantoms and the advantages and disadvantages of each. Briefly, existing computational phantoms involve a trade-off between realism and flexibility, and this affects their applicability to imaging simulations. Since they are based on patient data, voxel-based phantoms are realistic, but they remain fixed to a particular anatomy and resolution. Studies of the effects of anatomical variations or motion on medical imaging can be limited, and the generation of 105


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Computer phantom Model of imaging process

Simulated medical image

FIGURE 5.1 Computer-based medical imaging simulation. In this example, a chest x-ray is being generated from the computerized phantom.

XCAT

Evolution of computerized phantoms...

MOBY

MIRD

MCAT

NCAT

FIGURE 5.2 (See color insert following page 524.) Original MIRD phantom1 and phantoms developed in our laboratory that approach more ideal computerized phantoms.

the phantom at other resolutions requires interpolation, which introduces error. Stylized or mathematical phantoms, conversely, are defined mathematically, in order to allow for anatomical variation and generation at multiple resolutions. The simplicity of the mathematical equations, however, limits an exact modeling of the organ shapes. In order to get a more realistic phantom, the sets of equations defining the phantom have to become more complex; but with this increase in complexity comes a decrease in flexibility. Current work in phantom development has focused on the development of “hybrid” phantoms that seek to combine the realism of a patient-based voxelized phantom with the flexibility of a mathematical phantom. We have been leading the development of realistic and flexible digital phantoms for use in medical imaging research. Figure 5.2 shows the evolution of computerized phantoms toward more ideal hybrid computational phantoms and summarizes our work in this evolution with the development of the mathematical cardiac-torso (MCAT), NURBS-based cardiac-torso (NCAT), extended cardiac-torso (XCAT), and mouse wholebody (MOBY) phantoms.

The MCAT, NCAT, XCAT, and MOBY Computational Human and Mouse Phantoms

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5.2 The 4D Mathematical Cardiac-Torso Phantom The four-dimensional (4D) MCAT phantom, as seen in Figure 5.3, is a stylized anthropomorphic phantom that was developed in our laboratory for use in nuclear medicine imaging research, specifically for single-photon emission computed tomography (SPECT) and positron emission tomography (PET). The anatomy of the 4D MCAT phantom was constructed using simple geometric primitives based on the MIRD-5 computational phantom.1 We used overlap, cut planes, and intersections of the geometric objects, however, to form a more realistic anatomy. Using these mathematical formula, we modeled the size, shape, and configurations of the major thoracic structures and organs for imaging purposes. Table 5.1 shows a list of the organs modeled in the 4D MCAT phantom and their corresponding volumes. For the purpose of imaging simulation, the MCAT phantom is capable of simulating two physical models: a three-dimensional (3D) distribution of attenuation coefficients for a given photon energy and a 3D distribution of emission radionuclide activity for the various organs. Each of these computational phantoms is stored as a voxelized phantom of

Anterior

Posterior

Right lateral

FIGURE 5.3 3D surface renderings of the 4D MCAT phantom. Anterior, posterior, and right lateral views are shown.

TABLE 5.1 List of Organs Modeled in the MCAT Model and Their Corresponding Volume Organ

Volume (mL)

Liver

1825

Stomach (wall and contents)

367

Kidneys (2)

285

Spleen

175

Right lung

2216

Left lung

2150

Heart (blood and tissue)

640


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any user-defined resolution. Since the phantom is mathematically defined, there are no errors associated with generating the phantom at different resolutions. These voxelized representations can be used in combination with analytical or Monte Carlo-based models of the imaging process to simulate transmission (x-ray, x-ray CT) and emission (SPECT, PET) imaging data. The 3D distribution of attenuation coefficients classifies the thoracic tissues into one of four tissue types: muscle (soft tissue), lung, spinal bone (less dense), and rib bone (more dense). To generate the attenuation coefficient or the transmission phantom, the various organs are set with their individual attenuation coefficients, a user-defined photon energy. We can calculate the attenuation coefficients for the different tissue types from the physical densities and the mass attenuation coefficients of the tissues. The mass attenuation coefficients of the tissues, at any energy from 1 to 1000 keV, are calculated from the elemental compositions of the tissues2 and the energy-dependent mass attenuation coefficients for the elements.3 Figure 5.4 illustrates the use of the 4D MCAT as an attenuation coefficient phantom for the 72 keV radionuclide Thallium-201. Projection images, similar to those acquired from a patient during transmission imaging, can be simulated from the voxelized attenuation coefficient phantom using a computational phantom of the projection process. The 3D radionuclide distribution models the uptake of a radiopharmaceutical in the various organs. To generate a radionuclide uptake or emission phantom, the intensity values of the organs are set to their individual uptake ratios for the desired radiopharmaceutical. Figure 5.5 shows the use of the 4D MCAT as a radiopharmaceutical uptake phantom for Thallium-201. The projection images simulated from the uptake phantom emulate those that would be acquired during an emission-imaging scan. The projection images reflect the uptake for the particular radiopharmaceutical in the organs. Areas of higher uptake are indicated by the brighter intensities, whereas areas of lower uptake are indicated by darker intensities. In order to study the effects of heart motion on cardiac SPECT and PET imaging, the 4D MCAT phantom includes a beating heart computational phantom.4 The cardiac computational phantom in the 4D MCAT is based on ellipsoids and simulates the changes in chamber volume, left ventricular wall thickness, and heart rotation that occur throughout

Transaxial slices for 72 keV radionuclide (T1-201) A

B

C

D

Transmission projection (chest x-ray)

D A 4D MCAT

Human

FIGURE 5.4 4D MCAT used as an attenuation coefficient or transmission phantom. Left: transaxial slices are shown for a phantom simulated with each organ set to its individual attenuation coefficient for Thallium-201. Right: comparison of a transmission projection of the MCAT phantom with an actual patient chest x-ray.


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Transaxial slices with typical T1-201 uptake ratios Emission projections A

B

D

C

D

A Left

Anterior

FIGURE 5.5 4D MCAT used as a radiopharmaceutical uptake or emission phantom. Left: transaxial slices of the MCAT with organs set to typical uptake ratios for Thallium-201. Right: emission projections of the uptake phantom with the effects of attenuation. The increased uptake of the radiopharmaceutical by the liver and heart myocardium can be seen in the projections.

LV

RV

Heart: union of four partial ellipsoids

Z

LVext.

Z LVint.

X

X

LA RVint. RA

RVext. End diastole

End systole

End systole with end diastole overlay

FIGURE 5.6 Left: 4D MCAT cardiac model based on ellipsoids. The ventricles and atria are each defi ned by two ellipsoids, one for the inner and one for the outer boundary. Right: single LA slices of the beating heart during end-diastole and end-systole. End-systolic frame is shown with an end-diastolic overlay to illustrate the change in the LV during the cardiac cycle.

the cardiac cycle as seen in Figure 5.6. The beating heart motion in a normal human consists of a wringing-like twisting motion of the left ventricle (LV) and radial and longitudinal contraction of the heart walls.5–7 The changes and motion of the beating heart are simulated in the 4D MCAT by altering the parameters that define the ellipsoid models. A simplified version of the twisting motion of the heart is modeled by changing the rotation of the ellipsoids about the long-axis (LA) of the LV. The base of the ventricles is set to rotate clockwise, while the apex is set to rotate counterclockwise. The rotation is done as a pixel-by-pixel operation and is controlled by a parameter in the MCAT program that defines the maximum rotation. Altering the diameters of the axes that define the different ellipsoids in the phantom simulates the radial contraction and thickening of the heart walls during systole. We modeled this longitudinal contraction of the ventricles by changing the


110

Reference MCAT

120

LV volume (mL)

100

80

60

Systole

40 1

Diastole 6

11 Frame number

16

FIGURE 5.7 Volume curve for the LV of the 4D MCAT phantom as compared to that of an average male. (Adapted from Guyton, A. and Hall, J. Textbook of Medical Physiology, 9th edn., WB Saunders Co., Philadelphia, PA, 1996.)

valve plane between the atria and ventricles. The ellipsoids were altered so that the heart mass and volume remain constant throughout the cardiac cycle and equal to that of an average male,8 as seen in Figure 5.7. The heart computational phantom also includes the ability to model perfusion defects, such as those that would be seen in myocardial SPECT (Figure 5.8). Defects arise due to blocks in the coronary vessels that limit or cutoff bloodflow to the downstream myocardial tissue. These defects can be seen in myocardial SPECT as areas of low perfusion (low uptake of the radionuclide) in the left ventricular myocardium. Perfusion is indicative of the health of the tissue. Defects in the 4D MCAT are modeled as pie-shaped wedges in the wall of the LV by three parameters: circumferential width, LA width, and defect center.9,10 In producing the radionuclide uptake or emission phantom, the portion of the LV wall defined as the defect is given a lower uptake ratio than the rest of the LV myocardium. Figure 5.8 shows myocardial SPECT simulations with lesions located in three different locations within the LV. To investigate the effects of respiratory motion on SPECT and PET imaging, the 4D MCAT also includes a model for the breathing motion based on known respiratory mechanics.11 Respiratory motion involves the movement of the diaphragm, heart, thoracic cage, and lungs. During inspiration, the diaphragm contracts forcing the abdominal organs down and to the front of the body, expanding the volume of the chest. At the same time, the ribs rotate about an axis through their costal necks, moving outward and upward to further expand the chest volume. The lungs inflate due to the change in internal pressure that results from the increased chest volume.12 In order to simulate respiratory motion in


Short axis Base

111

Long axis

Circumferential width

Long-axis width Defect center

Apex FIGURE 5.8 (Left) Myocardial lesion in the 4D MCAT heart.9 The lesion is defined as a pie-shaped wedge in the left ventricular wall by the parameters: circumferential width, LA width, and defect center. (Right) Simulated myocardial SPECT imaging data showing three different locations for a lesion in the left ventricular wall. The lesions can be seen in the SA slices taken from above the LV and the LA slices taken along side of the LV. Lesions appear as areas of low perfusion (low intensity).

Heart

Liver

FIGURE 5.9 Parameters for the height of the diaphragm are altered to translate the diaphragm upward and downward in the geometry-based MCAT model. (Adapted from Segars, W.P., Lalush, D.S., and Tsui, B.M.W., IEEE Trans. Nucl. Sci., 48(1), 89, 2001.)

the MCAT phantom, we altered the geometric solids for the diaphragm, heart, ribs, and lungs through the manipulation of parameters defining them. To do this, we simulated the movement of the diaphragm during respiration by altering the parameters that define the height of the left and right diaphragm sections (Figure 5.9). The heart, liver, stomach, spleen, and kidneys were rigidly translated with the motion of the diaphragm. Tiled cut planes through the cylinder define the positions of the ribs in the MCAT (Figure 5.10a). This definition does not allow for the ribs to be rotated about the axis through their costal necks. Altering the tilt angle θ of the ribs and modifying the length of the rib short axis parameter allows us to approximate the rib length (Figure 5.10b). The RLA parameter was not adjusted. Figure 5.11 shows coronal cut slices of the 4D MCAT at end-inspiration and end-expiration. Two parameters, the height of the diaphragm and the anterior–posterior (AP) expansion of the chest, control the respiratory motion in the MCAT phantom. The height of the diaphragm controls the longitudinal motions of the liver, stomach, spleen, and heart while the AP


112

θ΄ Rib of length L

ΔHeight

θ

(a)

RSA

(b)

ΔRSA

Volume change (mL)

FIGURE 5.10 (a) Lateral view of the MCAT phantom showing the ribs defi ned as tilted cut planes through a cylinder. (b) Lateral view of the ribcage cylinder showing anteroposterior diameter change by altering the tilt angle and rib short axis RSA parameters. (Adapted from Segars, W.P., Lalush, D.S., and Tsui, B.M.W., IEEE Trans. Nucl. Sci., 48(1), 89, 2001.)

500

Expiration

Inspiration

250

0 0

1

2

3

4

5

Time (s)

3D anterior view

2D coronal slice

FIGURE 5.11 Top: normal respiratory curve. (Adapted from West, J. Respiratory Physiology, 5th edn., Williams & Wilkins, Baltimore, 1995.) Middle and bottom: 3D and 2D views of the MCAT at end-inspiration (left) and end-expiration (right).


113

expansion of the chest controls the lateral and AP motions of the organs as well as the motion of the ribcage. These time varying parameters were chosen to fit a volume curve for normal respiration12 (Figure 5.11). Time curves were derived for both the diaphragm motion and the AP expansion of the chest. The respiratory and cardiac models of the MCAT are parameterized so that a user can alter the magnitude or rates of each motion to simulate many different variations (normal and abnormal). In addition to motion, the MCAT phantom has the ability to model male and female anatomical variations. Adding breast extensions onto the male chest anatomy simulates female subjects. Variations in the male and female anatomy are generated by altering the parameters that define the different geometric shapes that compose the phantom. Figure 5.12 shows examples that demonstrate the ability of the phantom to vary patient anatomy. In each case, the phantom is altered to match the anatomy of the patient as determined by the patient’s PET scan.9,10 With this ability to modify the anatomy, the MCAT program can be used to simulate a patient population involved in patient studies. The 4D MCAT phantom provides a step up from the typical stylized phantom by providing a better representation of the anatomy while maintaining the flexibility to model anatomical variations and patient motion. With its capabilities, the 4D MCAT has been applied to many studies in emission imaging that seek to improve the quality of medical images. It has been used to research new image acquisition strategies and reconstruction algorithms, and to investigate the effects of physical factors, anatomy, and motion on medical images and to develop compensation methods for these effects. The 4D MCAT represents an advance in computerized modeling, but due to its geometrical design, is still lacking in terms of its level of realism that limits its applicability to higher resolution imaging techniques.

MCAT male population #92

#109

#117

MCAT female population #16

#43

PET

MCAT

FIGURE 5.12 MCAT phantoms created with varying anatomies. In each case, the MCAT phantom was altered through the parameters that defi ne the different structures to match the anatomy of the patient as determined by the patient’s PET scan. (Adapted from LaCroix, K.J. Evaluation of an attenuation compensation method with respect to defect detection in Tc-99m-MIBI myocardial SPECT images, PhD dissertation, The University of North Carolina at Chapel Hill, 1997; LaCroix, K.J. et al., J. Nucl. Med., 41, 502, 2000.)


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5.3 The 4D NURBS-Based Cardiac-Torso Phantom In order to create a more ideal computational phantom, a primitive is needed that will allow more realistic modeling of human anatomy than is possible with geometrical solids while maintaining the ability to model anatomical variations and patient motion. An appropriate primitive that will permit the kind of modeling needed is the nonuniform rational B-spline (NURBS).13 NURBS are widely used in computer graphics and geometrical modeling to describe complex 3D surfaces. NURBS surfaces offer many advantages in our 4D mathematical modeling of the human body. Because it is a continuous surface, a NURBS permits representation of a surface at any resolution. Thus, it is possible to generate the phantom at any spatial resolution without introducing any errors that might result from interpolation. The principal disadvantage of the NURBS, compared to 3D geometric shapes like those used in the 3D MCAT, is that a NURBS requires more parameters to describe a surface. A 3D NURBS surface is defined by an n × m matrix of control points, two knot vectors (one for latitude and one for longitude), and a set of basis functions whereas a simple mathematical relationship is all that is required for geometric surfaces. Another disadvantage of NURBS is the mathematical complexities they introduce. More complicated algorithms are required to calculate intersections, produce solids, and to handle shared surfaces. These drawbacks are hardly consequential anymore, though, given the vast increase in computer processing power and memory. NURBS can therefore provide a powerful tool for computational phantom development. In terms of phantom representations, NURBS surfaces can be based on specific patient image data. By fitting NURBS to actual patient data, the phantom is more realistic than those based on solid geometry or simple mathematical relationships. Also, NURBS can be altered easily via affine and other transformations to model variations in anatomy among patients and patient motions. The shape of a NURBS surface can be modified through the control points that define the surface (Figure 5.13). The control points form a convex hull around the NURBS surface and determine its shape. By applying transformations to the control points, the shape of the surface can be modified or sculpted as if it were made of clay.13 To perform each transformation, one merely needs to multiply the control points of the surface to be altered by the appropriate transformation matrix. With its flexibility and its ability to realistically model anatomy, NURBS is an excellent basis for a realistic and flexible computerized phantom.

V

z

y

z x

U

y x

FIGURE 5.13 Modification of the shape of a NURBS surface through its control points. Modification to the shape of the surface is done by manipulating its control points. The shaded control points are translated upward altering the shape of the surface.


115

FIGURE 5.14 Anterior and posterior views of the 4D NCAT phantom.

Lungs Body Stomach Liver

Skeleton

Kidneys

Spleen

FIGURE 5.15 Segmentation of the torso organs using the SURFdriver surface reconstruction program. CT slices are contoured (left) then reconstructed into smooth polygon models (right).

With NURBS as its basis, the 4D NCAT phantom was developed as the next generation MCAT phantom14 (Figure 5.14). The NURBS surfaces in the torso were constructed based on the Visible Human Male CT data set.15 The CT data consisted of axial CT scans of the entire body taken at 1 mm intervals at a resolution of 512 pixels × 512 pixels (pixel size of 0.898 mm per pixel). The structures in the torso were manually segmented using SURFdriver16 to display the CT images as shown in Figure 5.15. The contours defined in the segmentation process for each organ models were reconstructed into smooth polygon models using SURFdriver’s rendering engine and smoothing function. The software program Rhinoceros17 was used to fit smooth, cubic NURBS surfaces to contours taken from the polygon models created by SURFdriver (Figure 5.16). Figure 5.14 shows the resulting torso anatomy of the NCAT. Since it was based on imaging data, the anatomy of the NCAT is more realistic than that of the MCAT. Table 5.2 lists the organs modeled in the 4D NCAT and their volumes. Like the 4D MCAT, the NCAT phantom was extended into four dimensions to model common patient motions such as the cardiac and respiratory motions. The beating heart


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Lungs Body Liver

Skeleton

Kidneys

Stomach

Spleen

FIGURE 5.16 Generation of 3D NURBS models for the torso organs using the Rhinoceros NURBS program. Contours of the polygon models are lofted into NURBS surfaces (right). Contours obtained from the right kidney model are shown as example. The contours and their spacing are chosen to provide the best fit for the NURBS surface to the polygon model.

TABLE 5.2 List of Organs Modeled in the NCAT Phantom and Their Corresponding Volumes Organ Liver Gall bladder Stomach (wall and contents) Kidneys (2) Spleen Right lung Left lung Heart (blood and tissue)

Volume (mL) 1870 31 350 334 227 1900 1800 650

computational phantom of the 4D NCAT was based on 4D tagged magnetic resonance imaging (MRI) data obtained from Dr. Cengiz Ozturk of Johns Hopkins University, and Dr. Elliot McVeigh of the NIH and Johns Hopkins University, as seen in Figure 5.17. Three sets of tagged MR images from a normal subject were obtained and used to analyze the heart motion. The sets of data spanned the LV and were acquired for 26 time frames over the cardiac cycle. The data included two sets of parallel, short-axis (SA) images and one set of LA images. From the motion of the tag lines in the data, the full 3D motion of the heart over the cardiac cycle was analyzed and used to create time-dependent 3D NURBS surfaces for each of the four chambers of the heart. A 4D NURBS surface was then fit to the 3D surfaces creating a time-continuous 4D NURBS cardiac computational phantom (Figure 5.18) that was incorporated into the 3D anatomy of the NCAT phantom using the outer


117

SA image set 1 (SA1)

SA image set 2 (SA2)

LA image set 2 (LA1) FIGURE 5.17 Tagged MR data used as the basis for the NCAT beating heart. The data consisted of two sets of orthogonal SA images and one set of LA images. For each set, one image is shown at three different time frames from enddiastole (left) to end-systole (right). Tag lines begin as parallel stripes then deform with the movement of the heart tissue.

surface of the Visible Human heart as a guide. Since it was based on tagged MRI data, the NCAT heart illustrates the realistic contracting and twisting motion of the normal heart. With the flexibility of the NURBS surfaces, the NCAT heart was parameterized so that it could model a wide variety of beating heart motions, normal and abnormal. Many different parameters can be altered, such as ejection fraction, longitudinal and radial contraction, cardiac twist, heart rate, etc. Perfusion defects can be simulated in a similar manner to those of the MCAT (Figure 5.18). Global or regional cardiac motion abnormalities that result from these defects can also be simulated. Given the definition of an abnormal region, the control points that define the region in the heart are analyzed to characterize their normal motion throughout the cardiac cycle. The normal motion is parameterized in terms of the average regional radial (wall thickening) and longitudinal contractions and for the cardiac twist. These


118

End-diastole

RA

LA

RV

LV

End-systole

160 RV

140

Volume (mL)

120 100

LV RA

80

LA

60 40 20 0 0

0.2

0.4

0.6

0.8

1

Time (s) FIGURE 5.18 Cardiac model of the 4D NCAT. Plots of the volume change in the chambers are shown at the bottom.

parameters can be scaled to alter the motion of the selected region. The abnormal motion is blended in smoothly through transitional regions with that of the normal myocardium. With its realism and flexibility, the 4D NCAT cardiac computational phantom provides a useful tool in the study of cardiac imaging and the effects of cardiac motion in medical images. Respiration was modeled in the 4D NCAT based on a set of respiratory-gated CT data from the University of Iowa. The data were taken of a normal volunteer at 5%, 40%, 75%, and 100% of the total lung capacity (TLC). By identifying landmark points on and within the respiratory structures, and tracking their positions between the time frames, a general motion computational phantom for each respiratory structure and for different regions inside the lungs was formulated. The motions were scaled down to correspond to normal tidal breathing and incorporated into the phantom. We simulated the movement of the diaphragm by translating control points that define the left and right diaphragm surfaces (Figure 5.19). The heart, the stomach, and the spleen were translated up, down, backward, and forward with the movement of the diaphragm. The ribs were rotated about the axis through their costal necks to simulate their motion (Figure 5.20). Control points defining the lungs and body surfaces were altered, expanding or contracting them, depending on the rib and diaphragm motion. We set up the NCAT respiratory computational phantom in a similar fashion to that of the MCAT. Time curves were fit to the two time varying parameters for the diaphragm motion and the AP expansion in the chest. These motions


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Heart

Liver FIGURE 5.19 The control points (open squares) of the diaphragm in the NCAT are translated upward and downward to simulate its respiratory movement. The right portion of the diaphragm (top of the liver) is shown in the above example. (Adapted from Segars, W.P., Lalush, D.S., and Tsui, B.M.W., IEEE Trans. Nucl. Sci., 48(1), 89, 2001.)

(Cx, Cy, Cz) Y axis

Costal neck

θR

Z axis

To sternum FIGURE 5.20 Rotation of a rib in the NCAT about the axis through its costal neck. Lateral view is shown. (Adapted from Segars, W.P., Lalush, D.S., and Tsui, B.M.W., IEEE Trans. Nucl. Sci., 48(1), 89, 2001.)

were setup to work in concert to produce a normal respiratory volume curve.12 Figure 5.21 shows 2D and 3D views of the NCAT defined at end-inspiration and end-expiration. The respiratory motion is more realistic than that of the 4D MCAT (Figure 5.21). Similar to the beating heart, the respiratory computational phantom was parameterized in terms of chest and diaphragm breathing so as to model different types of respiratory motions. In addition to motion, the flexibility of the NURBS surfaces also allows for anatomical variations. One models anatomical variations in the 4D NCAT by applying transformations to the base anatomy of the phantom.18,19 These transformations can be based upon an analysis of patient imaging data. Figure 5.22 shows a population of patients of varying anatomy used to perform studies of compensations methods in myocardial SPECT. To create the anatomical variations in this work, we set up the 4D NCAT with parameters to scale the torso and organ sizes as well as to change the size, shape, orientation, and position of the heart. These same parameters were randomly sampled from distributions obtained from the Emory PET Torso Model Database20 and used to generate 24 phantoms, half male and half female. Figure 5.22 shows simulated data from six of these phantoms.


Volume change (mL)

120

Inspiration

500

Expiration

250

0 0

1

2 3 Time (s)

4

5

3D anterior view

2D coronal slice

FIGURE 5.21 Top: Normal respiratory curve. Middle and bottom: 3D and 2D views of the NCAT at end-inspiration (left) and end-expiration (right). (Adapted from West, J. Respiratory Physiology, 5th edn., Williams & Wilkins, Baltimore, 1995.)

FIGURE 5.22 First row shows one slice from six different models. Second row shows corresponding attenuation maps, and third row shows reconstructed SPECT images simulated from the phantoms.


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The 4D NCAT phantom offers a vast improvement over the geometry-based MCAT, by providing more realistic models of the anatomy, the cardiac system, and the respiratory motions of the human body. As such, the 4D NCAT has gained a widespread use in nuclear medicine imaging research, especially for evaluating and improving imaging instrumentation, data acquisition techniques, and image processing and reconstruction methods. It is widely used in myocardial SPECT, providing an excellent tool with which to study the effects of anatomy and patient motions.11,18,19,21–25 Despite this success, the 4D NCAT still has its limitations. The anatomy was based solely on the Visible Male CT dataset from the National Library of Medicine and was restricted to just the region of the torso. Also, as was the case with the MCAT, female subjects are modeled with the addition of user-defi ned breast extensions. Another limitation is that the phantom, although capable of being far more realistic, was originally designed for low-resolution imaging research and lacks the anatomical details for application to high-resolution imaging such as x-ray CT and MRI.

5.4 The 4D Extended Cardiac-Torso Phantom The 4D XCAT phantom was developed as the next version of the 4D NCAT. It includes more detailed and realistic anatomy and physiology, suitable for use in higher-resolution imaging applications. The XCAT phantom includes a whole-body male and female anatomy based on the high-resolution Visible Male and Female anatomical datasets from the NLM. The anatomical images are more detailed than that of the CT used to create the original NCAT male anatomy. The male data consist of 1878 anatomical slices of the body with a resolution of 2048 × 1216 and a pixel size of 0.33 mm and a slice width of 1 mm. The anatomical dataset of the female has the same characteristics as those of the male with one exception; the slices were obtained at 0.33 mm intervals. This results in over 5000 anatomical images over the body. Similar techniques as those used to create the NCAT organ models were used to create the detailed male and female anatomies for the XCAT phantom, as seen in Figures 5.23 and 5.24. As can be seen in the Figures, the XCAT includes more detailed organ models. Work is underway to include all the blood vessels and muscle tissue. Table 5.3 lists the current organs modeled in the male XCAT phantom. In addition to the structures shown, blood vessels, lymph nodes, and muscle tissue are also in the process of being included in the XCAT. In addition to the basic anatomy, we also updated the cardiac and respiratory motions in the XCAT phantom. High-resolution gated cardiac CT data of a healthy subject obtained from a dual source multislice CT (MSCT) scanner was used to define a more detailed anatomy for the cardiac computational phantom, as seen in Figure 5.25. The motion of the chamber surfaces was set up by combining information from the CT data as well as the gated tagged MRI data upon which the original 4D NCAT heart computational phantom was based. The cardiac twisting motion of the heart could not be ascertained from CT imaging data; therefore, the twisting motion in the original heart computational phantom was scaled to fit the new heart segmented from the CT data. Once the twisting motion was established, the radial and longitudinal contractions could be obtained by noting the epi- and endocardial borders in the gated MSCT images. We determined the motion of the vessels and other cardiac structures by tracking the landmark points located on or within


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Anatomical detail

FIGURE 5.23 (See color insert following page 524.) Male (left) and female (right) anatomies of the 4D XCAT phantom.

Head Illac vessels Lymph nodes Vas deferens

Chest

Hypogastric vessels Uterus Bladder

Seminal Bladder vesicles Prostate

Fallopian tube Ovary Cervix

Vagina Testes Ant.

Post Male abdomen

Right

Left Female abdomen

FIGURE 5.24 The 4D XCAT includes a high level of anatomical detail.

them for each subsequent time frame. Time curves were defined for the control points defining each surface creating the enhanced 4D computational phantom for the heart. The resulting heart computational phantom is more detailed than that in the previous NCAT version, giving it the ability to be applied to high-resolution cardiac imaging research.


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TABLE 5.3 List of Organs Modeled in the Male XCAT Model and Their Corresponding Volumes Organ Brain Liver Gall bladder Stomach (wall and contents) Kidneys (2) Spleen Right lung Left lung Heart (blood and tissue) Pancreas Large intestine (ascending, transcending, and descending) (wall and contents) Sigmoid/rectum (wall and contents) Small intestine (wall and contents) Bladder Prostate Testes (2) Thymus Thyroid Esophagus (wall and contents) Laryngopharynx Salivary glands Eyes (2) Adrenal (2) Pituitary Seminal vesicles Vas deferens Total skeleton Total body

Volume (mL) 1,400 1,853 31 462 348 240 1,893 1,826 670 90 1,680 212 1115 88 19 38 33 28 74 18 100 18 14 0.6 6 5 7,500 95,000

The respiratory motion of the 4D XCAT was similarly improved using more state-ofthe-art imaging data. A limitation to the respiratory motion of the 4D NCAT is that it was based on only one set of patient data. The data upon which it was based also had a resolution lower than that offered by more advanced CT scanners and consisted of only four time frames that did not adequately cover normal tidal breathing. Respiratory motion and its variations were better characterized in the 4D XCAT phantom through an analysis of several sets of 4D respiratory-gated CT image data obtained from Dr. George Chen of the Massachusetts General Hospital. Using automatic and semiautomatic techniques, the different respiratory structures were segmented from each time frame of each CT dataset. We used the time series of segmented structures to characterize the respiratory motion in each case. From an analysis of all patient datasets, the range in motion of the different structures was determined. This information was used to further parameterize the general respiratory computational phantom of the 4D XCAT to more realistically computational phantom respiratory variations. The respiratory motion was also extended to a wholebody computational phantom, including motion in the abdomen, as seen in Figure 5.25.


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End-diastole

End-expiration

End-systole

End-inspiration

FIGURE 5.25 Cardiac and respiratory motions of the 4D XCAT.

As mentioned above, the original XCAT male and female anatomies were based on the Visible Human data. New anatomically variable XCAT phantoms are under development based on several sets of pediatric and adult CT data, as seen in Figure 5.26. We are currently analyzing many sets of adult and pediatric imaging data to define statistical distributions that will characterize the change in size and shape of the organs at different ages. To aid in this analysis, we developed an interactive graphical application that could import patient data and provided several tools (manual and automatic) that could be used to alter the male or female anatomy of the XCAT to match the patient data. In matching the patient data, statistical models were derived for the deformable template of each organ or structure and categorized based on age range. Based on the statistical models derived from the patient data, any number of random patients of varying anatomy can be created. Combined with accurate computational phantoms of the CT imaging process, the phantom can provide a wealth of simulated patient data for CT research. The new 4D XCAT approaches that of an ideal computational phantom with its basis upon human data and the inherent flexibility of the NURBS primitives. The NURBS basis for the 4D XCAT is capable of providing a mathematical description that is as realistic as a voxelized computational phantom segmented from human data. The flexibility of the NURBS basis offers a tremendous advantage over voxelized computational phantoms. From a template anatomy, the 4D XCAT can realistically model a multitude of different


125

FIGURE 5.26 Anatomical variations from adult to pediatric produced for the XCAT based on CT data.

anatomical variations and patient motions from both male and female subjects (including adults and children). Combined with accurate models for the imaging process, the phantom can provide a wealth of simulated image data that are far more consistent with that of actual patients, as seen in Figure 5.27. There is essentially no limitation. Any number of different anatomies, cardiac or respiratory motions or patterns, and spatial resolutions can be simulated to perform research.

Imaging simulations using the phantom

Myocardial SPECT

22

PET (Brain)

44

Chest x-ray

66

In-111 ProstaScint SPECT FIGURE 5.27 Imaging simulations performed using the XCAT phantom.

x-ray CT

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5.5 The 4D Mouse Whole-Body Phantom The rapid growth in genetics and molecular biology in recent years, combined with the development of techniques for genetically engineering small animals, has led to increased interest in in vivo small animal imaging. With the rise of small animal imaging, new instrumentation, data acquisition strategies, and image processing and reconstruction techniques are being developed and researched. A major challenge remaining to researchers is how to evaluate the results of these new developments. Simulation techniques can provide a vital tool to evaluate and improve molecular imaging devices and techniques. Much research has been done in creating digital human phantoms for medical imaging research. Currently, there is a lack of realistic computer-generated phantoms modeling the mouse anatomy and physiological functions for use in molecular imaging research. The same methods and techniques used to develop the 4D NCAT and XCAT phantoms were used in the creation of a new 4D MOBY phantom.26 A 256 × 256 × 1024 3D magnetic resonance microscopy (MRM) dataset of a normal 16 week old male C57BL/6 mouse was used as the basis for the anatomy of the phantom. The dataset was obtained from G. Allan Johnson of the Duke Center for In Vivo Microscopy, an NIH Resource (P41 05959/R24 CA 92656). With a resolution of 110 μm over the whole body, the dataset is extremely detailed, allowing the creation of realistic models for several different anatomical structures. Figure 5.28 shows sample transaxial slices obtained from the MRM dataset.

FIGURE 5.28 Sample transaxial slices of the MRM dataset used to create the 3D anatomy for the mouse phantom. (From Segars, W.P. et al., Mol. Imag. Biol., 6, 149, 2004. With permission.)


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Models for the different structures were created using the same techniques developed in our laboratory to construct the 4D NCAT and XCAT phantoms. The anatomical structures were manually segmented using the software program SURFdriver and then used as input to the Rhinoceros NURBS modeling software, where NURBS surfaces were fit to the segmented structures. A gated black-blood MRI (bb-MRI) cardiac data set (Figure 5.29) of a normal 15 week old male C57BL/6 mouse was used to create a 4D beating heart computational phantom for the phantom. The study was obtained from Stuart S. Berr of the University of Virginia (UVa) and consisted of 12 time frames over a complete cardiac cycle. At each time frame, the 256 × 256 SA MR images had a pixel size of 0.1 × 0.1 mm and a slice thickness of 1 mm. Using the technique described above, 3D NURBS surfaces were created for the principal structures of the heart: the right and LVs, the atria, and the large vessels at each time frame. We set up the time correspondence between the control points defining a surface over the time frames based on the 4D NCAT human heart computational phantom. We also scaled back the cardiac twisting motion illustrated in the 4D NCAT cardiac computational phantom14 to fit the smaller heart size of the mouse. Once the twisting motion was established, the radial and longitudinal contractions of the control points could be obtained by noting the epi- and endocardial borders in the gated MRI images. Using this technique,

FIGURE 5.29 One SA slice through 12 frames of gated black-blood MRI cardiac data. (From Segars, W.P. et al., Mol. Imag. Biol., 6, 149, 2004. With permission.)


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we determined the 3D position of each control point defining a cardiac surface for each time frame and time curves were then defined for them completing the 4D computational phantom shown in Figure 5.30. Respiratory motion in the MOBY phantom was based on similar respiratory mechanics observed when creating the human NCAT and XCAT phantoms.11 The NURBS surfaces that define the respiratory structures in the phantom were altered through manipulation of their control points to simulate respiration. The diaphragm is defined in the phantom as the top of the liver that encompasses both left and right sections of the body. We simulated the movement of the diaphragm by simply translating the surface defining the liver (Figure 5.31). In addition to moving linearly up and down, the diaphragm was also set to move forward and backward with the changes in the AP diameter of the chest due to the movement of the ribs. This movement was observed in human respiration as well. The heart, stomach, spleen, and kidneys were translated with the movement of the diaphragm. In each case, the translation was applied to the control points defining the different structures in order to move them. The NURBS surfaces defining the lungs and body outline in the mouse phantom were set up to expand or contract with changes in the ribcage. Figure 5.32 shows 3D renderings of the respiratory computational phantom of the MOBY phantom.

End-diastole (ED) RA

LA

60

RA

LV

RV

End-systole (ES) LA

LV

RV

RV

Volume (μL)

50 40 LV 30 20 RA LA

10 0 0

5

10

15 Time frame

20

25

30

FIGURE 5.30 Top: 3D surface renderings of the epi- and endocardial surfaces of the RV and LV for the new NURBS-based mouse beating heart model at end-diastole and end-systole. Bottom: Volume curves for the atria and ventricles of the mouse heart model. (From Segars, W.P. et al., Mol. Imag. Biol., 6, 149, 2004. With permission.)


Heart

129

Heart

Liver Liver Stomach Stomach Spleen

Kidney

Spleen Kidneys

Kidney

FIGURE 5.31 Inspiratory motions of the liver (diaphragm), stomach, spleen, heart, and kidneys simulated in the mouse phantom. Expiratory motion was simulated as the reverse of the inspiratory motion. (From Segars, W.P. et al., Mol. Imag. Biol., 6, 149, 2004. With permission.) End-expiration

End-inspiration

FIGURE 5.32 Top: Anterior view of end-expiration (left) and end-inspiration (right) in the MOBY model. Bottom: Left lateral view at end-expiration (right) and end-inspiration (left). The dotted line indicates the movement of the diaphragm. (From Segars, W.P. et al., Mol. Imag. Biol., 6, 149, 2004. With permission.)


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(a)

(b) FIGURE 5.33 Top: Reconstructed SPECT coronal images generated from the mouse phantom simulating the uptake of Tc-99m MDP. Bottom: Coronal SPECT images obtained experimentally from an actual mouse. (From Segars, W.P. et al., Mol. Imag. Biol., 6, 149, 2004. With permission.)

Used in combination with accurate models of the imaging process, the 4D MOBY phantom can produce realistic imaging data to serve as a standard from which other molecular imaging devices and techniques can be evaluated and improved. The top of Figure 5.33 shows reconstructed SPECT images generated from the phantom simulating the uptake of Tc-99m MDP in a normal mouse without respiratory motion. The bottom of Figure 5.33 shows reconstructed SPECT images obtained from imaging a mouse


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(a)

(b) FIGURE 5.34 Top: Reconstructed cone-beam x-ray CT images generated from the mouse phantom. Bottom: Reconstructed cone-beam x-ray CT images obtained from a live mouse using a micro-CT system developed in our laboratory. (From Segars, W.P. et al., Mol. Imag. Biol., 6, 149, 2004. With permission.)

with the same radiopharmaceutical in our laboratory. Coronal image slices are shown. The top of Figure 5.34 shows reconstructed x-ray CT transaxial images simulated using the mouse phantom while the bottom of Figure 5.34 shows similar CT images obtained from a live mouse using a micro-CT system built in our laboratory. In both cases, the simulated images are comparable to those obtained experimentally. Like its human phantom counterparts, the MOBY phantom also has the ability to simulate different anatomies. Current work is underway to create anatomically variable models of the MOBY phantom as well as to create a computational phantom for the laboratory rat. Like the NCAT and XCAT, the 4D MOBY phantom can be applied to small animal imaging research to look at such things as the effects due to anatomy and motion, as well as

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to investigate image acquisition protocols and to research new reconstruction techniques. It can also be used to test and validate new small animal imaging scanners before entering production process and performing research into dosimetry in small animals. The MOBY phantom, as such, provides a unique and useful tool in molecular imaging research.

5.6 Summary The above discussion presents our developments toward more ideal hybrid computational phantoms for use in medical imaging research. We have found that the NURBS modeling technique is an efficient and flexible way to describe the anatomy and physiology for realistic phantoms. NURBS surfaces have the ability to model complex anatomy based on actual patient data. This gives them the level of realism offered by voxel-based phantoms. In addition, NURBS surfaces have the flexibility of stylized phantoms, in that they can be altered easily to model variations in the 3D anatomy and be extended to 4D to model patient motion. The NURBS-based phantoms, therefore, offer a major evolutionary advance in the development of computerized computational phantoms. Using NURBS as a basis, we developed the NCAT, XCAT, and MOBY phantoms for the purpose of medical imaging research. With the ability to simulate realistic predictive imaging data of a population of human or animal subjects, the phantoms have found a wide use to develop, evaluate, and improve imaging devices and techniques and to investigate the effects of anatomy and motion. They may also provide useful tools in the field of radiation dosimetry as can be seen in other chapters.

References 1. Snyder, W.S. et al. Estimates of absorbed fractions for monoenergetic photon sources uniformly distributed in various organs of a heterogeneous phantom, J. Nucl. Med., Suppl. 3, 7, 1969. 2. White, D.R. Tissue substitutes in experimental radiation physics, Med. Phys., 5, 467, 1978. 3. Hubbell, J.H. and Seltzer, S.M. Tables of X-Ray Mass Absorption Coefficients and Mass EnergyAbsorption Coefficients (Version 1.03) [online]. Available: http://physics.nist.gov, Gauthersbury, MD: National Institute of Standards and Technology, 1995. 4. Pretorius, P.H. et al. Evaluation of right and left ventricular volume and ejection fraction using a mathematical cardiac torso phantom, J. Nucl. Med., 38, 1528, 1997. 5. Park, J., Metaxas, D.N., and Axel, L. Analysis of left ventricular wall motion based on volumetric deformable models and MRI-SPAMM, Med. Imag. Anal., 1, 53, 1996. 6. Park, J., Metaxas, D.N., and Axel, L. Quantification and visualization of the 3D nonrigid motion of the left ventricle, in Proceedings of the SPIE Medical Imaging Conference (Physiology and Function), San Diego, CA, 177, 1997. 7. Park, J. et al. Deformable models with parameter functions for cardiac motion analysis from tagged MRI data, IEEE Trans. Med. Imag., 15, 278, 1996. 8. Guyton, A. and Hall, J. Textbook of Medical Physiology, 9th edn., Philadelphia, PA: WB Saunders Co., 1996. 9. LaCroix, K.J. Evaluation of an attenuation compensation method with respect to defect detection in Tc-99m-MIBI myocardial SPECT images, PhD dissertation, The University of North Carolina at Chapel Hill, 1997.


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10. LaCroix, K.J. et al. Receiver operating characteristic evaluation of iterative reconstruction with attenuation correction in 99mTc-Sestamibi myocardial SPECT Images, J. Nucl. Med., 41, 502, 2000. 11. Segars, W.P., Lalush, D.S., and Tsui, B.M.W. Modeling respiratory mechanics in the MCAT and spline-based MCAT phantoms, IEEE Trans. Nucl. Sci., 48, 89, 2001. 12. West, J. Respiratory Physiology, 5th edn., Baltimore: Williams & Wilkins, 1995. 13. Piegl, L. On nurbs—A survey, IEEE Comput. Graph. Appl., 11, 55, 1991. 14. Segars, W.P. Development and application of the new dynamic NURBS-based cardiac-torso (NCAT) phantom, PhD thesis, University of North Carolina at Chapel Hill, 2001. 15. Visible human male and female datasets, National Library of Medicine. Available at: http://www. nlm.nih.gov/research/visible/visible human.html. 16. Moody, D. and Lozanoff, S. A practical computer program for generating three-dimensional models of anatomical structures, Paper Presented at the 14th Annual Meeting of the American Association of Clinical Anatomists, Honolulu, HA, July 8, 1997. 17. Rhinoceros NURBS modeling software. Available at: http://www.rhino3d.com. 18. He, X. et al. Effect of anatomical and physiological factors and compensation methods on observer of performance for defect detection in myocardial perfusion, J. Nucl. Med., 44, 112P, 2003. 19. He, X. et al. A mathematical observer study for the evaluation and optimization of compensation methods for myocardial SPECT using a phantom population that realistically models patient variability, IEEE. Trans. Nucl. Sci., 51, 218, 2004. 20. Barclay, A.B., Eisner, R.L., and DiBella, E.V. PET Thorax Model Database, http://www.emory.edu/ CRL/abb/thoraxmodel, Atlanta, GA: Crawford Long Hospital of Emory University, 1996. 21. Segars, W.P. and Tsui, B.M.W. Study of the efficacy of respiratory gating in myocardial SPECT using the new 4-D NCAT phantom, IEEE Trans. Nucl. Sci., 49, 675, 2002. 22. Tsui, B.M.W., Segars, W.P., and Lalush, D.S. Effects of upward creep and respiratory motion in myocardial SPECT, IEEE Trans. Nucl. Sci., 47, 1192, 2000. 23. Smyczynski, M. et al. Impact of respiratory motion on the detection of solitary pulmonary nodules with SPECT imaging of NeoTect, in IEEE Medical Imaging Conference and Nuclear Science Symposium, Norfolk, VA, Nov. 10–16, 2002. 24. Smyczynski, M. et al. Modeling the respiratory motion of solitary pulmonary nodules for investigating SPECT tumor imaging, in IEEE Medical Imaging Conference and Nuclear Science Symposium, San Diego, CA, Nov. 4–10, 2001. 25. Tsui, B.M.W. et al. Quantitative cardiac SPECT reconstruction with reduced image degradation due to patient anatomy, IEEE Trans. Nucl. Sci., 41, 2838, 1994. 26. Segars, W.P. et al. Development of a 4-D digital mouse phantom for molecular imaging research, Mol. Imag. Biol., 6, 149, 2004.

6 The 3D and 4D VIP-Man Computational Phantoms X. George Xu, Tsi-Chian Ephraim Chao, Ahmet Bozkurt, Chengyu Shi, and Juying Zhang

CONTENTS 6.1 Introduction ............................................................................................................... 136 6.2 Development of VIP-Man and 4D VIP-Man.......................................................... 137 6.2.1 Original Images ........................................................................................... 137 6.2.2 Segmentation ................................................................................................ 138 6.2.2.1 General Organs............................................................................. 139 6.2.2.2 Red Bone Marrow ........................................................................ 139 6.2.2.3 GI Tract........................................................................................... 141 6.2.2.4 Miscellaneous Issues ................................................................... 142 6.2.3 Labeling ......................................................................................................... 144 6.2.4 Visualization ................................................................................................. 144 6.2.5 Implementation of VIP-Man into Monte Carlo Codes............................ 148 6.2.6 Extending 3D VIP-Man into 4D VIP-Man ................................................ 148 6.2.6.1 Conversion from Voxel Data to Surface Definition ....................................................................................... 149 6.2.6.2 Deformation of the Organ Surfaces ........................................... 149 6.2.6.3 Revoxelization of the 4D Computational Phantom for Each Respiratory Phase ......................................................... 150 6.3 Comparison between VIP-Man and Other Computational Phantoms .................................................................................................................... 151 6.4 Applications of the VIP-Man Computational Phantom ...................................... 152 6.4.1 Health Physics .............................................................................................. 152 6.4.1.1 External Photon Dosimetry ........................................................ 152 6.4.1.2 External Electron Dosimetry ...................................................... 153 6.4.1.3 External Neutron Dosimetry ...................................................... 153 6.4.1.4 External Proton Dosimetry ......................................................... 154 6.4.1.5 Internal Electron Dosimetry ....................................................... 154 6.4.1.6 Internal Photon Dosimetry for the GI Tract ............................. 155 6.4.1.7 RBM Dosimetry for External Irradiations ................................ 155 6.4.2 Applications in Radiological Imaging ...................................................... 156 6.4.2.1 Organ Doses from SPECT and PET Brain Imaging ................ 156 6.4.2.2 Organ Doses from X-Ray Radiographs ..................................... 156 6.4.2.3 Image Quality Optimization in Radiograph............................ 157 6.4.2.4 Organ Doses from Interventional Cardiological Examinations ................................................................................ 157 135


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6.4.3

Applications in Radiotherapy .................................................................... 157 6.4.3.1 External Beam Selection in Prostate Radiation Treatment ....................................................................................... 157 6.4.3.2 Organ Doses from Proton Radiation Treatments .................... 158 6.4.3.3 Dosimetry for Respiration Management in IGRT ................... 158 6.4.3.4 Organ Doses from Cone-Beam CT Imaging for IGRT ......................................................................................... 158 6.5 Summary .................................................................................................................... 158 Acknowledgments ............................................................................................................... 160 References ............................................................................................................................. 160

6.1 Introduction The visible photographic man (VIP-Man) computational phantom was developed during 1996–2000 at Rensselaer Polytechnic Institute (RPI, Troy, New York). The interest in anatomical and dosimetric modeling by the group at RPI originated from the doctoral research Dr. X. George Xu performed in early 1990s at Texas A&M University (TAMU, College Station, TX), under the guidance of Dr. W. Dan Reece, Dr. John Poston, and Dr. Wesley Bolch (now with the University of Florida) served on his dissertation committee. Dr. Xu’s work at TAMU was to implement the stylized adult male and female phantoms into the Monte Carlo N-Particle (MCNP) code in order to develop an algorithm that would link personnel dosimeter readings to effective dose equivalent that was adopted by the U.S. Nuclear Regulatory Commission (see Chapter 18 for more details). Dr. Xu joined the RPI faculty in 1995. Later that year, an international workshop on “Voxel Phantom Development” was held at the National Radiological Protection Board (Chilton, U.K.). It was clear that the so-called voxel phantoms would soon replace the stylized phantoms. Voxel phantoms reported prior to 1995 were developed from either computer tomography (CT) or magnetic resonance imaging (MRI) of volunteers. The majority of the subjects were adult males. The image slices were relatively thick due to the need to reduce the total whole-body scanning time. Furthermore, the computer memory at that time severely limited the number of voxels in the modeling. Xu and two of his students at RPI decided to develop a new voxel phantom and soon became impressed with the unique images from the Visible Human Project® (VHP) that became available around that time.1,2 During late 1990s, the computer technologies were rapidly improving and the VHP’s images were made freely available for research by the National Library of Medicine (NLM). After a couple of years of planning, Dr. Xu received a 4-year research grant from the National Science Foundation’s CAREER program in 1998. Dr. Tsi-chian Chao and Dr. Ahmet Bozkurt, PhD students at that time, immediately became totally absorbed into the project and soon made several breakthroughs that led to a series of papers related to a voxel-based phantom known as the VIP-Man. In 2004, Dr. Chengyu Shi, a former PhD student at RPI, Dr. Xu and Mr. Juying Zhang, a current PhD student, added respirationsimulating features into the VIP-Man phantom. This chapter describes the development of three-dimensional (3D) and four-dimensional (4D) versions of the image-based whole-body computational phantom, VIP-Man. These are followed by a summary of numerous applications to projects in health physics, diagnostic imaging, and radiation treatment.

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6.2 Development of VIP-Man and 4D VIP-Man Three-dimensional medical imaging techniques, such as CT and MRI, allow us to easily visualize the internal structures of the body and to store the images in versatile digital formats. In the past two decades, the radiotherapy community has increasingly depended on the use of Monte Carlo techniques with patient CT images for clinical treatment dose optimization.3 Compared to the medical community, however, health physicists faced the following unique and intractable technical challenges: (1) whole-body computational phantoms are needed for most health physics applications, but medical images are taken only for a portion of the body (CT procedures expose the patients to intense x-rays); (2) a large amount of internal organs/tissues have to be identified and segmented for organ dose calculations in health physics, while, in radiotherapy, only the tumor volume and the critical organs inside the field need to be specified; (3) the size of a whole-body computational phantom can be potentially too large for computers and Monte Carlo codes to handle; and (4) health physics dosimetry involves photons, electrons, neutrons, and protons, but majority of the clinical radiotherapy procedures involve only photon/electron beams or seeds (a few centers also involve neutron or proton beams). Because of these issues, only a few groups successfully constructed image-based wholebody computational phantoms (see Chapter 1 for a complete list of voxel phantoms).4–8 In 2000, Xu et al.9 was eager to share their contribution to the literature by reporting an adult male phantom, named VIP-Man, which had the best image resolution of that time: pixel size of 0.33 mm × 0.33 mm and slice thickness of 1 mm. In such a high resolution, the phantom was believed to offer attractive advantages in modeling tiny radiosensitive structures such as red bone marrow (RBM), skin, eye lens, thyroid, optical nerves, etc. The development of a voxel phantom includes several steps: 1. Selection of an original image set: The quality of original raw data set is crucial in constructing a tomographic computational phantom. The resolution of original images determines the ability to perform segmentation and modeling of human anatomy. 2. Segmentation: Each pixel in the original images was identified as belonging to a tissue/organ by manual or semiautomatic procedures involving computer programs, knowledge about the anatomy and visual inspection of the images. 3. Labeling: Each pixel is assigned an index number that specifies not only which tissue/organ it belongs to but also its density and chemical composition. 4. Visualization: Two-dimensional (2D) or 3D visualization is necessary to display and inspect the segmented, labeled, and registered images. 5. Implementing this phantom into a Monte Carlo code for radiation transport purposes.

6.2.1 Original Images In the mid-1990s, several unique sets of whole-body CT/MR/color photographic images from the NLM’s VHP became available (http://www.nlm.nih.gov/research/visible). The ambitious goal of the VHP, which was conceived in 1988 and initiated in 1991, was to build the most detailed digital image library about the anatomy of an adult male and an adult

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female.1,2 Cadavers that were considered “normal” and representative of a large population were evaluated. The donated body of an executed 38-year-old male from Texas was the first specimen to be selected for VHP. The individual was 186 cm tall and weighed 90 kg. Later, a 58-year-old female body was also obtained. To ensure the applicability, it was decided by NLM that the image data needed to be documented in several common formats used by radiologists and other physicians. Eventually, four modalities were used: traditional x-rays and CT scans to optimally visualize bone, MRI for soft tissue, and color photographs for definitive resolution. The color photographs, which had the finest resolution,9 were used to provide a standard for comparison. Generally, an image format consists of many pixels each representing a tissue volume in a 2D map. The 3D volume of the tissue is called a voxel, which is determined by multiplying the pixel size by the thickness of an image slice.10 In the VHP data set, the transversal MRI images of the head and neck and the longitudinal sections of the rest of the body, which were obtained at 4 mm intervals, are 256 × 256 pixels in resolution (each with 12 bits of gray tone resolution). The resulting voxel size for the MRI data set (torso portion) is 1.88 mm × 1.88 mm × 4 mm. The CT data consists of transversal CT scans of the entire body taken at 1 mm intervals at a resolution of 512 × 512 pixels where each pixel is made up of 12 bits of gray tone. The voxel size for the CT data set (torso portion) is 0.94 mm × 0.94 mm × 1 mm. The transversal anatomical photographs for both male and female cadavers are 2048 × 1216 pixels (each pixel is with 24 bits of color taken at 1 mm thick slices for the male cadaver and 0.33 mm for the female). For the visible male, there are a total of 1871 slices of both CT and anatomical photographs. The transversal anatomical images were obtained by photographing the top surface of the body block after removal of (by shaving) each successive millimeter (0.33 mm for the female) by a cryomacrotome. These color photographic data sets for whole body has a voxel size of 0.33 mm × 0.33 mm × 1 mm for the male (0.33 mm × 0.33 mm × 0.33 mm for the female). Since the first public debut on November 28, 1994, VHP images have been available in public domain. Since then, computer engineers and anatomists, working together, have devoted unprecedented effort to classify and visualize the data sets. Until recently, the Visible Human Male was the most complete computerized database of the human body ever assembled.11 Called “the greatest contribution to anatomy since Vesalius’s 1543 publication of De Humani Corporis Fabrica,” the VHP data sets are the seeds for a growing medical revolution. For more than a decade, scientists worldwide have been utilizing this national resource of anatomical information for biomedical sciences and engineering applications.12 Based primarily on the color photographic images, VIP-Man was constructed using the following detailed methods. 6.2.2 Segmentation To calculate organ doses, Monte Carlo simulations do not require the information of CT number, percentage distribution of water or color, but require the relationship between voxels and tissues/organs. Unfortunately, today’s computers are unable to automatically recognize tissues/organs from CT/MRI/cryosection images. Segmentation is a manual or semiautomatic procedure to recognize and classify the pixels on an original image into anatomical structures. VHP-cryosection images were previously segmented in the VHP by Spitzer and Whitlock to yield up to 1400 structures.11 Among these are 23 dosimetrically important tissues/organs. However, the other 16 tissues/organs were not segmented in the original data, including RBM, eye lenses, and gray/white matter in cerebrum, wall/content/mucosa in gastrointestinal (GI) tract, teeth, cerebrospinal fluid (CSF), and bladder wall/content. Several


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properties in the VHP-cryosection color photographs were helpful in identifying certain tissues and organs using various “measurement” techniques.13 For example, 1. Colors, such as red, green, and blue 2. Locations, such as x, y, z, distance to center, and distance to boundary 3. Connectivity, such as continuous component, boundary touching, hole, and isolated island 4. Others, such as shape, hue, saturation, intensity, etc. Organs with different compositions are expected to show variations in one, several, or all of the properties listed above. For this reason, the following sections will describe how these properties were used. 6.2.2.1 General Organs First of all this section will demonstrate how the “measurement” process works in segmenting the cerebrum, a tissue that is composed of the gray matter, white matter, and CSF. The color of all cerebrum pixels are examined and analyzed to yield the color histograms of red, green, and blue channels and the intensity. After several trials, the cerebrum is segmented by the following procedures. 1. Check the color of each pixel to see if R is larger than 190, G is larger than 160, and B is larger than 100, where R, G, and B are the intensity in red, green, and blue channels with a value from 0 (dark) to 255 (bright). If any two out of three checks mentioned above are passed, this pixel is identified as white matter. 2. Check the color of each pixel to see if R is smaller than 144, G is smaller than 102, and B is smaller than 50. If any two out of three checks mentioned above are passed, this pixel is identified as CSF. 3. Otherwise, this pixel is identified as gray matter. The gray matter, white matter, and CSF can be separated clearly by these procedures. Alternately, some tissues/organs cannot be segmented according to the color differences alone, such as the eye lenses/eyes, bladder wall/content, etc. The eye lenses can be segmented from the eye balls using the threshold of green channel (G > 114). However, other parts of the eyes also have the same green color as the eye lenses. The threshold of the green channel alone might yield some false pixels of the eye lenses. Methods like erosion, dilation, and median filter can be utilized jointly. In this method, the selection of the biggest connective compound followed by the median filter turned to be the favorite approach. The bladder content and the bladder wall were segmented as well in the similar way. 6.2.2.2 Red Bone Marrow The calculation of absorbed dose in the tissue of skeleton is a complex problem because of the difficulties in modeling the microscopic distribution of soft tissue and bone.14 In addition, there are two kinds of marrow inside the bone, yellow bone marrow (YBM) and RBM, both of which are difficult (if not impossible) to be identified and segmented directly from CT or MRI images. The distribution of RBM in the whole body has not been directly modeled in both stylized and voxel phantoms. However clinical measurements in cadavers

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suggest that the RBM tends to be located more in flat bones or skull, with concentrations varying by sex and age.15 The difficulty in defining RBM distribution has led researchers to derive different methods to estimate the RBM dose. Two general methods are currently used for skeletal dosimetry from external photons: (1) Snyder et al.16 assume a homogeneous representation and identical efficiencies to absorb energy of the skeleton. Therefore, the mass ratios are used for portioning the energy deposition to various target tissues (endosteum, RBM, etc.). They also state that potential overestimation of RBM dose will increase for photons at energies lower than 200 keV. (2) Cristy and Eckerman17 suggest another way to estimate skeleton dosimetry from the photon fluence through the trabecular bone. Previous studies of the mean chord lengths and electron absorbed fractions are utilized to estimate how the secondary particles are generated and deposited due to the fluence. The high-resolution color photographs of the VHP brought a unique opportunity to realistically quantify the 3D spatial and quantitative representation for whole-body RBM distribution. Since the RBM cavities are less than 10 μm, smaller than each pixel of the VHP data set (0.33 mm), the microscopic segmentation was not possible. However, the distribution of RBM at the “macroscopic” level was satisfactorily segmented by a threshold determined by the statistical properties of the original color images. After a careful calibration, the “redness” and “saturation” were tested to be the best properties for separating RBM distribution. The “redness” is defined as Redness =

R × 255 R +G +B

(6.1)

where R is the red channel of the corresponding pixel G is the green channel B is the blue channel “saturation” is the purity of color, such as ⎧ 0, ⎪ ⎪ ⎪ max − min × 255, Saturation = ⎨ ⎪ max + min ⎪ max − min × 255, ⎪ ⎩ 2 − (max + min)

if max = min if max + min ≤ 1 if max + min > 1

where max =

max(R , G, B ) 255

min =

min(R , G, B ) 255

For example, the saturation is 255 for pure color, and is 0 for black/white/gray.

(6.2)


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First, the colors of all bone pixels were measured. The “redness” of each bone pixel was then calculated according to Equation 6.1 and the “saturation” from Equation 6.2. A plot of saturation versus redness for all the bone voxels in whole body showed that there was a cluster of voxels that had a redness greater than 120. These pixels were then identified as having considerable amount of RBM and were then grouped to represent the macroscopic distribution of RBM in the whole body. A 3D visualization of RBM distribution was obtained to visualize the distribution which did not exist until the work associated with the VIP-Man phantom. As shown in Figure 6.1b, VIP-Man has more RBM in flat bones than long bones, as predicted by clinical data.5,16 A comparison of masses of the RBM and skeletal tissue between VIP-Man and the International Commission on Radiological Protection (ICRP) 70 Reference Man15 is given in Table 6.1. Although VIP-Man (104 kg) is heavier than the Reference Man (73 kg), the skeletal and RBM masses were selected to be similar. However, VIP-Man has more RBM in lower body and less RBM in upper body compared to the Reference Man. It should be pointed out that the YBM is not segmentable from the bone in VIP-Man because it has the similar color as the bone. In addition, the ICRP skeleton computational phantom18 suggests that the trabecular bones and the cortical bone be considered separately. However, in this study, the RBM in VIP-Man was segmented without distinguishing trabecular or cortical bone. In the case of the RBM, the term “segmentation” means a process in which a consistent, computer-aided method is used to select pixels that contain a large fraction of RBM. This way, there is at least a chance to try to model the RBM directly from these cadaver-based color images. In essence, this approach is not different from a method commonly used by many developers who selected the outermost layer in the voxel phantom as the skin—a tissue that has a size smaller than a voxel. 6.2.2.3 GI Tract

(a)

(b)

FIGURE 6.1 (a) Anterior 2D visualization for VIP-Man model and (b) 3D visualization for RBM of VIP-Man.

The GI tract in VIP-Man consists of 14 different tissues or organs, including esophagus content, esophagus mucosa, esophagus wall, lower large intestine (LLI) content (includes descending and sigmoid colons), LLI mucosa, LLI wall, rectum, small intestine, stomach content, stomach mucosa, stomach wall, upper large intestine (ULI) content (includes ascending and transverse colons), ULI mucosa, and ULI wall. The differences in color and texture are the key to separate the wall and content of the GI tract. The stomach, esophagus, and large intestine were visually segmented into content and wall because the former is much redder (darker) than the latter. In the small intestine, however, the color difference of wall and contents were not significant enough to be useful. In addition, the folds and the villi on the surface of small intestine wall are too small to be identified in the original image used to create VIP-Man. Mucosa, which is a superficial tissue on the inner wall of esophagus, stomach, ULI, and LLI, has been known to be more radiosensitive than the


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TABLE 6.1 Comparison of Skeleton and RBM Mass Distributions from VIP-Man and ICRP 70 Reference Man

Bone Structure

VIP-Man

RBM (g) ICRP 70 Reference Man

Skeleton (w/Marrow) (g) ICRP 70 Reference VIP-Man Man

Cranium

48.21

88.92

876.43

1,239.00

Mandible

2.00

9.36

89.14

126.00

Scapulae

46.43

32.76

319.24

378.00

Clavicles

13.41

9.36

102.68

84.00

Sternum

43.13

36.27

117.00

126.00

160.80

188.37

728.34

735.00

22.87

45.63

155.85

1,995.00

Ribs Cervical vertebrae Thoracic vertebrae

145.06

188.37

563.28

Lumber vertebrae

135.09

143.91

470.22

Sacrum

110.07

115.83

303.16

Innominate

315.93

204.75

1,071.43

1,113.00

41.37

78.39 –

2,027.67

1,606.50

1,376.93

1,186.50

768.96

661.50

647.61

556.50

379.68

378.00

244.64

241.50

Femora Tibiae

0.78

Other foot

2.01

Humeri

37.09

– 26.91 –

Radii and ulane

2.08

Other hand bone

1.11

–

Other

1.14

1.17

21.01

73.50

1,128.57

1,170.00

10,263.27

10,500.00

Total

Source: Cristy, M. and Eckerman, K.F., Specific absorbed fractions of energy at various ages from internal photon sources, ORNL/TM-8381/Volumes I–VII, Oak Ridge National Laboratory, Oak Ridge, TN, 1987.

wall tissue.19 From literature review, the average mucosal thickness can be 188 ± 52 μm 20 or 2.5 mm 21 in stomach, and 427 ± 98 μm in large intestine.20 In VIP-Man, a thin layer (0.33 mm for stomach and 0.67 mm for LLI and ULI) between wall and content is defined as the “mucosa.” The organ and tissue masses of VIP-Man are listed in Table 6.2. There is a significant difference in the mass of GI tract between VIP-Man and Reference Man, especially in the colon region (ULI + LLI). This suggests that the weight of adult intestine tract can be very different (varied from about 400 to 3400 g).21 6.2.2.4 Miscellaneous Issues 1. The teeth of VIP-Man were manually segmented by using an imaging software (Paint Shop Pro, version 6, Jasc Software, Inc., Eden Prairie, MN). 2. The male breasts of VIP-Man were 33.6 g in total weight, which were artificially constructed using the fat tissue that covered an area of 4.2 cm from a nipple and 0.99 cm beneath the skin. 3. Two layers of skin are important for radiation safety: 20–100 μm for epidermal effects, and 300–500 μm for dermal effects. The dose in the first layer is selected


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TABLE 6.2 Comparison of Organ Masses for VIP-Man, Scaled VIP-Man, and ICRP-23 Reference Mana Organs/Tissues Adrenals

VIP-Man (g)

Scaled VIP-Manb(g)

ICRP 23 (g)

8.3

5.8

Bladder (wall)

41.4

29.0

45.0

Bladder (urine)

43.2

30.2

102.0

1,574.0

1,101.9

1,429.0

Brain + nerve Breast (male)

14.0

33.6

23.5

26.0

265.1

185.6

121.0

Esophagus (wall)

38.9

27.2

Esophagus (lumen)

26.8

18.8

40.0 –

CSF

Esophagus (mucosa) Fat

–

3.5

2.5

36,326.6

25,430.7

17,200.0 10.0

Gallbladder (wall)

12.0

8.4

Gallbladder (bile)

21.0

14.7

60.0

Heart muscle

398.7

279.1

330.0

Kidneys

335.4

234.8

310.0

Lenses of eyes

0.4

0.4

1,937.9

1,356.6

1,800.0

LLI (wall)

290.8

203.6

160.0

LLI (lumen)

324.2

227.0

LLI (mucosa)

35.8

25.1

135.0 –

Liver

Lungs

0.54

910.5

637.4

1,000.0

43,002.6

30,104.3

28,000.0

Pancreas

82.9

58.0

100.0

Prostate

18.9

13.2

16.0

1,130.4

791.2

1,500.0

Muscle

RBM

1,0114.2

7,080.7

8,500.0

Skin

Skeleton

2,253.4

1,577.5

2,600.0

Small intestine

1,291.8

904.3

1,040.0

Spleen

244.0

170.8

180.0

Stomach (wall)

159.5

111.7

150.0

Stomach (content)

324.5

227.2

Stomach (mucosa)

13.7

9.6

250.0 –

Testes Thymus Thyroid

21 (1) 11.2

14.7

35.0

7.8

20.0

27.6

19.3

20.0

ULI (wall)

461.1

322.8

160.0

ULI (lumen)

905.7

634.0

ULI (mucosa)

63.4

44.4

135.0 –

Other Total a

b

1,688.0

1,181.7

4,382.0

104,277.2

73,000.0

70,000.0

Reference Man values are from ICRP 2322 and the MIRD model values from Cristy and Eckerman.1 Scaled VIP-Man was scaled down to be 176 cm in height and 73 kg in weight in accordance with ICRP 66.25

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to represent the skin dose by ICRP.22 However, Wells23 suggests that the skin dose should be evaluated at depths between 300 and 500 μm according to data on late dermal atrophy. The skin in VIP-Man is about 0.7 mm thick in the front portion of the body, and becomes thicker in the back, especially near the hips. 4. There is only one testicle (according to the medical records of the cadaver) in VIP-Man. 5. The eyes of VIP-Man are closed (an important factor in external electron calculations). 6. VIP-Man is 186 cm in height and 104 kg in weight. In comparison, the Reference Man is only 73 kg and 176 cm.24 Since VIP-Man (like other voxel phantoms) is different from the Reference Man, calculations for effective dose should be performed and reported carefully. It is so different that one journal reviewer once insisted that we report our result in “effective VIP-Man dose.”25 6.2.3 Labeling An index number representing different tissues/organs was assigned to each segmented pixel. There are a total of 72 different tissues/organs segmented in VIP-Man, which are summarized in Table 6.3. The densities of tissues/organs, which are mainly adopted from ICRP Publication 23, are also listed in Table 6.3.21 It should be noted that the suggested density for skeleton in ICRP Publication 23 is 1.4 g cm−3, which is an average value for all skeleton tissues. Alternatively, the density of bone in VIP-Man is 1.55 g cm−3 because RBM, cartilages, disks, and tendons have been separated from bone tissue. Chemical composition data of tissues/organs are also vital for Monte Carlo simulation and are taken from International Commission on Radiation Units and Measurements (ICRU) Report 44.26 When a tissue/organ is not available in ICRP 23 or ICRU 44, its density and chemical composition is assigned using values from similar tissues/organs. The approach of assigning uniform density and chemical composition for each organ is also used in other voxel phantoms.27 6.2.4 Visualization After being segmented and labeled, VIP-Man phantom was inspected by 2D and 3D visualization. To make sure each of these procedure was correctly done, one would need to construct and compare the newly constructed anatomy with that depicted by the original VHP-photograph. Furthermore, in order to check the continuity between each transverse slice, the images in axial view was reconstructed into images in sagittal and coronal views. For example, Figure 6.1a shows a segmented slice in coronal view that assures the continuities of skin, muscle, bone, lung, liver, heart, etc. This comparison should be repeated slice by slice for the whole data set, 3D surface rendering is more efficient. The 3D visualization of VIP-Man can be utilized to assure that the shape and relative location of each organ is correct. Two types of 3D rendering are used in this study: 1. Volume rendering, which takes a set of voxels as input to render a 2D projection image from a 3D object. A C++ code was written to render the distance to surface of the rendered tissues/organs. 2. Surface rendering takes a set of surfaces as input to render a 2D projection image from a 3D object. Visualization toolkit (VTK)28 was used to generate the surface rendition for VIP-Man.

Brain Brain

38.06 891.96

122.70 681.37 440.49 1.43

1.55

1.55

2.10

1.20

0.26

0.26

1.04

1.04

1.04

1.04

1.06

1.06

1.04

1.03

1.03

Other

Teeth

Tendon

Bronchus R

Bronchus L

Caudate nucleus

Cerebellum

Cerebrum—gray

Cerebrum—white

Coronary L

Coronary R

Corpus callosum

CSF, spinal

CSF, skull

7,193.05

97.75

167.31

16.92

1.41

8.95

1.73

1.59

1,097.18

–

–

–

–

Brain –

–

–

–

Bone –

Bone

Bone

Bone

Spine

87.15

1.55

1.55

Bone

Adrenals –

– –

ICRP 60 Organ

Cranium

806.79

854.70

0.18 328.97 8.28

Mass (g)

Mandible

Bone

1.02

1.06

Blood

1.21 × 10−3 1.21 × 10−3

Density (g cm−3)

Inside Outside Adrenals

Air

VIP-Man Organ

Male breast

Content

Wall

Mucosa

LLI

Lung

Liver

Lentiform nucleus

Lenses of eyes

Lateral ventricle

Kidneys

Heart wall

Bile

Wall

Gallbladder

Fronix

Fat

Eye

Lumen Mucosa Wall

Esophagus

VIP-Man Organ

Organs Used in VIP-Man and Their Corresponding Names Defined in ICRP 60

TABLE 6.3

0.92

1.04

1.04

1.04

0.26

1.05

1.07

1.10

1.03

1.05

1.03

1.03

1.03

1.04

0.92

1.03

1.04 1.04 1.04

Density (g cm−3)

33.56

324.24

290.84

35.75

910.51

1,937.93

13.41

0.54

7.08

335.37

398.71

20.98

11.96

2.22

36,326.63

14.91

26.75 3.47 38.87

Mass (g)

(continued)

Breast

–

Colon

Colon

Lung

Liver

–

Lenses

–

Kidneys

–

–

–

–

–

–

– Esophagus Esophagus

ICRP 60 Organ

The 3D and 4D VIP-Man Computational Phantoms 145

18.91

1.05

1.04

1.05

1.04

0.98

1.10

1.04

1.06

Pancreas

Pons and middle cerebellar peduncle Prostate

Rectum

RBM

Skin

Small intestine

Spleen

1.04

Mucosa

51.54

82.86

1.75

13.72

324.53

244.04

1,291.77

2,253.43

1,130.44

Source: Wells, J., Br. J. Radiol. Suppl., 19, 146, 1986.

1.04

Content

Stomach

24.57

1.04

Optic nerve

0.33

1.04

Optic chiasma

43,002.59

Mass (g)

1.04

Density (g cm−3)

Muscle

VIP-Man Organ

Stomach

–

Spleen

SI

Skin

RBM

–

–

–

Pancreas

–

–

Muscle

ICRP 60 Organ

Vestibulocochlear

Content

Wall

Urinary bladder

Content

Mucosa

Wall

ULI

Trachea

Thyroid

Thymus

Thalamus

Testes

Wall

VIP-Man Organ

Organs Used in VIP-Man and Their Corresponding Names Defined in ICRP 60


1.04

1.04

1.02

1.04

1.04

1.04

0.26

1.05

1.03

1.04

1.04

1.04

Density (g cm−3)

0.07

43.22

41.38

905.70

63.36

461.12

7.32

27.56

11.22

8.07

21.00

159.52

Mass (g)

–

–

Bladder

–

Colon

Colon

–

Thyroid

Thymus

–

Gonads

Stomach

ICRP 60 Organ



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As shown in Figure 6.1b, volume rendering has the advantage to represent the tiny and complicated structures such as RBM. However, this rendition does not promise good depth perception. Alternately, as shown in Figure 6.2, surface rendering offers stereo rendition but omits tiny structures because a lot of smooth filters are involved. Figure 6.3 shows the comparison between 3D images of the Medical Internal Radiation Dose (MIRD)-type stylized phantom and VIP-Man. Compared to VIP-Man, the stylized phantom appears to be crude in representing the realistic shapes of the GI tract.

(a)

(b)

(c)

FIGURE 6.2 (See color insert following page 524.) VIP-Man in 3D views showing (a) whole-body skin and skeletal structure; (b) details of internal organs with lungs in red, stomach in gold, ULI in purple, kidney in red, liver in maroon, LLI in brown, etc.; and (c) details of the head and brain containing skull in gold, white matter in white, gray matter in gray, nerve in blue, spinal cord in gold, thyroid in red, skin in white, etc. VTK was used in the surface rendering of the voxelized images.

Thyroid Esophagus Heart Lungs Liver Stomach Small intestine Colon Rectum

FIGURE 6.3 The GI tract in the mathematical model is too simple to represent the very twisted GI tract compared to VIP-Man.

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6.2.5 Implementation of VIP-Man into Monte Carlo Codes Due to its high resolution, implementation of VIP-Man into Monte Carlo codes required a huge amount of random accessible memory (RAM). In 1998, the maximum “useable” RAM for a typical personal computer (PC) was less than 1 GB and it was impossible to handle the size of VIP-Man, which contains a total of about 3.7 billion voxels and additional coding. A significant amount of effort was required to reduce the memory burden by using an innovative look-up table (LUT) algorithm. The LUT algorithm was successfully implemented in the Monte Carlo code EGS4, allowing the computer to store only the key anatomical and physical data; the details are unfolded from specific tables when needed. The memory saving with the LUT algorithm in VIP-Man/EGS4 is about a factor of 20.29 In 2001, VIPMan/EGS4 was run using a PC of 512-MB RAM at the original 0.33 mm × 0.33 mm × 1 mm voxel size. This made VIP-Man/EGS4 the “finest” voxel-based phantom ever developed for Monte Carlo calculations at that time. MCNP and Monte Carlo N-Particle eXtended (MCNPX), on the other hand, proved to be much harder to use for this phantom. The default code options had to be changed to optimize memory. Even with these improvements, however, the voxel size of VIP-Man/MCNP/X had to be compromised to 4 mm × 4 mm × 4 mm (or about 6 million voxels for the whole body) in order to run it on the same PC at that time. Others were reportedly able to handle a head computational phantom of 65 million voxels in MCNP4A using the Advanced Simulation and Computing Program (ASCI) Blue Mountain supercomputer (over 6000 Parallel central processing units [CPUs] from Silicon Graphics, Inc. [SGI]) at Los Alamos National Laboratory.30 In a later study, various attempts were made to increase the total number of voxel in the MCNP with limited success.31 Therefore, VIP-Man and other voxel phantoms from the Chinese VHP (see Chapter 11) that have original voxel size of 0.33 mm or less will continue to encounter the problem with the MCNP/X codes. Although the resolution for VIP-Man/MCNP/X is limited by the current computer technologies, VIP-Man/MCNP/X was the first voxel-based computational phantom constructed for neutron and proton dose calculations. All of our calculations were performed on PCs operated under Linux environment, which is a complete operating system that is similar but not identical to Unmultics Information and Computing System (UNIX). The Parallel Virtual Machine (PVM) in Linux has enabled us to use multiple CPUs for very time-consuming tasks. Compilers, such as g77, had to be used in EGS4 to accommodate the large integral format.29 Since both EGS4 and MCNP4B codes could transport photons and electrons, we were able to “benchmark” the modeling and Monte Carlo coding by making sure both codes yielded the same results for VIP-Man (at an identical voxel size of 4 mm × 4 mm × 4 mm resolution). A simple comparison was performed between organ doses calculated using different Monte Carlo codes for 1 MeV parallel photon beams under anterior–posterior (AP) irradiation. At the time this work was performed, the calculations took about 50 h for 10 million photons in MCNP and about 25 h for 25 million photons in EGS4 (on a 450 MHz Pentium II PC of 512 MB RAM). Both codes tracked electrons by different transport algorithms with carefully optimized electron step settings. Results indicate remarkable agreement within the statistical uncertainty between EGS4 and MCNP versions of the VIP-Man phantom.29 6.2.6 Extending 3D VIP-Man into 4D VIP-Man The sections above described the processes to develop the 3D, static VIP-Man computational phantom. For certain dosimetry applications, however, it is useful to consider organ


149

motions and changing human postures that are dependent on the fourth dimension. In external beam radiation treatment of lung cancer, the treatment plan must take into account the degree and pattern of patient breathing. For example, to reconstruct a nuclear accident, the dose assessment will depend on how a worker moves around the radiation source. A time-dependent, 4D VIP-Man computational phantom was quite a challenge but at the same time it would open the door for interesting applications. The following section describes a method to extend the 3D VIP-Man into 4D by adding respiration-caused organ motions for the purpose of external beam treatment planning for the lung lesion.32,33 A 4D respiration-simulating anatomical computational phantom is a series of computergenerated 3D phantoms whose shape, size, and location change according to specified respiratory motion patterns. 6.2.6.1 Conversion from Voxel Data to Surface Deﬁnition The VIP-Man whole-body phantom contains voxels at a size of 0.33 mm × 0.33 mm × 1 mm. Figure 6.3 shows the internal organs of the 3D VIP-Man computational phantom adopted for this study. Organs, each originally defined as a group of voxels, were converted into polygon surfaces of anatomical features. Polygon models were then translated into nonuniform rational B-spline (NURBS) surfaces.34 Organ surfaces defined using NURBS could be easily deformed by changing control points as first demonstrated by Segars et al.35–37 and later by Xu and Shi32 and Zhang et al.33 The NURBS approach was also used to manually adjust shape/size for a series of pediatric computational phantoms.38 For this study, a commercial program called Rhinoceros (McNeel North America, Seattle, WA) was used to create, edit, analyze, and translate NURBS curves, surfaces, and solids. The VIP-Man organ files were changed into a format readable by Rhinoceros using a free software program called vtkEditor (http://www.esat.kuleuven.ac.be/∼vtkedit). Once the organs were imported into Rhinoceros, organ contours were regenerated and lofted into 3D NURBS surfaces. At that time, we performed this process on the lungs, heart, skin, rib cage, spine, kidneys, stomach, spleen, and liver. After generating the NURBS files, control points for each organ were obtained by exporting the NURBS organ file to a text file. These control points contain basic anatomical features of the original 3D VIP-Man computational phantom. 6.2.6.2 Deformation of the Organ Surfaces To simulate time-dependent deformation caused by the respiratory motion, the control points for each of the interlinked organs were transformed using a rigid motion defined in Equation 6.3 C new = S × R × C old + T where Cold is a 3 × N matrix defining the original control points Cnew is the 3 × N matrix of the translated points N is the number of one organ’s control points S is a scalar matrix R is a 3 × 3 matrix which defines rotation T is a 3 × N matrix which defines translation

(6.3)

150


By using parameters that are functions of time, t, in matrices S, R, and T, the control points are extended from the original 3D space into 4D. In this study, the respiratory motion patterns were based on the clinical data described previously by Segars et al.36 The rotation angle of the rib cage around its intersection with the spine was defined as a piecewise linear function with respect to time, t, as shown in Equation 6.4. Δθ × t ⎧ ⎪ θ = ⎨2 ⎪⎩ 3 × Δθ × (5 − t )

0≤t < 2 2≤t < 5

(6.4)

where we selected the angular step, Δθ, to be 2.5° and the entire respiration cycle to last 5 s. The first 2 s were used for the inspiration and the last 3 s were for the expiration. The positions of the sternum and skin were defined according to the positions of the rib tips. The lungs were modeled by referring to the position of the fifth rib in the transverse direction and the top of the liver in the longitudinal direction. For other organs, such as liver, stomach, heart, kidneys, and a lesion in the left lung, we first created a normalized motion curve as shown in Equation 6.5. ⎧ 1⎛ ⎛ π ⎞⎞ 1 − cos ⎜ × t ⎟ ⎟ ⎪ ⎜ ⎝ 2 ⎠⎠ ⎪ 2⎝ x=⎨ ⎪ 1 ⎛ 1 − cos ⎛ π × (5 − t )⎞ ⎞ ⎜⎝ ⎟⎠ ⎟ ⎪ 2 ⎜⎝ ⎠ 3 ⎩

0≤t < 2 (6.5) 2≤t < 5

where x is the normalized motion distance t is the time The magnitude of such organ motions was then controlled by multiplying different amplifying factors in the x, y, and z directions, respectively. We used MATLAB® (The MathWorks, Inc., Natick, MA), version 6.5, to develop a software program to automate the calculations and a NURBS toolbox (http://www.aria.uklinux.net/nurbs.php3) for MATLAB was used for the final integration of the 4D VIP-Man computational phantom. The reconstructed organ and body surfaces using the NURBS are shown in Figure 6.4, which displays the frontal and side views of the skin, lungs, heart, rib cage, spine, and liver.33 6.2.6.3 Revoxelization of the 4D Computational Phantom for Each Respiratory Phase For the calculation of radiation dose distributions under each of the respiratory phases, the 4D phantom was treated as a combination of a series of 3D phantoms representing the anatomy at different points of the respiratory cycle. Once the 4D VIP-Man phantom was constructed, each of the 3D phantoms was re-created from the 4D phantom for Monte Carlo calculations. This process was accomplished by converting the NURBS surfaces back to voxels for each given respiratory phase. The control points for an organ were saved in a 3D matrix and the contours were calculated by specifying the cutting plane coordinate in MATLAB. A total of eight respiration phases, each having 70 2D-slices, were sampled and revoxelized to represent the entire respiratory cycle in eight phases: peak exhale,


(a)

151

(b)

FIGURE 6.4 (a) Front view and (b) side view of the reconstructed NURBS surfaces in phase 1 (middle inhale phase).

early inhale, middle inhale, late inhale, peak inhale, early exhale, middle exhale, and late exhale.33 The resolution for each of the revoxelized computational phantoms is 2.1 mm × 1.2 mm × 6.0 mm. Finer resolutions can be obtained if the 2D-slice size and cutting plane number are increased.

6.3 Comparison between VIP-Man and Other Computational Phantoms VIP-Man is first compared with the ICRP Reference Man21 in terms of organ mass. As listed in Table 6.2, VIP-Man is heavier, taller, with much more fat and muscle, and has heavier GI tract tissues compared to the Reference Man. For other internal organs, the mass differences between VIP-Man and the Reference Man are less significant and, in particular, the volume of the lungs is slightly smaller as expected from the cadaver images. Comparison of detailed anatomical information in VIP-Man with stylized mathematical computational phantoms was one of the motivations of this study. It is very obvious that the stylized mathematical computational phantoms have very simplified shapes for the GI tract and stomach. The relative locations of the stomach, large intestine, and thyroid are also quite different from those in the VIP-Man. VIP-Man also contains many tissues such as RBM, GI tract mucosa, teeth, gray/white matter of cerebrum, optic chiasma, and vestibulocochlear nerve that were previously not well defined (or not available) in stylized mathematical computational phantoms. Compared with other tomographic computational phantoms developed prior to 2006, VIP-Man has a smaller voxel size which allows some of the small organs to be more accurately defined. Our experiences with the VIP-Man phantom have made us recognize that, because all tomographic phantoms are developed from medical images taken from

152


real individuals, they are necessarily different in anatomy. As such, a composite of these computational phantoms will have to be used to represent a population of workers.

6.4 Applications of the VIP-Man Computational Phantom Since 2000, the group at RPI has used the VIP-Man phantom for a variety of radiological studies in health physics, diagnostic imaging, and radiotherapy. These studies are briefly summarized below. 6.4.1 Health Physics Health physics dosimetry typically involves organ dose and effective dose quantities for external and internal sources under standard irradiation conditions. The VIP-Man phantom offered an opportunity to perform quantitative comparison of these quantities with those derived previously from other phantoms. These studies were not meant to establish VIP-Man as a standard reference phantom. Instead, we were interested in demonstrating how similar or different the dosimetry data from VIP-Man, a large-sized individual of 40 years old, would be with respect to the ICRP Reference Man. Details of these studies have been reported for different radiation types including photons,29,39 electrons,40,41 neutrons,25,42 and protons.43 Here we provide brief descriptions. 6.4.1.1 External Photon Dosimetry Using the VIP-Man phantom, Chao et al. reported a new set of conversion coefficients from kerma free-in-air to absorbed dose and kerma free-in-air to “effective VIP-Man dose” for external monoenergetic photon beams from 10 keV to 10 MeV.30 (A correction for the reported data was subsequently published.44) This study noted that kerma approximation, which assumes secondary electrons from photon interactions to deposit their energies at an interaction site, could lead to potential uncertainty for high-energy photons incident on shallow tissues (such as breast, skin, eye lenses, or gonads). Photon sources considered in Chao et al. were monoenergetic parallel beams with energies from 10 keV to 10 MeV.30 The irradiation geometries included AP, posteroanterior (PA), left lateral (LLAT), right lateral (RLAT), rotational (ROT), and isotropic (ISO). For all sources, the photons were generated on a fixed plane as broad parallel beams. Results from this study were tabulated and compared with those obtained from the stylized computational phantoms, ADAM and EVA involving a GSF Monte Carlo code. The “effective VIP-Man doses” differed from the previously reported data by 10%–50% for photons between 100 keV and 10 MeV. The discrepancies were greater for lower energies and for individual organ doses. The study concluded that the size of computational phantom, kerma approximation, and the anatomical difference were three main factors in causing dosimetric discrepancies. These comparisons also suggested possible ways to improve the stylized phantoms. For example, the stomach is situated too close to the left side of the body compared to VIP-Man. The liver is situated too close to the right side of the body compared to VIP-Man, and the esophagus is too close to the back. VIP-Man has the smallest voxel size among existing computational phantoms for many years until the Chinese VHP generated several cadaver image sets that are 0.2 mm in thickness using specialized milling machines.45,46


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Although VIP-Man has its own limitations, it is the representation of a real person and contains many tissues that were previously not well defi ned (or not available) in other computational phantoms. 6.4.1.2 External Electron Dosimetry Compared to photon dosimetry, electron dosimetry is complicated because electrons undergo a large number of interactions when passing through materials. For radiation protection purposes, tables of conversion coefficients are needed to estimate the dose from the measurable field quantities, such as the particle fluence. Only a limited number of researchers have published organ dose conversion coefficients for electrons calculated from mathematical computational phantoms. The approach of using standard and simplified computational phantoms based on the Reference Man works well for penetrating radiation, such as photons and neutrons. Indeed, comparison of photon doses derived from different phantoms have showed impressive agreement.42 However, the uncertainty caused by the anatomical computational phantoms for electrons can be potentially very high because slight differences between body computational phantoms can cause dramatic dosimetric deviations for less-penetrating radiation. Chao et al.40 presented the results of organ dose calculations on the VIP-Man which is a taller and heavier computational phantom than the Reference Man. Organ doses for 10 MeV AP electrons are compared between results from VIP-Man using EGS4-VLSI Monte Carlo code, ADAM using MCNP4,47 hermaphrodite phantom using FLUKA,48 and MIRD-5 phantom using EGS4.49 For shallower organs such as skin and thyroid, the deviation between these results is about 80%. For deeper organs such as lung, stomach, bladders, liver, and esophagus, significant differences more than an order of two can be observed. The differences between VIPMan and the MIRD-based stylized phantoms suggest possible range of errors caused by using the MIRD-based stylized computational phantoms on a specific individual as large in body size as the VIP-Man. The study noted that Schultz and Zoetelief47 used thymus dose to substitute esophagus dose, which seemed to be a poor assumption. The lung dose reported by Ferrari et al.48 are much lower than those obtained from other phantoms because of the extra shielding by female breasts, which were inserted into a male hermaphrodite computational phantom used by the authors. This study suggested that a careful consideration is necessary before using hermaphrodite mathematical phantom for electron dosimetry. These comparisons suggest again that, at least for electron dosimetry, a single standard body computational phantom does a very poor job in representing individuals of diverse anatomy. The study further concluded that it was clear that a large number of voxel phantoms would need to be investigated before the degree of dose variation was understood. 6.4.1.3 External Neutron Dosimetry Using the VIP-Man phantom, Bozkurt and his coauthors reported a new set of fluenceto-absorbed dose and fluence-to-effective dose conversion coefficients calculated for both low-energy (10−9 to 20 MeV) and high-energy (20–10,000 MeV) neutrons.25,42 Organ dose calculations were performed using the Monte Carlo code MCNPX for 20 monoenergetic neutron beams under six different irradiation geometries: AP, PA, RLAT, LLAT, ROT, and ISO. The absorbed dose for 24 major organs and effective dose results based on the realistic VIP-Man were presented and compared with those based on the simplified MIRD-based phantoms reported in the literature. For high-energy neutrons, although VIP-Man has

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detailed and realistic anatomical representations, neutron effective dose results did not vary significantly from the values obtained from much simpler MIRD-5 stylized phantoms for this range of energies. However, individual organ doses from VIP-Man were considerably different, suggesting that the existing neutron dose data need to be reevaluated at the organ levels. For low-energy neutrons, results in this study yielded up to 40% larger values for the effective dose, and for many organ doses, thus suggesting that the results reported in the past may not be conservative. The authors concluded that several factors might have contributed to the discrepancies: the differences in anatomical computational phantoms which cause around 10% difference in effective dose because VIP-Man is heavier and taller, and how the Monte Carlo codes treat the transport of high-energy particles, including the use of evaluated data and theoretical computational phantoms. 6.4.1.4 External Proton Dosimetry Bozkurt and Xu43 applied the VIP-Man phantom to calculate fluence-to-absorbed dose and fluence-to-effective dose conversion coefficients under high-energy proton environment. These organ dose calculations were performed using the Monte Carlo code MCNPX under six different irradiation geometries: AP, PA, LLAT, RLAT, ROT, and ISO and for 10 monoenergetic proton beams between 20 and 10,000 MeV. The absorbed dose results were presented for 24 major organs of VIP-Man and the calculated data were compared with those based on mathematical phantoms reported in the literature. Some discrepancies in organ dose and effective dose were observed which were within 40% due to the use of different transport computational phantoms employed by different Monte Carlo codes. 6.4.1.5 Internal Electron Dosimetry Based on the VIP-Man phantom, Chao and Xu calculated, for the first time, complete sets of specific absorbed fractions (SAF) for internal electron emitters.41 Electron emitters with energies from 100 keV to 4 MeV were studied and the results provided a set of complete dosimetry data for protection against internal electron exposures. This was also the first time to report internal electron data for walled organs such as esophagus, LLI, stomach, and ULI. Although electrons are considered as weakly penetrating radiation and researchers have usually ignored the dose to organs other than the source organ, results from this study shows that doses to neighbor organs and nearby organs can be too great to be neglected. In examining the effect of the partial penetrating of electrons in internal dosimetry, the authors classified the target organs into four categories according to the levels of absorbed dose. (1) The “highest target” for the organ receiving the highest dose which was usually the source organ itself. From the radiotherapeutic point of view, dose about a few tens of Sv is usually prescribed to the highest target. (2) On the other hand, compared to dose limits recommended by ICRP 60,22 a few tens of mSv of organ dose is critical to protect normal organs against radiation. Therefore, the organ receiving a dose less than 0.1% of the dose in the highest target was classified as the “irrelevant organ.” (3) If the SAF in the target organ was larger than 1% of SAF in the highest target, it was classified as the “neighbor target.” (4) Finally, the “nearby targets” were those receiving doses between 0.1% and 1% in the highest target. Chao and Xu41 also tabulated the neighbor targets and the nearby targets for 26 source organs emitting electrons from 100 keV to 2 MeV. This study provided convincing evidence that internal electrons do affect organs beyond the source organ.


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6.4.1.6 Internal Photon Dosimetry for the GI Tract In this study, the VIP-Man phantom was used by Chao and Xu to calculate SAFs for the GI tract.39 SAFs for sources in GI tract have been previously studied based on stylized phantoms. However, there are three shortcomings in these previous calculations: (1) the definition of GI tract in the stylized phantom, including those by Cristy and Eckerman17 and Poston et al.27 were anatomically simplified. (2) These stylized phantoms, including Smith et al.50 did not model the mucosal layer, resulting in a potential uncertainty in the assessed risk to the stomach. (3) The secondary electrons were often not considered in these Monte Carlo calculations because the range of secondary electrons was assumed to be too short. In this study using the VIP-Man phantom, the authors compared SAFs for the stomach wall from VIP-Man to those previously published by Cristy and Eckerman17 for photon sources in the stomach content. SAFs of stomach wall in the Cristy and Eckerman phantom is practically the same as those from VIP-Man, peaking at about 20 keV and leveling off at 100 keV and above. These results suggest that, for photon sources in the stomach content, the dose to the stomach wall is not dependent on the phantom (mass or anatomy). This is perhaps easy to understand for penetrating photon sources in the centrally located stomach content. On the other hand, the data line for the stomach mucosa as the target in VIP-Man is clearly above the data line for the stomach wall and the trend decreases as the energy increases from 10 to 100 keV. This suggests that, even for penetrating photons, the risk to the stomach may have been significantly underestimated (by about 60% at 20 keV) if the stomach wall is the only target under consideration, as was often done in the past. Results also show that the dose to the mucosa is much higher than dose to the wall for all the organs of the GI tract causing the possibility of underestimating the risk. This study clearly demonstrated the advantage of the VIP-Man phantom whose small voxel size allowed the dosimetry to be performed on small tissues structures, such as the mucosal layer in the GI tract. 6.4.1.7 RBM Dosimetry for External Irradiations In his doctoral research at RPI, Dr. Caracappa used two sets of Visible Human images for the same cadaver to develop an algorithm for assessing the dose to the RBM from external photon and electron sources.51 Dosimetry phantoms commonly in use assume that the marrow space throughout the body consists of a uniform mixture of active and inactive bone marrow. In reality, however, bone sites in different parts of the body are known to consist of varying combinations of active and inactive bone marrow. A Monte Carlo phantom was constructed in this study from the CT images of the VHP, and compared to the VIP-Man phantom, which was derived from color photographs of the same individual. These two data sets for the same individual offered interesting information that was not available elsewhere. RBM doses were calculated for the CT phantom using the uniform mixture assumptions and the cellularity factors adopted by ICRP. The goal was to test the previous assumptions and evaluate the accuracy of the computed doses in Monte Carlo simulations. Based on the newly developed algorithms, three dosimetry applications were investigated and tested. Broad beam photon irradiation in occupational exposure results in similar doses for high energies, but differences as great as 40% for low energies. In nonuniform photon exposures from selected CT examinations, the differences in the computed marrow dose are significant, 25% and 33% for the two cases modeled. An electron total body irradiation procedure for treating skin cancer is also studied, with a 39% difference in RBM dose between the existing method and the proposed revised method. These

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results demonstrate the advantage of the new algorithms by accounting for marrow cellularity and distribution various bone sites in the anatomical and dosimetry computational phantoms. 6.4.2 Applications in Radiological Imaging 6.4.2.1 Organ Doses from SPECT and PET Brain Imaging To estimate internal dosimetry for brain imaging, a head and brain portion of the VIPMan was used to implement into the Monte Carlo code, EGS4-VLSI.52 Fifteen subregions were modeled including caudate nucleus, cerebellum, cerebral cortex, cerebral white matter, corpus callosum, eyes, lateral ventricles, lenses, lentiform nucleus, optic chiasma, optic nerve, pons and middle cerebellar peduncle, skull CSF, thalamus, and thyroid. This is the first time that several small structures are modeled for nuclear medicine dosimetry purposes. S-values were calculated for the most important sources and targets encountered in single photon emission computed tomography (SPECT) and positron emission tomography (PET) brain imaging. These results were then compared to those from the stylized head/brain phantom recommended by the MIRD.53 Although heavier individuals (such as VIP-Man) will usually receive lower radiation doses, however, the stylized head/brain phantom underestimates the S-values by 15% on average for a patient similar to the VIPMan phantom. More voxel head/brain phantoms are needed in order to compare various brain sizes and anatomical variations. Before such an intercomparison is performed, the results presented in this chapter are useful for patients who are similar to VIP-Man in body size or weight. 6.4.2.2 Organ Doses from X-Ray Radiographs VIP-Man was used by Mark Winslow, a PhD student at RPI, in collaboration with Dr. Walter Huda from University of Syracuse, to calculate values of energy imparted (ε) and effective dose (E) for monoenergetic photons (30–150 keV) in radiographic examinations. Energy deposition in the organs and tissues of the human phantom were obtained using Monte Carlo simulations. Ratios of effective doses to dose imparted (E/ε) were obtained for three common projections: AP, PA, and LAT of head, cervical spine, chest, and abdomen, respectively. For head radiographs, all three projections yielded similar results. At 30 keV, E/ε was 1.6 mSv J−1, which increased to 7 mSv J−1 for 150 keV photons. The AP cervical spine was the only projection investigated where the E/ε decreased with increasing photon energy. Above 70 keV, cervical spine E/ε showed little energy dependence and ranged between 8.5 mSv J−1 for PA projections and 17 mSv J−1 for AP projections. The values of E/ε for AP chest examinations showed very little variation with photon energy, and had E/ε of 23 mSv J−1. Values of E/ε for PA and LAT chest projections were substantially lower than the AP projections and increased with increasing photon energy. For abdominal radiographs, differences between PA and LAT projections were very small. All abdomen projections showed an increase in the E/ε ratio with increasing photon energy, and reached a maximum value of 13.5 mSv J−1 for AP projections, and 9.5 mSv J−1 for PA/lateral projections. These monoenergetic E/ε values can generate values of E/ε for any x-ray spectrum, and can be used to convert values of energy imparted into effective dose for patients undergoing common head and body radiological examinations.54


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6.4.2.3 Image Quality Optimization in Radiograph In his doctoral research, Mark Winslow, in collaboration with Professor Birsen Yazici of RPI, studied image optimization by examining the organ doses and image quality by analyzing approximately 2000 simulated chest x-ray images for the VIP-Man using the receiver operator characteristic/area under the ROC curve (ROC/AUC) analysis55,56 (see Chapter 23 for more information on that particular application). 6.4.2.4 Organ Doses from Interventional Cardiological Examinations After graduating from RPI, Dr. Bozkurt and his colleagues57 used the VIP-Man phantom to simulate both the patient and the physician during an interventional cardiological examination procedure. The patient was lying on the operation table while the physician was standing 15 cm from the patient. Organ equivalent doses and effective doses for both individuals were calculated simultaneously involving seven different x-ray spectra (between 60 and 120 kVp). The calculations were performed using the Monte Carlo code MCNP. The patient’s heart was irradiated by x-rays generated by a point source of a conical distribution. Five major projections typical to a coronary angiography examination were investigated. The authors reported the mean effective doses from 0.092 to 0.163 mSv/(Gy cm 2) for the patient and from 0.027 to 1.153 μSv/(Gy cm 2) for the shielded physician. The effective doses for the patient determined in the study were found to be lower than those reported in the literature partially because the VIP-Man phantom ignores possible higher ovarian dose contribution to the gonadal equivalent dose. On the other hand, the effective doses determined for the physician agreed reasonably well with the literature data. 6.4.3 Applications in Radiotherapy 6.4.3.1 External Beam Selection in Prostate Radiation Treatment The abdominal portion of the VIP-Man phantom was used in the doctoral research by Brian Wang at RPI to develop and demonstrate an Adjoint Monte Carlo (AMC) method for optimizing the external beam directions in the so-called 3D conformal radiation treatment of the prostate cancer.31 The AMC method had been widely used in nuclear reactor physics research but was never demonstrated for treatment planning in realistic 3D patient anatomy. With the VIP-Man phantom which was already implemented in the MCNP code that has multigroup adjoint cross sections, it was possible to test the theory in clinically relevant scenarios. This study was in collaboration with Dr. Moshe Goldstein, a nuclear engineering from Israel who first proposed the method during a sabbatical at ORNL, and Dr. Narayan Sahoo who was a clinical therapeutic physicist at Albany Medical Center. In this application, the adjoint fluxes for the prostate (Planning Target Volume [PTV]) and the rectum and bladder (organs at risk [OARs]) in the VIP-Man phantom were calculated on a spherical surface of approximately 1 m radius, centered at the center of gravity of PTV.31 An “importance” ratio, defined as the PTV dose divided by the weighted OAR doses, was calculated for each of the available beamlets to select the best beam angles. Finally, the doses in PTV and OAR were calculated using the forward Monte Carlo method. The Pinnacle treatment planning system was used to generate dose volume histograms (DVHs) for the 3D plan with beam angles obtained from the AMC method and a standard six-field conformal radiation therapy plan. Results showed that the DVHs for the prostate from these

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two plans are very similar, suggesting that both methods can deliver necessary dose to the PTV. However, DVHs for bladder and rectum were found to be superior for the AMC method. This study demonstrated the feasibility of the AMC method in optimizing external beam directions based on anatomical information in a 3D and realistic patient anatomy. The study also identified issues to be further addressed before this method could become clinically useful.31 6.4.3.2 Organ Doses from Proton Radiation Treatments In a separate study, doctoral student Brian Wang, worked with Drs. Harald Paganetti and Hongyu Jiang of MGH in Boston to adopt the VIP-Man computational phantom to assess organ doses (including the RBM dose) from secondary radiation in proton radiation treatment.31 More information about this application for organ dose assessment from secondary radiation in proton treatment can be found in Chapter 25. 6.4.3.3 Dosimetry for Respiration Management in IGRT After graduating from RPI, Dr. Chengyu Shi worked with RPI doctoral student Mr. Juying Zhang, to apply the respiration-simulating 4D VIP-Man phantom for image-guide radiotherapy (IGRT) of lung cancer.33 Detail information can be found in Chapter 26. 6.4.3.4 Organ Doses from Cone-Beam CT Imaging for IGRT Recently, RPI doctoral students Mr. Jianwei Gu and Mr. Bryan Bednarz, used the VIPMan phantom to calculate organ doses using a recent procedure associated onboard imaging to localize the patient in IGRT.58 As the IGRT becomes widely and frequently practiced in clinics, it is becoming clear that such an “imaging dose” to the patient, in addition to scattered radiation dose to the healthy organs, is no longer at a level that can be entirely excluded from the treatment planning. The VIP-Man phantom and the MCNPX code were used in this study to simulate two imaging procedures: kV and MV cone beam CTs (CBCTs). The results indicate that thyroid receives the highest dose in head and neck scans for both kV and MV CBCTs, and the bladder receives the highest dose in prostate scan for both kV and MV CBCTs. The effective doses for H&N scan and for prostate scan are at the same level in both kV and MV CBCTs. This study provided a method to compute organ doses and effective dose that are useful in treatment planning and risk assessment.

6.5 Summary VIP-Man is a voxel-based computational phantom constructed in late 1990s from segmented color photographic images of the adult male from the VHP. The motivation of this development was to make available an anatomical realistic phantom that had better voxel resolution and more anatomical information than those developed earlier. Since the segmentation was based on color cadaver images of extra fine resolution, for several years, VIP-Man represented the world’s finest and most complete human anatomical computational phantom, containing small tissues, such as skin, GI tract mucosa, eye lenses,


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and RBM. The phantom was adopted into the state-of-the-art Monte Carlo codes, EGS4, MCNP, and MCNPX (and also GEANT4) for radiation transport studies involving nearly all radiation types of interest in radiological science such as photons, electrons, neutron, and protons. This allowed a systematical investigation on the potential errors the radiation protection dosimetry caused by a person-specific phantom. The work by Xu et al.7 contained a lot of detail that allowed readers to follow the procedure to construct the imagebased computational phantoms, especially from cadaver images later available in Korea and China. The VIP-Man phantom was used in many projects, such summarized in this chapter, to evaluate and compare a large number of important dosimetric quantities for external and internal sources under standard radiation protection dosimetry irradiation conditions, as well as various applications to imaging and radiotherapy. For more information about projects based on the VIP-Man phantom, please visit http://www.rpi.edu/dept/ radsafe/public_html/. To date, the VIP-Man phantom has been shared with more than 40 users internationally. The work by Xu et al. reviewed most of the radiation protection dosimetry data derived from the VIP-Man phantom.39 In operational personnel dosimetry, uncertainties in dose measurements of up to 50% are common as a result of dosimeter energy calibration, positioning, environmental noise, etc. It seems that a 30% improvement in the estimate of effective dose from external photon exposures, as reported for the VIP-Man and other phantoms, suggest that the decade-long effort to develop a new class of voxel phantoms did not directly improve upon the radiation protection dosimetry parameters. For electrons and protons, the dose differences caused by the anatomy are too great to rely on a single voxel phantom, being either stylized or tomographic. The authors further noted that the use of the newly developed standard voxel male and female phantoms might not lead to any real improvement in dose estimate for a population.38 Instead, the authors suggested that a library of person-specific voxel phantoms would have to be considered in the future. The authors then went on to predict that, although the tomographic images indeed provided realistic anatomical information of human body, the voxelized geometry was not technically suitable for developing dosimetry standards due to the difficulty in adjusting the voxel data. In contrast, advanced surface modeling tools, such as nonuniform rational B-splines or computer-aided design, were flexible in designing population-adjustable and time-dependent phantoms. The authors stated that if the same amount of resources were invested in refining the existing stylized phantoms, one could have achieved the same degree of dosimetric precision. The significance of the voxel phantom development was that it opened the door for person-specific dosimetry—a philosophy that the ICRP has yet to fully embrace. Using the versatile VIP-Man phantom and associated tools, exiting projects were carried at RPI one after the other in the past 10 years. In retrospect, the VIP-Man phantom brought the opportunity for us at RPI to aggressively experiment. Although the VIP-Man was originally developed for radiation protection dosimetry studies, it was recognized early on that, by shifting from stylized phantoms to voxel-based data, it became possible to also perform patient-specific radiation dosimetry studies in the fields of diagnostic imaging and radiation treatment where more precise dose assessment was required. It is interesting to note that the VIP-Man phantom was also successfully adopted for a few applications in surgical simulations by coupling the data with novel biomechanical information.59 As predicted by Xu et al.,7 by combining the fine anatomical information in the VIP-Man with physical properties that are radiological, electrical, thermal, chemical, mechanical, or biological, the VIP-Man phantom would someday become a useful tool for multidisciplinary applications.

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Acknowledgments The development of VIP-Man computational phantom was crucial in our earlier research efforts at RPI. The authors are grateful to the National Science Foundation/Biomedical Engineering Program for a Faculty Early Career Development Award (#BES-9875532), to the National Library of Medicine and Dr. Victor Spitzer for the Visible Human images, to Dr. Keith Eckerman at Oak Ridge National Laboratory for his insightful discussions during the planning and execution of the project. The VIP-Man phantom was subsequently used and refined by other students at RPI and we would like to acknowledge their works that went into this chapter: Dr. Mark Winslow who, in collaboration with Dr. Walter Huda and Dr. Birsen Yazici, studied image quality optimization for radiography; Dr. Brian Wang who, in collaboration with Dr. Moshe Goldstein and Dr. Narayan Sahoo, studied adjoint Monte Carlo -based external beam optimization, and then, in collaboration with Dr. Harald Paganetti and Dr. Hongyu Jiang, on proton treatment; Dr. Peter Caracappa who developed new algorithms for red bone marrow dosimetry, and finally, Mr. Jianwei Gu and Dr. Bryan Bednarz, in collaboration with Dr. Steve Jiang, studied organ doses from image-guided procedures involving cone beam CTs.

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14. Eckerman, K.F. and Stabin, M.G. Electron absorbed fractions and dose conversion factors for marrow and bone by skeletal regions, Health Phys, 78, 199, 2000. 15. ICRP. Basic Anatomical and Physiological Data for Use in Radiological Protection: The Skeleton, ICRP Publication 70, Pergamon Press, Oxford, 1995. 16. Snyder, W.S., Ford, M.R., and Warner, G.G. Estimates of specific absorbed fractions for photon sources uniformly distributed in various organs of a heterogeneous phantom, MIRD Pamphlet No. 5, revised, Society of Nuclear Medicine, New York, 1978. 17. Cristy, M. and Eckerman, K.F. Specific absorbed fractions of energy at various ages from internal photon sources, ORNL/TM-8381/Volumes I–VII, Oak Ridge National Laboratory, Oak Ridge, TN, 1987. 18. ICRP. Limits for Intake of Radionuclides by Workers, ICRP Publication 30, Pergamon Press, Oxford, 1979. 19. Pizzarello, D.J. and Witcofski, R.L. Basic Radiation Biology, Lea and Febiger, Philadelphia, PA, 1967. 20. Poston, J.W. et al. A revised model for the calculation of absorbed energy in the gastrointestinal tract, Health Phys, 71, 307, 1996. 21. ICRP. Report of the Task Group on Reference Man, ICRP Publication 23, Pergamon Press, Oxford, 1975. 22. ICRP. 1990 Recommendations of the International Commission on Radiological Protection, ICRP Publication 60, Pergamon Press, Oxford, 1991. 23. Wells, J. Problems associated with localised skin exposures, Br J Radiol Suppl, 19, 146, 1986. 24. ICRP. Human Respiratory Tract Model for Radiological Protection: A Report of a Task Group of the International Commission on Radiological Protection, ICRP Publication 66, Pergamon Press, Oxford, 1994. 25. Bozkurt, A., Chao, T.C., and Xu, X.G. Fluence-to-dose conversion coefficients from monoenergetic neutrons below 20 MeV based on the VIP-man anatomical model, Phys Med Biol, 45, 3059, 2000. 26. ICRU. Tissue substitutes in radiation dosimetry and measurement, ICRU Report 44, Bethesda, MD, 1989. 27. Dimbylow, P.J. The development of realistic voxel phantoms for electromagnetic field dosimetry, Proceedings of the Workshop on Voxel Phantom Development, Chilton, U.K., 1996. 28. Schroeder, W., Martin, K.W., and Lorensen, W. The Visualization Toolkit: An Object-Oriented Approach to 3-D Graphics, Prentice Hall PTR, Upper Saddle River, NJ, 1996. 29. Chao, T.C., Bozkurt, A., and Xu, X.G. Conversion coefficients based on the VIP-Man anatomical model and EGS4-VLSI code for external monoenergetic photons from 10 keV to 10 MeV, Health Phys, 81, 163, 2001. 30. McKinney, G.W. Voxelized Model for MCNP, personal communication, 1999. 31. Yang, L. et al. A simulation for effects of RF electromagnetic radiation from a mobile handset on eyes model using the finite-difference time-domain method, Conf Proc IEEE Eng Med Biol Soc, 2007, 5294, 2007. 32. Xu, X.G. and Shi, C.Y. Preliminary development of a 4D anatomical model for Monte Carlo simulations, Monte Carlo 2005 Topical Meeting: The Monte Carlo Method: Versatility Unbounded in a Dynamic Computing World, Chattanooga, TN, 2005. 33. Zhang, J. et al. Development of a geometry-based respiratory motion-simulating patient model for radiation treatment dosimetry, J Appl Clin Med Phys, 9, 2700, 2008. 34. Piegl, L. On NURBS—A survey, IEEE Comput Graphics Appl, 11, 55, 1991. 35. Garrity, J.M. et al. Development of a dynamic model for the lung lobes and airway tree in the NCAT phantom, IEEE Trans Nucl Sci, 50, 378, 2003. 36. Segars, W.P. Development and Application of the New Dynamic NURBS-Based Cardiac-Torso (NCAT) Phantom, PhD thesis, University of North Carolina at Chapel Hill, Chapel Hill, NC, 2001. 37. Segars, W.P. et al. Development of a 4-D digital mouse phantom for molecular imaging research, Mol Imaging Biol, 6, 149, 2004.

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38. Lee, C. et al. A series of 4D pediatric hybrid phantoms developed from the UF series B tomographic phantoms, Med Phys, 33, 2006, 2006. 39. Xu, X.G., Chao, T.C., and Bozkurt, A. Comparison of effective doses from various monoenergetic particles based on the stylised and the VIP-Man tomographic models, Radiat Prot Dosimetry, 115, 530, 2005. 40. Chao, T.C., Bozkurt, A., and Xu, X.G. Organ dose conversion coefficients for 0.1–10 MeV electrons calculated for the VIP-Man tomographic model, Health Phys, 81, 203, 2001. 41. Chao, T.C. and Xu, X.G. Specific absorbed fractions from the image-based VIP-Man body model and EGS4-VLSI Monte Carlo code: Internal electron emitters, Phys Med Biol, 46, 901, 2001. 42. Bozkurt, A., Chao, T.C., and Xu, X.G. Fluence-to-dose conversion coefficients based on the VIPMan anatomical model and MCNPX code for monoenergetic neutrons above 20 MeV, Health Phys, 81, 184, 2001. 43. Bozkurt, A. and Xu, X.G. Fluence-to-dose conversion coefficients for monoenergetic proton beams based on the VIP-Man anatomical model, Radiat Prot Dosimetry, 112, 219, 2004. 44. Chao, T.C., Bozkurt, A., and Xu, X.G. Correction to conversion coefficients based on the VIPMan anatomical model and EGS4-VLSI code for external monoenergetic photons from 10 keV to 10 MeV, Health Phys, 84, 390, 2003. 45. Zhang, B.Q. et al. CNMAN: A Chinese adult male voxel phantom constructed from color photographs of a visible anatomical data set, Radiat Prot Dosimetry, 124, 130, 2007. 46. Zhang, G.Z., Liu, Q., and Luo, Q.M. Monte Carlo simulations for external neutron dosimetry based on the visible Chinese human phantom, Phys Med Biol, 52, 7367, 2007. 47. Schultz, F.W. and Zoetelief, J. Organ and effective doses in the male phantom ADAM exposed in AP direction to broad unidirectional beams of monoenergetic electrons, Health Phys, 70, 498, 1996. 48. Ferrari, A., Pelliccioni, M., and Pillon, M. Fluence to effective dose and effective dose equivalent conversion coefficients for electrons from 5 MeV to 10 GeV, Radiat Prot Dosimetry, 69, 97, 1997. 49. Katagiri, M. et al. Effective doses and organ doses per unit fluence calculated for monoenergetic 0.1 Mev to 100 MeV electrons by the MIRD-5 phantom, Radiat Prot Dosimetry, 90, 393, 2000. 50. Smith, T., Petoussi-Henss, N., and Zankl, M. Comparison of internal radiation doses estimated by MIRD and voxel techniques for a “family” of phantoms, Eur J Nucl Med, 27, 1387, 2000. 51. Caracappa, P.F., Chao, T.C., and Xu, X.G. A study of predicted bone marrow dose from external radiation exposures using two sets of image data for the same individual. Health Phys, 96, 661, 2009. 52. Chao, T.C. and Xu, X.G. S-values calculated from a tomographic head/brain model for brain imaging, Phys Med Biol, 49, 4971, 2004. 53. Bouchet, L.G. et al. A revised dosimetric model of the adult head and brain, J Nucl Med, 37, 1226, 1996. 54. Winslow, M. et al. Use of the VIP-Man model to calculate energy imparted and effective dose for x-ray examinations, Health Phys, 86, 174, 2004. 55. Son, I.Y. et al. X-ray imaging optimization using virtual phantoms and computerized observer modelling, Phys Med Biol, 51, 4289, 2006. 56. Winslow, M., Xu, X.G., and Yazici, B. Development of a simulator for radiographic image optimization, Comput Methods Programs Biomed, 78, 179, 2005. 57. Bozkurt, A. and Bor, D. Simultaneous determination of equivalent dose to organs and tissues of the patient and of the physician in interventional radiology using the Monte Carlo method, Phys Med Biol, 52, 317, 2007. 58. Gu, J. et al. Assessment of patient organ doses and effective doses using the VIP-Man adult male phantom for selected cone-beam CT imaging procedures during image guided radiation therapy, Radiat Prot Dosimetry, ncn200, 2008. 59. Jin, W. et al. Improving the visual realism of virtual surgery, Proceedings of Medicine Meets Virtual Reality 13, Long Beach, CA, 2005.

7 The FAX06 and the MAX06 Computational Voxel Phantoms Richard Kramer, Helen Jamil Khoury, José Wilson Vieira, Vanildo Júnior de Melo Lima, Eduardo César de Miranda Loureiro, Gabriela Hoff, and Iwan Kawrakow

CONTENTS 7.1 Introduction ............................................................................................................... 163 7.2 Materials and Methods ............................................................................................ 165 7.2.1 Voxel Phantoms ............................................................................................ 165 7.2.2 The MAX Phantom ...................................................................................... 166 7.2.2.1 Database......................................................................................... 166 7.2.2.2 Adjustment of Organ and Tissue Masses ................................. 168 7.2.3 The FAX Phantom ........................................................................................ 168 7.2.3.1 Database......................................................................................... 168 7.2.3.2 Segmentation ................................................................................ 168 7.2.3.3 Addition of Head and Arms ....................................................... 170 7.2.3.4 Anatomical Corrections .............................................................. 170 7.2.4 The FAX06 and the MAX06 Phantoms ..................................................... 170 7.2.4.1 ICRP103 and New Concepts for Skeletal Dosimetry .............. 170 7.2.4.2 Segmentation of New Organs and Tissues .............................. 171 7.3 Results ......................................................................................................................... 177 7.3.1 Anatomical Results ...................................................................................... 177 7.3.1.1 The MAX06 Phantom .................................................................. 177 7.3.1.2 The FAX06 Phantom .................................................................... 179 7.3.2 Dosimetric Results ....................................................................................... 179 7.3.2.1 Skeletal Dosimetry Based on CT Images of Spongiosa .......... 179 7.4 Conclusions ................................................................................................................ 194 Acknowledgments ............................................................................................................... 194 References ............................................................................................................................. 194

7.1 Introduction Matter, after having been exposed to ionizing radiation, cannot express itself in terms of absorbed or equivalent dose. Consequently, the equivalent dose in tissues of the human body cannot be measured directly. Indirect measurements of equivalent dose can be made by radiation detectors, but they are restricted to locations on the surface of the human 163

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body, thereby excluding most of the radiosensitive organs and tissues from this method of equivalent dose assessment. In order to establish relationships between protection quantities to be determined and operational quantities that can be measured, one has to make use of an exposure model: a physical or computational structure for the simultaneous determination of equivalent dose in the human body and of measurable quantities for exposure conditions relevant in radiation protection. It must therefore incorporate sources and fields of the radiations involved, a representation of the human body, a method for the determination of equivalent dose inside the human body, and a possibility to determine measurable quantities of interest. The results are usually expressed as conversion coefficients (CCs) as a function of the exposure conditions, which are ratios between equivalent dose to organs and tissues at risk and measurable quantities. Routine measurements in radiation protection can be interpreted in terms of organ and tissue equivalent dose by multiplying the instrument’s reading with the appropriate CC, provided that the irradiation conditions simulated with the exposure computational phantom correspond to the real exposure situation. Previously, CCs for radiation protection were determined using physical exposure model.1 The human body was represented by a physical phantom, which consisted of a real human skeleton embedded in tissue-equivalent material having the form and shape of a human body. At the same time, speed and memory capacity of computers increased significantly, which made it possible to develop computational exposure model that used virtual representations of the human body, called computational human phantoms (hereafter called phantoms), and radiation transport simulation methods to determine CCs.2 Today, absorbed or equivalent dose assessments in the human body are usually made with computational exposure model. The most important equivalent dose quantity in radiological protection is the effective dose, defined in 1991 by the International Commission on Radiological Protection (ICRP) in Publication 60.3 At that time, the effective dose represented a sum over the weighted equivalent doses in 23 organs and tissues at risk. Consequently, any type of human phantom should ideally contain at least these 23 organs and tissues. In 2000, at the Department of Nuclear Energy of the Federal University of Pernambuco (DEN/UFPE) in Recife/Brazil, a computational exposure model for the determination of CCs for the area of radiological protection using Monte Carlo (MC) codes was developed. For this purpose it was necessary to design two human phantoms. The FAX06 (Female Adult voXel) and the MAX06 (Male Adult voXel) phantoms have been developed in a two-step process. In the first step, two adult human voxel phantoms have been developed, called FAX4 and MAX,5 which contain homogeneously segmented skeletons and all 23 organs and tissues necessary to calculate the effective dose as defined by ICRP60.3 Care was taken that the masses of the organs and tissues matched the reference masses given by ICRP896 as closely as possible. The new recommendations of the ICRP, Publication 103,7 revise the concept of the effective dose with respect to the number of organs and tissues considered at risk and therefore included in the determination of the effective dose. Consequently, in the second step, the two adult phantoms underwent additional segmentation, which added six new organs and tissues according to ICRP103,7 and were called from then on FAX06 and MAX06.8 In order to open the possibility for the application of μCT images of trabecular bone in the FAX06 and the MAX06 phantoms, the second segmentation was extended to the skeletons by segmenting cortical bone, spongiosa (=trabecular bone filled with soft tissue),

The FAX06 and the MAX06 Computational Voxel Phantoms

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medullary yellow bone marrow (YBM) in the long bones, and cartilage under observation of ICRP-based skeletal tissue masses.9 At the time of their introduction, MAX and FAX were the first human adult phantoms with soft-tissue organ masses based on the anatomical reference data recommended by ICRP Publication 896 and the updated versions FAX06 and MAX06 were the first human adult phantoms with ICRP-based skeletons, segmented into cortical bone, spongiosa, medullary YBM, and cartilage. Since then, the further development of the FAX06 and the MAX06 phantoms occurred mainly inside their skeletons where the spongiosa voxels have been further segmented into trabecular bone and marrow for the purposes of skeletal dosimetry.10,11 Over the years, the phantoms have been applied to many areas of radiation protection, such as medical exposures,12 occupational and environmental exposures,13 and to accidental exposures.14 With respect to patient dosimetry in x-ray diagnosis, a software tool for absorbed dose calculation based on the FAX06 and the MAX06 phantoms has been made available to the public.15,16 Until now, most of the absorbed or equivalent dose calculations in the FAX06 and the MAX06 phantoms have been made with the EGS417 and EGSnrc18,19 MC codes for photons and electrons. Meanwhile, the phantoms have been successfully connected to the GEANT420 MC code, which allows for the transport of many other particles besides electrons and photons in the future.21 During the two-step development of the FAX06 and the MAX06 phantoms, certain organs and tissues had to be resegmented and certain dosimetric methods had to be revised, especially in the area of skeletal dosimetry. The description given in this chapter will therefore focus on the updated versions of FAX06 and MAX06 and will report on their predecessors FAX and MAX only as far as necessary. For concepts that have now only “historical value,” the reader is kindly asked to consult the original publications.4,5 This book, in which this chapter appears, represents the most comprehensive and upto-date description of “Anatomical Computational Phantoms for Radiation Dosimetry.” Therefore, it is considered unnecessary to review the literature of phantom development in this chapter.

7.2 Materials and Methods 7.2.1 Voxel Phantoms A computerized tomography (CT) image is a two-dimensional (2D) picture of a crosssection through the body of a human, which is composed of pixels (picture elements) representing different gray values distributed mostly on a scale between 0 and 255. When presented in the format normally used in x-ray diagnosis, the human eye differentiates apparent homogeneous regions in CT images, which represent organs, tissues and, sometimes, pathological abnormalities. If amplified, however, these regions reveal that the homogeny is, in reality, composed of an intrinsic mixture of pixels with different gray values. Therefore, constructing anthropomorphic phantoms composed of homogeneous organs and tissues requires segmentation, a process, which divides a CT image into various organ-specific regions by assigning one label, called the organ ID number, to all pixels within that region, through which they become part of that organ or tissue.

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A consecutive set of segmented CT images from head to toe represents a threedimensional voxel (volume element) matrix, called a human voxel phantom, and with ID numbers assigned to each voxel depending on the organ or tissue to which it belongs to. Human voxel phantoms can be introduced into the MC radiation transport codes for the purpose of equivalent dose calculation in segmented organs and tissues. 7.2.2 The MAX Phantom 7.2.2.1 Database Three segmented voxel phantoms were available on the Web site of Yale University22,23 at the time when the phantom development at the DEN/UFPE began • VOXELMAN: A torso voxel phantom with head, but without arms and legs • MANTISSUE3-6: The VOXELMAN phantom with legs and arms, which are closed in front of the abdomen • VOXTISS8: The MANTISSUE3-6 voxel phantom with the arms straightened laterally along the body All three phantoms have been constructed from the same database: namely 78 CT images acquired from neck to midthigh with 1 cm slice thickness, 51 CT images of the head and neck region with 0.5 cm slice thickness, and 124 high-resolution transverse MRI images with 0.15 cm slice thickness from a patient, who was scheduled for head, thorax, abdomen, and pelvic scans for a diagnosis of diffuse melanoma. His height was 175 cm and the weight was 70.2 kg. VOXELMAN represents the combination of the segmented head and body CT images with a 4 mm cubic voxel size. Later, Stuchly24 added arms and legs, segmented from the Visible Man’s red color cross-sections,25 to the torso phantom, which was called MANTISSUE3-6. This phantom has been rescaled to achieve a 3.6 mm cubic voxel size. Finally, Sjogreen26 straightened along the sides of the body the arms of the MANTISSUE3-6 phantom maintaining the 3.6 mm cubic voxel size. This version is called VOXTISS8, and it consists of 487 segmented body cross-sections, each of which expands into a 192 × 96 pixel matrix. About 40 organs and tissues have been segmented in the trunk, arms, and legs, and about 56 organs and tissues in the head. The VOXTISS8 phantom contains the high-resolution MRI head, and it was this voxel computational phantom, which has been chosen as database for the construction of the MAX phantom. Table 7.1 shows organs and tissue masses for the VOXTISS8 phantom and for the ICRP Reference Man.6 The last column shows the percentage deviation of the VOXTISS8 organ masses relative to the ICRP data. As one can see, the agreement between the two sets of data is poor. The differences are smaller than 10% only for the eyes, the brain, and the adipose tissue. For all other organs and tissues, the deviations are at least 15%, sometimes even higher than 100%. The thymus and one adrenal were not segmented at all in the VOXTISS8 phantom, and the volume of the other adrenal was too small. The bladder wall was too thick compared to the dimensions shown in anatomical textbooks or other phantoms. Voxels inside the body had the organ ID number of the testes, which made this organ too heavy, apart from causing an unrealistic distribution of its volume, and several transversal images showed that the lungs had been segmented sometimes outside the ribcage. Also it was found that about 4.5 kg of blood had falsely been segmented as muscle and adipose tissue.


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TABLE 7.1 Comparison of Organ and Tissue Masses between the ICRP Reference Man and the VOXTISS8 Phantom

Organ/Tissue Adipose (fat) Adrenals Bladder wall Skeleton Brain Colon Eyes Kidneys Liver Lungs Muscle, skeletal Esophagus Pancreas RBM Small intestine Skin Spleen Stomach Testes Thymus Thyroid Trachea Total body Height

ICRP89

YALE

Percentage Differences

Reference Man (g)

VOXTISS8 (g)

VOXT/Reference Man (%)

14,600 14 50 10,500a 1,450 670 15 310 1,800 1,200 29,000 40 140 1170 1,000 3,300 150 400 35 25 20 10 73,000 176 cm

14,970 3 154.2 13,869.4b 1491 895.7 15.8 373.2 1434 756.5 36,070 31.5 38.8 570.9 1,296 6,676 272.8 251.5 72.5 Not segmented 5.1 14.3 81,728 175.3 cm

+2.5 −78.6 +208.4 +32.1 +2.8 +33.7 +5.3 +20.4 −20.3 −37.0 +24.4 −21.3 −72.3 −51.2 +29.6 +102.3 +81.9 −37.1 +107.1 – −74.5 +43.0 +12.0 −0.4

Source: Kramer, R. et al., Phys. Med. Biol., 48, 1239, 2003. With permission. Stomach, colon, and small intestine include contents. a Bone, marrow, cartilage, misc. b Bone and marrow.

The VOXTISS8 skeleton has a volume of 7884.7 cm3, 1387.1 cm3 of which have been segmented as “bone marrow.” The linear voxel dimensions of the VOXTISS8 phantom are 3.6 mm, whereas the linear dimensions of bone marrow cavities are in the range of 50–2000 μm.27 As it is impossible to segment an object with a pixel size that is greater than the linear dimension of the object to be segmented, consequently the volume segmented within the VOXTISS8 skeleton cannot represent bone marrow. In addition, one finds that the volume of that “bone marrow” represents only 17.6% of the total skeletal volume, whereas according to ICRP709 the bone marrow occupies 47.4% of the volume of the skeleton. Consequently with 13,869.4 g the weight of the VOXTISS8 skeleton is quite heavy for a person with a body height of 175.3 cm as Table 7.1 indicates. Apart from the bone marrow problem it was also found that part of the clavicles had not been segmented in the VOXTISS8 phantom.

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7.2.2.2 Adjustment of Organ and Tissue Masses Our objective with the new segmentation was to change the volume of already segmented organs, and to add new organs that have not been segmented, such that the new voxel phantom corresponds with its organ and tissue masses as closely as possible to the organ masses given for the ICRP Reference Man.6 For the soft-tissue organs, a method of voxel exchange with surrounding general tissues, such as adipose, muscle, blood, etc. was applied, taking into account relevant information from anatomical textbooks. In the case of the testes, for example, the ID numbers of the voxels falsely segmented as testes inside the body have been changed into the ID numbers for muscle and/or adipose tissue, thereby reducing the volume of the testes. In order to reduce the volume of the bladder wall, voxels at the inner surface of the bladder have been changed into urine voxels until the remaining wall volume had the desired value. According to the mass differences shown in Table 7.1, the volumes of the adrenals, the liver, the lungs, the esophagus, the pancreas, the stomach, and the thyroid have been increased, while the volumes of the bladder wall, the colon, the kidneys, the skin, the small intestine, the spleen, and the testes have been decreased. The thymus and one adrenal have been added based on data found in ICRP2328 and anatomical textbooks. The thyroid has not only been enlarged, but its position has also been changed according to ICRP23, where 2 cm of tissue overlying the thyroid are mentioned for the adult male. While the trunk and head of the VOXTISS8 phantom represent a patient with a body height of 175.3 cm and a body weight of 70 kg, the arms and legs have been added to the torso from a person with a body height of 186 cm and a body weight of 104 kg. Therefore, we can assume that the muscle and adipose tissue of the arms and the legs are mainly responsible for the soft-tissue part of the 12% excess weight. In order to achieve a reduction of the total body weight, the superficial layer of skin voxel was uniformly removed from the arms and the legs. Afterwards the first layer of the remaining voxels received the ID number of the skin. 7.2.3 The FAX Phantom 7.2.3.1 Database The main set of data used for the construction of the FAX phantom consisted of 151 consecutive CT images of a 37-year-old female patient. The patient’s height was 165 cm, and her weight was 63.4 kg. The images covered the trunk, the neck, and the lower part of the head including the mandible with the lower teeth. The pixel size was 0.073 cm × 0.073 cm, and the distance between two consecutive images was 0.5 cm. CT Screening International, Irvine, California, provided the images in October 2002. A second set of data consisted of 206 consecutive CT images of the legs and feet of a 62-year-old woman. The pixel size was 0.07 cm × 0.07 cm, and the distance between two consecutive images was 0.25 cm. The images had been provided by the university hospital of the city of Porto Alegre, Brazil in September 2003. 7.2.3.2 Segmentation The CT images of the patients have been obtained in the digital imaging and communications in medicine (DICOM) format, and have been visualized by means of the software OSIRIS,29 which is available on the Internet. OSIRIS has several types of filters, which improve the visualization of boundaries between organs. After editing, the images have


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been saved as bitmap files with the software PAINT, which is included in the Microsoft WINDOWS accessories. Segmentation was done in each of the 151 torso images with PAINT by manually painting every organ and tissue of interest with a different color, while the 206 images of the legs have been segmented automatically.30 Thereby the gray values of the pixels, which belong to a specific organ, were replaced by a specific color, which corresponds to a specific ID number. The automatic segmentation of the 206 images of the legs was possible, because the legs contain only a few different tissues, like skin, muscle, adipose, bone, and marrow. In the first step, a vector was defined to store in increasing order 256 gray values shown on the screen of the computer, beginning with the color white, then gradually turning into gray and finally reaching black. In this way, the position of a gray value stored in the vector corresponds to a “pixel value” to be used in the process of automatic segmentation, indicated by Figure 7.1, where (A) represents the original image in bitmap format. First, the size of the image was reduced from 512 × 512 original pixels to 256 × 256 new pixels (B), where each new pixel is the arithmetic mean of the gray values of four neighboring original pixels. This reduction provides a homogenization of the gray values that compose a certain region. Without changing the size of the image any further, this averaging process was repeated two more times (C) and (D), with the eight nearest neighbors of each pixel. Then a fi lter was used to remove “isolated pixels,” which represent pixels with a gray value significantly different from those of its neighbors (E). Finally, certain boundaries were chosen to define intervals on the scale between 0 and 255. Then, to all pixels with values lying within a certain interval, one value and its corresponding color were assigned. The regions defined by the intervals correspond to organs or tissues and the “colored value” is their ID number (F). According to the definition of the effective dose given in ICRP Publication 60,3 the following organs and tissues have been segmented: the adrenals, the bladder wall, the

FIGURE 7.1 Graphical representation of steps (A) through (F) for automatic segmentation of the FAX06 legs.

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skeleton, the brain, the breasts, the colon, the kidneys, the liver, the lungs, the muscle, the esophagus, the ovaries, the pancreas, the small intestine, the skin, the spleen, the stomach, the thymus, the thyroid, the trachea, and the uterus. Although not included in the effective dose of Publication 60, the heart and adipose tissue have also been segmented. 7.2.3.3 Addition of Head and Arms The head of the MAX phantom was attached to the neck of the FAX phantom, after scaling down the MAX phantom’s head according to the anatomical differences between the Male and the Female Reference Adult as defined by ICRP89.6 The same procedure was applied to the addition of the arms, which also have been taken from the MAX phantom. 7.2.3.4 Anatomical Corrections During screening, the patient was asked to raise her arms behind her head. Therefore, we have redesigned the shoulders and the upper part of the arms according to data from anatomical textbooks. The images of the trunk, the head, the arms, and the legs originated from three different patients. Therefore, with respect to the representation of the skeleton some adjustments had to be made again based on anatomical textbooks, but at the same time taking into account as much as possible the reference distribution of bone mass fractions given by ICRP89.6 The form of the breasts has also been modified in order to represent an upright standing adult female. As in the case for the MAX phantom, all segmented organ and tissue volumes of the FAX phantom were adjusted based on voxel exchange to match the reference masses given by ICRP89.6 Finally all segmented images have been rescaled to achieve the same voxel size of 0.36 cm × 0.36 cm × 0.36 cm = 0.046656 cm3 as in the case of the MAX phantom, and the total number of slices has been adjusted to 453, which corresponds to a body height of 163 cm. 7.2.4 The FAX06 and the MAX06 Phantoms 7.2.4.1 ICRP103 and New Concepts for Skeletal Dosimetry According to the recommendations of the ICRP released in Publication 60, 3 the determination of the effective dose, the most important dose quantity in radiological protection, requires the calculation of the equivalent dose to radiosensitive organs and tissues shown in columns 1 and 2 of Table 7.2. The trachea replaced the upper large intestine,31 which was part of the original list of tissues,3 but actually is already included in the colon. Meanwhile, the ICRP extended by six the number of radiosensitive organs and tissues to be taken into account for the determination of the effective dose in its new recommendations released in Publication 103,7 due to recently reported new evidence about stochastic radiation effects in these organs and tissue. These new organs and tissues are shown in the third column of Table 7.2. The segmented organs and tissues of the FAX and the MAX phantoms include those mentioned in the first two columns of Table 7.2. The new organs and tissues from column 3 had either not been segmented (the gallbladder, the extra thoracic airways, the lymphatic nodes, the salivary glands) or their masses had not been adjusted (the heart wall, the prostate). In order to prepare the phantoms for the effective dose calculations based on ICRP103, it was decided to segment and/or adjust the organs and tissues shown in column 3 of


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TABLE 7.2 Radiosensitive Organs and Tissues to be Included in the Calculation of the Effective Dose Organs and Tissues (ICRP60)

New Organs (ICRP103)

Adrenals Bladder Bone surface Brain Breast Colon Gonads Kidneys Liver Lungs Muscle

Extra thoracic airways Gallbladder Heart wall Lymphatic nodes Prostate Salivary glands

Esophagus Pancreas RBM Skin Small intestine Spleen Stomach Thymus Thyroid Trachea Uterus

Source: Kramer, R. et al., Phys. Med. Biol., 51, 3331, 2006. With permission.

Table 7.2 in the MAX and the FAX phantoms, and to use this opportunity to segment the walls and contents in the stomach, the colon, and the small intestine, as well as the cortical bone, the medullary yellow the bone marrow, the spongiosa, and the cartilage in the skeletons, segmentations which had not been done during the design of the MAX and the FAX phantoms. Skeletal tissues had not been segmented because the CT number method, used to calculate the equivalent dose to the red bone marrow (RBM), required the application of the skeletal gray values contained in the original CT images of the patient. As these skeletal gray values have to match the segmented bone pixels exactly, it was not possible to segment regions inside the bones. New methods for skeletal dosimetry are based on CT images of spongiosa. Their applications to the skeletons of the MAX and the FAX phantoms, however, require the segmentation of the cortical bone, the spongiosa, the medullary bone marrow, and the cartilage in the skeletons. 7.2.4.2 Segmentation of New Organs and Tissues The segmentation of the new organs and tissues, in the FAX06 and MAX06 phantoms, was based on the original CT images, on anatomical textbooks, 32,33 on ICRP70,9 and on ICRP89,6 and if not stated otherwise, the following description refers to both phantoms. The images of the two phantoms have been edited with the SCION software, 34 and adjustments were made to the volumes by changing the organ and tissue identification numbers literally pixel by pixel. Tissue compositions and densities from ICRU4435 shown in Table 7.3 have been used to control the match with the ICRP reference masses. Adipose tissue, already segmented in the FAX and the MAX phantoms, was adjusted to match the reference mass and connective tissue was segmented for the first time. In 2006 when the phantom update was initiated, adipose and connective tissue were still considered by the ICRP as tissues at risk for the determination of the effective dose, but then eventually excluded in the fi nal version of the new recommendations, which was approved in 2007.


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TABLE 7.3 Elemental Tissue Composition Used in the MAX06 and the FAX06 Phantoms Based on ICRU44

Atom H C N O Na Mg P S Cl K Ca Fe ρ (g cm−3)

Soft Tissue (%) 10.4 12.4 2.6 73.5 0.2 0.2 0.2 0.2 0.2 0.02 0.02 1.05

Muscle Tissue (%)

Adipose Tissue (%)

Skin Tissue (%)

Lungs Tissue (%)

Skeleton Skeleton Cartilage Cortical (%) (%)

10.2 14.3 3.4 71.0 0.1

11.4 59.8 0.7 27.8 0.1

10.0 20.4 4.2 64.5 0.2

10.3 10.5 3.1 74.9 0.2

9.6 9.9 2.2 74.4 0.5

0.2 0.3 0.1 0.4

0.1 0.1

0.1 0.2 0.3 0.1

0.2 0.3 0.3 0.2

2.2 0.9 0.3

3.4 15.5 4.2 43.5 0.1 0.2 10.3 0.3

22.5 1.05

0.95

1.09

0.26

1.1

1.92

Skeleton Spongiosa Skeleton (%) YBM (%) 8.5 40.4 2.8 36.7 0.1 0.1 3.4 0.2 0.2 0.1 7.4 0.1 1.18

11.5 64.4 0.7 23.1 0.1

0.1 0.1

0.98

Source: Kramer, R. et al., Phys. Med. Biol., 51, 3331, 2006. With permission.

7.2.4.2.1 Adipose Tissue Adipose tissue had already been segmented in the original versions of the two phantoms. Only minor adjustments of the masses had to be made. 7.2.4.2.2 Connective Tissue Separable connective tissue includes primarily periarticular tissue, tendons, and fascia. Periarticular tissue has been segmented at the shoulders, the knees, the hips, and the elbows, between the vertebrae, between vertebrae and ribs, and between the sacrum and the ilium, and also at the sinfisis pubis for the FAX06 phantom. The fascia has been introduced between the deepest layer of the subcutaneous tissue and the muscle tissue distributed over the whole body, while the tendons have been segmented mainly at the joints. 7.2.4.2.3 Extra Thoracic Airways ICRP896 and ICRP1037 clarify that the extra thoracic airways consist of the anterior and posterior nasal passages, the mouth cavity, the pharynx and the larynx. Reference masses for the larynx are given in ICRP89.6 The volumes for the pharynx have been determined with data from anatomical textbooks, while estimates for the volumes of the nasal passages and the mouth cavity have been made based on data provided by ICRP896 on page 92 and also based on anatomical textbooks.32,33 7.2.4.2.4 Gallbladder The gallbladder was segmented below the liver, separately by wall and contents. 7.2.4.2.5 Heart Wall The heart walls had already been segmented in both phantoms, but now the volumes have been adjusted to match the masses recommended by ICRP89.6


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7.2.4.2.6 Lymphatic Nodes Lymphatic nodes have been segmented at the armpit, at the groins, behind the knees, in front of the elbows, at the neck, and around abdominal soft-tissue organs. ICRP89 states “Except for the lymphocytes, which are present in most tissues, the lymphatic tissue is contained in the RBM and in the lymphatic organs: lymph nodes, spleen, thymus, mucous membranes, tonsils, adenoids, Peyer’s patches, and the vermiform appendix.”6 Therefore the segmented volume of the lymphatic nodes was confined to 50% of the volume recommended by ICRP89. 7.2.4.2.7 Prostate The prostate had already been segmented in the MAX phantom, but now the volume has been adjusted in order to match the mass recommended by ICRP89.6 7.2.4.2.8 Salivary Glands The salivary glands have been segmented around the mandible as parotid, submaxillary, and sublingual parts, observing the mass ratios 10:5:2 between them according to ICRP89.6 7.2.4.2.9 Stomach, Colon, and Small Intestine Introducing a wall thickness of one voxel layer, i.e., of 3.6 mm, the walls and contents of the stomach and the colon have been segmented. Because of the many twists and turns of the small intestine it is almost impossible to segment a continuous and steady wall based on voxels. Therefore the total volume of the small intestine was divided between the wall and contents by distributing the wall voxels homogeneously throughout the volume of the organ. 7.2.4.2.10 Skeleton The skeletons of the MAX and the FAX phantoms had been segmented with respect to the surrounding muscle tissue, but no attempt was made until now to segment tissues within the skeletal structures, like cortical bone, spongiosa, medullary YBM in the shafts of the long bones and cartilage. The images of the VOX_TISS8 phantom,23 which had been the basis for the development of the MAX phantom, show areas of segmented bone marrow, however, it was demonstrated that this bone marrow segmentation was faulty. In order to prepare the phantoms for advanced skeletal dosimetry in the future, we decided to segment the cortical bone, the spongiosa, the medullary YBM in the shafts of the long bones, and the cartilage in the skeletons of the MAX and the FAX phantoms, based on the original CT images, as well as by using anatomical textbooks,32,33 and the color photographs of the Visible Human.25 However, the new skeletons should also be based on recommendations published by the ICRP as far as anatomically possible, similar to the procedure applied to the segmented soft-tissue organs. Therefore the segmentation of the skeletal tissues took into account the following data provided by ICRP896 and ICRP70:9 1. Reference masses for bone, RBM, YBM, cartilage, and miscellaneous tissues (teeth, periostenum, and blood vessels) as shown in Table 7.4 for the reference adult male and female.6 For the cartilage masses only half of the recommended mass was taken into account because a part of the cartilage included by ICRP89 into the mass of the skeleton actually is located “off-bone,” like in the ear, in the nose, etc.,


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TABLE 7.4 Skeletal Tissue Masses, Volumes, and Densities Based on ICRP89 and ICRU44 Skeletal Tissues Bone RBM YBM Cartilage Misc. Total

Density, r(g cm−3)

Reference, m(g)

Male, v(cm3)

Reference, m(g)

Female, v(cm3)

1.92a 1.03a 0.98a 1.10a 1.20b 1.37b

5500 1170 2480 550 250 9950

2864.6 1135.9 2530.6 500.0 208.3 7239.4

4000 900 1800 450 200 7350

2083.3 873.8 1836.7 409.1 166.7 5369.5

Source: Kramer, R. et al., Phys. Med. Biol., 51, 3331, 2006. With permission. BM, bone marrow; MISC, teeth, periostenum, and blood vessels. a ICRU densities. b Calculated densities.

and also because in order to achieve smooth surfaces between bone and skeletal muscle, the cartilage is sometimes segmented as bone or sometimes as muscle. The density for the miscellaneous tissues has been calculated based on data from ICRP2328 as 1.20 g cm−3. With the ICRU4435 densities and the calculated densities shown in column 2, the corresponding skeletal tissue volumes have been determined and are presented in columns 4 and 6 of Table 7.4. The average density of 1.37 g cm−3 calculated from the total skeletal mass and volume is, of course, greater than the 1.3 g cm−3 recommended by ICRP89 because of the smaller cartilage mass. 2. For nine important bones or bone groups, Table 7.5 presents sex-specific mass fractions, the mass ratios between cortical and trabecular bone, the RBM mass fractions, taken or derived from data recommended in ICRP89,6 and the cellularity factors, which indicate the fraction of the marrow volume occupied by RBM, taken from ICRP70.9

TABLE 7.5 Skeletal Tissues Data Based on ICRP70 and ICRP89 ICRP 70/89 Skeletal Region

Male Bone Mass Fractions

Female Bone Mass Fractions

Mass Ratio of Bone Cortical/Trabecular

RBM Mass Fractions

Cellularity Factor

Lower arm bone Upper arm bone Ribcage Spine Skull Mandible Pelvis Upper leg bone Lower leg bone

0.059 0.053 0.126 0.190 0.118 0.012 0.106 0.153 0.183

0.056 0.047 0.104 0.204 0.119 0.012 0.105 0.159 0.193

87/13 80/20 94/6 30/70 95/5 90/10 90/10 70/30 80/20

0 0.023 0.228 0.422 0.076 0.008 0.176 0.067 0

0 0.25 0.60 0.70 0.38 0.38 0.48 0.25 0

Source: Kramer, R. et al., Phys. Med. Biol., 51, 3331, 2006. With permission. Ribcage, ribs, sternum, clavicles, scapulae.


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Tables 7.6 and 7.7 show theoretical distributions of skeletal tissue volumes for the reference adult male and female, respectively, which have been calculated with the data from Tables 7.4 and 7.5. The volumes of the miscellaneous tissues have been added to the volumes for cartilage. As an example, the calculation, which created the distribution of skeletal tissue volumes in Tables 7.6 and 7.7, will be demonstrated for the spine of the adult male: Application of the RBM mass fraction From Tables 7.4 and 7.5 one can calculate the amount of RBM mass in the spine as 1170 g × 0.422 = 493.7 g, or the RBM volume in the spine as 493.7 g/1.03 g cm−3 = 479.3 cm3.

TABLE 7.6 Theoretical Volume Distribution of Skeletal Tissues for the Adult Male Adult Male Skeletal Region Lower arm bone Upper arm bone Ribcage Spine Skull Mandible Pelvis Upper leg bone Lower leg bone Total volume

Cortical Bone, cm3 164.9 85.8 351.0 163.2 422.5 40.2 258.8 201.9 470.5 2158.8

Spongiosa, cm3

YBM Med., cm3

Cart/Misc., cm3

Total, cm3

144.4 215.8 454.1 1065.2 249.3 28.5 445.5 669.3 489.2 3761.3

59.0 89.9

42.6 37.4 89.7 134.8 83.1 8.5 74.7 108.8 128.8 708.4

410.9 428.9 894.8 1363.2 754.9 77.2 779.0 1257.4 1273.3 7239.6

277.4 184.8 611.1

Source: Kramer, R. et al., Phys. Med. Biol., 51, 3331, 2006. With permission. Cart/misc., cartilage + miscellaneous tissues; ribcage, ribs, sternum, clavicles, scapulae; YBM med., medullary YBM in the shafts of the long bones.

TABLE 7.7 Theoretical Volume Distribution of Skeletal Tissues for the Adult Female Adult Female Skeletal Region Lower arm bone Upper arm bone Ribcage Spine Skull Mandible Pelvis Upper leg bone Lower leg bone Total volume

Cortical Bone, cm3 118.8 50.9 177.0 130.0 307.9 29.3 181.7 162.1 377.1 1,534.8

Spongiosa, cm3

YBM Med., cm3

Cart/Misc., cm3

Total, cm3

94.6 150.7 343.3 829.9 190.9 21.6 340.6 502.2 358.8 2,832.6

38.4 57.6

32.4 27.2 59.8 117.6 68.9 6.9 61.4 91.2 110.4 575.8

284.2 286.4 580.1 1,077.5 567.7 57.8 583.7 953.8 978.5 5,369.7

198.3 132.2 426.5

Source: Kramer, R. et al., Phys. Med. Biol., 51, 3331, 2006. With permission. Cart/misc., Cartilage + miscellaneous tissues; ribcage, ribs, sternum, clavicles, scapulae; YBM med., medullary YBM in the shafts of the long bones.

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Application of the cellularity factor According to the cellularity factor from Table 7.5 the RBM volume of 479.3 cm3 represents 70% of the total marrow volume in the spine. Therefore the YBM volume in the spine is 479.3 cm3 × 3/7 = 205.4 cm3, or the YBM mass in the spine is 205.4 cm3 × 0.98 g cm−3 = 201.3 g. Application of the male bone mass fraction From Tables 7.4 and 7.5 one can calculate the total mass of the spine as 9950 g × 0.190 = 1890.5 g. From Table 7.4 one can calculate the mass for Cart/misc as (550 g + 250 g)/9950 g × 1890.5 g = 152 g, or the volume for Cart/misc as 152 g/1.128 g cm−3 = 134.8 cm3. Application of the mass ratio between cortical and trabecular bone With the ratios between cortical and trabecular bone from Table 7.5 one can calculate the masses and volumes for cortical and trabecular bone in the spine as cortical mass = (1890.5 − 493.7 − 201.3 − 152 g) × 0.3 = 313.1 g, and the cortical volume as 313.1 g/1.92 g cm−3 = 163.2 cm3, and the trabecular mass = (1890.5 g − 493.7 g − 201.3 g − 152 g) × 0.7 = 730.5 g, and the trabecular volume as 730.5 g/1.92 g cm−3 = 380.5 cm3. The spongiosa volume is 479.3 + 205.4 + 380.5 = 1065.2 cm3. The application of this method to all nine bones yielded 1036 g of YBM in all RBM containing bones. Taking into account the total YBM mass from Table 7.4 one finds 2480 − 1036 = 1444 g of YBM which is located in the shafts of the long bones, in the spongiosa of the lower part of the upper long bones and in the lower long bones. These regions of spongiosa do not contain RBM. Finally, it was possible to determine the volume of the YBM in the shafts of the long bones, also called medullary YBM using the bone mass fraction of the long bones, and anatomical data from the CT images and the textbooks shown in column 4 of Tables 7.6 and 7.7. The segmentation of the skeleton into cortical bone, spongiosa, medullary YBM, and cartilage should take the volumes shown in Tables 7.6 and 7.7 into account as far as skeletal anatomy would permit to do so. In the bones of the human skeleton spongiosa is usually surrounded by regions of cortical bone. Measurements on cortical bone thickness in various bones in the CT images used for the design of the MAX06 and the FAX06 phantom have shown that for both sexes ca. 1.2 mm can be considered as a minimum thickness for cortical bone covering the spongiosa. This value has been confirmed independently by similar measurements performed by Brindle and Bolch.36 However, a cortical bone thickness of 1.2 mm cannot be represented by a 3.6 mm cubic voxel. Therefore the voxel matrices of both phantoms were rescaled in order to achieve a 1.2 mm cubic voxel matrix. The rescaling procedure divided each dimension of a 3.6 mm cubic voxel of the MAX and the FAX phantoms by 3, which gave 3 × 3 × 3 = 27 1.2 mm cubic voxels for every 3.6 mm cubic voxel. With this finer voxel resolution it became possible to realize anatomically meaningful distributions between cortical bone voxels and spongiosa voxels, as well as between skin voxels and adipose voxels. For example, in the MAX and the FAX phantoms the absorbed dose to the skin was calculated with a special algorithm in the first 1.5 and 1.2 mm depths of the surface voxels, respectively, because the voxel depth of 3.6 mm is not representative for the depth of the skin. In the new MAX06 and FAX06 phantoms the skin absorbed dose is now calculated as the absorbed dose to the 1.2 mm cubic surface voxel layer, i.e., averaged over a depth of 1.2 mm for both phantoms.


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7.3 Results 7.3.1 Anatomical Results 7.3.1.1 The MAX06 Phantom For the most part, the skeleton of the MAX phantom is the skeleton of the VOX_TISS8 phantom.23 Table 7.8 shows the total volumes of bones and bone groups for the MAX phantom together with the ICRP-based data from Table 7.6. The percentage deviations in the last column are significant, especially for RBM containing bones, like the ribcage, the skull, the mandible, and the pelvis. At the first release of the MAX phantom5 the skeleton was hardly changed, because of the requirements of the CT number method as explained above, and also because the total volume of the MAX skeleton deviated only 9% from the ICRP-based total skeletal volume. But we felt that a segmentation of skeletal tissues should be based on anatomically reasonable volumes for the main bones and bone groups. The attempt to repair the falsely segmented MAX skeleton turned out to be complicated and showed that this process would probably take many months to achieve the objective. Therefore we decided to “borrow” the FAX skeleton, and to increase the dimensions of this female skeleton to arrive at the dimensions of the male skeleton, while at the same time observing the sex-specific mass ratios from Table 7.5. Table 7.9 shows the skeletal tissue volumes, which have been segmented in the MAX06 skeleton. For some bones it was possible to realize the ICRP-based volumes from Table 7.6 without compromising the skeletal anatomy. The total cortical bone volume of the MAX06 skeleton is 8.6% greater, and the total spongiosa volume is 4.9% smaller than the ICRPbased value from Table 7.6; however, for the total skeleton, the volume equals the theoretical value. After completion of the segmentation of the skeletal tissues, the soft-tissue organs of the MAX phantom, plus the newly segmented soft-tissue organs, were assembled together TABLE 7.8 Comparison of Bone Volumes between the MAX Phantom and the ICRP Reference Adult Male Adult Male Skeletal Region Lower arm bone Upper arm bone Ribcage Spine Skull Mandible Pelvis Upper leg bone Lower leg bone Total

MAX, cm3 308.0 600.2 1537.2 1131.1 497.0 102.4 1020.0 1132.0 1597.7 7925.6

ICRP-Based, cm3 410.9 428.9 894.8 1362.4 754.9 77.2 778.9 1257.5 1274.0 7239.5

Source: Kramer, R. et al., Phys. Med. Biol., 51, 3331, 2006. With permission. Ribcage, ribs, sternum, clavicles, scapulae.

Difference, % −25 +40 +72 −17 −34 +33 +31 −10 +25 +9


178

TABLE 7.9 Volume Distribution of the Segmented MAX06 Skeletal Tissues MAX06 Phantom Skeletal Region

Cortical Bone, cm3

Spongiosa, cm3

YBM Med., cm3

Lower arm bone Upper arm bone Ribcage Spine Skull Mandible Pelvis Upper leg bone Lower leg bone Total volume

172.1 100.5 376.3 293.8 422.5 40.2 258.8 210.2 470.5 2344.9

137.1 201.1 429.7 934.6 249.0 28.5 445.5 661.1 489.1 3575.7

59.9 89.9

277.0 183.7 610.5

Cart/Misc., cm3 41.6 37.4 88.8 133.8 73.0 18.6 74.7 109.4 130.9 708.2

Total, cm3 410.7 428.9 894.8 1362.2 744.5 87.3 779.0 1,257.7 1274.2 7239.3


with the MAX06 skeleton to form the new MAX06 phantom, which consists of 1461 transversal images, each of which has 474 pixel × 222 pixel, i.e., the phantom matrix has 153738108 voxel, 41461410 of which are filled with human tissues. Figures 7.2 through 7.5 present images of the MAX06 phantom, which show some of the additionally segmented organs and tissues. Table 7.10 presents a comparison of the organ and tissue masses of the MAX06 phantom with those recommended in Table 2.8 of the ICRP89 report. The main differences between the organ and tissue masses of the two data sets occur for the skeleton (cartilage) and the lymphatic nodes for reasons which have been explained above; and for some tissues, like extra thoracic airways, spinal cord, etc., which are not listed in the Table of ICRP89. As these differences partly compensate, the total weight of the MAX06 phantom turns out to be about half a kilogram less than the reference weight of 73 kg, which corresponds to a difference of 0.7%, while at the same time the total sum of the ICRP recommended organ and tissue masses exceeds the reference weight by about half a kilogram.

Parotid

Submaxillar

Sublingual

FIGURE 7.2 MAX06 phantom: salivary glands. (From Kramer, R. et al., Phys. Med. Biol., 51, 3331, 2006. With permission.)


179

MAX06, SLICE, 349 Skin Adipose Lungs Cortical bone ribcage Spongiosa ribcage Heart wall Soft tissue Lymphatic nodes Muscle Cortical bone upper arm bone Yellow marrow upper arm bone Esophagus Cortical bone spine Spongiosa spine Ribcage cartilage Spinal chord Spinel cartilage

FIGURE 7.3 MAX06 phantom: transversal image in the heart region. (From Kramer, R. et al., Phys. Med. Biol., 51, 3331, 2006. With permission.)

7.3.1.2 The FAX06 Phantom Table 7.11 shows the skeletal tissue volumes segmented for the FAX06 phantom, and again for some bones the spongiosa and the cortical bone volume match the ICRP-based value exactly. The total cortical bone volume is 17.1% greater and the total spongiosa volume 9.3% smaller than the corresponding value from Table 7.7. These numbers differ from those found for the MAX06 skeleton above, because both sexes have different bone mass fractions as Table 7.5 indicates. Again the total skeletal volume equals the theoretical value from Table 7.7. Figures 7.6 through 7.8 present images of the FAX06 phantom, which show especially some of the additionally segmented organs and tissues. The comparison of the organ and tissue masses between the ICRP reference adult female and the FAX06 phantom is presented in Table 7.12. Here the sum of the ICRP masses from Table 2.8 of Publication 896 exceeds the reference weight of 60 kg by 193 g. Apart from the differences between the masses of the skeleton (cartilage) and the lymphatic nodes, the FAX 06 phantom has 1 kg of fat less than the reference adult FIGURE 7.4 MAX06 phantom. female. The FAX06 phantom consists of 1359 transversal images each of (From Kramer, R. which has 474 pixels × 222 pixels, i.e., the phantom matrix has 143004852 et al., Phys. Med. voxel, 34208854 of which are fi lled with human tissues. Biol., 51, 3331, 2006. Figures 7.9 and 7.10 show three-dimensional representation of the With permission.) skeletons and the body surface of the MAX06 and the FAX06 phantom, respectively, while Figure 7.11 presents the skeletons and internal organs of the FAX06 and the MAX06 phantoms with adipose and muscle tissue removed. 7.3.2 Dosimetric Results 7.3.2.1 Skeletal Dosimetry Based on CT Images of Spongiosa 7.3.2.1.1 Skeletal Soft Tissues at Risk The two skeletal tissues at risk, when the human body is exposed to ionizing radiation, are the hematopoietic stem cells of the marrow, called RBM, and the osteogenic cells on


180

the endosteal surfaces, called bone surface cells (BSCs) or bone endosteum, both of which are located in the irregularly shaped marrow cavities of trabecular bone with diameters ranging from 50 to 2000 μm.27 Skeletal dosimetry is concerned with the determination of the equivalent dose to the RBM and the BSC, which is a quite challenging task because of the complicated microstructure of trabecular bone. “Trabecular bone plus its supported soft tissue is sometimes also referred to as spongiosa.”9 Discussion about the final location and the distribution of the two skeletal tissues at risk is still ongoing with respect to the revision of the thickness of the BSC layer from 10 to 50 m, the exclusion of the Haversian canals of cortical bone, the inclusion of cortical surfaces of the medullary cavities,37 the consideration of trabecular bone remodeling,38 and the inhomogeneous distribution of RBM cells in the marrow.39 For the methods discussed here, the BSC represent the part of the marrow volume that is located within a

Tendons Periarticular tissue Fascia

FIGURE 7.5 MAX06 phantom: lymphatic nodes and connective tissues. (From Kramer, R. et al., Phys. Med. Biol., 51, 3331, 2006. With permission.)

TABLE 7.10 Comparison of Organ and Tissue Masses between the MAX06 Phantom and the ICRP Reference Adult Male Organ/Tissue Adult Male Adrenals Salivary glands Esophagus Stomach wall Small intestine wall Colon wall Liver Gallbladder wall Pancreas Brain Heart wall Adipose Skin Muscle Lungs Skeleton Spleen Thymus Thyroid Kidneys Bladder wall Testes Prostate

ICRP89 (g)

MAX06 (g)

14.0 85.0 40.0 150.0 650.0 370.0 1,800.0

14.0 85.0 40.0 150.0 650.0 370.0 1,800.0

10.0 140.0 1,450.0 330.0 14,500.0 3,300.0 29,000.0 1,200.0 10,500.0 150.0 25.0 20.0 310.0 50.0 35.0 17.0 64,146.0

10.0 140.0 1,450.0 330.0 14,544.1 3,383.9 29,000.0 1,200.0 9,950.4 150.0 25.0 20.0 310.0 50.0 35.0 17.0 63,724.4

Difference (g)

+44.1 = +0.3% +83.9 = +2.5%

−549.6 = −5.2%

−421.6 = −0.7%


181

TABLE 7.10 (continued) Comparison of Organ and Tissue Masses between the MAX06 Phantom and the ICRP Reference Adult Male Organ/Tissue Adult Male Breasts Tongue Larynx Extra thoracic airways GI content Gallbladder content Trachea Tonsils Ureter/urethra Epididymes Pituitary gland Eyes Optic nerve Blood Hard palate Feces Spinal cord

ICRP89 (g)

Difference (g)

25.0 73.0 28.0 900.0 58.0 10.0 3.0 26.0 4.0 0.6 15.0

133.4 900.0 58.0 10.3

15.1 1.6

+0.1 = +0.7%

4900a

70,188.6 Connective tissue Lymphatic nodes

MAX06 (g)

2,600.0 730.0 73,518.6

Soft tissue 73,518.6

33.6 39.2 183.8 65,099.4 2,600.0 365.0 68,064.4 4,426.3b 72,490.7

−5,089.2 = −7.3% −365 = −50% −5,454.2 = −7.4% −1,027.9 = −1.4%

Source: Kramer, R. et al., Phys. Med. Biol., 51, 3331, 2006. With permission. Without lungs. b Includes blood. a

TABLE 7.11 Volume Distribution of the Segmented FAX06 Skeletal Tissues FAX06 Phantom Skeletal Region Lower arm bone Upper arm bone Ribcage Spine Skull Mandible Pelvis Upper leg bone Lower leg bone Total volume

Cortical Bone, cm3 130.1 93.2 280.5 273.0 278.1 29.1 174.2 162.2 377.2 1797.6

Spongiosa, cm3

YBM Med., cm3

Cart/Misc., cm3

Total, cm3

83.2 108.4 239.6 686.9 220.0 21.6 348.5 502.2 358.8 2569.2

38.4 57.6

32.4 27.2 59.8 117.6 59.7 16.8 60.9 91.0 110.4 575.8

284.1 286.4 579.9 1077.5 557.8 67.5 583.6 953.7 978.6 5369.1

198.3 132.2 426.5



182

FAX06. SLICE 497

Adipose Small intestine content Small intestine wall Colon content Colon wall Lymphatic nodes Kidneys Spongiosa spine Spinal chord Soft tissue Cortical bone lower arm bone Yellow marrow lower arm bone Connective tissue (fascia) Muscle Cortical bone spine Periarticular tissue (connect. tissue)

FIGURE 7.6 The FAX06 phantom: transversal image in the abdominal region. (From Kramer, R. et al., Phys. Med. Biol., 51, 3331, 2006. With permission.)

FAX06

Anterior nasal passage Posterior nasal passage

Pharynx

Mouth Larynx

FIGURE 7.7 The FAX06 phantom. (From Kramer, R. et al., Phys. Med. Biol., 51, 3331, 2006. With permission.)

distance of 10 m from the surfaces of trabecular bone,40 while the RBM occupies a part of the remaining marrow volume given by the cellularity factor.9 7.3.2.1.2 Previous Methods of Skeletal Dosimetry The history of skeletal dosimetry is connected with the name of F. W. Spiers. Spiers and his coworkers at the University of Leeds in England can be considered the most important contributors to the development of skeletal dosimetry from its beginning in the early 1950s until the late 1980s. Conversion factors for marrow in trabecular bone from exposure to


183

Skin Pharynx Soft tissue Muscle Skull cartilage Brain Spongiosa skull Cortical bone skull Adipose

FIGURE 7.8 The FAX06 phantom: transversal extra thoracic airways image between mouth and nasal passage. (From Kramer, R. et al., Phys. Med. Biol., 51, 3331, 2006. With permission.)

TABLE 7.12 Comparison of Organ and Tissue Masses between the FAX06 Phantom and the ICRP Reference Adult Female Organ/Tissue Adult Female Adrenals Salivary glands Esophagus Stomach wall Small intestine wall Colon wall Liver Gallbladder wall Pancreas Brain Breasts Heart wall Adipose Skin Muscle Lungs Skeleton Spleen Thymus Thyroid Kidneys Bladder wall Ovaries

ICRP89 (g)

FAX06 (g)

13.0 70.0 35.0 140.0 600.0 360.0 1,400.0 8.0 120.0 1,300.0 500.0 250.0 19,000.0 2,300.0 17,500.0 950.0 7,800.0 130.0 20.0 17.0 275.0 40.0 11.0

13.0 70.0 35.0 140.0 600.0 360.0 1,400.0 8.0 120.0 1,300.0 500.0 250.0 18,000.0 2,300.0 17,497.9 950.0 7,355.5 130.0 20.0 17.0 275.0 40.0 11.0

Difference (g)

−1,000 = −5.3% −2.1 < −0.1% −444.5 = −5.7%

(continued)


184

TABLE 7.12 (continued) Comparison of Organ and Tissue Masses between the FAX06 Phantom and the ICRP Reference Adult Female Organ/Tissue Adult Female Uterus Tongue Larynx Extra thoracic airways GI content Gallbladder content Trachea Tonsils Ureter/urethra Fallopian Tub. Pituitary gland Eyes Optic nerve Blood Hard palate Feces Spinal cord Connective tissue Lymphatic nodes

ICRP89 (g)

FAX06 (g)

Difference (g)

80.0 52,919.0 60.0 19.0

80.0 51,472.4

−1,446.6 = −2.7%

112.6 830.0 48.0 8.1

+0.1 = + 1.3%

830.0 48.0 8.0 3.0 18.0 2.1 0.6 15.0

15.0 1.3

3570a

57,492.7 2,100.0 600.0 60,192.7

Soft tissue 60,192.7

30.0 33.6 72.2 52,623.3 2,100.0 300.0 55,023.2 3,979.8b 59,003.0

−4869.5 = −8.5% −300 = −50% −5,169.5 = −8.6% −1,189.7 = −2.0%

Source: Kramer, R. et al., Phys. Med. Biol., 51, 3331, 2006. With permission. a Without lungs. b Includes blood.

x-rays,27,41,42 the measurement of chord-length distributions in trabecular bone,43 and the calculation of skeletal dose factors for incorporated radionuclides44–50 can be considered to be fundamental contributions to skeletal dosimetry, and many of them became the data base for research made in scientific laboratories all over the world. Based on the work of the group at the University of Leeds, another important center for skeletal dosimetry was developed at the Oak Ridge National Laboratory (ORNL) by Eckerman in connection with the development of the MIRD5 phantom series.51,52 With respect to the investigation presented here, the photon fluence-to-dose response (FDR) functions for RBM and BSC have to be mentioned, 53 which are based on the chord-length distributions measured by Darley54 and Beddoe et al.43 Application of the conversion factors for marrow in trabecular bone from external exposure to x-rays42 has initiated the development of skeletal dosimetry at the National Research Center for Environment and Health in Munich in Germany. Based on a proposal by Rosenstein,55 Kramer56 developed an algorithm to be applied to the MIRD5-type phantoms ADAM and EVA57 for external photon radiation, which used three correction factors (3CF), among them rad/Roentgen conversion factors for marrow in five different


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FIGURE 7.9 Lateral and frontal three dimensional. (From Kramer, R. et al., Phys. Med. Biol., 51, 3331, 2006. With permission.)

bones of the human skeleton published by Spiers.42 These so-called Spiers factors, or later “King–Spiers factors,”41 have also been integrated into the CT number method, developed by Zankl and Wittmann,58 which for the first time applied a heterogeneous tissue distribution to the skeleton of a phantom. While the University of Leeds was the center of skeletal dosimetry for about four decades, today the most advanced methods in this area come from the University of Florida. Bolch and his coworkers from the Nuclear and Radiological Engineering Department succeeded in segmenting trabecular bone and marrow in μCT and NMR images of the spongiosa of human bone samples, and subsequently connected these segmented images to a MC radiation transport code for the purposes of skeletal dosimetry.59–62 Spongiosa in the human skeleton is usually surrounded by cortical bone. Therefore, the group at the University of Florida developed a special MC transport code, called paired-image radiation transport (PIRT), which transports the particles through a “macro” matrix with a voxel size of some hundred micron, representing spongiosa, cortical bone and surrounding soft tissues, and at the same time through a “micro” matrix with cubic voxel sizes down to 30 μm, representing the microstructure of spongiosa with segmented volumes of marrow and trabecular bone. So far this new method has mainly been applied to isolated bone samples for nuclear medicine applications, but not to a complete skeleton embedded in a human body so far. 7.3.2.1.3 The 8 SP (Systematic–Periodic) Cluster Method The 8 SP cluster method for skeletal dosimetry was introduced in two publications.10,11 The first paper developed the fundamentals of the method, while the second paper

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added the algorithm, which allowed for the calculation of the BSC equivalent dose in subvolumes of segmented trabecular microvoxels. The description given here is a compressed one. Readers interested in all the details are kindly requested to consult the original publications.10,11 In recent years, samples of trabecular bone extracted from human skeletons have been scanned for various reasons with microcomputed tomography using resolutions typically between 15 and 80 μm. The results are three-dimensional μCT images of human spongiosa, which depict the trabecular bone structure and the marrow cavities, like the one shown in Figure 7.12, which represents one of many images available to the computational dosimetry group at the DEN/UFPE for skeletal dosimetry studies. These μCT images are stacks of rectangular 2D digital images, which form parallepipeds with dimensions varying between 6.7 mm × 6.7 mm × 6.0 mm and 22.8 mm × 17.1 mm × 4.2 mm. The segmented spongiosa macrovoxels of the FAX06 and the MAX06 phantoms have cubic voxel dimensions of 1.2 mm. Introducing the μCT images into the spongiosa macrovoxels means that one has to extract 1.2 mm cubes from the μCT images, here called micromatrices. The MAX06 and the FAX06 phantoms have about 2 million and 1.5 million 1.2 mm cubic spongiosa macrovoxels, respectively. Applying the μCT images of spongiosa to skeletal

FIGURE 7.10 Three-dimensional frontal view of the skeletons of the FAX06 phantom view of the surface of the FAX06 (on the left) and the MAX06 (on the right) phantoms. (From Kramer, R. et al., Phys. Med. Biol., 51, 3331, 2006. With permission.)

FIGURE 7.11 (See color insert following page 524.) Frontal and lateral views of the skeletons and internal organs of the FAX06 and the MAX06 phantoms (adipose and muscle tissues removed).


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FIGURE 7.12 3D μCT image of vertebral trabecular bone.

dosimetry one might think at first about filling these millions of spongiosa macrovoxels of a human skeleton with different micromatrices made of microvoxels containing marrow or trabecular bone. Apart from the expenditure of coding millions of micromatrices, however, this would lead to an explosion of memory space and execution time of the MC code; i.e., this is simply not feasible. Instead, for a given trabecular bone volume fraction, the method developed at the DEN/ UFPE calculates the equivalent dose to the RBM and the BSC in the spongiosa of the MAX06 and the FAX06 phantoms based on a cluster (=parallelepiped made of a small number) of micromatrices observing the following conditions: 1. The cluster of micromatrices must be selected from the CT image in such a way that the trabecular bone volume fraction of the cluster is equal to that of the whole CT image 2. The number of micromatrices of the cluster can be reduced as long as the equivalent doses to the RBM and the BSC do not change within the margins of the statistical errors During MC radiation transport, every time a particle enters a spongiosa voxel coming from a cortical bone voxel the transport is transferred to a micromatrix made of trabecular bone and cavities filled with marrow and BSC, whose matrix index (= its position in the cluster) is selected depending on the particle’s position in the phantom’s macromatrix and in the cluster. However, when a particle travels from one spongiosa voxel to another, then the index of the micromatrix is determined as the index of the neighboring micromatrix in the cluster,

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or the algorithm assumes that the micromatrices repeat periodically if the particle leaves the cluster. Compared to a random selection of micromatrices, this procedure is systematic and periodic, thereby preserving coherently the spongiosa structure represented by the cluster. If the particle leaves the spongiosa, transport is retransferred to the macromatrix of the phantom. During particle transport through the micromatrices, energy deposition is registered in the marrow cavities. If the interaction takes place within a distance of 10 μm from trabecular bone surfaces or from cortical bone surfaces neighboring spongiosa, a special algorithm assigns a fraction of the energy to be deposited to the BSC based on the step length inside the 10 m layer. Otherwise the entire energy of that step is scored to the RBM equivalent dose taking into account the cellularity factor for the corresponding bone. Kramer et al.10,11 have shown that the SP selection applied to a cluster of only 2 × 2 × 2 = 8 micromatrices is capable of accurately calculating the whole-body RBM and BSC equivalent doses for external exposure to photons. Figure 7.13 represents a graphical display of the 8 SP cluster method.

3D micro-CT image of spongiosa Resolution: 30 μm Trabecular bone volume: 15% Size: 7.9 mm × 7.9 mm × 7.7 mm

Cluster of 81.2 mm cubic micromatrices with 15% trabecular bone volume extracted from the 3D micro-CT image

1 micromatrix systematically periodically selected from the cluster at runtime to be used in a spongiosa voxel

Spongiosa

FIGURE 7.13 The use of a cluster of eight micromatrices with 15% trabecular bone volume extracted from a μCT image scanned at a resolution of 30 μm. (From Kramer, R. et al., Phys. Med. Biol., 51, 6265, 2006. With permission.)


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7.3.2.1.4 RBM and BSC Equivalent Doses for External Exposure to Photons 7.3.2.1.4.1 Comparison with RBM Data Determined by Other Methods The results of the 8 SP cluster method based on a vertebral bone sample scanned at 30 μm resolution are compared in Figure 7.14 with the results of the 3CF and the FDR method for the equivalent dose to the RBM of the FAX06 phantoms. The 3CF and FDR CCs are greater than the 8 SP cluster CC for the whole range of incident photon energies between 10 keV and 10 MeV. The reasons for this difference are the additional use of cortical bone and medullary YBM apart from spongiosa for RBM equivalent dose calculation and the application of kerma approximation when using the 3CF/FDR methods. In contrast, the 8 SP cluster method calculates the equivalent dose to the RBM only in soft tissue filled cavities of spongiosa surrounded by cortical and trabecular bone based on secondary electron transport. For external exposure to a real human skeleton, the radiosensitive soft tissues are shielded by cortical, and to a lesser extent, by trabecular bone, especially for photon energies below 500 keV. What serves as a shielding in real human skeletons, becomes a region contributing to the RBM equivalent dose in the case of the homogeneous skeleton used by the 3CF/FDR methods. Figure 7.14 shows that energy depositions by photons in “homogenized cortical bone” and also in “homogenized medullary YBM” increase the equivalent dose to the RBM above the values given by the 8 SP cluster method. The latter considers as contributing regions only the trabecular cavities filled with marrow having densities between 0.99 and 1.02 g cm−3 depending on the cellularity, whereas the 3CF/FDR methods use all skeletal regions with a density of 1.4 g cm−3 as contributors to the RBM equivalent dose, which increases the equivalent dose even more. For incident photon energies above 500 keV the shielding effect of cortical bone becomes less important, because secondary electrons have increasingly enough kinetic energy to penetrate the cortical bone shielding surrounding the spongiosa voxels, and consequently the differences between the 8 SP cluster and the 3CF/FDR curves in Figure 7.14 become smaller. But the 3CF/FDR data remain greater than the 8 SP cluster results especially when incident photon energies approach the range of 3–10 MeV, because the use of the kerma

Equivalent dose/air kerma (Sv/Gy)

1.0 0.8 0.6 0.4 RED BONE MARROW Fax06 phantom Whole skeleton AP BSC thickness: 10 μm

0.2 0.0 0.01

0.1

Photon energy (MeV)

8 SP cluster

FDR

1

10 3CF

FIGURE 7.14 RBM equivalent dose per air kerma free-in-air in the FAX06 phantom as a function of the photon energy for AP-incidence for different methods of skeletal dosimetry. FDR, fluence-to-dose response functions; 3CF, three correction factor method; 8 SP cluster, 8 SP cluster method based on a vertebral bone sample scanned at 30 μm resolution. (From Kramer, R. et al., Phys. Med. Biol., 51, 6265, 2006. With permission.)


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Equivalent dose/air kerma (Sv/GY)

approximation “keeps the energy deposited in the skeleton,” whereas the 8 SP cluster results suggest that the RBM equivalent dose from the secondary electrons entering, created in and leaving the marrow cavities remains constant or finally decreases, implicating a net electron escape from the marrow cavities with increasing energy. Differences between RBM equivalent doses calculated with the 8 SP cluster method and the older methods may also arise from different trabecular bone volume distributions, a fact which on the other hand may explain additionally the relatively good agreement between the 3CF and the FDR methods, because, apart from both methods using homogeneous skeletons and kerma approximation, these two methods are based on the same trabecular chord-length distribution published by Darley54 and Beddoe et al.43 Explanations given above for the differences between the 8 SP cluster and the 3CF/FDR results apply also to the interpretation of the differences to be observed in Figure 7.15, which compares BSC equivalent doses calculated with different methods. Like before, “FDR” represents the FDR functions, here calculated for a thickness of 10 m, while “SKEL” represents the average equivalent dose to the homogeneous skeletal mixture, because the 3CF method does not provide correction factors for the calculation of the BSC equivalent dose. In the past, the “SKEL” equivalent dose was considered to be a conservative estimate for the BSC equivalent dose, which, according to Figure 7.15, is not true, compared to the FDR and the 8 SP cluster results for incident photon energies above 80 and 200 keV, respectively. The 8 SP cluster results are smaller than the FDR results for the above-mentioned reasons, namely the shielding by cortical bone and the secondary electron escape from the spongiosa for high incident photon energies. All BSC CCs are significantly greater than the RBM CCs shown in Figure 7.14, however, because of the additional contribution to the BSC equivalent dose by the photoelectrons released in trabecular bone entering the marrow cavities. These electrons have short ranges; i.e., they deposit their energy in the cavities mainly close to the trabecular bone surface. Figures 7.14 and 7.15 indicate that methods based on energy deposition in all skeletal regions filled with the same average elemental composition using kerma approximation 2.5 Bone surface cells Max06 phantom Whole skeleton AP BSC thickness: 10 μm

2.0 1.5 1.0 0.5 0.0 0.01

0.1

1

10

Photon energy (MeV) 8 SP cluster

FDR

SKEL

FIGURE 7.15 BSC equivalent dose per air kerma free-in-air in the FAX06 phantom as a function of the photon energy for AP-incidence for different methods of skeletal dosimetry. FDR, fluence-to-dose response functions; SKEL, average equivalent dose in the homogeneous mixture; 8 SP cluster, 8 SP cluster method based on a vertebral bone sample scanned at 30 μm resolution.


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will usually overestimate the equivalent dose to the RBM and the BSC, mainly because these methods neglect the shielding effect of cortical bone for lower photon energies; and, additionally the escape of secondary electrons from the trabecular cavities for higher photon energies. At least, usually these earlier methods give a conservative estimate of the equivalent dose to the RBM and the BSC.

Equivalent dose/kerma in air (SV/Gy)

7.3.2.1.4.2 Comparison with RBM Data Determined with Other Exposure Models Figure 7.16 shows male RBM equivalent doses per air kerma free-in-air as a function of the photon energy for AP-incidence for the MAX06 phantom calculated with the 8 SP cluster method, and for the following male phantoms: MAX,5 ADAM,63 GOLEM,64 VIP-MAN,65,66 NORMAN-05,67 and REX.68 ADAM is a MIRD5-type mathematical phantom, while all other computational phantoms are true to nature voxel-based phantoms. MAX06, MAX, NORMAN-05, and REX have organ and tissue masses adjusted to ICRP-recommended data, whereas VIP-MAN represents an adult male much taller and heavier than the ICRP reference adult male, and GOLEM has about 4 kg less whole-body weight compared to the reference weight. The methods applied to the calculation of RBM equivalent dose vary significantly. For the ADAM phantom the 3CF method based on early Spiers factors27 and on an outdated distribution of RBM mass28 was used in a homogeneous skeleton. The CTN method combined with the 3CF method using the KS factors41 was applied to a heterogeneous skeleton in the MAX phantom, while the same method but with the older Spiers factors was used for the GOLEM phantom. For the NORMAN-5 phantom the authors developed a dose factor based on the photon energy and the MEA coefficients of RBM and the skeletal mixture to be multiplied with the photon fluence in a homogeneous skeleton with bone-specific materials taken from ICRU 46,69 which actually represents a type of FDR method. For the REX phantom, considered the adult male ICRP reference phantom, the authors apply a type of modified 3CF method. They use this method in segmented spongiosa

1.2 1.0

Red bone marrow AP

0.8 0.6

CTN: CT number method

0.4

8SP: 8SP cluster method 3CF: 3 correction factor method

0.2 0.0 0.01

0.1

1

10

Photon energy (MeV) MAX_CTN/3CF

MAX06_8 SP

GOLEM_CTN/3CF

NORMAN-05

VIPMAN

REX

ADAM_3CF

FIGURE 7.16 RBM equivalent dose per air kerma free-in-air as a function of the photon energy for AP-incidence for different male phantoms and methods of skeletal dosimetry.

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voxels applying, among others, the KS factors. As the KS factors have been determined by King and Spiers41 for photon radiation incident on a vertebral body, their application only in the spongiosa is problematic because the shielding by the cortical bone shell is neglected. As a consequence, the photon energy used for the KS factors is too high, because not degraded by the cortical bone shell, which probably leads to elevated RBM equivalent doses. Another reason for the REX RBM equivalent doses being higher than the MAX06 RBM equivalent doses could be the fact that in order to match ICRPrecommended skeletal volumes exactly, the authors70 had to make anatomical compromises with regard to the representation of the cortical bone shell surrounding the spongiosa. There are some regions in the skeleton of the REX phantom where the spongiosa is not embedded in cortical bone, which leads to higher RBM equivalent doses because of the missing shielding. Taking advantage of the color photographs of the Visible Human,25 the authors of the VIP-MAN phantom65,66 have segmented the RBM in order to provide a direct macroscopic calculation of RBM equivalent dose. The VIP-MAN RBM CC shown in Figure 7.16 is significantly greater and smaller than the CCs for all other phantoms below 70 keV and above 3 MeV, respectively. This suggests that below 70 keV the VIP-MAN RBM is less shielded by cortical and trabecular bone than the RBM in all other phantoms. Above 3 MeV the VIPMAN RBM decreases because of secondary electron escape. The MAX06 and the REX CCs include also secondary electron escape, however, for these phantoms this effect becomes visible only above 6 MeV showing a much smaller decrease. A comparison of the RBM equivalent doses of the MAX, GOLEM, ADAM, and VIP-MAN phantoms has been discussed earlier,5 identifying, for example, the use of the older Spiers factors, a significantly higher calcium content in bone and less body mass as main reasons for the GOLEM RBM curve showing greater values than the MAX RBM curve. Although the CTN method has a conceptual defect as discussed by Kramer et al.,8 the MAX curve and the MAX06 curve agree reasonably well, because both methods use heterogeneous skeletons, where spongiosa voxels are surrounded by cortical bone voxels. It seems that the shielding effect of cortical bone especially at low photon energies is mainly responsible for the MAX and the MAX06 RBM curves being well below the NORMAN-05 RBM curve, which uses a homogeneous skeleton with relatively great average skeletal densities. Reduced RBM and BSC equivalent doses due to shielding by cortical bone compared to the results from methods that use homogeneous skeletal mixtures have also been observed by Lee et al.71 in a study on external photon exposure to different bone specimens using the PIRT method.62 In addition these authors also found good agreement between their CT image-based method and the CTN method. Figure 7.17 shows female RBM equivalent doses per air kerma free-in-air as a function of the photon energy for AP-incidence for the FAX06 phantom calculated with the 8 SP cluster method, for the FAX phantom calculated with the CTN/3CF method, and for the following female phantoms: EVA,63 DONNA,64 and REGINA.68 The arguments of the discussion for the male CCs in Figure 7.16 also apply here for the RBM equivalent doses of the female phantoms. The whole-body mass of the DONNA phantom, however, is not less, but is significantly greater than, the reference mass. Consequently the RBM equivalent dose of DONNA is less than the RBM equivalent dose of GOLEM. 7.3.2.1.5 Effective Dose for External Exposure to Photons Kramer et al.4,5 have discussed effective dose comparisons between the FAX and the MAX phantoms and other computational phantoms. Similar data for the FAX06 and the MAX06 phantoms will be calculated soon, because the discussion within ICRP with respect to the


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Equivalent dose/kerma in air (Sv/Gy)

1.2 1.0

Red bone marrow AP

0.8 0.6 CTN: CT number method

0.4

3CF: 3 correction factor method 0.2

8SP: 8SP cluster method

0.0 0.01

0.1

1

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Photon energy (MeV) FAX_CTN/3CF

FAX06_8 SP

EVA_3CF

REGINA

DONNA_CTN/3CF

Equivalent dose/kerma in air (Sv/Gy)

FIGURE 7.17 RBM equivalent dose per air kerma free-in-air as a function of the photon energy for AP-incidence for different female phantoms and methods of skeletal dosimetry. 1.5 Effective dose AP 1.0

0.5

0.0 0.01

0.1

1

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Photon energy (MeV) REX/REGINA

MAX06/FAX06

FIGURE 7.18 Effective dose per air kerma free-in-air as a function of the photon energy for AP-incidence for the MAX06/ FAX06 and the REX/REGINA phantoms.

tissues and organs to be included in the calculation of the effective dose terminated only shortly before the approval of the new recommendations in March 2007. As a preview, Figure 7.18 shows a comparison of the effective dose between the FAX06/ MAX06 and the REX/REGINA68 exposure computational phantoms. The percentage differences are 60%, 31.4%, 19.3%, 10.0%, 5.7%, 2.9%, and 1.8% for incident photon energies of 10, 15, 20, 30, 40, 50, and 60 keV, respectively. It is difficult to explain these differences because anatomical descriptions of the REX and the REGINA phantoms have not been published so far. But one can assume that anatomical and/or compositional differences between frontal superficial organs and tissues of the phantoms involved

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are responsible for the observed percentage differences. The value of the REX/REGINA effective dose for 70 keV seems to be too small, an error also to be observed for the RBM equivalent doses in Figures 7.16 and 7.17. Between 100 keV and 1 MeV the effective doses for the two exposure computational phantoms agree very well, which also supports the assumption that the deviations seen for lower energies are related to differences between superficial organs. Noticeable differences can be observed also above 1 MeV; however they never exceed 5%.

7.4 Conclusions Human phantom development is an ongoing process. Meanwhile new types of phantoms, called hybrid phantoms,72,73 have been published, which supersede the traditional voxel phantoms with respect to the true to nature representation of anatomical structures. Nevertheless, FAX06 and MAX06 represent milestones in the phantom development, because they were the first adult human voxel phantoms with ICRP89-based organ and tissue masses, with ICRP89-based segmented cortical bone and spongiosa in their skeletons and with a trabecular microstructure introduced into their spongiosa voxels where skeletal dosimetry based on μCT images of trabecular bone has been successfully applied for the first time for a complete skeleton embedded in a human body. The ICRP is going to recommend the use of traditional voxel phantoms for radiation protection. Therefore it seems that this type of human phantom will be still around for some time to come. Readers interested in using the FAX06 and/or the MAX06 phantoms or software tools based on these phantoms should visit the Web site http://www.grupodoin.com and follow the link Caldose.

Acknowledgments We acknowledge with thanks the permission of IOP Publishing Ltd. in allowing reproduction of previously published material from the journal Physics in Medicine and Biology. The authors would also like to thank the Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq) and the Fundação de Amparo à Ciência do Estado de Pernambuco (FACEPE) for their financial support.

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4. Kramer, R. et al. All about FAX: A Female Adult voXel phantom for Monte Carlo calculation in radiation protection dosimetry, Phys Med Biol, 49, 5203, 2004. 5. Kramer, R. et al. All about MAX: A male adult voxel phantom for Monte Carlo calculations in radiation protection dosimetry, Phys Med Biol, 48, 1239, 2003. 6. ICRP. Basic Anatomical and Physiological Data for Use in Radiological Protection: Reference Values, ICRP Publication 89, Pergamon Press, Oxford, 2003. 7. ICRP. The 2007 recommendations of the International Commission on Radiological Protection, ICRP publication 103, Ann ICRP, 37, 1, 2007. 8. Kramer, R. et al. MAX06 and FAX06: Update of two adult human phantoms for radiation protection dosimetry, Phys Med Biol, 51, 3331, 2006. 9. ICRP. Basic Anatomical and Physiological Data for Use in Radiological Protection: The Skeleton, ICRP Publication 70, Pergamon Press, Oxford, 1995. 10. Kramer, R. et al. Skeletal dosimetry in the MAX06 and the FAX06 phantoms for external exposure to photons based on vertebral 3D-microCT images, Phys Med Biol, 51, 6265, 2006. 11. Kramer, R. et al. Skeletal dosimetry for external exposure to photons based on microCT images of spongiosa from different bone sites, Phys Med Biol, 52, 6697, 2007. 12. Kramer, R. et al. Equivalent dose to organs and tissues in hysterosalpingography calculated with the FAX (Female Adult voXel) phantom, Br J Radiol, 79, 893, 2006. 13. Kramer, R. et al. MAX meets ADAM: A dosimetric comparison between a voxel-based and a mathematical model for external exposure to photons, Phys Med Biol, 49, 887, 2004. 14. Kramer, R. et al. Application of the MAX/EGS4 exposure model to the dosimetry of the Yanango radiation accident, Phys Med Biol, 50, 3681, 2005. 15. Kramer, R., Khoury, H.J., and Vieira, J.W. CALDOS_X—An on-line interface for absorbed dose assessment in diagnostic radiology, 11th International Conference on Radiation Shielding, Callaway Gardens, Pine Mountain, GA, April 13–18, 2008. 16. Kramer, R., Khoury, H.J., and Vieira, J.W. CALDOS_X—A software tool for the assessment of organ and tissue absorbed doses in diagnostic radiology, 12th International Congress of the International Radiation Protection Association, Buenos Aires, Argentina, 19–24 October, 2008. 17. Nelson, W.R., Hirayama, H., and Rogers, D.W.O. The EGS4 Code System, SLAC-265, Stanford Linear Accelerator Centre, Stanford University, Stanford, CA, 1985. 18. Kawrakow, I. Accurate condensed history Monte Carlo simulation of electron transport: I. EGSnrc, the new EGS4 version., Med Phys, 27, 485, 2000. 19. Kawrakow, I. and Rogers, D.W.O. The EGSnrc code system: Monte Carlo simulation of electron and photon transport, NRC Report PIRS-701, National Research Council of Canada, Ottawa, 2003. 20. Allison, J. et al. Geant4 developments and applications, IEEE Trans Nucl Sci, 53, 270, 2006. 21. Cassola, V.F. et al. Organ equivalent doses in the MAX06 and the FAX06 phantoms for external exposure to photon radiation calculated with the EGSnrc and the GEANT4 Monte Carlo codes, International Nuclear Atlantic Conference, Santos, SP, Brazil, September 30–October 5, 2007. 22. Zubal, I.G. et al. Computerized three-dimensional segmented human anatomy, Med Phys, 21, 299, 1994. 23. Zubal, I.G. et al. High resolution, MRI-based, segmented, computerized head phantom, in The Zubal Phantom Data, Voxel-Based Anthropomorphic Phantoms, http://noodle.med.yale.edu/ phantom, 2001. 24. Stuchly, M. The Zubal Phantom Data, Voxel-based Anthropomorphic Phantoms, http://noodle.med. yale.edu/phantom, 1996. 25. Spitzer, V.M. and Whitlock, D.G. Atlas of the Visible Human Male, Jones and Bartlett Publishers, Sudbury, MA, 1998. 26. Sjogreen, K. The Zubal Phantom Data, Voxel-Based Anthropomorphic Phantoms, http://noodle.med. yale.edu/phantom, 1998. 27. Spiers, F.W. Sources, fields, measurements and applications, in Transition-Zone Dosimetry Radiation Dosimetry, vol. III, 2nd edn., Academic Press, New York, 1969. 28. ICRP. Report of the Task Group on Reference Man, ICRP Publication 23, Pergamon Press, Oxford, 1975.

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8 The University of Florida Pediatric Phantom Series Choonsik Lee, Daniel L. Lodwick, Deanna Hasenauer Pafundi, Scott R. Whalen, Jonathan L. Williams, and Wesley E. Bolch

CONTENTS 8.1 Introduction ............................................................................................................... 199 8.2 Materials and Methods ............................................................................................ 200 8.2.1 UF Newborn Voxel Phantom ..................................................................... 200 8.2.2 Phantom Development Procedure ............................................................ 200 8.2.2.1 Polygonization of the Voxel Phantom ....................................... 200 8.2.2.2 NURBS Modeling ......................................................................... 201 8.2.2.3 Voxelization of the Polygon Computational Phantom............ 202 8.2.3 Standardization of Hybrid Phantom ........................................................ 203 8.2.3.1 Match Body Dimension to Anthropometric Data ................... 203 8.2.3.2 The Matching of the Organ Volume to the ICRP Publication 89 Values ................................................................... 204 8.2.3.3 Alimentary and Respiratory Systems ....................................... 204 8.2.3.4 Creation of the Male Newborn Phantom from the Female Phantom .................................................................... 205 8.2.4 Extended Development of Pediatric Phantoms ....................................... 206 8.3 Results and Discussion ............................................................................................ 206 8.3.1 Voxelization Algorithm ............................................................................... 206 8.3.1.1 UF Hybrid Phantoms for Newborn Male and Female ........... 208 8.3.2 Comparison with UF Voxel Newborn Phantom and ICRP89 ............... 214 8.3.3 Advantages of Hybrid Newborn Phantom .............................................. 214 8.3.4 The Extended Development of Older Phantoms..................................... 216 8.3.5 Pediatric MicroCT-Based Skeletal Computational Phantoms ............... 217 8.4 Conclusions ................................................................................................................ 217 Acknowledgment ................................................................................................................. 218 References ............................................................................................................................. 218

8.1 Introduction This chapter reviews the development of a series of pediatric whole-body phantoms based on non-uniform rational B-spline (NURBS) surfaces as constructed at the Advanced Laboratory for Radiation Dosimetry Studies (ALRADS), University of FL, Gainesville, FL. We introduce the development procedure mainly for the newborn phantom, as that was the 199

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first hybrid phantom in which NURBS technology was incorporated. Other older phantoms ranging from 1 to 15 years old were extensions of this newborn phantom. We further subdivided the phantom series into two groups—Group A and Group B. Group A is composed of male and female phantoms of the newborn, 1 year old, 5 years old, 10 years old, and 15 years old, where phantom stature, total weight, and individual organ masses are targeted to within 1% of ICRP Publication 89 reference values. While the newborn phantoms were constructed from whole-body CT scans of a newborn cadaver, the remaining phantoms of Group A were constructed from segmented head and chest–abdomen–pelvis CT images from live pediatric patients all of normal anatomy. Group B phantoms are constructed as upwardly and downwardly scaled versions of the Group A phantoms, thus providing a phantom at each 1 year interval, from newborn to 15 years old. The intent of the University of Florida (UF) pediatric series is to provide a reference library of phantoms for matching to individual patients in organ dose assessment in radiography, fluoroscopy, CT imaging, and radiotherapy. In traditional medical dose reconstruction, 50th percentile reference phantoms are assigned to individual patients based upon their age. In the UF approach, phantoms are assigned to individual patients, not based on age, but on trunk height, thus reducing residual uncertainties in organ volumes from as high as 50% to as low at 15%. Next, leg lengths are adjusted to match patient statue, and finally, the outer body contour of the phantom is adjusted to match as closely as possible the patient’s body distribution of subcutaneous fat and/or muscle. The overall approach thus yields a highly patient-specific phantom for dose assessment than afforded by existing applications. Finally, we discuss in this chapter our efforts to provide, through tissue sampling and microCT image analysis, detailed and agespecific skeletal computational phantoms for each phantom of the UF series.

8.2 Materials and Methods 8.2.1 UF Newborn Voxel Phantom UF hybrid newborn phantoms represent an evolution in the UF newborn voxel phantom developed by our same research group.1 We segmented the UF newborn voxel phantom from 485 CT slices of 6 day old female cadaver, which weighed 3.83 kg. The subject died in an attempt to correct congenital abnormalities of the great vessels, and was scanned within 24 h of death. Unlike typical CT scans of live patients, the arms were positioned parallel to the body and included within the scan coverage. We examined the CT images, and found that the cadaver was free of any physical defects that might cause problems in phantom development. We also employed two additional datasets from a 1 month old patient and a 2 month old male to replace the collapsed lungs of the 6 day old cadaver, and to model adrenal glands which were invisible in the original CT data, respectively. A total of 66 different regions were segmented through several image processing steps and manual contouring. 8.2.2 Phantom Development Procedure 8.2.2.1 Polygonization of the Voxel Phantom The UF voxel newborn phantom in the three-dimensional (3D) voxel matrix form should be converted into a polygon mesh form, which is the base framework for the NURBS surface

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modeling. We used 3D-DOCTOR (Able Software Corp., Lexington, MA), a 3D modeling and image processing software for tomography data, to convert the voxel geometry of the UF newborn voxel phantom into a polygon mesh geometry. We imported a binary voxel phantom array consisting of 480 × 211 × 485 voxels into 3D-DOCTOR, and the organ contours were extracted from the binary file by utilizing an interactive segmentation tool organ-by-organ. The interactive segmentation tool automatically rendered organ contours on two-dimensional (2D) pixel slices with a certain organ tag assigned to the thresholding windows of the interactive segmentation tool. Contours for contents and gas in the intestinal tract such as the stomach, the colon, and the small intestine were not extracted, since we assumed that the intestinal tract was filled with soft tissue in the hybrid phantoms. We only obtained the outer contours from those walled organs, including the urinary bladder and the gall bladder, of which the wall thickness was exactly redesigned using NURBS surfaces. Both outer and inner contours, however, were acquired for bone sites such as cranium and vertebrae. Other than the internal organs, the skeletal contours were completely resegmented from the original CT data to more accurately describe the skeleton volumes of the newborn subject. After we obtained the contours of the whole anatomy in the voxel phantom, we generated 3D polygon mesh computational phantoms by the built-in 3D rendering function, and exported them into a Wavefront Object file format, which retains the dimensional, the position, and the organ tag information of the original voxel phantom. 8.2.2.2 NURBS Modeling We incorporated polygon mesh computational phantoms extracted from the voxel phantom into a NURBS modeling tool to generate NURBS anatomical surface computational phantoms. We employed Rhinoceros (McNeel North America, Seattle, WA), a NURBS modeling, rendering, and analysis software, to model the NURBS surfaces that exactly fit the polygon mesh computational phantoms generated from 3D-DOCTOR. To manipulate polygon anatomy computational phantoms independently and effectively, we generated Wavefront Object files from 3D-DOCTOR within five groups (body contour, alimentary system, respiratory system, other soft tissue organs, and the skeleton) and imported as five different layers in Rhinoceros. Each layer could be turned on or off, and was not affected by objects in other layers. Smooth NURBS surfaces were made from polygon mesh computational phantoms on an organ-by-organ basis. First, we obtained several contours from polygon mesh organ computational phantoms as needed, and the NURBS surfaces were fit to the contours using the LOFT tool. After generating NURBS surfaces, we removed the original polygon mesh computational phantoms from the geometry. The NURBS surfaces were generated for all of the internal organs and tissues, excluding the brain and skeleton, of which the shapes could not be effectively described by the NURBS surfaces. Pafundi et al. (2008) have presented the details of skeleton modeling in hybrid phantoms. Even though the eyeballs, lenses, ovaries, urinary bladder, and breast can be described by spherical or ellipsoidal shapes, it is usually difficult to model smooth 3D shapes by stacking manually segmented 2D contours in the development of a voxel phantom. In the context of the hybrid phantom, we carefully designed the NURBS-based stylistic computational phantoms by closely referring to their original shapes and positions. Eyes and ovaries were modeled as spherical objects, and the lens as a flatten ellipsoid. The breasts are not well developed at newborns, but were modeled by small button-like ellipsoid and placed at the corresponding position to facilitate breast dose calculation. The shape of the urinary bladder varies depending on the amount of urine inside, so this organ was also stylistically modeled by a walled ellipsoid. We modeled the contents

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of the walled organs, such as the heart, the gall bladder, and the urinary bladder, by contracting organ surfaces. Each control points surrounding the NURBS surfaces was inwardly moved to a normal direction so that another NURBS surfaces for the contents could be generated. In this way, the volumes of walled organs were exactly matched to the reference data. We designed in a new way the tongue and the tonsils, of which the reference masses are presented in the International Commission on Radiological Protection (ICRP) Publication 89, in the UF hybrid phantoms. We described the tongue by bending and flattening a truncated ellipsoid. We modeled two palatine tonsils with spheres, and positioned them on the left and right sides toward the back of the tongue. We described the three layers of kidneys, the cortex, the medulla, and the pelvis, by contracting the original kidney volumes and matching each volume to the reference data of ICRP89: the cortex represents about 70% of the volume of the kidneys, the medulla about 25%, and the pelvis about 5%. 8.2.2.3 Voxelization of the Polygon Computational Phantom The existing Monte Carlo transport codes cannot recognize NURBS or polygon geometries, and so the NURBS computational phantoms cannot be directly incorporated into Monte Carlo radiation transport codes. A hybrid phantom voxelization process is thus key technology for the phantom in dosimetry studies. A voxelization algorithm for the purpose of this study should meet the following criteria. First, it must be able to voxelize multiple polygon objects involved in the whole-body NURBS computational phantom in the 3D voxel medium. Second, it should have the capability to handle outermost surfaces as well as artificial holes which are a problem in case of the cranium or vertebrae. Third, the original NURBS volumes must be as accurately maintained in the resulting voxel volume as much as possible. To meet these criteria, we wrote an in-house MATLAB® (The MathWorks, Inc., Natick, MA) code, named Voxelizer, exclusively for the purpose of this study. Voxelization was performed in three steps: triangulation, intersection calculation between polygon and z-grid, and ray-casting process. All smooth NURBS surfaces were triangulated using the built-in function of Rhinoceros, where the user can specify a meshing tolerance (MT) defined as the maximum angle between adjacent faces in the resulting polygon mesh. Smaller values of MT result in slower triangulation, more accurate meshes, and a higher polygon count. After the NURBS surfaces were triangulated into polygons, we saved the vertices of each polygon in ASCII Raw Triangles format. We then imported the vertex data into the Voxelizer, and calculated polygon-by-polygon the intersections between single polygons and z-grid, dictated by a predefined voxel resolution (VR). If the z-grid intersects a given polygon, two vertices of the intersection are stored in an array. Finally, we assigned user-defi ned organ tags to voxels involved in certain organ computational phantoms by determining if a voxel is inside or outside a given organ surface. In order to test whether or not a voxel is inside an object surface, one has to count the number of times a ray traveling from the center of the voxel to minus infinity of y-axis intersect surface of the polygon. If the number is odd, the point is inside the surface. How close the resulting voxel phantom is to the original NURBS computational phantom depends on two parameters: the MT and VR. To quantitatively understand the effect of those parameters on volume difference between NURBS computational phantom and voxel phantom, we performed a sensitivity study. The largest and smallest NURBS organs, the liver and the pituitary gland, were chosen for this sensitivity study. First, those organs were triangulated using five different MTs of 50°, 40°, 30°, 20°, and 10° and the volumes of


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the resulting polygon computational phantoms were compared with those of the original NURBS computational phantoms. Second, they were voxelized using five different VRs, and the volumes of the voxelized organs were compared with the original polygon organ volumes. 8.2.3 Standardization of Hybrid Phantom It is generally desirable to make the computational phantom representative of various body dimensions and anatomical characteristics of a reference individual or patient to perform consistent and reproducible dosimetry calculations for radiation protection and medical situations. For the purpose of standardization, we made the following efforts by referring to the literature resources of anthropometric and anatomical data. 8.2.3.1 Match Body Dimension to Anthropometric Data To match the body dimensions of the newborn phantom to anthropometric data, we made two approaches to the NURBS computational phantom. First, we developed a deformable outer body contour and the initiative of a posture change to the original cadaver-based voxel phantom. We limited the original body contours extracted from UF voxel newborn phantom to fixed body dimensions and postures, and only the uniform scaling of the whole anatomy could be made by changing the segmented VR. We developed new body contours, in which dimensions of the head, torso, and limbs can be independently formed, from framework of the artistic 3D CHILD computational phantoms given by the 3D modeling community (http://www.3dcafe.com). We imported the polygon mesh computational phantom of the 3D CHILD into Rhinoceros, separately generated the NURBS surfaces for the head, the torso, the arms, the legs, the hands, and the feet, and combined them all into the whole-body contour. We carefully deformed each body contour to match the original body contour of the UF voxel newborn phantom by manipulating the control points surrounding the NURBS surfaces. We also deformed the body contours of the fingers and toes to fully represent the individual bones of the fingers and toes. Each body part of the resulting NURBS body contour is deformable to match any targeted anthropometric data. This deformability is also a useful feature used to accommodate the difference in body dimensions of other age groups, as well as different body shapes, such as the 25th or 75th height and weight percentiles. Second, we carefully modeled the original posture of the newborn cadaver in a curled posture with bent arms and legs, and thus represented a more neutral posture. We rotated and transported the arm bones (humerus, radii, ulnae, and hand bones) and the leg bones (femur, tibiae, fibulae, patellae, ankle, and feet bones) accordingly. Currently, three anthropometric data sets are available for the newborn: the standing height, the sitting height, and the head circumference (HC). We obtained the standing and the sitting heights for newborn from ICRP Publication 89.2 We took the Crown-Rump Length (CRL) of the newborn as a suitable substitute for sitting height. CRL is defined as the distance between the vertex of the skull and the ischial tuberosities. The standing height and the CRL of the reference newborn were 51 and 34 cm, respectively. We calculated the HC as 33.1 cm, using a correlation equation between HC and CRL as reported by Yang et al.3 We matched perfectly the standing height of the original UF voxel newborn phantom to the ICRP reference height, and the CRL was 2.9% shorter than ICRP data. The HC of the UF voxel newborn phantom was 32.5 cm, which was 1.7% shorter than the reference value.

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8.2.3.2 The Matching of the Organ Volume to the ICRP Publication 89 Values We matched all of the organs and tissues in the hybrid phantom to the reference data of the ICRP Publication 89, with the exception of the brain, eye balls, skin, and walled organ contents. In the case of voxel phantom development, this matching process is usually performed by manually adding or removing pixels in 2D slice images, so that organ volumes can be accurately adjusted to the target values. As for hybrid phantoms, however, this matching process should be performed by manipulating the NURBS control points in the 3D medium, which is anatomically more realistic than pixel-based modification, as performed in voxel phantoms. Even though the volume of the eyeballs in the voxel phantom could be accurately matched to the reference values by modifying the pixel map of 2D slices, it is difficult to guarantee if the resulting 3D object would be anatomically realistic. As for hybrid phantoms, an expansion of the 3D spheres representing the eyeballs was not allowed beyond a certain space within the cranium, so it was impossible to increase the eye volume to the reference values. This issue was the same problem for the brain data, as the cranium restrained it in the same manner. No attempts were made to expand the cranium volume to accommodate these restrictions, since we gave first priority to a realistic skeletal computational phantom that faithfully represented the skeleton of the ICRP89 reference newborn. We adjusted the total body mass of the voxelized phantom to the reference mass of 3.5 kg by modifying the outer body contour. We generated skin by assigning a skin index to the outermost voxel layer following NURBS phantom voxelization. To match the skin volume to reference values, we adjusted the VR to the skin thickness of the Oak Ridge National Laboratory (ORNL) newborn phantom: 0.07 cm.4 We did not match the volumes of the contents of the walled organs, such as the small intestine, the colon, the stomach, the urinary bladder, and the heart, to the reference data as described in Lee et al.5 The NURBS surfaces of organs were so close to each other that a change in one organ’s volume would affect the volume of the neighboring organs. In an attempt to match the organ volumes to reference data at the NURBS modeling stage, every surface intersection should be avoided, since Rhinoceros cannot identify intersections between pairs of NURBS surfaces. Although overlapping volumes should be removed from those both organs, Rhinoceros independently calculates volumes of each organ without considering organ overlap. As a result, there could be differences in volumes reported in the NURBS computational phantom and the corresponding voxel phantoms. To avoid these volume change problems, we carefully adjusted and separated the NURBS organ surfaces from each other without sacrificing the original anatomical realism. Since it was difficult to completely recognize any intersections in the NURBS modeling stage, this adjustment was performed through an iterative process of adjusting the NURBS surfaces and recognizing the overlaps in the voxelized computational phantoms where the organ overlaps are easily seen in 2D image slices. 8.2.3.3 Alimentary and Respiratory Systems Special consideration was given to modeling the alimentary and respiratory systems. Although we obtained the organs in these two systems from the UF voxel newborn phantom, anatomical problems were evident, due to image discontinuities and other defects. These problems are most likely caused by poor vision-based segmentation of low contrast CT images. In the current study, we applied a stylistic approach to alimentary and respiratory systems in the context of hybrid phantom. First, the central luminal traces of the esophagus, the small intestine, the colon, and the rectosigmoid were approximately as given in the UF voxel newborn phantom and


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her original CT images. In the small intestine, we found it nearly impossible to follow the central track, so that wavy contours were constructed according to reference anatomy resources; whereas we obtained those of the colon from the base voxel phantom. We generated a NURBS pipe structure of a given radius along these central tracks. We also made organ contents along the central track with a pipe of smaller radius. We did not attempt the separation of the wall and content for the esophagus, since it is nearly collapsed except when a food bolus is passing through. The stomach is transformable, depending on the stage of food digestion. Zankl et al.6 investigated the stomach dose of seven adult male and female voxel phantoms and reported that the stomach position varies largely among different individuals and even in a single individual. Therefore, a stylistic NURBS stomach computational phantom was redesigned referring to the stomach position and shape within the original voxel phantom, along with the anatomy literatures. We matched the anatomical features of the resulting NURBS alimentary system to reference data provided by the ICRP Publication 89.2 The publication reported reference lengths of the esophagus, the small intestine, and the colon as 10, 80, and 45 cm, respectively. These are physiological length measured in a living person, rather than anatomical lengths that are measured at autopsies, and are usually longer than the physiological lengths. We matched the central tracks of those three organs to the reference lengths within 5%. As for the colon, the original central track is much shorter than the reference length. Through a discussion with experienced radiologist, we extended the central track of transverse colon to the lower small intestine region. We also matched the organ masses of the esophagus, the stomach wall, the small intestine, and the colon to the reference masses of the ICRP 89. Radiosensitive tissues in the respiratory system are composed of an anterior nasal passage, a posterior nasal passage, a mouth, a nasal and an oral pharynx, a larynx, a trachea, a main bronchi, and the lungs.7 The anterior nasal passage is defined as extra-thoracic (ET)1, and ET2 comprises the posterior nasal passage, the mouth, the pharynx, and the larynx. Additionally, researchers categorize the trachea and the main bronchi into the bronchial region (BB). We designed and attached the external nose, including the two nostrils, to the face at the same position of the nose in the UF voxel newborn phantom. We obtained the central tracks of trachea and main bronchi from the newborn voxel phantom and the NURBS pipes, with a certain thickness generated along the tracks. We also matched the resulting respiratory system to the reference data provided in the ICRP 89. Two reference values, the masses of the larynx and the trachea, were available for the newborn individual: 1.18 and 0.45 g, respectively. We adjusted the volumes of the larynx and the trachea by manipulating the control points. The reference lung mass of newborn is 60 g, and the volume would be calculated as 202.7 cm3 by using a nominal lung density of 0.296 g cm−3 adopted by ORNL newborn stylized phantom.4 However, the volume of the NURBS lungs was 103.88 cm3, which is significantly smaller than the targeted reference volume at this density. Since any modification of the lung volumes was not permitted due to the fixed geometries of the rib cage, the heart, and the thymus, an effective lung density was assigned so that the total lung mass would match its reference mass. A similar approach was adopted by other investigators developing reference stylized or voxel phantoms.8,9 Lung density assigned to hybrid newborn phantom was 0.572 g cm−3.

8.2.3.4 Creation of the Male Newborn Phantom from the Female Phantom We developed the hybrid newborn phantom of the opposite sex using the original female phantom. The implicit assumption we made here is that there are no significant differences in organ volumes, positions, and depths between the reference male and the reference


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female newborn child, with the exception of the reproductive organs. We obtained computational phantoms of the male-specific organs (scrotum, testes, and prostate) from the UF 9 month male voxel phantom10 and we inserted them in the hybrid female phantom following the corresponding removal of the ovaries and the uterus. We reduced the volumes of the male sex organs to match the reference data in ICRP 89. The bladder of the UF 9 month male voxel phantom also replaced the urinary bladder in the female phantom, since the uterus usually presses it anteriorly in the female anatomy. 8.2.4 Extended Development of Pediatric Phantoms We applied the same technology outlined above for the newborn phantom to the older phantom of the UF series: 1, 5, 10, and 15 year old, both male and females. Contours of major organs and tissues were converted or segmented from the previous UF voxel phantoms, and the computed tomography (CT) data as needed. We reconstructed and imported the polygon mesh computational phantoms for the major organs and tissues into Rhinoceros for the NURBS surface modeling. We matched the resulting NURBS/polygon mesh computational phantoms representing the body contour and the internal anatomy to anthropometric data and the reference organ mass data provided by the CDC and the ICRP, respectively. Finally, we completed a total of eight hybrid male and female phantoms, where we matched a total of eight anthropometric data categories to standard values within 4%, and organ masses matched to ICRP data within 1%, with the exception of the skin. We voxelized the hybrid phantoms from the NURBS phantoms at resolutions of skin thickness for Monte Carlo calculation.

8.3 Results and Discussion 8.3.1 Voxelization Algorithm To convert a NURBS-based hybrid newborn phantom into a voxel phantom, which is actually utilized for Monte Carlo dosimetry calculation, researchers developed a voxelization process. Figure 8.1 shows the voxelization process of newborn left lung as an example.

(a) Original voxel

(b) Polygon mesh

(c) NURBS

(d) Voxel (2 × 2 × 2 mm3)

(e) Voxel (1 × 1 × 1 mm3)

FIGURE 8.1 Example of voxelization process starting from (a) original voxel model of UF voxel newborn phantom through, (b) polygon mesh model, and (c) NURBS model to (d) voxelized model with different VR (1 × 1 × 1 mm3 and 2 × 2 × 2 mm3). (From Lee, C. et al., Phys. Med. Biol., 52, 3309, 2007. With permission.)


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The original voxel lung of the UF voxel newborn phantom was converted to a polygon mesh lung computational phantom using 3D-DOCTOR (Figure 8.1a and b). We imported a polygon mesh lung computational phantom into Rhinoceros and generated a smooth NURBS-based lung computational phantom (Figure 8.1c). We triangulated the NURBSbased lung using a MT of 10°, and then voxelized it using two VRs: 2 × 2 × 2 mm3 and 1 × 1 × 1 mm3 (Figure 8.1d and e). Even though the resolutions of the resulting two voxel lung computational phantoms were lower than original VR of UF newborn voxel phantom, 0.0562 × 0.0562 × 0.0959 cm3, the smooth surfaces of real human anatomy were better described in hybrid lung computational phantoms. To study the effect of meshing the tolerance and the VR on the resulting organ volume, we chose to voxelize the largest and smallest NURBS organs, the liver and the pituitary gland, using difference parameters. First, we triangulated the NURBS organs using different MTs, 50°, 40°, 30°, 20°, and 10°. A comparison of the liver and the pituitary gland volumes for different MTs is tabulated in Table 8.1. The percent differences between the original NURBS volume and the triangulated polygon volume are presented in this table as well. As the MT decreases from 50° to 10°, the polygon organ volumes become closer to the NURBS volumes, and their corresponding percent differences decrease correspondingly. As for the liver, about 21 times more polygons were needed to reduce the volume difference from 1.904 to 0.105. The liver converged on its NURBS volume faster than the smaller pituitary gland did. Differences between the NURBS and the polygon computational phantoms were thus reduced to less than 1% by using an MT of 10°. We voxelized the polygon liver and pituitary gland that had been triangulated using MT of 10° by using different VRs. A comparison of organ volumes for different VRs is summarized in Table 8.2 with percent difference between polygon and voxel organs. As the VR increased, voxel organ volume converged to polygon organ volumes. We selected VRs to give a comparable difference for two different-sized organs. For both organs, we observed fluctuations in the volume differences for differences less than 1%. This feature may be caused by relatively low VRs, which reflect small amounts of voxel change below 1%.

TABLE 8.1 Effect of MT on Polygon Count, Organ Volume, Percent Differences in Organ Volumes for Both the Liver and Pituitary Gland Organs Liver

Pituitary gland

MT (°)

Polygon Count

Polygon Organ Volume (cm3)

Difference (%)a

50

3048

121.6750

−1.997

40

5098

122.8179

−1.076

30

8796

123.2308

−0.744

20

18816

123.7330

−0.339

10

64892

124.0242

−0.105

50

48

0.0744

−23.216

40

224

0.0908

−6.267

30

224

0.0908

−6.267

20

960

0.0954

−1.596

10

3968

0.0965

−0.401

Source: Lee, C. et al., Phys. Med. Biol., 52, 3309, 2007. With permission. Note: Volumes of the NURBS lungs and pituitary gland are 124.6643 and 0.0969 cm3, respectively. a Difference (%) = ( (Polygon volume – NURBS volume)/NURBS volume) × 100.


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TABLE 8.2 Effect of VR on Organ Volume, Voxel Count, and Volume Differences for the Liver and Pituitary Gland That Were Polygonized from NURBS Models Using an MT of 10° Organs Liver

Pituitary gland

VR (cm)a

Voxel Organ Volume (cm3)

Voxel Count

Difference (%)b

1.5

131.6250

39

1

125.0000

125

5.694 0.374

0.5

122.7500

982

−1.432

0.2

124.0960

15512

−0.352

0.1

124.0250

124025

−0.409

0.1

0.1010

101

4.663

0.0663

0.0968

332

0.265

0.05

0.0973

778

0.777

0.02

0.0965

12061

−0.012

0.01

0.0965

96460

−0.041

0.005

0.0965

772024

0.003

Source: Lee, C. et al., Phys. Med. Biol., 52, 3309, 2007. With permission. Note: Volumes of the polygon liver and pituitary gland are 124.5338 and 0.0965 cm3, respectively. a X, Y, and Z sides of the all voxels are of the magnitude indicated in the column. b Difference (%) = ( (Voxelized volume – Polygon volume)/Polygon volume) × 100.

8.3.1.1 UF Hybrid Phantoms for Newborn Male and Female Researchers developed UF Hybrid (UFH) newborn phantoms by applying polygonization, NURBS modeling techniques, and voxelization processes to previous UF voxel newborn phantom. Researchers have developed hybrid phantoms with newborn male anatomy from female phantoms by replacing female-specific organs (ovaries and uterus) with male-specific organs (testes and prostate), which were obtained from UF 9-month voxel phantoms. Figure 8.2 shows the front views of the 3D rendering of the UF voxel newborn phantom, and the NURBS- and voxel-versions of UFH female newborn phantoms. We named the NURBS-version of hybrid phantom UFH-NURBS, and the voxel-version UF-voxel. We made the skin and the muscle transparent to better view the internal organs and the skeleton. As shown in Figure 8.2a, it is relatively difficult to distinguish organs in the abdominal region of the UF voxel newborn phantom, due to discontinuities in the z-direction, with the exception of the skeleton. NURBS surfaces in the UFH-NURBS newborn phantom have better continuity and more smoothness of the abdominal anatomy, especially in the large and small intestine, as shown in Figure 8.2b. We converted this detail to the UFH-voxel newborn phantom, even though its VR (3.43 × 10−4 cm3) is lower than that of the original UF voxel newborn phantom (3.03 × 10−4 cm3). The detail would be enhanced as the VR increases. The difference of anatomy between the male and female phantoms is depicted in Figure 8.3, where the left lateral 3D views of the lower abdominal regions with skin and left-hand bones are removed. We replaced the female-specific organs, the ovaries, and the uterus by the male-specific organs, testes, and prostate. We can observe the differences in the shape and position of the urinary bladder between the male and female anatomy. The organs and tissues in the UFH-NURBS male and female newborn phantoms are listed in Table 8.3, along with the mass and reference density obtained from the ICRU Report 46.11 We added comments on surrogate density for some organs, for which newborn


(a) UF voxel newborn phantom

209

(b)UFH-NURBS newborn phantom

(c)UFH-voxel newborn phantom

FIGURE 8.2 Front views of 3D rendering of (a) UF voxel newborn phantom, (b) UFH-NURBS female newborn phantom, and (c) UFH-voxel female newborn phantom. VR of (a) UF voxel and (c) UFH-voxel newborn phantoms were 0.0562 × 0.0562 × 0.0959 cm3 and 0.07 × 0.07 × 0.07 cm3, respectively. Body contours were made transparent for better viewing internal organs and skeleton. (From Lee, C. et al., Phys. Med. Biol., 52, 3309, 2007. With permission.)

Colon Small intestine Left ovary Urinary bladder Uterus

(a) UFH-NURBS female phantom

Colon Small intestine Urinary bladder Prostate Scrotum Left teticle Penis

(b) UFH-NURBS male phantom

FIGURE 8.3 Left lateral views of 3D rendering of (a) female and (b) male UFH newborn phantoms showing difference of gender-specific anatomy. Skin and left hand bones including cartilage were made transparent to better view gender-specific organs.

Alimentary system Tongue Salivary glands Tonsils Esophagus—wall Stomach—wall Stomach—contents Small Intestine—wall Small Intestine—contents Colon Right—wall Right—contents Left—wall Left—contents

Respiratory system ET1 (anterior nasal layer) ET2 (posterior nasal layer) ET2 (oral cavity layer) ET2 (larynx) ET2 (pharynx) Trachee Bronchi—extrapulmonary Lungs (inclusive of blood) Left lung Right lung Total

Organ System

Muscle (newborn) Ave soft tissue (male) Ave soft tissue (male) Gastrointestine Gastrointestine Ave soft tissue (male) Gastrointestine Ave soft tissue (male) Gastrointestine Ave soft tissue (male) Gastrointestine Ave soft tissue (male)

1.03 1.03 1.03 1.03

Calculated Calculated Calculated

0.62 0.62 0.62

1.05 1.03 1.03 1.03 1.03 1.03 1.03 1.03

Ave soft tissue (male) Ave soft tissue (male) Ave soft tissue (male) 50:50 soft tissue/cartilage Ave soft tissue (male) 50:50 soft tissue/cartilage 50:50 soft tissue/cartilage

(ICRU 46)

Comment

1.03 1.03 1.03 1.07 1.03 1.07 1.07

(g/cm )

3

Density

7.46 5.21 7.35 7.20

Not defined 5.99 Not defined 2.59 7.00 6.14 33.97 22.73

32.52

1.30 0.25 0.60 Not defined

Not defined

Mass (g)

7 −78 5 −40

30 0 −85 13 −59

−0.2

−46

20

0

% Diff

UF Voxel Female

28.86 31.16

7.01 17.93 7.01 17.93

3.52 5.99 0.10 1.99 6.99 25.44 30.12 54.92

60.02

0.09 0.85 0.74 1.30 0.31 0.50 0.39

Mass (g)

0.2 −25 0.2 49.4

0.7 −0.1 0.8 −0.6 −0.1 −36 0.4 −2

3.4 −2.9 0.0

−0.5

−0.3

% Diff

UFH-NURBS

28.75 30.92

7.01 12.01 7.01 12.01

3.50 5.98 0.10 2.00 6.97 25.34 29.86 29.97

59.68

0.08 0.72 0.72 1.31 0.31 0.50 0.38

Mass (g)

0.2 −50 0.2 0.0

−0.1 −0.4 −0.3 0.1 −0.4 −37 −0.5 −46

3.0 −3.7 −0.8

−0.2

0.9

% Diff

UFH-Voxel

28 32

7 24 7 12

3.5 6 0.1 2 7 40 30 56

60

1.3 Not defined 0.5 Not defined

Not defined Not defined

Mass (g)

ICRP 89

Summary of Organ Masses within Three Computational Phantoms of the Newborn: (1) UF Voxel Female, (2) UF Hybrid–NURBS, and (3) UF Hybrid–Voxel. The Latter Two Phantoms Include Separate Models of the Male and Female Newborn. These Masses Are Then Compared to ICRP Publication 89 Reference Masses by Organ, Organ System, and for Total Body Tissues and Total Body Masses for Both Sexes

TABLE 8.3


Cortical bone (infant) Red marrow (adult) Dentine Ave soft tissue (male) Effective ave density

1.65 1.03 1.30 1.03 1.47

1.1

Integumentary system Sking Skin (newborn)

Cartilage (adult)

1.10

Kidney (40 week fetus) Bladder (adult-empty) Urine of ave density Muscle (newborn) Ave soft tissue (male) Testes (adult) Ave soft tissue Ovaries (adult) Ovaries (adult)

Heart (40 week fetus) Blood (newborn)

Gastrointestine Ave soft tissue (male) Liver (40 week fetus) Ave soft tissue (male) Ave soft tissue (male) Ave soft tissue (male)

Skeletal system Bone associated cartilaged Bone tissues Bone (CB, TB) Active marrowe Teeth Miscellaneousf Total

1.03 1.04 1.01 1.05 1.03 1.04 1.03 1.05 1.05

1.04 1.07

Circulatory system Heart—wall Heart—content Bloodb

Urogenital system Kidneys (all regions) Urinary Bladder—wall Urinary Bladder—contentsc Penis Scrotum Testes (2) Prostate gland Ovaries (2) Uterus

1.03 1.03 1.04 1.03 1.03 1.03

Rectosigmoid—wall Rectosigmoid—contents Liver Gall Bladder—wall Gall Bladder—contentsa Pancreas

102.10

280.71

−42

17

−3 −12

0.29 3.52

Not defined

−14 0

21.58 4.00 6.48

−54

−78

1.33

21.13

−8 −75 −16 −34

2.77 2.99 109.13 2.19

Not defined

238.6

127.8

24.97 4.01 10.08 0.82 1.65 0.85 0.80 0.30 4.00

20.01 6.05

2.99 3.40 129.12 0.50 2.81 6.00

−0.6

0.4

0.3 −0.1 0.0 −0.1

−0.1 0.3 −16.0

0.1 −77

0.2 −72 −0.7 0.4 0.4 0.0

163.65

237.8

126.4

24.92 3.97 10.05 4.90 4.90 0.85 0.80 0.30 3.98

19.97 6.04

3.40 3.40 128.86 0.50 2.81 5.99

−6.5

−0.9

−0.7

−0.4 0.2 0.0 −0.5

−0.3 −0.7 −16.3

−0.2 −77

0.2 −72 0.2 0.3 0.3 −0.2

170.0 50.0 0.7 19.3

(continued)

175

240.0

127.32

25 4 12 Not defined Not defined 0.85 0.8 0.3 4

20 26 290

3 12 130 0.5 2.8 6

The University of Florida Pediatric Phantom Series 211

Totals by organ system Respiratory system Alimentary system—tissues of organ walls Alimentary system—Gl tract and gall bladder content

Additional tissues Adrenal glands (2) Brain Breasts (2) Eyeballs (2) Lens (2) Pituitary gland Spinal cord Spleen Thymus Thyroid Rest of body (ROB) Separable fat Skeletal muscle Separable connective tissues Fixed lymphatic tissuesh i Blood (large vessels) External nose Cartilage—ears j Miscellaneous ROB Total

Organ System Ave soft tissue (male) Brain (newborn) Adipose (newborn #2) Ave soft tissue (male) Eye lens (adult) Ave soft tissue (male) Brain (newborn) Spleen (40 week fetus) ICRP 89—Para 606 Thyroid (adult) Adipose (newborn #2) Muscle (newborn) Ave soft tissue (male) Ave soft tissue (male) Blood (newborn) 66:33 soft tiss/cartilage Cartilage (adult) Ave soft tissue (male) Effective ave density

0.99 1.05 1.03 1.03 1.07 1.05 1.10 1.03 1.02

(ICRU 46)

Comment

1.03 1.03 0.99 1.03 1.07 1.03 1.03 1.04 1.07 1.05

(g/cm )

3

Density

122.44

44.27

Not defined

0.45 1.43

6.00 322.39 0.09 6.01 0.13 0.10 6.76 9.51 13.00 1.30

Mass (g)

64.20 201.36

23

−20 −23 −57

−51

−50 −23

% Diff

0.1 0.0 −0.1

0.0 −15 0.1 0.2 0.6 −0.2

% Diff

UFH-NURBS

34.67 179.78

2509.17

3.01 291.38 Not defined 2.93 Not defined Not defined 15.13 7.64 10.00 0.56

Mass (g)

UF Voxel Female % Diff

85.54

63.69 201.19

2173.14

6.6

5.98 −0.4 321.33 −15.4 0.09 0.7 5.99 −0.2 0.13 −0.2 0.10 −0.3 6.44 9.47 −0.3 12.93 −0.5 1.29 −0.8

Mass (g)

UFH-Voxel

146.8

61.8 202.1

890 800 120 35 75 0.45 1.43 116.7 2038.8

6 380 0.09 6 0.13 0.1 Not defined 9.5 13 1.3

Mass (g)

ICRP 89

Summary of Organ Masses within Three Computational Phantoms of the Newborn: (1) UF Voxel Female, (2) UF Hybrid–NURBS, and (3) UF Hybrid–Voxel. The Latter Two Phantoms Include Separate Models of the Male and Female Newborn. These Masses Are Then Compared to ICRP Publication 89 Reference Masses by Organ, Organ System, and for Total Body Tissues and Total Body Masses for Both Sexes



5.10

3.33 127.84 238.59 Not defined 365.29 Not defined

3.81

0.00 0.00 280.71 102.10

2509.17

3538

3488 1.1

10.08

6.48

4.4

28.98

25.58

330.7

26.06

21.13

3389 3400 3485 3496

2173.14

363.75

237.77 163.65

126.40

10.65

5.08

10.05

28.89

25.92

1.4 1.7 −0.4 −0.1

3341 3342 3500 3501

2038.8

416.1

240.0 175.0

127.3

0.9

5.10

12.0

29.0

46.0

Source: Lee, C. et al., Phys. Med. Biol., 52, 3309, 2007. With permission. Note: UF hybrid—NURBS and UF hybrid—voxel include separate models of the male and female newborn. These masses are then compared to ICRP Publication 89 reference masses by organ, organ system, and for total body tissues and total body masses for both sexes. a In the UF Voxel Phantom, the wall and content of the heart and gall bladder are not separated—% difference given for the combined mass. b In this table, total blood volume is partitioned into three regions: (1) heart content, (2) individual organs, and (3) rest of body (major vessels). c No reference value is given in ICRP 89 and thus an approximate value of 12.3 g is used as defined in the ORNL stylized newborn phantom. d Skeletal cartilage excludes the following nonbone associated regions of cartilage: external nose and ears, larynx, trachea, and extrapulmonary bronchi. e Assumed to include the 7% of total blood volume (20.3 of 290 g) in the newborn as per Section 7.7.2 of ICRP 89. f As per Section 9.2.15 of ICRP 89, miscellaneous skeletal tissues include periosteum and blood vessels, but exclude periarticular tissue and blood. g Skin masses given here are for the female phantom, and are 0.15% higher in the male phantom due to the addition of the penls and scrotum. h Estimated from the reference adult values given in Section 7.8.2 of ICRP Publication 89 and scaled by newborn to adult total body mass. i Taken as 25.92% of total blood pool as per Section 7.7.2 of ICRP 89 (other tissues, arota, large arteries, large veins). j Miscellaneous rest-of-body is added to force the total body mass to its ICRP 89 reference value of 3500 g. k Male phantom masses additionally include soft tissuea occupied by the uterus and ovaries in the corresponding female phantom.

Total body tissues (F) Total body tissues (M) Total body mass (F) Total body mass (M)

Additional tissues—excluding rest of body Additional tissues—rest of body

Circulatory system—heart wall and content Urogenital system—kidneys and urinary bladder wall Urogenital system—urinary bladder content Urogenital system—Internal sex organs (ovaries, uterus, prostate)k Urogenital system—external sex organs (penis, scrotum, testes) Skeletal system—bone associated cartilage Skeletal system—bone tissues Integumentary system

The University of Florida Pediatric Phantom Series 213

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reference data are not available. For example, the heart wall density for the newborn was not available, and the density of the adult heart was used in the surrogate. Also, we estimated the effective homogeneous lung density as the ratio of the reference lung masses given in the ICRP 89 to the lung volumes of the UFH phantoms. We separated the left and right organs for the adrenals, kidneys, and lungs. We also separated the wall and the content of some walled organs: the colon, the gall bladder, the heart, the rectosigmoid, the small intestine, the stomach, and the urinary bladder. The external nose, the pituitary gland, the tongue, and the tonsil were newly modeled in the UF hybrid phantoms. 8.3.2 Comparison with UF Voxel Newborn Phantom and ICRP89 The organ masses of the UFH-NURBS and UFH-voxel phantoms are compared to those of the original UF voxel newborn phantom and ICRP 89 as listed in Table 8.3. Even though we matched the total body mass of the UF voxel newborn phantom to the ICRP reference mass within 1%, other organs showed a significant mass difference from the reference mass. The Gall bladder wall was up to 194% heavier than reference. The percent differences of organ masses between the UFH-NURBS phantom and the ICRP 89 values were also evaluated in Table 8.3. All are within 1% for internal organs, except for the brain and the eyeballs. As mentioned previously, the expansion of the brain and the eyes was not attempted, as the skeleton—including the cranium—was already well matched to the ICRP 89 reference values. Skin mass was not available at the stage of NURBS modeling, since skin was generated only after the phantom voxelization. The UFH-NURBS newborn phantom was triangulated and voxelized using an MT of 10° and a VR of 0.07 × 0.07 × 0.07 cm3. According to the results of the sensitivity study, as shown in Tables 8.1 and 8.2, we expected these parameters to reduce the percent differences between the NURBS and the voxel computational phantoms below 1% for all organs. Table 8.3 evaluates and lists the percent difference of organ masses between the UFH-voxel phantom and the ICRP 89. The mass of the small intestine in the UF voxel phantom was 2.4% smaller than its reference value, while the NURBS small intestine was matched to within 1%. This difference was attributed to the self-overlap of small intestine, especially at the curved regions. Assigning a skin tag to the outermost voxel layer of body contour generated the skin. Since the VR was set to the skin thickness of ORNL newborn phantom, the skin thickness of the UFH-voxel phantom was the same as with the ORNL phantom. The resulting skin mass of the UFH-voxel phantom was 166.396 g, which was 4.92% smaller than its reference value. The number of voxels in the UFH-voxel phantom was 3.301 × 107, which was 32% smaller than in the original UF voxel newborn phantom. Hybrid phantoms thus make it possible to more effectively represent human anatomy using optimized computational resources. 8.3.3 Advantages of Hybrid Newborn Phantom Hybrid newborn phantoms developed in this study have several advantages over existing stylized and voxel phantoms. First, hybrid phantoms have both the flexibility of stylized phantoms, in changing the computational phantoms of individual organs, the outer body contour, and the extremities, while still preserving the realistic anatomy of voxel phantoms. Organs for which anatomical detail cannot be fully extracted from the original CT images due to the limited image contrast can be described by stylistic computational phantoms incorporated within the hybrid NURBS computational phantom. This advantage was highlighted in the case of the alimentary system. To show this advantage of hybrid phantoms, Figure 8.4 depicts a comparison between the alimentary systems of the stylized,


Esophagus

215

Esophagus

Esophagus

Stomach Stomach

Stomach Colon

Colon

Small intestine Small intestine Colon Rectosigmoid

(a) ORNL newborn

Small intestine Rectosigmoid

(b) UF voxel newborn

Rectosigmoid

(c) UFH voxel newborn

FIGURE 8.4 Alimentary systems of (a) ORNL stylized, (b) UF voxel, and (c) UFH-voxel newborn phantoms. Esophagus, stomach, small intestine, colon, and recto-sigmoid colon are labeled.

the voxel, and the UFH-NURBS newborn phantoms. Those of the stylized phantom and voxel phantom were obtained from the latest revised ORNL newborn phantom8 and UF newborn voxel phantom, respectively. The alimentary tract in the revised ORNL stylized newborn phantom is significantly simplified and unrealistic compared with the more realistic anatomy of both the voxel and hybrid phantoms. Even though the overall shapes of the large and small intestines in the UF voxel newborn phantom are more realistic than those of stylized phantom, they have partial volume defects and image slice discontinuities due to the limited resolution of the CT images. We voxelized the alimentary tracts of the UFH-voxel newborn phantom from the NURBS surfaces based on the original anatomical shapes in the UF voxel newborn phantom and the ICRP Publication 89 reference data. Other advantages of hybrid phantoms is (1) their flexibility to modify the outer body contour to represent nonreference individuals, (2) their ability to modify organ shape, position, and depth, and (3) their ability to generate voxel phantoms—whole-body or individual organ systems—at resolutions that are user-defined and thus optimized to the dosimetry problem at hand. Manipulations of control points surrounding the NURBS surfaces of body contours and internal organs allow the shapes to be modified to generate different sized organ or body shapes. Due to this feature, we can accurately match the organ masses of the UF hybrid newborn phantom to the reference data. It is also possible to add or remove certain organs as needed, which makes it possible to create a male phantom from the original female newborn phantom while maintaining the framework of all nonsex-organ anatomy. In the case of existing voxel phantoms, even though the x–y in-plane VR can be as high as the resolution of the original CT images, the resolution in the z-direction (slice thickness) cannot be increased, thus restricting it to the original scanning interval.


216

8.3.4 The Extended Development of Older Phantoms We developed the older members of the UF hybrid phantom series using the same technology as we incorporated into the newborn hybrid phantom. These additional phantoms are those of the ICRP 89 reference 1, 5, 10, and 15 year old male and female. We used the same CT-based anatomy for the development of the male and female at ages 1, 5, and 10 year, with the exception of the reproductive organs (i.e., the gonads, the uterus, and the prostate). The 15 year old male and female phantoms were developed independently from different CT data as described in Lee et al.12 A total of eight body dimensions were matched to the standard anthropometric data listed in the Table 8.4 within 4% for all phantoms. Figure 8.5 presents the 3D frontal view of the series of UF hybrid pediatric male and female phantom series. TABLE 8.4 Anthropometric Data Obtained from NHANES III/IV, Anthrokids, and ICRP89 0 M

1 F

M

5

15

10

F

M

F

M

F

M

F

109

109

138

138

167

161

Height Standing

51

51

76

76

Sitting

34

34

49.5

48.1

60.6

60.2

73.1

73.6

88.8

85.5

32.6

32.6

47.2

47

62

59.9

75

70.7

47.9

46.7

Length Arm Circumference 51.5

50.7

52.9

52.7

55.4

54.3

Neck

25.3

24.5

28.1

27.6

32.8

30.8

Waist

55

54.9

67.7

65.7

80.1

78.8

Buttock

57.3

58.4

74.5

75.8

92.5

93.4

25

24.9

31.2

31.1

38.8

36.3

Head

33.1

33.1

Breadth Biacromial

1-year male

1-year female

5-year male

5-year female

10-year male

10-year female

15-year male

15-year female

FIGURE 8.5 (See color insert following page 524.) Series of UF hybrid pediatric male (left) and female (right) phantoms: newborn, 1, 5, 10, and 15 year old phantoms.


217

FIGURE 8.6 Representative microCT images of age-dependent skeletal microstructure in the pediatric spine. Upper panel includes images of the cervical, thoracic, and lumbar vertebral bodies (left to right) of the newborn skeleton, while the lower panel gives corresponding images of 18 year old spine.

8.3.5 Pediatric MicroCT-Based Skeletal Computational Phantoms In their current configurations, the bones of the skeleton in the UF pediatric series of Figure 8.5 are homogeneous in tissue composition and skeletal microstructure, yet are of a reference total volume to contain all ICRP 89 reference tissue masses. Current efforts are devoted to endowing each phantom with a unique and age-dependent microCT-base skeletal computational phantom for not only internal radiation sources (alpha and beta particles as needed for nuclear medicine dose assessment), but also for the construction of fluence-to-dose response functions for assessing active marrow and endosteal tissue doses from both internal and external photon sources. Details of the dose–response function are given in Eckerman et al.13 Representative images of both newborn and midteen skeletal microstructures are shown in Figure 8.6 as needed for paired-image radiation transport simulation.14,15 Implementation of these skeletal computational phantoms requires subsegmentation of each pediatric bone in the UF hybrid series into regions of spongiosa, cortical bone, and medullary marrow using methods similar to that described by Zankl et al.16 and by Kramer et al.17 for their adult phantoms.

8.4 Conclusions To evaluate radiation dose distribution within human anatomy, researchers have developed and utilized simplified mathematical equation-based stylized phantoms for several dosimetry calculation purposes since the 1960s. Voxel phantoms have been subsequently

218


developed to overcome the anatomical limitations of stylized phantoms since the mid1980s. Even though the anatomical realism of computational phantoms has been improved significantly through the use of voxel phantoms, they are more difficult to match of individual patient body morphometry. To merge the advantages of the stylized and voxel phantoms, we have developed hybrid male and female newborn phantoms. We adopted NURBS, an advanced mathematical modeling tool, to replace the limited mathematical surface equations of stylized phantoms, and we utilized the precedent UF voxel newborn phantom as its realistic anatomical framework. The development of hybrid phantom was performed in three steps: polygonization of the UF voxel newborn phantom, NURBS surface modeling, and voxelization of NURBS computational phantom. We used two 3D graphic tools, 3D-DOCTOR and Rhinoceros, to triangulate voxel phantom and generate NURBS surfaces, and an in-house MATLAB code was written to voxelize resulting NURBS computational phantom into fi nal voxel phantom that is ready for Monte Carlo radiation transport calculation. The hybrid male newborn phantom was constructed from the female phantom by replacing female-specific organs with male-specific organs. We adjusted the resulting NURBS computational phantoms represent the body contour and the internal anatomy to match the anthropometric and the reference newborn data reported by ICRP and other sources. Finally, the UFH-voxel newborn male and female phantoms were constructed by voxelizing the UFH-NURBS phantoms used a targeted VR of 0.07 × 0.07 × 0.07 cm3. Researchers can make the NURBS-based hybrid phantoms realistic based on medical images, while maintaining the flexibility to model smooth organ surfaces and even organ motion. Body contours can be modified by manipulating control points to accommodate different body shape among individuals. The hybrid technologies established in this study, including polygonization of existing voxel phantom, NURBS surface modeling technique, and conversion of NURBS computational phantom into voxel phantom, can be applied to the development of a wide range of human phantoms in the future. This new class of anthropomorphic computational phantoms can be widely applied to dose evaluation in radiation protection, medical imaging, and radiation therapy, where realistic and flexible dynamic computational human phantoms are required.

Acknowledgment This work was performed under grant RO1 CA116743 from the National Cancer Institute (subcontract from Rensselaer Polytechnic Institute) with the University of Florida.

References 1. Nipper, J.C., Williams, J.L., and Bolch, W.E. Creation of two tomographic voxel models of paediatric patients in the first year of life, Phys Med Biol, 47, 3143, 2002. 2. ICRP. Basic Anatomical and Physiological Data for Use in Radiological Protection: Reference Values, Publication 89, International Commission on Radiological Protection, Oxford: Pergamon Press, 2002.


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3. Yang, L. et al. A simulation for effects of RF electromagnetic radiation from a mobile handset on eyes model using the finite-difference time-domain method, Conf Proc IEEE Eng Med Biol Soc, 2007, 5294, 2007. 4. Cristy, M. and Eckerman, K.F. Specific absorbed fractions of energy at various ages from internal photon sources, ORNL/TM-8381 V1-V7, Oak Ridge, TN: Oak Ridge National Laboratory, 1987. 5. Lee, C. et al. Hybrid computational phantoms of the male and female newborn patient: NURBSbased whole-body models, Phys Med Biol, 52, 3309, 2007. 6. Zankl, M. et al. Organ dose conversion coefficients for external photon irradiation of male and female voxel models, Phys Med Biol, 47, 2367, 2002. 7. ICRP. Human Respiratory Tract Model for Radiological Protection: A Report of a Task Group of the International Commission on Radiological Protection, ICRP Publication 66, Oxford: Pergamon Press, 1994. 8. Han, E., Bolch, W., and Eckerman, K. Revisions to the ORNL series of adult and pediatric computational phantoms for use with the MIRD schema, Health Phys, 90, 337, 2006. 9. Lee, C., Lee, C., and Bolch, W.E. Age-dependent organ and effective dose coefficients for external photons: A comparison of stylized and voxel-based paediatric phantoms, Phys Med Biol, 51, 4663, 2006. 10. Lee, C. et al. Whole-body voxel phantoms of paediatric patients—UF Series B, Phys Med Biol, 51, 4649, 2006. 11. ICRU. Tissue substitutes in radiation dosimetry and measurement, ICRU Report 44, Bethesda, MD, 1989. 12. Lee, C. et al. Hybrid computational phantoms of the 15-year male and female adolescent: Applications to CT organ dosimetry for patients of variable morphometry, Med Phys, 35, 2366, 2008. 13. Eckerman, K.F. et al. Response functions for computing absorbed dose to skeletal tissues from photon radiation, Radiat Prot Dosim, 127, 187, 2008. 14. Shah, A. et al. Accounting for beta-particle energy loss to cortical bone via Paired-Image Radiation Transport (PIRT), Med Phys, 32, 1354, 2005. 15. Shah, A.P. et al. A paired-image radiation transport model for skeletal dosimetry, J Nucl Med, 46, 344, 2005. 16. Zankl, M., Eckerman, K.F., and Bolch, W.E. Adult male and female voxel-based models representing the ICRP reference adult—The skeleton, Radiat Prot Dosim, 127, 174, 2007. 17. Kramer, R. et al. Skeletal dosimetry for external exposure to photons based on microCT images of spongiosa from different bone sites, Phys Med Biol, 52, 6697, 2007. 18 Pafundi, D. et al. Image-based pediatric skeletal dosimetry for the UF hybrid computational phantom series, 2008. Annual Meeting of the European Association of Nuclear Medicine, Munich Germany, October 11–15, 2008 (Supplement to Eur J Nucl Med 35(2), S135, 2008).

9 Japanese Computational Phantoms: Otoko, Onago, JM, JM2, JF, TARO, HANAKO, Pregnant Woman, and Deformable Child Kimiaki Saito, Kaoru Sato, Sakae Kinase, and Tomoaki Nagaoka

CONTENTS 9.1 Introduction ............................................................................................................... 221 9.2 Construction of Computational Phantoms ...........................................................222 9.2.1 JAEA Computational Phantoms ................................................................222 9.2.2 NICT Computational Phantoms ................................................................ 226 9.2.2.1 Adult Male and Female Computational Phantoms................. 229 9.2.2.2 Pregnant Women Computational Phantoms ........................... 230 9.2.2.3 Deformed Child Computational Phantoms ............................. 231 9.2.2.4 Computational Phantoms of Arbitrary Posture ...................... 232 9.2.2.5 Arbitrary High Resolution Human Voxel Computational Phantoms ....................................................................................... 233 9.2.2.6 Applications in Electromagnetic Dosimetry ............................ 233 9.3 Dose Calculation ....................................................................................................... 233 9.3.1 Monte Carlo Code Systems......................................................................... 233 9.3.2 External Dose Calculation .......................................................................... 236 9.3.2.1 Photon ............................................................................................ 236 9.3.2.2 Electron .......................................................................................... 239 9.3.3 Internal Dose Calculations ......................................................................... 240 9.3.3.1 Absorption Fractions ................................................................... 240 9.3.3.2 S Value............................................................................................ 242 9.3.4 Whole-Body Counting ................................................................................ 245 9.4 Application to Radiotherapy ................................................................................... 247 9.5 Conclusion (Future Plan) ......................................................................................... 250 References ............................................................................................................................. 251

9.1 Introduction The majority of human computational phantoms for radiation protection purposes, including stylized computational phantoms, have been constructed for Caucasian body types. For example, both the conventional MIRD computational phantoms (the Medical Internal 221

222


Radiation Dose Committee of the Society of Nuclear Medicine Pamphlet No. 5 type computational phantoms)1 and the newly released voxel computational phantoms by ICRP2 are both based on Caucasian anatomical data. Further, basic data for radiation protection, like dose conversion factors, have been prepared using Caucasian computational phantoms. Therefore, Asian researchers have desired a clarification of the difference in doses due to the anatomical differences between Caucasian and Asians. Voxel computational phantoms enable us to reasonably investigate the dose differences between these two different races, since they can simulate anatomical structures realistically. From this viewpoint, the Japan Atomic Energy Agency (JAEA) constructed the first Asian voxel computational phantom called Otoko in 2001,3 and several Japanese computational phantoms have been constructed since then, mainly for radiation protection purposes. Additionally, the National Institute of Information and Communication Technology (NICT) has developed Japanese voxel computational phantoms for the evaluation of the exposure to electromagnetic fields, which has become a concern in recent years. At NICT, several advanced computational phantoms, like a variable posture computational phantom and a pregnant computational phantom, have been developed. In this chapter, we introduce these Japanese computational phantoms, and explain their applications to ionizing and nonionizing radiation dosimetry in diverse conditions.

9.2 Construction of Computational Phantoms 9.2.1 JAEA Computational Phantoms At the JAEA, five Japanese voxel computational phantoms have been completed from computed tomography (CT) data: three male adult computational phantoms and two female adult computational phantoms. All CT data were taken for healthy volunteers at the Fijita Health University Hospital after receiving the approval by the Ethics Committee. Pictures of four of the developed voxel computational phantoms are shown in Figure 9.1, and the physical characteristics are listed in Table 9.1. Researchers segmented the first Asian computational phantom, Otoko,3 in collaboration with GSF in Germany with the commercially available image-processing equipment MIPRON (Kontron Elektonik, Eching, Germany), and the other computational phantoms by applying the GSF techniques with the commercial software Visilog4 (Noesis, Orsay, France) at JAEA. In these computational phantoms, compact bone and bone marrow are separately modeled in each skeletal voxel according to the CT value, enabling users to calculate doses with considering the bone marrow distribution in the body. The first generation computational phantoms Otoko3 and Onago4 developed by Saito et al. have a voxel size of 0.98 × 0.98 × 10 mm,3 while the second generation computational phantoms JM5 and JF6 developed by Sato et al. have a finer voxel size of 0.98 × 0.98 × 1 mm3. Performance of CT scanners has greatly improved in a short period, and it has become possible to take CT pictures at a high resolution with less exposure resulting in the development of the high-resolution computational phantoms. Researchers expect the former long-shaped voxel not to cause significant effects on dose accuracy, since the voxel volume is small; however, in some small organs, a voxel length of 10 mm could lead to insufficient resolution of the organ computational phantoms. Figure 9.2 shows examples of organ computational phantoms consisting of voxels at 10 mm thickness and at 1 mm. Voxels at 10 mm

Japanese Computational Phantoms

Otoko

223

Onago

JM

JF

FIGURE 9.1 (See color insert following page 524.) Japanese voxel models developed at JAEA.

TABLE 9.1 Physical Characteristics of Developed Voxel Phantoms at JAEA

Gender Weight (kg) Height (cm) Slice thickness (mm) Pixel side length (mm)

Otoko

Onago

JM

JF

Male 65 170 10 0.98

Female 57 162 10 0.98

Male 66 171 1 0.98

Female 44 152 1 0.98

Standard Asian Man Male 64 170 – –

Female 46 155 – –

thickness can properly model a stomach having a large size, while voxels at 1 mm thickness would be necessary for realistic modeling of a thyroid. Organ masses of the developed computational phantoms are shown in Tables 9.2 and 9.3. The both male computational phantoms Otoko and JM have body sizes close to the Asian Reference Man (ARM) defined by Tanaka;7 however, in general the organ masses of JM are closer to the reference values. The portion of organs whose masses coincide the reference values within 30% deviation is 85% for JM and 55% for Otoko. In Otoko, especially, small organs tend to deviate from the reference values. This is considered partly because the slice thickness of 10 mm is not small enough to precisely model small organs with high resolution. Organ masses of female computational phantoms should not be directly compared to the reference values, because Onago has larger body size than the Asian Reference Man, Female (ARMF) while JF has smaller body size. In Figure 9.3, we compare the body thicknesses and widths of the developed voxel computational phantoms with the reference values for the thorax, the abdomen, and the

224


Stomach

0.98 × 0.98 × 10 mm3

0.98 × 0.98 × 1 mm3 Thyroids

0.98 × 0.98 × 10 mm3

0.98 × 0.98 × 1 mm3

FIGURE 9.2 Examples of organ models consisting of voxels at 10 mm height and at 1 mm height.

buttocks. They are all within 2σ deviations around the reference values, and it was confirmed that the developed voxel computational phantoms do not much deviate from the average body shapes of the Japanese. In radiation protection, dose calculations are usually performed using an assumed upright position of human bodies, while voxel computational phantoms have been constructed from CT data taken in lying position. The structures of a human body and its organs are considered to slightly change according to the posture; therefore, researchers have investigated the effect of posture on organ doses. For this purpose, Sato et al. constructed the voxel computational phantom JM2 from CT data taken in an upright position9 for the same person as JM. Using a cone beam CT scanner, four sets of CT pictures of spherical regions at a diameter of 25 cm were taken after changing the height position. The CT pictures could not cover some peripheral parts of the body because of the limited diameter; the missing parts were complemented by the data of JM. This complement is considered not to affect the calculated dose significantly, since dominant organs were mostly included in the corn beam CT data.


225

TABLE 9.2 Comparison of Organ and Tissue Masses of the Japanese Male Voxel Models Developed at JAEA and the ARM Ratio

Weight (g) Organ or Tissue

Otoko

JM

ARF

Otoko/ARM

Otoko/ARFM

Adrenals

20.9

11.9

14

1.49

0.85

Eyes

20

13.5

15

1.33

0.90

Lenses Gall bladder Esophagus Stomach Intestine

0.2

0.4

0.4

0.50

0.93

11.7

6.7

8

1.46

0.84 0.93

16.1 122.3 1,009

40

0.40

124

37.1

140

0.87

0.89

684

920

1.10

0.74

Small intestine

691

431

590

1.17

0.73

Large intestine

318

253

330

0.96

0.77

Upper large intestine

175

135

180

0.97

0.75

Lower large intestine

143

118

150

0.95

0.79

Heart

476

528

360

1.32

1.47

Kidney Liver Lung Muscle (skeletal)

320

0.83

1191

265 1,304

0.83

1,600

0.74

0.82

1,546

1,549

1,100

1.41

1.41

266

30,560

24,547

24,600

1.24

1.00

Pancreas

109

130

0.84

0.91

Skeleton

11,368

119 11,051

8,300

1.37

1.33

7,582

7,318

4,500

1.68

1.63

Hard bone Bone marrow

3,786

3,734

3,800

1.00

0.98

Skin

2,195

2,217

2,400

0.91

0.92

138

140

0.54

0.99

37

0.74

0.99

Spleen

75.7

Testes

27.4

36.8

Thymus

4.58

31.6

33

0.14

0.96

Thyroid

9.93

21.8

19

0.52

1.15

Trachea Urinary bladder

8.93 38.8

10.2

9

0.99

1.13

37.7

40

0.97

0.94

In Table 9.4, we compare the organ masses between the constructed upright computational phantom JF2 and JF. The organ masses agree reasonably, and this suggests the segmentation was properly carried out. The lateral CT pictures in Figure 9.4 indicate that the spine above the waist was bent backward for a standing position in comparison to the lying position. This bending is considered necessary to keep body balance in standing position. Further, the force direction by gravity is different between standing and lying positions resulting in different shapes and positions of flexible organs. The abdominal shape apparently differs between the two positions. We tabulated the distances between several organs for JM and JM2 in Table 9.5. Here, the gravity center of an organ was considered as the representative position. The distances relative to the brain hardly change according to the posture for the esophagus, the lungs, the


226

TABLE 9.3 Comparison of Organ and Tissue Masses of the Japanese Female Voxel Model Developed at JAEA and the ARMF Ratio

Weight (g) Organ or Tissue

Onago

JF

ARMF

Onago/ARMF

JF/ARFM

Adrenals

19.7

6.2

13

1.52

0.48

Eyes

23.3

15.6

12

1.94

1.30

Lenses

0.7

0.3

0.3

2.33

1.09

Gall bladder

4.1

3.8

6

0.68

0.63

30

Esophagus Stomach Intestine

9.3 99.4 914

0.31

1.65

106

49.5

110

0.90

0.97

790

980

0.93

0.81

Small intestine

742

552

720

1.03

0.77

Large intestine

172

238

260

0.66

0.91

Upper large intestine

106

121

140

0.76

0.87

Lower large intestine

67

117

120

0.56

0.97

Heart

476

280

320

1.49

0.88

Kidney

257

213

280

0.92

0.76

Liver

1,448

1,179

1,400

1.03

0.84

Lung

996

1,245

910

1.09

1.37

Ovaries

10.4

6.8

11

0.95

0.61

Pancreas

52.6

95.9

110

0.48

0.87

Skeleton Hard bone

10,354

7,389

6,400

1.62

1.15

7,133

4,658

3,400

2.10

1.37

Bone marrow

3,222

2,731

3,000

1.07

0.91

Skin

1,975

1,753

1,800

1.10

0.97

Spleen Thymus

90.6

55.5

120

0.76

0.46

1.6

19.4

29

0.05

0.67 0.43

Thyroid

5.8

7.3

17

0.34

Trachea

6.2

17.5

7

0.91

2.57

23.5

20.2

30

0.78

0.67

47.5

70

2.18

0.68

Urinary bladder Uterus

152

lower large intestine, and the urinary bladder; while the positions of the liver, the stomach, the gall bladder, and the kidneys in standing posture shifted to a lower direction because of gravity. The change in distance is within several percent in most cases. Positional relations among neighboring organs in the middle of body are difficult to explain with simple principles; some cases show a large distance change like the case for stomach content and pancreas. This change in positional relation affects the specific absorption fraction (SAF) as discussed later. 9.2.2 NICT Computational Phantoms This section gives an overview of the work at the NICT on voxel computational phantoms developed for electromagnetic-field dosimetry. In the mid-1990s, anatomically realistic


227

400

300 2σ

Width (mm)

Thickness (mm)

300 200

100

× Reference JM JF Otoko Onago

0

200

× Reference JM JF Otoko Onago

100

0 T

A B Male

T

A B Female

T

A B Male

T

A B Female

FIGURE 9.3 Body thicknesses and widths of the developed voxel models at the thorax (T), abdomen (A), and buttock (B) parts. The reference values are shown together.

TABLE 9.4 Organ Masses of JM and JM2 Mass (kg) Organ or Tissue

JM

JM2

Adrenals

0.012

0.011 (0.93)

Brain

1.704

1.704 (1.00)

Heart wall

0.528

0.501 (0.95)

Kidneys

0.265

0.263 (0.99)

Liver

1.304

1.400 (1.07)

Lower large intestine wall

0.118

0.117 (0.99)

Lungs

1.549

1.603 (1.03)

Pancreas

0.119

0.117 (1.00)

Skin

2.217

2.237 (1.01)

Small intestine wall

0.431

0.425 (0.99)

Spleen

0.138

0.139 (1.01)

Stomach wall

0.124

0.122 (0.98)

Testes

0.037

0.037 (1.01)

Thymus

0.032

0.032 (1.00)

Thyroid

0.022

0.022 (0.99)

Upper large intestine wall

0.135

0.137 (1.02)

Urinary bladder wall

0.038

0.037 (0.98)

Note: Values in parentheses are the ratios of organ masses of JM2 to JM.


228

Spine

Abdomen

(a) Lying position

(b) Standing position

FIGURE 9.4 Comparison of body structures between JM in lying position and JM2 in standing position.

TABLE 9.5 Distances between Several Dominant Organs in JM2 and JM in Terms of the Gravity Center Organ Distance (mm) Organs

JM

JM2

Ratios of JM2 to JM

Brain and esophagus Brain and lungs Brain and liver Brain and stomach wall Brain and gall bladder wall Brain and kidneys Brain and lower large intestine wall Brain and urinary bladder wall Kidneys and liver Kidneys and lower large intestine wall Kidneys and pancreas Stomach content and liver Stomach content and lower large intestine wall Stomach content and pancreas

322 355 480 511 510 544 710

318 351 490 526 529 563 713

0.99 0.99 1.02 1.03 1.04 1.03 1

748 94 174

748 98 155

1 1.05 0.89

64 109 206

62 116 198

0.97 1.06 0.96

31

47

1.5


(a)

(b)

229

(c)

(d)

(e)

(f)

FIGURE 9.5 3D images of voxel models developed at NICT: (a) adult male and (b) adult female models; (c) 26 week pregnant woman model; (d) 7 year old, (e) 5 year old, and (f) 3 year old models.

voxel computational phantoms of a cranium were developed and have been used for dosimetry of human heads exposed to electromagnetic radiation from cellular phones.10–14 These head computational phantoms, however, cannot be used for safety evaluations of wearable communication terminals worn on other parts of the body. Since the late 1990s, several research groups have been developing high-resolution whole-body voxel computational phantoms for electromagnetic-field dosimetry.15–19 These computational phantoms, however, are based on anatomical data for non-Japanese subjects and their body proportions differ substantially to those of Japanese population. Therefore, researchers at NICT have developed realistic high-resolution whole-body voxel computational phantoms of Japanese body size, as shown in Figure 9.5.20–22 9.2.2.1 Adult Male and Female Computational Phantoms Nagaoka et al. have developed voxel computational phantoms by collecting magnetic resonance imaging (MRI) data from volunteer Japanese adult males and females with average body size (Figure 9.5a and b).22 The average height and weight of Japanese adults between the ages 18 and 30 years are 171.4 cm and 63.3 kg for male and 159.1 cm and 52.6 kg for female, respectively.23 On the bases of this data, a male and a female subject for the computational phantoms were chosen whose height and weight were close to the Japanese average values. The male volunteer, who was 22 years old, was 172.8 cm tall and weighed 65.0 kg; the female volunteer, who was also 22 years old, was 160.0 cm tall and weighed 53.0 kg. Magnetic resonance images of the male and female subjects were acquired using a 1.5 Tesla (T) MRI system. Analysts used whole-body transverse (axial) images for the male and female to identify tissues and organs for each voxel. It is impossible to automatically segment the voxels with sufficient accuracy using currently available image-processing technologies. All tissue- and organ-identification processing was therefore performed manually by medical personnel using PC software. Researchers checked the three-dimensional images and the three orthogonal planes and then smoothed the boundaries of the


230

TABLE 9.6 Identified Tissues and Organs in the Male and Female Models Developed at NIST No.

Tissue and Organ

No.

Tissue and Organ

No.

Tissue and Organ

1

Adrenals

20

Gray matter

38

Seminal vesiclea

2

Air (internal)

21

Heart

39

Skin

3

Bile

22

Hypothalamus

40

Small intestine

4

Bladder

23

Kidney

41

Small intestine contents

5

Blood

24

Large intestine

42

Spleen

6

Bone marrow and cancellous bone

25

Large intestine contents

43

Stomach

7

Breast fatb

26

Lens

44

Stomach contents

8

Cartilage

27

Ligament

45

Tendon

9

Cavernous bodya

28

Liver

46

Testisa

10

Cerebellum

29

Lung

47

Thalamus

11

Cornea

30

Muscle

48

Thyroid

12

Cortical bone

31

Nerve (spinal cord)

49

Tongue

13

CSF

32

Ovaryb

50

Tooth

14

Diaphragm

33

Pancreas

51

Trachea

15

Duodenum

34

Pineal glands

52

Urine

16

Esophagus

35

Pituitary

53

Uterusb

a

54

Vaginab

55

White matter

17

Eye tissue (sclera)

36

Prostate

18

Fat

37

Salivary gland

19

Gall bladder

a b

Only male model. Only female model.

following tissues and organs: axial, sagittal, and coronal. The final positioning and shaping of the tissues and organs were performed under the supervision of medical doctors using three-dimensional visualization software (INTAGE RV; Kubota Graphics Technologies, Inc., Tokyo, Japan). The developed male and female computational phantoms were assumed to stand in upright position with their hands at both sides of their bodies. The computational phantoms are composed of voxels of 2 × 2 × 2 mm3 and are divided into 51 different tissues and organs, as shown in Table 9.6. The masses of the main tissues and organs of the developed computational phantoms were compared with the average values of the Japanese Reference Man,24 while the masses of the tissues and organs not compiled in the data of the Japanese Reference Man were compared with those of the ARM.7 The masses of 60% of the identified tissues and organs in the male computational phantom were within 30% of the average values. For the female computational phantom, the masses of 80% of the identified tissues and organs were within 30% of the average values.22,24 9.2.2.2 Pregnant Women Computational Phantoms Developing a high-resolution computational phantom on the basis of only the medical images of pregnant women is difficult because of ethical issues regarding obtaining high-resolution whole-body tomographic images of healthy pregnant women. Therefore, Nagaoka et al. have developed a whole-body pregnant woman computational phantom by


231

Control point

Skin

Muscle

Skin

Muscle

FIGURE 9.6 Change in shape of abdomen of female model obtained using FFD algorithm. Female model before deforming abdomen (left) and after deforming abdomen (right).

combining a computational phantom of a fetus and the adult female computational phantom (Figure 9.5c).21 The researchers segmented the tissues and organs of the fetus computational phantom using image analysis software (SliceOmatic ver. 4.3; Tomovision Inc., Montreal, Canada). They obtained the images of a healthy 26 week pregnant woman from an abdominal MRI, and according to the MR images a fetus computational phantom was constructed consisting of six tissues: the fetus, the fetal brain, the fetal eyes, the amniotic fluid, the placenta, and the uterine wall. In addition, the researchers dilated the abdomen of the female computational phantom by applying the free-form deformation (FFD) algorithm25 in order to match the abdominal shape at 26 weeks of pregnancy. The advantage of this technique is that the object shape is not limited and it can maintain the continuity of deformed objects. These tissues were deformed in relation to the original MR images (abdomen) of the pregnant woman. Figure 9.6 shows the deformation of the abdomen of the female computational phantom. Finally, using the original MR images of the subject as a reference, the fetus computational phantom was combined with the abdomen of the deformed pregnant woman computational phantom. The computational phantom is composed of voxels of 2 × 2 × 2 mm3, and it is divided into 56 different tissues and organs. The fetal occiput is directed toward the mother’s left anterior side as shown in Figure 9.7. 9.2.2.3 Deformed Child Computational Phantoms Nagaoka et al. transformed the adult male computational phantom into child figure computational phantoms (Figure 9.5d through f).20 The transformation was performed because developing new computational phantoms of children based on the MRI data of healthy child subjects is difficult due to ethical issues. It is not possible to rescale an adult

232


computational phantom to match the size of a child, because the resulting computational phantom does not accurately represent the specific figure of the child. Nagaoka et al. obtained their own reference data on 3, 5, and 7 year old children because statistical data are hardly available on the figures of children under the age of 7 years. The original adult male computational phantom was transformed into figure computational phantoms of children using the FFD algorithm.25 Researchers adjusted the child computational phantoms to have the average heights, weights, and proportions of Japanese children. Like the adult male computational phantom, the developed child computational phantoms consist of 2 × 2 × 2 mm3 voxels and are divided into 51 different tissues and organs. From Figure 9.5, it can be seen that the proportions of the child computational phantoms differ from those of the adult computational phantoms. The sectional images of the child computational phantoms in Figure 9.8 clearly show that the anatomical structures are maintained.

FIGURE 9.7 Position of fetus in pregnant woman model.

9.2.2.4 Computational Phantoms of Arbitrary Posture Because most of whole-body voxel computational phantoms are based on MRI or CT data, a straight posture is assumed. This limits the simulation of actual situations when wireless telecommunication devices are used. Therefore, we have been developing computational phantoms that can be transformed into any arbitrary posture.26,27 By applying FFD,25 we transform the posture of computational phantoms. An example of a voxel computational phantom in a different posture is shown in Figure 9.9.

FIGURE 9.8 Sagittal images of child models.

3 years old

5 years old

7 years old


233

9.2.2.5 Arbitrary High Resolution Human Voxel Computational Phantoms

FIGURE 9.9 Example of variable posture model. Male model in sitting pose.

The 2 mm resolution20–22 computational phantoms currently used by NICT enable us to evaluate exposure to high-frequency electromagnetic radiation up to 3 GHz. We anticipate, however, that wireless communication devices will be used at frequencies above 3 GHz in the near future. This motivated us to develop higher resolution computational phantoms to be able to evaluate exposures to electromagnetic fields of higher frequencies. The spatial resolution of the computational phantoms is doubled when the voxel size is reduced by half. Therefore, it is possible to double the applicable frequency in this approximated manner. Nevertheless, problems could arise if the staircase shape of the curvature boundaries is maintained, because such rough modeling can cause significant effects on the simulation at higher frequencies. To solve the problem, Nagaoka and Watanabe created a technique for developing arbitrary high-resolution computational phantoms by smoothing the irregularities between tissue boundaries.28 Examples of computational phantoms on which this technique has been applied are shown in Figure 9.10. The figure shows that the improved smoothness of the outer and internal boundaries. 9.2.2.6 Applications in Electromagnetic Dosimetry

Researchers evaluated the safety of radio frequency electromagnetic field (RF-EMF) based on the specific absorption rate (SAR), which is the amount of RF energy absorbed per unit weight of a body. SAR is used as a measure of the thermal effects of RF-EMF exposure. SARs in the human body were estimated by applying the finite-difference time-domain (FDTD) method29 to realistic human voxel computational phantoms. The method is very effective for use with inhomogeneous human computational phantoms that have complex shapes because the region being simulated is divided into Yee cells (minute blocks) and the elements of the electromagnetic field of each Yee cell are calculated for every discrete time step. An example of an FDTD analysis to evaluate the SAR distribution in a human voxel computational phantom of a male in a sitting pose is shown in Figure 9.11.

9.3 Dose Calculation 9.3.1 Monte Carlo Code Systems Monte Carlo code systems combined with the voxel computational phantoms for calculating doses and related quantities have been constructed as user codes of the multipurpose photon and electron transport calculation code EGS4.30 The outlines of the constructed

234


Original model (2 mm resolution)

Improved model (1 mm resolution)

FIGURE 9.10 Example of arbitrary high-resolution human voxel model using female model.

code systems are listed in Table 9.7. In the whole code systems, we added the function to calculate radiation transport in voxel geometry. In addition to the main physical computational phantoms of the EGS4, Rayleigh scattering, Doppler broadening in Compton scattering, linearly polarized photon scattering, and electron impact ionization can also be considered. UCPIXEL3,31 calculates organ doses for external exposures in different irradiation conditions: (a) basic irradiation geometries of AP, PA, RLAT, LLAT, ROT, ISO, AB, BA with monoenergetic photons or electrons; (b) a cylindrical plane source around the voxel computational phantom emitting photons having arbitrary energy and angular spectra; (c) uniform volume source in air, or uniform plane source in ground emitting electrons having an arbitrary energy spectrum where the voxel computational phantom stands vertically on the ground; (d) a point source emitting monoenergetic photons or electrons uniformly over directions viewed by an arbitrary rectangle. When emitted photons have energy and angular spectra, the energy and angle of a radiation are sampled using the probability distribution functions previously prepared. UCSAF32 calculates absorption fractions (AFs) for internal exposures from source distributed uniformly in an assigned region that usually corresponds to an organ or a tissue, or in plural assigned regions. Arbitrary point sources having different intensities in the body can also be assumed. The energy can be selected from (a) monoenergy, (b) an arbitrary energy spectrum, (c) and a beta spectrum expressed by a theoretical formula.


235

SAR(W/kg) 10–4

10–3

10–2

10–1

100

101

FIGURE 9.11 (See color insert following page 524.) Example of SAR distribution in a model exposed to plane wave (80 MHz). Incident power density is 1 mW cm−2.

TABLE 9.7 Developed Monte Carlo Code Systems at JAEA Code Name

Object

Obtained Quantity

UCPIXEL

External exposure

Organ dose

UCSAF

Internal exposure

Organ dose

UCWBC

In vivo measurement

Detection efficiency

UCRTP

Radiation therapy

Dose distribution

Specific absorption fraction Pulse height spectrum

UCWBC33 obtains calibration factors for in vivo measurements to quantify radionuclide uptake in the body. In this code system, combinatorial geometry is available in addition to voxel geometry, and this enables to easily model detectors set around the human body. Basically, the same source conditions as UCSAF are assumed; additional source distributed

236


uniformly in the whole body can be simulated considering radionuclides like potassium or cesium. Detector responses can be calculated for photons emitted in the body; in case of a scintillation detector the scintillation efficiency as a function of electron energy can be taken into account. UCRTP34 is a code system to calculate dose distribution in a patient body in radiation therapy. In this system, energy deposition is recorded per each voxel, while in other systems energy deposition is recorded per organ or tissue. Typical irradiation conditions for radiotherapy can be considered in this program. 9.3.2 External Dose Calculation 9.3.2.1 Photon Sato et al. investigated variations in organ doses for external photons8 using the developed voxel computational phantoms at JAEA. Organ doses were calculated for six typical external irradiation conditions of AP, PA, LLRAT, RLAT, ISO, ROT with 25 kinds of monoenergetic photons ranging from 0.01 to 10 MeV. In addition to the developed four Japanese voxel computational phantoms Otoko, Onago, JM, and JF, Caucasian male and female computational phantoms Rex and Regina2 having body sizes close to the reference man defined by ICRP35 Caucasian male computational phantom VIP-man36 having a large size, and stylized computational phantoms Adam and Eva37 based on the reference man data were used for analysis of dose variation by difference factors. The absorbed doses of 20 dominant organs were analyzed for the male and 22 dominant organs for the female. The organs considered are as follows: the bone marrow, the upper large intestine, the lower large intestine, the lung, the stomach, the bladder, the liver, the esophagus, the thyroids, the skin, the cortical bone, the adrenals, the brain, the small intestine, the kidneys, the pancreas, the spleen, the thymus, the testes (ovaries), the breasts, and the uterus. We adjusted the histories of the Monte Carlo calculations so that the statistical uncertainty of each considered organ would become less than 5%. Figure 9.12 gives the ratios of organ doses for the Japanese computational phantoms to those for the Caucasian computational phantoms. We selected five dominant organs and two energies of 0.05 and 1 MeV in this figure. We normalized organ doses for Japanese male computational phantoms to Rex, and doses for Japanese female computational phantoms to Regina. Several tendencies are confirmed from this figure. Obvious differences are larger for 0.05 MeV. In terms of irradiation geometries, the difference is large in the order of LAT > PA > AP, which is considered due to the path length difference to reach the organs. Thyroid doses indicate a large variation up to a several factor, while dose different for other organs seem within a factor of 2 in the energy range over 50 keV. Figure 9.13 shows energy dependencies of thyroid doses for Onago, JF, and Regina in LLAT geometry. The positions of thyroids tend to change according to individuals, and this could vary the dose. The positions of thyroid are clearly different even for Japanese computational phantoms as shown in Figure 9.14. The thyroids of Regina are expected to locate at deeper positions than those for Japanese, though the detailed anatomical data have not been released on Regina. It is not yet clear if the large difference is due to racial difference or due to individual difference, and further analysis is necessary for understanding systematically the difference. The bladder wall doses are shown in Figure 9.15 for AP and PA geometries. Doses are larger in AP geometry since the bladder exists near the front surface of a human body. In most cases, the VIP-man shows the smallest doses because it has a larger body size

Dose ratio (Japanese/Caucasian)

1.4

Dose ratio (Japanese/Caucasian)


3

1.2

(0.05 MeV)

1.2

1

1 0.8

0.6

0.6

0.4

0.4

JM JF

0.2 Th

Otoko Onago

Lu

Li JM JF

St

Bl

1.8 1.6 1.4 1.2 1 0.8 0.6 0.4 0.2

Otoko Onago

RLAT (0.05 MeV)

5 1 5 0

Th Lu

Li

AP (1 MeV)

St

0

Bl

Otoko Onago

JM JF

0.2 0

5 2

2 1.8 1.6 1.4 1.2 1 0.8 0.6 0.4 0.2 0

1.4 AP

0.8

0

237

Th

Lu

Li

St

Bl Otoko Onago

JM JF

RLAT (1 MeV)

Th Lu Li

St

Bl

5 4.5 4 3.5 3 2.5 2 1.5 1 0.5 0

1.4 PA (0.05 MeV)

1.2

PA (1 MeV)

1 0.8 0.6 0.4 JM JF

0.2

Otoko Onago

Th Lu Li

St

Bl

0

JM JF

Otoko Onago

Th Lu Li

St

Bl

2.5 JM JF

Otoko Onago

LLAT (0.05 MeV)

JM JF

2

Otoko Onago

LLAT (1 MeV)

1.5 1 0.5

Th Lu

Li

St

Bl

0

Th Lu

Li

St

Bl

FIGURE 9.12 Ratios of organ doses for Japanese models to those for Caucasian models in the thyroid (Th), lungs (Lu), liver (Li), stomach (St), and bladder wall (Bl) for external photon exposure. Organ doses for Japanese male models were normalized to Rex, and doses for Japanese female models to Regina.

Organ dose per air-absorbed dose (Gy/Gy)

1.4 1.2 1.0 0.8 0.6 0.4

JF Onago

0.2 0.0 0.01

Regina

0.1

1

10

Photon energy (MeV) FIGURE 9.13 Comparison of thyroid doses among Onago, JF, and Regina for external photon exposure in LLAT geometry.


238



FIGURE 9.14 Thyroid positions of JF and Onago.

2 AP

1 Japanese JM Otoko JF Onago 0 0.01

Caucasian Rex Regina


10

2 PA

1

0 0.01


10

FIGURE 9.15 Comparison of bladder wall doses among different models for external photon exposure in AP and PA geometry.

than other computational phantoms. Doses for Japanese computational phantoms are smaller than those for Caucasian computational phantoms in AP geometry, and larger in PA geometry. This might indicate some anatomical difference between Caucasian and Japanese: possible reasons are that the bladder exists in deeper position in the Japanese, and that Caucasian has bigger hip size. It is difficult to draw a conclusion only from these data, and further investigation is desired. In total, concerning individual organs, the dose difference among different computational phantoms becomes up to a factor of 4–5 at 50 keV. Further, some differences need to be considered due to systematic difference that we observed. Further analyses are needed to clarify the reason of the difference: some systematic difference due to anatomical difference may exist like the case of bladder wall. The difference in effective dose was within 10% for almost all of cases and hardly exceeds 20% in the energy range of 50 keV to 10 MeV. The complete data set of the calculated organ doses is under preparation, and detailed discussion will be made elsewhere.


239

It must be noted that, in case of partial irradiation by narrow beams, organ doses change drastically by the incident position and angle.38 In such cases, an appropriate individual computational phantom is considered to be necessary to obtain realistic organ doses and effective doses. 9.3.2.2 Electron Saito et al. calculated external electron doses for Otoko and Onago in AP, PA, and ISO geometries,4 and compared the data with a MIRD computational phantom by Ferrari.39 Figure 9.16 demonstrates examples of the dose coefficients for the three computational phantoms: the left data for liver doses in AP geometry is an example where a good agreement was observed among the computational phantoms; while the right illustrates the kidney doses in PA geometry. Which is an example where there was apparent difference between the MIRD computational phantom and the voxel computational phantoms. In external electron exposure, the organ depth is a dominant factor to determine the energy dependency of the dose coefficients. The Figure indicates the effective depth is quite similar for three computational phantoms in the left case, and obviously different in the right case. Figure 9.17 shows organ doses for Otoko and Onago normalized to those for the MIRD computational phantom. Large differences between different computational phantoms take place mainly around 10 MeV where the electron range is several centimeter. It makes a great difference if electrons reach organs or not; and dominant organs lie mostly at depths of several centimeter. The maximum dose difference observed in this comparison was a factor of 50 in kidney dose in PA geometry. A factor of 50 does not have any concrete meaning, but the electron dose for an individual organ sometimes shows a large discrepancy between different computational phantoms. The maximum difference observed in an effective dose was a factor of 2, which coincides with data of Kramer.40 Concerning external electron doses, we find that the available data are still limited and that we desire further investigations.

80 Equivalent dose per fluence (fSv/m2)

Equivalent dose per fluence (fSv/m2)

80 Liver (AP)

70 60 50 40 30 20

Otoko Onago MIRD

10 0

100

101

102

103

Electron energy (MeV)

104

Kidneys (PA)

70 60 50 40 30 20 10 0

100

101

102

103

104

Electron energy (MeV)

FIGURE 9.16 Examples of organ doses for external electron exposure calculated using Otoko, Onago and a MIRD model. (From Saito, K. et al., Jpn. J. Health Phys. 43, 122, 2008. With permission.)


10

Lungs (PA) Relative dose

Relative dose

10

1 Otoko/MIRD

10 Stomach (AP)

1

0.1 Otoko/MIRD

Onago/MIRD

0.1 101

102

103

Kidneys (PA) Relative dose

240

1

0.1 Otoko/MIRD Onago/MIRD

Onago/MIRD

104

0.01 101

10

102

103

104

0.01 101

102

103

104

10 Small int. (AP)

Bone marrow (AP)

Effective dose

Otoko/MIRD

1 Otoko/MIRD

Onago/MIRD

0.1 101

102 103 104 Electron energy (MeV)

Relative dose

1

Relative dose

Relative dose

2

1 AP PA ROT

Onago/MIRD

0.1 101


0 101


FIGURE 9.17 Organ doses and effective doses for Otoko and Onago normalized to those for a MIRD model in external electron exposure. (From Saito, K. et al., Jpn. J. Health Phys. 43, 122, 2008. With permission.)

9.3.3 Internal Dose Calculations 9.3.3.1 Absorption Fractions Internal dosimetry requires the fraction of energy emitted as a specified radiation in a source organ that is absorbed in a unit target organ, the so-called specific absorbed fractions (SAFs). The SAFs currently used by the International Commission on Radiological Protection (ICRP) have been obtained from calculations using MIRD computational phantoms. Positional relation among organs that is an important factor to determine SAFs could change significantly according to conditions; hence, SAF calculations for sophisticated computational phantoms are necessary to accurately evaluate internal doses. Kinase et al.32 calculated SAFs for photons in the Otoko, Onago, and MIRD computational phantom using the UCSAF code and then compared these results with published data to investigate the influence of several parameters on the SAFs. The source was assumed to emit monoenergetic photons in the energy range of 10 keV to 4 MeV and uniformly distributed in the source organ. The source organ was taken as the kidneys, but more than 100 target organs were considered. We show the calculated SAFs for kidneys as the target organ, as well as the source organ, in Figure 9.18, together with the data for Golem and Voxelman. A good agreement was found between the SAFs for all computational phantoms except for Voxelman, whose kidneys have a larger mass than those of the other computational phantoms, resulting in lower SAF values. Sato et al.5 calculated self-absorption fractions (Self-AFs) using Otoko, JM, and a MIRD computational phantom. Since Self-AFs are not explicitly affected by organ masses, unlike SAFs, they are suitable to discuss characteristics of energy deposition by radiations emitted from a source organ. Examples of Self-AFs are shown for six organs in Figure 9.19. It is clear that Self-AFs are similar for large organs having identical shapes among different


241

101 Otoko Onago MIRD 5 type voxel MIRD 5 type Golem Voxelman

SAF (/kg)

100

10–1

10–2 10–2

100

10–1

101

Photon energy (MeV)

FIGURE 9.18 SAFs for the kidneys as the source and target organs calculated using several different models. (From Kinase, S. et al., Radiat. Prot. Dosim., 105, 557, 2003. With permission.)

Self-AF (–)

1

1

Brain

0.1

1

0.1

0.1

JM Otoko Voxel-MRD

0.01

0.01

(a)

0.1 1 Energy (MeV)

0.01

0.01

(b) 1

0.1 1 Energy (MeV)

0.01

0.01

(c) 1

Thyroid

Pancreas

Self-AF (–)

JM Otoko Voxel-MRD

JM Otoko Voxel-MRD

1

0.1 1 Energy (MeV) Urinary bladder wall

0.1 0.1

0.1

0.01 JM Otoko Voxel-MRD

0.01 (d)

Spleen

Kidneys

0.01

0.1 1 Energy (MeV)

0.01 (e)

JM Otoko Voxel-MRD

JM Otoko Voxel-MRD

0.01

0.1 1 Energy (MeV)

0.001 (f)

FIGURE 9.19 Self-AFs for six different organs calculated using Otoko, JM, and a MIRD model.

0.01

0.1 1 Energy (MeV)

242


computational phantoms, such as brain and kidneys. In case of small organs, however, the masses and shapes affect the Self-AFs significantly. When the shapes are identical, Self-AFs are determined by the masses: the portion of energy absorbed by the organ itself is largely affected by the size. The difference in spleen doses could be explained by the masses. While, in pancreas, thyroid and unitary bladder wall, the Self-AFs are not explained by the masses, and this indicated the shapes are important factors for these cases. Sato et al. investigated the effect of the posture of a human body on SAFs.41 In Figure 9.20, SAFs were compared between JM and JM2 for the esophagus, the lungs, the lower large intestine and the unitary bladder as target organs, when 16 organs were taken as source organs. JM is a computational phantom based on CT data taken in supine position, while JM2 is based on CT data in upright position. Data at photon energies of 0.03, 0.1, and 4 MeV are shown in this figure. Obviously, the difference is larger for lower energy photons whose large attenuations tend to affect the SAFs. We selected the four organs because they did not change the positions relative to the head between supine and upright position as indicated in Table 9.5. Other organs moved to lower directions in upright position because of the gravity. These movements are reflected in SAFs. Concerning the esophagus and lungs which are located in the upper parts of the body, the SAFs in upright position are generally smaller than those in supine position because the distances among organs increase according to the downward movements of other organs. Conversely, in case of the lower large intestine wall and unitary bladder wall located in the lower parts, the SAFs become larger in an upright position. Under the conditions considered here, the maximum observed difference was a factor of 3. Sato et al. examined the relation of SAFs to organ distance using Otoko, Onago, JF, and JM.42 In Figure 9.21, SAFs for several different combinations of source and target organs in the four computational phantoms are plotted together as a function of distance between the centers of gravity. The distance between the centers of gravity may not be the most suitable parameter to analyze SAFs in every case; nevertheless, it was confirmed that SAFs would be roughly expressed as a function of this distance. 9.3.3.2 S Value Kinase et al.43,44 evaluated S values—which are the mean absorbed doses per unit cumulated activity to the target organ from uniformly distributed radioactivity within the source organ—for several beta-ray emitters using the UCSAF code. S values have been used for dose estimates in radiological protection and medical diagnostic procedures. In particular, self-dose S values for positron emitters in the brain, the heart, and the urinary bladder play important roles for an accurate quantification of the doses to patients administered a radiopharmaceutical for clinical PET imaging. The evaluation of S values for the urinary bladder wall is indispensable for designing patient protocol strategies intended to minimize the dose for a specific radiopharmaceutical. In general, self-dose S values for positron emitters in the brain and the heart follow the inverse first power of the mass, especially if the absorbed fraction is approximated by unity (meaning no energy escape from the brain and the heart). S values for positron emitters within the urinary bladder are currently derived on the assumption that the dose at the surface of the content is approximately half the dose within their volume. Figure 9.22 shows the self-dose S values for the brain and the heart of the computational phantoms, for 11C, 13N, 15O, and 18F; the contributions from positrons and the two annihilation photons are distinguished. Readers can see that, as expected, the S values increase with increasing mean energy of beta ray. In emitters with high positron energies, positron

(a)

Number of source organs

0

5

10

0

5

Ratio of SAFs (upright/supine)

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