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Optical Coherence Tomography in Cardiovascular Research
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Optical Coherence Tomography in Cardiovascular Research Editors Evelyn Regar MD PhD Department of Interventional Cardiology Thoraxcenter Erasmus Medical Center Rotterdam The Netherlands
Ton G van Leeuwen MSc PhD Laser Center Academic Medical Center University of Amsterdam Amsterdam and Biophysical Engineering (BPE) Biomedical Technology Institute Faculty of Science and Technology University of Twente Enschede The Netherlands
Patrick W Serruys MD PhD Department of Interventional Cardiology Thoraxcenter Erasmus Medical Center Rotterdam The Netherlands
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© 2007 Informa Healthcare, an imprint of Informa UK Limited First published in the United Kingdom in 2007 by Informa Healthcare, 4 Park Square, Milton Park, Abingdon, Oxon OX14 4RN. Informa Healthcare is a trading division of Informa UK Ltd. Registered Office: 37/41 Mortimer Street, London W1T 3JH. Registered in England and Wales number 1072954. Tel: +44 (0)20 7017 6000 Fax: +44 (0)20 7017 6699 Email:
[email protected] Website: informahealthcare.com All rights reserved. No part of this publication may be reproduced, stored in a retrieval system, or transmitted, in any form or by any means, electronic, mechanical, photocopying, recording, or otherwise, without the prior permission of the publisher or in accordance with the provisions of the Copyright, Designs and Patents Act 1988 or under the terms of any licence permitting limited copying issued by the Copyright Licensing Agency, 90 Tottenham Court Road, London W1P 0LP. Although every effort has been made to ensure that all owners of copyright material have been acknowledged in this publication, we would be glad to acknowledge in subsequent reprints or editions any omissions brought to our attention. Although every effort has been made to ensure that drug doses and other information are presented accurately in this publication, the ultimate responsibility rests with the prescribing physician. Neither the publishers nor the authors can be held responsible for errors or for any consequences arising from the use of information contained herein. For detailed prescribing information or instructions on the use of any product or procedure discussed herein, please consult the prescribing information or instructional material issued by the manufacturer. A CIP record for this book is available from the British Library. Library of Congress Cataloging-in-Publication Data Data available on application ISBN 1 84184 611 2 ISBN 978 1 84184 611 8 Distributed in North and South America by Taylor & Francis 6000 Broken Sound Parkway, NW, (Suite 300) Boca Raton, FL 33487, USA Within Continental USA Tel: 1 (800) 272 7737; Fax: 1 (800) 374 3401 Outside Continental USA Tel: (561) 994 0555; Fax: (561) 361 6018 Email:
[email protected] Distributed in the rest of the world by Thomson Publishing Services Cheriton House North Way Andover, Hampshire SP10 5BE, UK Tel: +44 (0)1264 332424 Email:
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Contents
List of contributors Preface Acknowledgments
ix xv xvi
SECTION 1 – BASIC PRINCIPLES AND PHYSICS 1
An introduction to tissue optics for OCT Johan F Beek, Dirk J Faber
3
2
Principles of OCT James G Fujimoto, Joseph M Schmitt
19
3
Design of an OCT imaging system for intravascular applications Christopher L Petersen, Joseph M Schmitt
35
4
Light and sound: parallels and differences Gijs van Soest, Anton FW van der Steen
43
SECTION 2 – CURRENT CARDIOVASCULAR APPLICATIONS 2A CORONARY APPLICATIONS 5
Intracoronary OCT application: methodological considerations Evelyn Regar, Francesco Prati, Patrick W Serruys
53
6
OCT: comparison to histology Teruyoshi Kume, Takashi Akasaka
65
7
OCT: plaque morphology in the clinical setting Francesco Prati, Maria Cera, Tamer Fouad, Vito Ramazzotti
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8
OCT plaque characterization: comparison to angioscopy Yoshihiro Takeda, Jean-François Surmely, Takahiko Suzuki
79
9
OCT plaque characterization: comparison to multislice computed tomography Carlos AG Van Mieghem, Nico R Mollet, Pim J de Feyter
87
10
OCT plaque characterization: comparison to IVUS-VH Gastón A Rodríguez-Granillo, Patrick W Serruys
95
11
How to match different imaging technologies Nico Bruining, Ronald Hamers
103
2B POTENTIAL IN VULNERABLE PLAQUE RESEARCH 12
Vulnerable plaque: the present and the future John A Ambrose, Ralph J Wessel
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13
Resolution versus imaging depth: every advantage has it disadvantage Freek J van der Meer, Ton G van Leeuwen
115
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CONTENTS
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OCT imaging of vulnerable plaque: the Massachusetts General Hospital experience Christopher Raffel, Guillermo J Tearney, Brett E Bouma, Ik-Kyung Jang
121
15
Clinical lessons from OCT imaging of Apo E knockout mice Mehmet Cilingiroglu, Jung Hwan Oh, Pramod K Sanghi, Nate J Kemp, Sharon Thomsen, Thomas E Milner, Marc D Feldman
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2C ASSESSMENT PROCEDURAL OUTCOME 16
Long-term effects of endovascular radiation after balloon angioplasty: assessment by OCT and histology Heleen MM van Beusekom, Evelyn Regar, Ilona Peters, Wim J van der Giessen
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17
Acute OCT findings after stenting Jean-François Surmely, Yoshihiro Takeda, Tatsuya Ito, Takahiko Suzuki
153
18
OCT findings in drug-eluting stents Eberhard Grube, Victor Lim, Lutz Buellesfeld
161
2D TOTAL CHRONIC OCCLUSION 19
20
Chronic total occlusion: do we need intravascular imaging guidance? Jun Tanigawa, Osamu Katoh, Carlo Di Mario
171
OCT-guided wiring technique for chronic total coronary occlusion Yoshihiro Takeda, Osamu Katoh
183
2E OCT IN NON-CORONARY ANATOMY 21
OCT in peripheral arteries Oliver A Meissner, Johannes Rieber
22
OCT: gaining insights into cerebral aneurysm healing after endovascular treatment William E Thorell
23
Cardiac development in chicken and mouse embryos Florence Rothenberg, Michael W Jenkins, Andrew M Rollins
191
203 209
SECTION 3 FUTURE DEVELOPMENTS 3A ADVANCED SIGNAL ANALYSIS 24
New parallel frequency domain techniques for volumetric OCT Boris Povazˇay, Wolfgang Drexler, Rainer A Leitgeb
221
25
Spectroscopic analysis of arterial wall components using OCT Dirk J Faber, Freek J van der Meer, Ton G van Leeuwen
231
26
Polarization-sensitive OCT: detection of vulnerable atherosclerotic plaque Seemantini K Nadkarni, Mark C Pierce, Brett E Bouma, Guillermo J Tearney, Johannes F de Boer
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27
OCT elastography: a possibility to detect vulnerable plaque? Gijs van Soest, Frits Mastik, Patrick W Serruys, Evelyn Regar, Anton FW van der Steen
249
28
Limiting ischemia by fast Fourier-domain imaging Joseph M Schmitt, Robert Huber, James G Fujimoto
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3B CONTRAST ENHANCED OCT IMAGING 29
Microsphere contrast agents for OCT Stephen A Boppart, Kenneth S Suslick
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CONTENTS
30
Molecular contrast OCT Brian E Applegate, Joseph A Izatt
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3C OCT DOPPLER 31
Why do we need flow measurements? Role of flow and shear stress in atherosclerotic disease Jolanda J Wentzel, Frank JH Gijsen, Johan CH Schuurbiers, Harald C Groen, Alina G van der Giessen, Anton FW van der Steen, Patrick W Serruys
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32
Principles of Doppler OCT Victor XD Yang, I Alex Vitkin
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33
OCT blood flow imaging Stephen J Matcher, Julian Moger
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34
Summary and outlook Ton G van Leeuwen, Evelyn Regar, Patrick W Serruys
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Index
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Contributors
Takashi Akasaka MD PhD Department of Cardiovascular Medicine Wakayama Medical University Wakayama Japan
Stephen A Boppart MD PhD Department of Electrical and Computer Engineering, Bioengineering, and Medicine Biophotonics Imaging Laboratory Beckman Institute for Advanced Science and Technology University of Illinois at Urbana-Champaign Urbana, Illinois USA
John A Ambrose MD Comprehensive Cardiovascular Center Saint Vincents Catholic Medical Centers of New York New York, New York USA
Brett E Bouma PhD Cardiology Division and Wellmam Center for Photomedicine Massachusetts General Hospital Harvard Medical School Boston, Massachusetts USA
Brian E Applegate PhD Department of Biomedical Engineering Texas A&M University College Station, Texas USA Johan F Beek MD PhD Laser Center Academic Medical Center University of Amsterdam Amsterdam The Netherlands
Nico Bruining PhD Department of Cardiology Thoraxcenter Erasmus Medical Center Rotterdam The Netherlands
Heleen MM van Beusekom PhD Department of Cardiology Thoraxcenter Erasmus Medical Center Rotterdam The Netherlands
Lutz Buellesfeld MD Department of Cardiology and Angiology Helios Heart Center Siegburg Siegburg Germany
Johannes F de Boer PhD Harvard Medical School and Health Sciences and Technology Wellman Center for Photomedicine Massachusetts General Hospital Boston, Massachusetts USA
Maria Cera MD Catheterization Laboratory San Giovanni Hospital Rome Italy
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LIST OF CONTRIBUTORS
Mehmet Cilingiroglu MD FACP FESC Divison of Cardiology Department of Medicine University of Texas Health Science Center San Antonio, Texas USA Wolfgang Drexler PhD University of Vienna Institute of Medical Physics Christian Doppler Laboratory Vienna Austria Dirk J Faber PhD Laser Center Academic Medical Center University of Amsterdam Amsterdam The Netherlands Marc D Feldman MD University of Texas Health Science Center in San Antonio San Antonio, Texas USA Pim J de Feyter MD PhD Department of Cardiology and Radiology Thoraxcenter Erasmus Medical Centre Rotterdam The Netherlands Tamer Fouad BSC Catheterization Laboratory San Giovanni Hospital Rome Italy James G Fujimoto PhD Department of Electrical Engineering and Computer Science and Research Laboratory of Electronics Massachusetts Institute of Technology Cambridge, Massachusetts USA Alina G Van der Giessen MSC Department of Biomedical Engineering Thoraxcenter Erasmus Medical Center Rotterdam The Netherlands Wim J van der Giessen MD PhD Department of Cardiology Thoraxcenter Erasmus Medical Center Rotterdam The Netherlands
Frank JH Gijsen PhD Department of Biomedical Engineering Thoraxcenter Erasmus Medical Center Rotterdam The Netherlands Harald C Groen MSC Department of Biomedical Engineering Thoraxcenter Erasmus Medical Center Rotterdam The Netherlands Eberhard Grube MD Department of Cardiology and Angiology Helios Heart Center Siegburg Siegburg Germany Ronald Hamers PhD Department of Cardiology Thoraxcenter Erasmus Medical Center Rotterdam The Netherlands Robert Huber PhD Department of Electrical Engineering and Computer Science and Research Laboratory of Electronics Massachusetts Institute of Technology Cambridge, Massachusetts USA Tatsuya Ito Department of Cardiology Toyohashi Heart Center Toyohashi, Archi Japan Joseph A Izatt PhD Department of Biomedical Engineering Duke University Durham North Carolina USA Ik-Kyung Jang MD PhD Massachusetts General Hospital Harvard Medical School Boston, Massachusetts USA Michael W Jenkins MSC Department of Biomedical Engineering Case Western Reserve University Cleveland, Ohio USA
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LIST OF CONTRIBUTORS
Osamu Katoh MD Cardiovascular Center Toyohashi Heart Center Toyohashi, Aichi Japan Nate J Kemp PhD Department of Biomedical Engineering University of Texas Austin, Texas USA Teruyoshi Kume MD Department of Cardiology Kawasaki Medical School Kurashiki Japan Ton G van Leeuwen MSC PhD Laser Center Academic Medical Center University of Amsterdam Amsterdam and Biophysical Engineering (BPE) Biomedical Technology Institute Faculty of Science andTechnology University of Twente Enschede The Netherlands Rainer A Leitgeb PhD Laboratoire d’Optique Biomedicale Lausanne Switzerland Victor Lim MD Department of Cardiology and Angiology Helios Heart Center Siegburg Siegburg Germany Carlo Di Mario MD PhD Department of Interventional Cardiology Royal Brompton Hospital London UK Frits Mastik Department of Biomedical Engineering Thoraxcenter Erasmus Medical Center Rotterdam The Netherlands Stephen J Matcher PhD BSC School of Physics University of Exeter Exeter UK
Freek J van der Meer PhD Laser Center Academic Medical Center University of Amsterdam Amsterdam The Netherlands Oliver A Meissner MD Institute for Clinical Radiology Ludwig-Maximilians-University Munich Germany Carlos AG Van Mieghem MD Department of Cardiology and Radiology Thoraxcenter Erasmus Medical Center Rotterdam The Netherlands Thomas E Milner PhD Department of Biomedical Engineering University of Texas Austin, Texas USA Julian Moger BSC PhD School of Physics University of Exeter Exeter UK Nico R Mollet MD PhD Department of Cardiology and Radiology Thoraxcenter Erasmus Medical Center Rotterdam The Netherlands Seemantini K Nadkarni PhD Department of Dermatology Wellman Center for Photomedicine Massachusetts General Hospital Harvard Medical School Boston, Massachusetts USA Jung Hwan Oh MS Department of Biomedical Engineering University of Texas Austin, Texas USA Ilona Peters BSC Department of Cardiology Thoraxcenter Erasmus Medical Center Rotterdam The Netherlands
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LIST OF CONTRIBUTORS
Christoper L Petersen PhD System Development and Intellectual Property LightLab Imaging Westford, Massachusetts USA Mark C Pierce PhD Department of Dermatology Wellman Center for Photomedicine Massachusetts General Hospital Harvard Medical School Boston, Massachusetts USA Boris Povazˇ ay PhD Zentrum für biomedizinische Technik und Physik Medizinische Universität Wien Vienna Austria Francesco Prati MD Catheterization Laboratory San Giovanni Hospital Rome Italy Christopher Raffel MD FRACP Cardiology Division and Wellmam Center for Photomedicine Massachusetts General Hospital Harvard Medical School Boston, Massachusetts USA Vito Ramazzotti MD Catheterization Laboratory San Giovanni Hospital Rome Italy Evelyn Regar MD PhD Department of Interventional Cardiology Thoraxcenter Erasmus Medical Center Rotterdam The Netherlands Johannes Rieber MD Department of Cardiology Medizinische Poliklinik Ludwig-Maximilians University Munich and Department of Radiology Massachusetts General Hospital Harvard Medical School Boston, Massachusetts USA
Gastón A Rodríguez-Granillo MD PhD Department of Interventional Cardiology Thoraxcenter Erasmus Medical Center Rotterdam The Netherlands Andrew M Rollins PhD Departments of Biomedical Engineering and Medicine Case Western Reserve University Cleveland, Ohio USA Florence Rothenberg MD MS Division of Cardiovascular Diseases University of Cincinnati Cincinnati, Ohio USA Pramod K Sanghi MD Divison of Cardiology Department of Medicine University of Texas Health Science Center San Antonio, Texas USA Joseph M Schmitt PhD LightLab Imaging Westford, Massachusetts USA Johan CH Schuurbiers BSC Biomedical Engineering Thoraxcenter Erasmus Medical Center Rotterdam The Netherlands Patrick W Serruys MD PhD Department of Interventional Cardiology Thoraxcenter Erasmus Medical Center Rotterdam The Netherlands Gijs van Soest PhD Department of Thoraxcenter Biomedical Engineering Thoraxcenter Erasmus Medical Center Rotterdam The Netherlands
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LIST OF CONTRIBUTORS
Anton FW van der Steen PhD Interuniversity Cardiology Institute of the Netherlands Rotterdam and Department of Biomedical Engineering Thoraxcenter Erasmus Medical Center Rotterdam The Netherlands Jean-François Surmely MD Department of Cardiology Toyohashi Heart Center Toyohashi, Archi Japan Kenneth S Suslick PhD Beckman Institute for Advanced Science and Technology Department of Chemistry University of Illinois at Urbana-Champaign Urbana, Illinois USA Takahiko Suzuki MD Department of Cardiology Toyohashi Heart Center Toyohashi, Aichi Japan Yoshihiro Takeda MD Department of Cardiology Toyohashi Heart Center Toyohashi, Aichi Japan Jun Tanigawa MD Division of Cardiology First Department of Internal Medicine Osaka Medical College Takatsuki, Osaka Japan
Guillermo J Tearney MD PhD Wellman Center for Photomedicine Massachusetts General Hospital Harvard Medical School Boston, Massachusetts USA Sharon Thomsen MD Department of Biomedical Engineering University of Texas Austin, Texas USA William E Thorell MD Department of Neurosurgery University of Nebraska Medical Center Omaha, Nebraska USA I Alex Vitkin PhD MCCPM Departments of Medical Biophysics and Radiation Oncology University of Toronto Toronto, Ontario Canada Jolanda J Wentzel PhD Department of Biomedical Engineering Thoraxcenter Erasmus Medical Center Rotterdam The Netherlands Ralph J Wessel MD Department of Cardiology University of California San Francisco (UCSF) Fresno, California USA Victor XD Yang MD PhD PEng Imaging Research Sunnybrook Health Sciences Center Toronto, Ontario Canada
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Preface
Over the past decade, optical coherence tomography (OCT) technology has experienced a tremendous development. Its unique capability to visualize biological tissue in vivo at very high resolution, even beyond the cellular level, as well as a variety of possibilities for signal processing has stimulated researchers from many different disciplines. OCT has successfully entered the medical area in the field of ophthalmology, where it soon became an established clinical tool. Preliminary clinical studies have been initiated in other clinical specialities, such as gastroenterology, gynecology and dermatology. Atherosclerosis is the major cause for morbidity and mortality in Western-civilizations. As a systemic disease it affects such different organs as the brain, heart, kidneys and peripheral vessels. The presentation of the disease is two-fold, with a chronic progressive component and sudden life-threatening events such as acute coronary syndrome or stroke. Diagnosis and therapeutic interventions rely on medical imaging. The unique imaging capabilities of OCT may offer new ways for the analysis of the disease, the understanding of acute events and therapy. The cardiovascular application of OCT represents a relatively new and challenging task, as the sample volume is not directly accessible and imaging is principally hampered by the presence of blood. We are pleased to present this handbook dedicated to the application of OCT in cardiovascular research. In a unique approach, it summarizes the current applications and state-of-the-art developments of OCT in different disciplines. Knowledge from basic science, such as molecular biology and chemistry, engineering and preclinical and clinical research is presented in a concise way. Each section is introduced by a general overview from a recognized expert in the field, outlining the scope of the problem, followed by several chapters addressing specific issues in detail. This structure
enables the reader to gain a rapid general understanding as well as specific information on the different aspects of cardiovascular OCT application. The first section discusses the basic principles and physics of the OCT technology. The second section summarizes the current possibilities in cardiovascular application and addresses specifically the possibilities for coronary plaque characterization, the assessment of vulnerable plaque, the analysis of therapeutic interventions and the guidance of complex procedures such as percutaneous coronary intervention of chronic total occlusions. The third section is dedicated to future developments including advanced signal analysis, molecular contrast imaging and OCT Doppler. The OCT in Cardiovascular Research provides biologists, physicians and cardiologists insights into the technical possibilities of advanced OCT signal processing, and offers present and future options for qualitative, quantitative, morphometric and functional analysis. It provides basic scientists and engineers with insights into the needs of applied science in the specific setting of clinical research. It is our hope that the OCT in Cardiovascular Research will prove useful to the medical community, in its effort to serve as a comprehensive guide to understanding many complexities of OCT imaging and its role in cardiovascular research. As an instructional tool, the handbook will help to enhance diagnostic capabilities, prevention and therapeutic strategies in our collective efforts to reduce the morbidity and mortality in our patients with cardiovascular disease. We hope that this book may serve as a guide and inspire atherosclerosis researchers to intensively use and further challenge the fascinating potential of OCT. Evelyn Regar Ton G van Leeuwen Patrick W Serruys
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Acknowledgments
Many thanks to all of our contributors, recognized and respected worldwide for their work with OCT. The clinical research presented in this handbook would not have been possible without the enthusiasm, cooperation and trust of our patients.
We would also like to acknowledge the dedication of Oliver Walter and Alan Burgess of Informa Healthcare for their tremendous efforts in the collection, coordination and subsequent production of this handbook.
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SECTION 1 Basic principles and physics
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CHAPTER 1 An introduction to tissue optics for OCT Johan F Beek, Dirk J Faber
INTRODUCTION
where I(d) denotes the intensity at distance d, which represents the distance within the tissue to the source (or, in the case of transmission through a sample, the physical thickness of the sample through which the light travels), I0 denotes the light intensity incident on the tissue (also called the irradiance), and µa [m−1] denotes the absorption coefficient of the tissue. The absorption coefficient is defined such that, when a photon propagates over an infinitesimal distance ∆d, the probability of absorption in this distance is µa times this distance. In other words, the mean free path for an absorption event is 1/µa. Tissue absorption varies for different wavelengths. Examples of light-absorbing tissue constituents or chromophores are melanin, hemoglobin, oxyhemoglobin, bilirubin, cytochrome and water. Figure 1.1 gives the absorption coefficient of the most important of these chromophores at 250 nm to 2.5 µm. The wavelength range of interest for OCT is approximately from 700 nm to 2 µm, because the emission spectra of light sources used in most commercial and experimental OCT systems are in this range. The µa’s of water, hemoglobin, oxyhemoglobin and melanin were collected from the literature by Scott Prahl and Steve Jacques and can be found together with other data on http:// omlc.org.edu/spectra. For water, the data of Hale and Querry were used2. The wavelength range between 600 nm and 1200 nm is often called the therapeutic window. Figure 1.1 illustrates that the most important tissue chromophores at that wavelength range show relatively low absorption. As a result, the penetration depth at these wavelengths for therapeutic and diagnostic applications is relatively large, allowing OCT imaging to greater depths.
At present, many colleagues working in various medical fields are enthusiastically using optical coherence tomography (OCT) in their daily clinical practice and lines of research. For most medical applications of OCT, a fair knowledge of human anatomy, pathology and tissue function suffices to allow interpretation of clinically obtained images. However, a general understanding of tissue optics can be helpful and sometimes is necessary for correct interpretation of OCT images. Furthermore, during recent years, papers have been published in which OCT is used to determine optical properties of tissue and tissue constituents. The aim of this chapter is to provide an introduction to tissue optics for clinical users of OCT, in a way in which an extensive understanding of physics is not required. Basic principles of tissue optics such as absorption, single and multiple scattering and scattering anisotropy are addressed, followed by a brief discussion of the theory of light transport in tissue. The most widely used methods to determine optical properties of tissue are discussed, and this paragraph will be complemented by a description of the use of OCT to measure the optical properties of tissue. The chapter is concluded with a selected overview of optical properties of tissue with relevance to OCT.
BASICS OF TISSUE OPTICS Absorption If tissue would simply be absorbing matter without scattering, biomedical optics would be straightforward and much less fun. Light attenuation in nonscattering media can be described by an exponential decay, commonly referred to as Lambert–Beer’s law1: I(d) = I0e-µad
Scattering Scattering can be elastic or non-elastic. In this chapter, we only consider elastic scattering in which the photon energy is unaltered. An example of non-elastic scattering is fluorescence, in which part of the photon
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melanosome
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Figure 1.1 Absorption coefficient as a function of wavelength at 250 nm to 2.5 µm for water, hemoglobin (Hb), oxyhemoglobin (HbO2) and melanin (for practical reasons given as the average absorption coefficient of the interior of melanosomes). Data in the spectral range of 700 nm to 2 µm are of special interest, because most light sources used in commercial and experimental optical coherence tomography systems emit in this range. The data were collected by Scott Prahl and Steve Jacques (see http://omlc.org.edu/ spectra). For water the data of Hale and Querry were used2
energy is absorbed by the fluorophore and another part is emitted as a photon with a longer wavelength. In first-order elastic scattering, the scattered light can be treated in a similar manner to that of absorbed light in non-scattering media. Attenuation due to scattering can be described with a simple exponential decay using Lambert–Beer’s law*. In single particle optics, conservation of energy requires that (1) the energy absorbed inside a particle may by definition be put equal to the energy incident on the cross-sectional area for absorption σa; (2) the total energy scattered in all directions is equal to the energy of the incident wave falling on the cross- sectional area for scattering σs; and (3) the energy removed from the original beam may by definition be put equal to the energy incident on the cross-sectional area for extinction or total attenuation σt; and that in general (4) the cross-sectional area for total attenuation equals the sum of the cross-sectional areas for absorption and scattering (σt = σa + σs)3. For a medium with multiple identical scatterers, the scattering coefficient
µs of this medium can be defined by µs = nsσs, where ns is the number of scattering particles per unit volume and σs is the scattering cross section of an infinitesimal volume dV in a tissue volume V. In a similar way, the average scattering coefficient of a medium with a number of n different scatterers can be defined as µs = ns1σs1 + ns2σs2 + ns3σs3 + . . . + nsnσsn. Tissue absorption can be described in a similar manner. In other words, it is usually assumed that scattering and absorption coefficients increase linearly with the concentration of scattering and absorbing particles, respectively. This assumption generally holds for the absorption coefficient and, in case of low to moderately high scattering, also for the scattering coefficient. However, for increasingly high concentrations of scattering particles, the scattering coefficient µs does not increase linearly with the concentration of scattering particles due to shielding of one particle by another, leading to an overlap (and, therefore, a reduction) of scattering cross sections. This is also called dependent (or cooperative) scattering3. Most tissues typically contain many scatterers of different sizes and shapes. The body consists of a wide variety of cell organelles, nuclei, cells and intercellular structures, all with their own tissue architecture and organization. Besides a large variation in shape between different types of cell there may be, with respect to scattering, significant variation between cells of one type. Due to local differences in index of refraction (between and within these structures), light is scattered within tissue through diffraction. Variations in, for instance, perfusion, oxygenation, water content (edema) and respiration are all factors that further add to the complexity of tissue as an absorbing and scattering (or turbid) medium. As a result, scattering in tissue is not a welldefined problem. It seems to be unfeasible to distinguish all the different types of scatterers in tissue and then obtain µs by adding products of the number ns and cross section σs of different types of scattering particles. Therefore, normally bulk scattering properties of tissue are measured and used for modeling. In analogy with the definition of the absorption coefficient given in the previous paragraph, the scattering coefficient is then also defined such that, when a photon propagates over infinitesimal distance ∆d, the probability of scattering in this distance is µs times this distance.
*When singly scattered photons are detected in OCT, the relevant light from the source that is collected by the interferometer detector travels in the tissue a distance of 2d, which represents the sum of two equal distances, one in the tissue from the source to the point where the light is reflected or backscattered and another from this point to the detector. As a result, the light is attenuated twice over that distance. In general, Lambert–Beer’s law is used to calculate the total attenuation. Using equation 1, instead of the µa, the total attenuation coefficient µt (= µa + µs) is used. If an image is made of a multilayered structure such as the retina or the wall of the esophagus, attenuation by the various layers should be considered, both to and from the point of reflection or backscattering within the tissue.
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A. Rayleigh and Henyey– Greenstein for g = 0
B. Henyey–Greenstein for g = − 0.5
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Figure 1.2 Polar plots of the Rayleigh phase function [p(cosθ) = 3/4(1 + cos2θ)] and examples of the Henyey–Greenstein phase function [p(cosθ) = (1−g2) / (1 + g2 − 2gcosθ)3/2] for different values of g (equations from references 3 and 4)
Phase functions and scattering anisotropy Although the direction of propagation of a photon is not necessarily different from that photon’s direction before scattering, most scattering events do alter the direction of photon propagation. Therefore, knowledge of the probability of scattering in tissue (given by µs) should be complemented by information on the direction in which the photons propagate after scattering. For a single scattering event, the distribution of all scattering angles is given by the scattering probability density function, also called the (scattering) phase function. The choice of phase function is of key importance in any calculation on single or multiple scattering. For well-defined physical problems, the phase function is given and not chosen3. Given the above-mentioned complex nature of scattering in tissue, the phase function for tissue is not given. The (single scattering) phase function that therefore is chosen should as accurately as possible describe the average distribution of scattering angles after scattering of light by bulk tissue that contains many different types of scatterer. Over the years, several theoretical phase functions have been proposed to represent single scattering phase functions, such as
the Henyey–Greenstein, modified Henyey– Greenstein, δ-Eddington, Rayleigh and Reynolds functions. For tissue, the (modified) Henyey–Greenstein phase function, that originally was proposed to describe scattering in astronomy, is most widely used4. It approximates Mie scattering by particles comparable in size to the wavelengths of the visible spectrum. It is in better agreement with the mostly forward scattering of tissue than the Rayleigh phase function, where forward and backward scattered intensities are the same. If scattering intensities are the same in all directions, we speak of isotropic scattering. In tissue, scattering is anisotropic. The anisotropy factor g, which is defined as the expectation value of the cosine or mean cosine of the scattering angle, is a measure of scattering anisotropy. Total forward scattering means that g = 1, in isotropic scattering g = 0 and in total backward scattering g = −1. For most tissues, g ranges from 0.69 to 0.9988. Therefore, when light is scattered by tissue, the mean scattering angle will be between 2.8° and 46.4°. Consequently, the scattering angle in tissue will be predominantly between these values. Figure 1.2 shows the Rayleigh phase function and examples of the Henyey–Greenstein phase function for different values of g3,4.
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A
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Depth (mm)
Figure 1.3 Simulated A-scans for three different positions of the focus in a weakly scattering sample (µt= 6 mm−1). The total signal (solid curves) is split into the single backscattering (dashed curves) and multiple scattering (dotted curves) contribution. Arrows denote the focus position in panels A, B and C. Note the logarithmic vertical scales and the obvious suppression of multiple scattered light at the focus position. a.u., arbitrary units
Consequences for optical coherence tomography: single versus multiple scattering It is clear from the discussion above that, with increasing scattering coefficient or increasing imaging depth, the probability of detecting multiply scattered light increases. The detection of multiple scattered light can hamper both OCT imaging and the quantitative measurement of optical properties from the OCT data, and thus the identification of different (pathologic) tissues. The contribution of multiple scattering to the OCT data is consequently the subject of many investigations5–8. It depends on the optical properties of the tissue as well as on the parameters of the OCT system. As is discussed in Chapter 2, OCT detection schemes are based on low-coherence interferometry (or white light interferometry), in which light returning from the sample is correlated to light that has traveled a known distance in the OCT system’s reference arm. The path length of the detected photons may be known, but they may have traveled any imaginable path (of that length) through the tissue, which makes it close to impossible to pinpoint an exact ‘reflection site’. Remember that in OCT image formation, it is
implicitly assumed that the light travels straight paths to and from the reflection site in the tissue. There are two reasons why images can be formed. First of all, as argued above, for most tissues the anisotropy is larger than 0.7 which means that scattering is predominantly forward directed and the photons actually do travel more or less straight paths. Second, OCT set-ups essentially have a confocal optical layout, which means that the detection probability of light originating from the focal volume of the imaging lens is maximized. It is the combination of path length (coherence) gating with location (confocal) gating that allows image formation in OCT. To assess the influence of confocal gating, we used the model of Thrane and colleagues7. The influence of the confocal gating on multiple scattering rejection is shown in Figure 1.3, in which the calculated OCT signal for three different positions of the focus in a scattering medium is depicted. Note the obvious suppression of multiple scattered light at the focus position (indicated by the arrows). The total OCT signal closely matches the single backscattering contributions over a large part of the depth scan. Similar calculations demonstrate that the imaging depth, defined here as the position in the tissue at
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7
3.0 2.5 Imaging depth (mm)
200
150
100
120
2.0
100
1.5
80 60
1.0
40 0.5
20
50
0.0 0
0 400
0
10
20
30
40
50
60
Hematocrit (%) Linear fit for low hematocrit Calculated packing factors: Twersky Hard spheres Cylinders
Figure 1.4 Attenuation coefficient of whole blood samples (subjected to simple shear 300 s−1) vs. hematocrit. Also shown are the best linear fit for low hematocrit and calculated curves using different packing factors. Clearly, the linear relation between attenuation coefficient and concentration only holds for low hematocrit
which the contribution of single and multiple scattered photons to the OCT signal is equal, increases with the numerical aperture of the OCT system. In other words, high numerical aperture optics are required to measure single backscattered light at large probe depths – a familiar result from confocal microscopy in turbid media9. When dependent or cooperative scattering occurs, the scattering coefficient µs no longer scales linearly with concentration (see above). Van de Hulst3 states, as a rule of thumb, that when the particles are separated by less than three times the particle radius, dependent scattering occurs. This situation typically holds for blood, which has a volume concentration of scatterers (the red blood cells) of ~40%. Cooperative scattering effects are usually addressed by introducing packing factors. Next to Twersky’s well-known quadratic formula, packing factors for hard spheres and for cylinders have been utilized in ultrasound measurements of whole blood. To illustrate the diversity of results that can be obtained, combining our recent calculations of the scattering cross section of an erythrocyte10 with Twersky’s packing factors and packing factors for hard spheres and cylinders leads to theoretical predictions of µs of 129 mm−1, 9 mm−1 and 33 mm−1, respectively, at 800 nm and 40% hematocrit11. We determined the attenuation coefficient as a function of hematocrit of whole blood samples (see Figure 1.4), under experimental conditions described by Steenbergen and colleagues12. Clearly, the linear
Required bandwidth (nm)
140
µt (mm−1)
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2000
Figure 1.5 Imaging depth (blue curve) and bandwidth of the OCT source required to obtain a resolution of 10 µm (red curve) as a function of wavelength as calculated for typical parameters of an optical coherence tomography (OCT) system and µs (chosen at µs = 6 mm−1). Imaging depth is defined as the depth at which the contributions of the single and multiple scattering to the OCT signal are equal
relation between attenuation coefficient and concentration only holds for low hematocrit. The fact that red blood cells exhibit extremely forward-directed scattering (g > 0.99) combined with the high-volume concentration makes it extremely difficult to interpret optical (or more specifically, OCT) signals from whole blood.
Contrast enhancement Owing to the strongly forward-scattering nature of tissue, only a small fraction of the photons emitted by the source contribute to the interference signal used to construct the OCT image. As a result, efficiency is poor and signal-to-noise ratios can be better. Many researchers are working on improvements. A new approach is to enhance contrast by use of nanoparticles. These highly scattering particles that often demonstrate more backward scattering than tissue are either homogeneous or shells. The particles are manufactured from, for instance, biodegradable polymers, silica and/or metal. The particles can be doped with various substances, such as absorbing dyes or fluorophores. Leaky tumor vasculature or conjugation to antibodies allows specific targeting. Particles may be designed in such a way that their optical properties change due to optical, thermal or magnetic stimuli.
Imaging depth The imaging depth of OCT systems depends on parameters of the system and the optical properties of the tissue that is imaged. Figure 1.5 shows, as a function of wavelength for a typical OCT system and µs (chosen at µs = 6 mm−1), the imaging depth (blue curve) as well as the bandwidth required to obtain a resolution of 10 µm (red curve). For this figure, the
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imaging depth is defined as the depth at which the contributions of the single and multiple scattering to the OCT signal are equal.
THEORY OF LIGHT TRANSPORT IN TISSUE Historically, there are two distinct theories that are used to describe scattering and absorption in turbid media such as tissue: analytical theory based on Maxwell’s equations and transport theory or radiative transfer theory based on Bolzmann’s equation1,3. Although the theories have different starting points, they deal with the same phenomena and therefore it is not surprising that there are fundamental relationships between them. The basic differential equation of the transport theory (Bolzmann’s equation, also known as the equation of radiative transfer and the Maxwell–Boltzmann collision equation), is also used in other fields such as statistical mechanics, e.g. kinetic theory of gases and neutron transport theory1. The equation describes the losses of the incident light due to absorption and scattering, at a certain position and coming from a certain direction in the tissue, and the gain of light at this position due to scattering from all other directions. Unfortunately, an exact analytic solution for this equation is not available. Therefore, approximations are used to describe light transport in tissue. These approximations accurately predict remittance and transmittance out of the tissue, as well as the fluence rate* at a certain point within the tissue. Although transport theory lacks strictness, its predictions have proved to be satisfactory. In the 1980s, Martin van Gemert developed transport theory for one dimension. In his approach he assumes that light propagates in either a positive or a negative direction on a line representation of tissue with homogeneously distributed absorbers and scatterers. Especially for estimation of attenuation of fluorescence excitation and emission photon fluxes and reflections in multilayered geometries, onedimensional transport theory is quite useful13. Much earlier, a model for plane geometries with many fluxes at discrete angles was developed by Kubelka and Munk14,15. In this theory, Kubelka–Munk coefficients for absorption and scattering of diffuse radiation are defined. With these parameters, differential equations are formed in which photons are lost due to absorption and scattering and gained due to scattering of light from the opposite direction. The use of the Kubelka–Munk theory for tissue optics has rapidly declined after the introduction of other methods that allow description of light
propagation in three dimensions. One example is diffusion theory. This gives a relatively accurate description of light propagation in tissue that can be solved numerically for the three-dimensional problem16–18. However, in the diffusion approximation of the equation of radiative transfer the scattering is assumed to be diffuse and therefore it is unsuitable for some predictions, e.g. predictions of fluence rate or remittance close to the source. Other numerical approaches to the transport equation are the inverse adding–doubling method and the Monte Carlo method. The inverse adding–doubling method was introducedto tissue optics by Scott Prahl in the late 1980s19.The technique had been used earlier by van de Hulst for solving problems of multiple scattering in other fields. In the inverse adding–doubling method, reflection and transmission for an arbitrary slab of tissue are calculated from values for a thin slab with known optical properties by doubling its thickness until the desired thickness is achieved. By using the adding method, the results can be extended to a multilayered geometry with layers with different optical properties. In the Monte Carlo method an approach is used that allows modeling of more complex geometries20,21. Tissue geometry and optics are modeled in a computer program and light propagation is calculated using random numbers and statistics (hence the name of the method). In these programs, that are both available in Cartesian and cylindrical coordinates, light beams, detector opening angles and different tissue structures (e.g. multiple layers, blood vessels, etc.) with specific indices of refraction and absorption and scattering properties are programed. The optical path of a large number of photons is then simulated, using a random number generator to determine the distance between collisions. In this approach, the accuracy of the results is proportional to the square root of the number of photons that is generated by the program. An additional advantage of this method is that paths of excitation and emission photons in fluorescence can also easily be simulated22.
DETERMINATION OF OPTICAL PROPERTIES OF TISSUE Prediction of light propagation in tissue with a certain level of accuracy requires knowledge of the absorption coefficient µa, the scattering coefficient µs, the scattering phase function or the anisotropy factor g and the index of refraction of the tissue n. In this paragraph, several methods are described to measure these
*The fluence rate (J/cm2) is defined as the radiant power incident on an imaginary infinitesimal small sphere located at a certain point within the tissue, divided by the cross-sectional area of that sphere.
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parameters. Furthermore, methods are discussed that are not suitable for separate determination of the various optical properties but that are used to measure combinations of these parameters, such as the total attenuation coefficient µt (also called extinction coefficient or, in case of scattering only, turbidity), the reduced scattering coefficient µs′ and the effective attenuation coefficient µeff, where the total attenuation coefficient µt = µa + µs , the reduced scattering coefficient µs′ = µs(1 − g), which some authors also call the transport scattering coefficient, and the effective attenuation coefficient µeff = √ (3µa (µa + µs′)). There is a large biological variation in optical properties of tissue with respect to tissue absorption, scattering, scattering anisotropy and, to a lesser extent, index of refraction. Some examples of variations are obvious, such as variation in absorption in various skin types or in transparent structures of the eye versus absorption in the pigment epithelium of the retina. Others are less obvious, such as the possibly tremendous differences in absorption and scattering of lung tissue (e.g. absorption in the pink lungs of a non-smoker living in an area with little air pollution versus that of the black lungs of a heavy smoker who lives in an air-polluted environment). Many factors have to be considered when reviewing the available data on tissue optical parameters. Besides differences in measurements due to biological variation, large differences between in vivo and in vitro measurements can be expected. Some methods are only applicable for in vitro measurements, while other methods that also can be employed for in vivo measurements have their specific drawbacks. When considering in vitro measurements, the method of tissue harvesting, the time interval between harvesting and measurement, storage (e.g. with or without saline, with or without freezing), dehydration and tissue preparation (cutting, grinding) all affect the outcome of the measurements. Although discussion of the influence of these factors is beyond the scope of this chapter, investigators should be aware that these factors might lead to large variations in results.
Methods that are suitable only for determination of in vitro optical properties Optically thin samples, which predominantly demonstrate single scattering, can be used directly to measure absorption, scattering and the scattering phase function23–25. The total attenuation coefficient µt is calculated using Lambert–Beer’s law from measurement of the attenuation of the flux of a collimated beam. This is correct because, in such thin samples, there is no gain in flux due to scattering from other directions. In a second measurement, the scattered light is captured and measured with a photodetector or photomultiplier in a so-called
9
integrating sphere (which basically is a sphere with a highly reflective coating), while the collimated light that is transmitted by the sample leaves the sphere through a small aperture opposite the source aperture. In this method, losses of scattered light through the apertures are calculated and taken into account when calculating the optical properties. In a third measurement, the phase function and the anisotropy factor g are determined by rotating a photodetector or photomultiplier around the sample, thus measuring the intensity of scattered light as a function of the scattering angle. Thus, from these three measurements, µa, µs and g are derived. Although the experiments seem to be straightforward, accurate determination of optical properties using this method is extremely difficult. A major difficulty is that, in order to meet the condition of predominant single scattering (and, therefore, negligible multiple scattering), the physical thickness d of the sample should be much smaller than one mean free path δ, where δ = 1/µt, or, more correctly, one mean free path for scattering (1/µs). Especially for highly scattering tissues, the consequence of this requirement is that very thin samples have to be used. It is practically impossible to cut those samples from fresh (unfrozen) tissue. Other difficulties include scattering artifacts through mounting of the tissue between glass slides, measurement of non-uniform light distributions in the integrating sphere, and low signal-to-noise ratios. In optically thick samples (1< τ < 10, where τ = µtd and d is the physical thickness of the sample), the optical properties can be measured indirectly, using a method similar to the one described above26,27. In this method, normally two integrating spheres are used to measure the diffuse reflectance and transmittance of an intervening sample. Alternatively, one sphere can be used for two measurements, by moving the sphere to the opposite side of the sample or by turning the sample. From those two measurements, µa and µs′ are determined, using for instance inverse adding–doubling or (inverse) Kubelka–Munk theory, or look-up tables generated with Monte Carlo programs. Additional measurement of the collimated transmission allows determination of both µs and g. One of the drawbacks of this method (and of the method described above) is that it is very difficult to take all light losses into account. For instance, the sample is usually positioned in a sample holder consisting of a spacer and glass slides. Light is easily lost from the spheres to the side of the sample holder by reflections within these glass slides. As a result, when using this method, the absorption coefficient of the sample is often overestimated28. Nevertheless, this method is the most widely used to determine the optical properties of tissue (see also Table 1.2 at the end of this chapter).
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Methods that are suitable for determination of in vitro and in vivo optical properties A method that can be used for both in vivo and in vitro determination of optical properties of bulk tissue uses interstitial optical fibers to measure the effective attenuation by the tissue. There are several approaches with broad-beam irradiation of the tissue surface. However, the most straightforward technique is to use a source fiber and a detection fiber to measure the light fluence rate as a function of distance between the fibers. Bare fibers may be used, but preferably the fibers have a spherically diffusing tip that is isotropic at the wavelength of interest. From these measurements, the effective attenuation coefficient µeff can be determined using the diffusion approximation to the equation of radiative transfer. The method and calculations are easy to perform and have been used to measure in vivo effective attenuation coefficients of various tissues, including those of the lung as a function of respiration29. A method based on a similar principle measures the local diffuse reflectance or remittance from the tissue surface as a function of distance from a source. In one approach, a source fiber irradiates the tissue surface and the light coming out of the tissue is measured using one or more detection fibers30–32. With this method the absorption coefficient µa and the reduced scattering coefficient µs′ can be assessed. Again, the diffusion approximation is used to determine those parameters. In a second approach, the total diffuse reflectance is measured. Measurement of the total diffuse reflectance yields only the reduced albedo a′, where a′ = µs′ / (µa + µs′). The method is useful to determine the contribution of tissue absorption of specific chromophores. For instance, when assuming that µs′ is constant, multiple measurements at one wavelength at different oxygen saturation levels would allow assessment of hemoglobin and oxyhemoglobin absorption. In a third approach the local diffuse reflectance (or transmittace) of a short light pulse is measured as a function of time (time domain; time-of-flight measurements), or a high-frequency modulated source is used and the phase shift and amplitude modulation of the detected light are measured (frequency domain). Pulsed photothermal radiometry and photoacoustic spectroscopy are techniques that may be used to determine in vivo optical properties by measuring either the thermal response or the acoustic response as a function of time after one or more diagnostic light pulses33,34. Neither technique is solely dependent on optical properties of the tissue. In the case of pulsed photothermal radiometry the signal is also dependent on the thermal properties of the tissue (e.g. volume specific heat and thermal diffusivity, emissivity and convective properties). If the
thermal properties are known, µa can be derived from the measurements. If the tissue scatterers and absorbers are distributed homogeneously within the tissue, both µa and µs′ may be derived, albeit with limited accuracy. In photoacoustic spectroscopy, the technique is also dependent on the acoustic properties of the tissue. The absorbed light from, for example, a frequencymodulated light source generates acoustic pressure waves that can be detected with a microphone. In principle, as in pulsed photothermal radiometry, both µa and µs′ may be derived. The technique is especially useful to determine the µa of tissues in the ultraviolet range of the electromagnetic spectrum.
Determination of optical properties in vivo using optical coherence tomography In the past few years, researchers have explored the use of OCT to determine the optical properties of tissue. In general, tissues are differentiated by OCT by qualitative differences in gray levels and structural appearance. A quantitative analysis of the OCT signal has been proposed that allows measurement of the total light attenuation µt by the local tissue components8,35,36. The OCT signal is influenced by the optical properties of the tissue (absorption and scattering) as well as by the optical components of the OCT imager. The attenuation coefficient can be measured from the OCT signal by fitting a model relation to this signal from a region of interest in an OCT image. Two types of model are available, based on the single backscattering assumption37,38 or models taking multiple scattering explicitly into account7,39. For mathematical simplicity, descriptions based on the single scattering model are favored. So, an important question is: can multiple scattering effects be ignored? Since imaging depths generally do not exceed about 1 mm, this may well be justified for weakly scattering media. We compared the single scattering model to the extended Huygens–Fresnel multiple scattering model by Thrane et al.7 using calibrated scattering samples with µt ranging from 2 mm−1 to 6 mm−1.35 It was concluded that, for low numerical aperture set-ups (such as the clinically used, catheter-based systems), the single scattering model is valid for about six scattering depths, i.e. for imaging depths of 1 mm with µt < 6 mm−1, which is in accordance with approximately four mean free paths (mfp = µtd) in the findings by Bizheva et al., Wax et al. and Pan et al.5,40,41. Figure 1.5 presents calculations based on the multiple scattering model7, which support these findings. As discussed previously, focusing optics in the sample arm suppresses the detection of light scattered from outside the focal volume, similar to confocal microscopy 9. In clinically used probes and catheters, the optical components of the sample
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A
11
100
Absorption coefficient (cm−1)
Caucasian skin50 10
1 51 50 Porcine skin
0.1
Caucasian skin49 0.01 700
800
900
1000 1100 1200 1300 1400 1500 1600 1700 1800 1900 2000 Wavelength (nm)
B
Reduced scattering coefficient (cm−1)
50
40 Porcine skin51
30
20 Caucasian skin50 10 Caucasian skin49 0 700
800
900
1000
1100
1200
1300
1400
1500
1600
1700
1800
1900
2000
Wavelength (nm)
Figure 1.6 (A) Absorption coefficient µa as a function of wavelength between 700 nm and 2 µm for skin as derived from published graphs49–51. Method of assessment. Caucasian skin at 700–980 nm: 12 skin samples consisting of epidermis attached to the dermis of four individuals, using double integrating spheres and Monte Carlo simulations49. Caucasian skin at 1000–2000 nm: 22 skin samples consisting of epidermis attached to the dermis of 14 individuals, using double integrating spheres and the adding–doubling method50. Porcine skin at 920–1520 nm: double integrating spheres and Monte Carlo simulations51. (B) Reduced scattering coefficient µs′ as a function of wavelength between 700 nm and 2 µm for skin as derived from published graphs49–51. Method of assessment: see (A)
arm are fixed. Therefore, for quantitative extraction of µt, the confocal properties of the OCT system have to be taken into account, i.e. the change of the OCT signal with increasing distance between the probed location in the tissue and location of the focus. Recently a general expression has been derived for the confocal axial point spread function
for single-mode fiber-based OCT systems 42. The major advantage of this point spread function is that it is described by one parameter only, the Rayleigh length Z0 (half the depth of focus), which can easily be determined experimentally. In that case, the OCT signal as a function of depth is then given by combining the point spread function
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Table 1.1
Index of refraction of different tissues of different species (from references 44 to 48) Species
Tissue Blood Muscle Kidney Liver Spleen Lung Adipose tissue Skin stratum corneum epidermis dermis
Human
Canine
Porcine
Bovine
1.400 1.417 1.367
1.400 1.400 1.380 1.400 1.380
1.390 1.390 1.400 1.380
1.412 1.390 1.390
λ source (reference) 633 nm (44)
1.455 1.415 *1.51, *1.47 1.41, *1.43 #, *1.34 ## *1.34, *1.41
Vascular wall 18°C intima media calcification Lipid pool
1.354 ± 0.002 1.391 ± 0.007 1.64 ± 0.02 1.52 ± 0.02
37°C intima media calcification lipid pool
1.352 ± 0.002 1.382 ± 0.006† 1.63 ± 0.05 1.42 ± 0.04†
1300 nm (45, 46, 47)
800 nm (48)
*, in vivo measurement; #, granular layer; ##, basal layer; †, p < 0.05, compared to 18°C
with the single scattering model, which is the underlying model of the results presented in Chapter 25. High- resolution OCT images at 800 nm and 1300 nm were used to determine µt of constituents of atherosclerotic plaque: for diffuse intimal thickening (µt = 55 ± 12 cm−1 and µt = 32 ± 12 cm−1 at 800 nm and 1300 nm, respectively), media (99 ± 18 cm−1 vs. µt = 67 ± 11cm−1), lipidrich regions (32 ± 11 cm−1 vs. µt = 23 ± 5 cm−1), calcifications (111 ± 49 cm−1 vs. µt = 260 ± 32 cm−1) and thrombi (112 ± 23 cm−1 at 800 nm)36,43.
Determination of the index of refraction Finally, it should be stressed that, for accurate prediction of fluence rates within the tissue and remittance and transmittance from the tissue, knowledge of the index of refraction of the tissue and different tissue layers is of utmost importance. Few investigators have extensively investigated variations in the index of refraction of tissue. Most data are based on measurement of the critical angle
for total reflection against the tissue surface. One particularly elegant method to determine the index of refraction of tissue was introduced by Bolin and colleagues44. The average index of refraction of various tissues was measured by inserting an optical fiber without a cladding in the tissue of interest, followed by alignment of the bare tip with the tissue surface and measurement the angle of the emerging cone of light. Tissues were used from four species: bovine, human, porcine and canine. It was found that in some cases significant differences occurred from the commonly assumed value of about 1.400. Table 1.1 summarizes the average results for whole tissue samples and compares the data with measurements of the index of refraction using an OCT set-up44–48. Interestingly, Bolin et al. found a difference of only 0.001 in both average values and standard deviation when comparing 12 samples of whole tissue from one tissue block to those samples in a homogenized state. The possibility of homogenizing before assessment considerably simplifies measurements of the index of refraction of the tissue of interest44.
normal
normal
normal
normal
Human
Human
Human
Pig
Cartilage Human
Atherosclerotic artery Human thickened intima lipid media calcified thrombus Human thickened intima lipid media calcified
1060 1064
normal normal
Aorta Pig Human
g
3.0 1.7
9.4
16.9
13.0
7.0
4.5 6.0
µeff
(cm−1)
Integrating spheres, DT Integrating sphere, goniometer, MC Integrating sphere, goniometer, DT Integrating sphere, goniometer, MC Integrating sphere, goniometer, DT Integrating spheres, DT
Method and theory
800 1064
23 67 260 Integrating sphere, IAD
OCT
0.9
0.9
(-)
1300
5.1 2.6
18.3
17.8
23.3
22.4
20.1 23.9
(cm−1)
µs(1-g)
32 99 111 112 32
233
239
µs
(cm−1)
OCT
0.52 0.34
1.5
4.3
2.2
0.7
0.33 0.5
µa
(cm−1)
55
235
240
µt
(cm−1)
800
1320
1320
1320
1064
(nm)
(continued)
54
43
36,43
52
52
53
52
52 53
Reference
10:19 AM
Tissue
10/11/2006
λ
Table 1.2 Selected overview of tissue optical properties between 700 nm and 2 µm with relevance to optical coherence tomography (OCT); total attenuation coefficient µt, the absorption coefficient µa, scattering coefficient µs, scattering anisotropy g, reduced scattering coefficient µs′, and effective attenuation coefficient µeff for in vitro and in vivo aorta, atherosclerotic artery, cartilage, eye, gastric wall, muscle, myocardium and skin. Data printed in italic derived from graphs. If missing in the original publication, results for µt and µeff were calculated from the available data
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AN INTRODUCTION TO TISSUE OPTICS FOR OCT 13
trabecular meshwork
Cow
Muscle Pig
fundus
Gastric wall Human antrum
Human
retinal pigment epit. (rpe) choroid
retina
sclera
λ
1064 1320
1060
700 to 950 700 to 950
700 740 780 820
700 780 1064 700 780 1064 700 780 1064 700 780 1064
(nm)
106 100 95 90
702 600 420 240 231 191 2110 2090 1680 730 700 530
µt
(cm−1)
1.2 2.3
2.0
0.5 to 1.2 0.8 to 1.0
0.8 0.4 0.2 0.1
1.9 0.2 0.1 1 0.8 0.6 690 480 80 140 100 10
µa
(cm−1)
105 100 95 90
700 600 420 240 230 190 1420 1610 1600 590 600 520
µs
(cm−1)
2.8 2.4
11 to 8 13 to 10
(cm−1)
µs(1-g) g
0.9 0.9 0.9 0.9
0.87
0.84
0.97
0.9
(-)
3.8 5.7
4.2 to 5.8 5.8 to 5.7
5.2 3.5 2.4 1.7
48.2
284
3.4
3.6
µeff
(cm−1)
*
* *
*
Photoacoustic transducer, AT Integrating sphere
Reflectometry, MC
Integrating sphere, KM converted µa and µs using estimated g
Integrating spheres, MC
Method and theory
(continued)
59
58
57
56
55
Reference
10:19 AM
Eye Bovine
Tissue
14
(Continued)
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Table 1.2
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dermis
normal arm foot sole forehead epidermis
790 850 790 850 1300 790 700 750 800 850 900 950 1000 1064
700 700
322
411 405 255 285
165 178
µt
(cm-1)
0.70 0.22 0.25 0.15 0.19 0.16 0.29 0.39 0.23
0.38 0.09 0.037 0.024 2.4 1.6 1.8 0.33
0.98 0.3
µa
(cm-1)
321
409 403 254 285
164 178
µs
(cm-1)
28.7 8.1 9.5 15.4 19.6 15.3 14.0 9.1 10 18.4 19 17 13 12 11 11 9.8 9.1
9.3 6.4
(cm-1)
µs(1-g) g
0.940
0.952 0.962 0.945 0.968
0.943 0.964
(-)
µeff
5.8 1.5 1.0 1.1 12.5 8.7 9.2 3.1 8.1 6.3 3.6 3.6 2.4 2.6 2.3 3.1 3.5 2.5
4.5 2.5
(cm-1)
* * * *
OCT Integrating spheres, IAD Integrating sphere, IAD
Integrating spheres, IAD
Integrating spheres, IAD
Reflectometry, MC Reflectometry, DT
Integrating spheres, IAD Integrating sphere, DT, KM
Method andtheory
AT, acoustic theory; DT, diffusion theory; IAD, inverse adding–doubling; KM, Kubelka–Munk theory; MC, Monte simulations; OCT, optical coherence tomography. *in vivo measurement
Pig Rabbit Rat
Pig
Pig
Skin Human
790 1064
Myocardium Dog normal Pig normal
λ
(nm)
47 28 23
28
28
31 32
28 60
Reference
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Tissue
(Continued)
10/11/2006
Table 1.2
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SELECTED OVERVIEW OF OPTICAL PROPERTIES OF TISSUE WITH RELEVANCE TO OPTICAL COHERENCE TOMOGRAPHY Figure 1.6 shows absorption and reduced scattering coefficients as a function of wavelength for human skin, consisting of epidermis attached to the dermis49,50. For comparison, data on porcine skin are included51. Table 1.2 gives an overview of the total attenuation coefficient µt, the absorption coefficient µa, scattering coefficient µs, scattering anisotropy g, reduced scattering coefficient µs′, and effective attenuation coefficient µeff for in vivo and in vitro aorta, arteriosclerotic artery, bone, cartilage, eye, gastric wall, muscle, myocardium and skin.
REFERENCES 1. Ishimaru A. Wave Propagation in Scattering and Random Media, Vols 1 and 2. New York: Academic Press, 1978 2. Hale GM, Querry MR. Optical constants of water in the 200 nm to 200 µm wavelength region. Appl Opt 1973; 12: 555–63 3. Van de Hulst HC. Multiple Light Scattering, Vols 1 and 2. New York: Academic Press, 1980 4. Henyey LG, Greenstein JL. Diffuse radiation in the galaxy. Astrophysics 1941; 93: 70–83 5. Bizheva KK, Siegel AM, Boas DA. Path-lengthresolved dynamic light scattering in highly scattering random media: the transition to diffusing wave spectroscopy. Phys Rev E 1998; 58: 7664–7 6. Yadlowsky MJ, Schmitt JM, Bonner RF. Multiple scattering in optical coherence tomography. Appl Opt 1995; 34: 5699–707 7. Thrane L, Yura HT, Andersen PE. Analysis of optical coherence tomography systems based on the extended Huygens Fresnel principle. J Opt Soc Am 2000; 58: 7664–7 8. Faber DJ. Functional optical coherence tomography. PhD thesis, University of Amsterdam, Amsterdam, The Netherlands, 2005 9. Schmitt JM, Knüttel A, Yadlowsky M. Confocal microscopy in turbid media. J Opt Soc Am 1994; 11: 2226–35 10. Faber DJ, Aalders MCG, Mik EG, et al. Oxygen saturation dependent absorption and scattering of blood. Phys Rev Lett 2004; 93: 028102–1 11. Faber DJ, Aalders MCG, Mik EG, van Leeuwen TG. Light absorption of (oxy-)hemoglobin assessed by optical coherence tomography. Opt Lett 2003; 28: 1436–8 12. Steenbergen W, Kolkman R, de Mul F. Light scattering properties of undiluted human blood subjected to simple shear. J Opt Soc Am 1999; 16: 2959–67 13. Van Gemert MJC, Welch AJ, Star WM. Onedimensional transport theory. In: Welch AJ, van Gemert MJC, eds. Optical–thermal Response of Laser Irradiated Tissue. New York: Plenum Press, 1995 14. Kubelka P, Munk F. Ein Betrag zur Optik der Farbanstriche. Z Techn Phys 1931; 12: 593–601
15. Kubelka P. New contributions to the optics of intensely light-scattering materials. Part II: Nonhomogeneous layers. J Opt Soc Am 1954; 44: 330–5 16. Patterson MS, Wilson BC, Wyman DR. The propagation of optical radiation in tissue I. Models of radiation transport and their application. Lasers Med Sci 1991; 6: 155–68 17. Patterson MS, Wilson BC, Wyman DR. The propagation of optical radiation in tissue II. Models of radiation transport and their application. Lasers Med Sci 1991; 6: 379–90 18. Yoon G, Welch AJ, van Gemert MJC. Development and application of three dimensional light distribution model for laser irradiated tissue. IEEE J Quantum Electron 1987; QE-23: 1721–33 19. Prahl SA. Light transport in tissue. PhD dissertation, University of Texas, Austin, TX, USA, 1988 20. Metropolis N, Ulam S. The Monte Carlo method. J Am Stat Assoc 1949; 44: 335–41 21. Wilson BC, Adam G. A Monte Carlo model for the absorption and flux distributions of light in tissue. Med Phys 1983; 10: 824–30 22. Bogaards A, Aalders MCG, Zeyl CC, et al. Localization and staging of cervical intraepithelial neoplasia using double ratio fluorescence imaging. J Biom Opt 2002; 7: 215–20 23. Jacques SL, Alter CA, Prahl SA. Angular dependence of He-Ne light scattering by human dermis. Lasers Life Sci 1987; 1: 309–33 24. Flock ST, Wilson BC, Wyman DR. Total attenuation coefficients and scattering phase functions of tissues and phantom materials at 633nm. Med Phys 1987; 14: 835–42 25. Marchesini R, Bertoni A, Andreola S, et al. Extinction and absorption coefficients and scattering phase functions of human tissues in vitro. Appl Opt 1989; 28: 2318–24 26. Pickering JW, Moes CJM, Sterenborg HJCM, et al. Two integrating spheres with an intervening sample. J Opt Soc Am 1992; 9: 621–31 27. Pickering JW, Prahl SA, van Wieringen N, et al. Double-integrating-sphere system for measuring the optical properties of tissue. Appl Opt 1993; 32: 399–410 28. Beek JF, Blokland P, Posthumus P, et al. In vitro doubleintegrating-sphere optical properties of tissues between 630 and 1064 nm. Phys Med Biol 1997: 42; 2255–61 29. Beek JF, van Staveren HJ, Posthumus P, et al. The optical properties of lung as a function of respiration. Phys Med Biol 1997; 42: 2263–72 30. Groenhuis RAJ, Ferwerda HA, ten Bosch JJ. Scattering and absorption of turbid materials determined from reflection measurements. Appl Opt 1983; 22: 2456–62 31. Dögnitz N, Wagnières G. Determination of tissue optical properties by steady-state spatial frequencydomain reflectometry. Lasers Med Sci 1998; 13: 55–65 32. Doornbos RMP, Lang R, Aalders MC, et al. The determination of in vivo human tissue optical properties and absolute chromophore concentrations using spatially resolved steady-state diffuse reflectance spectroscopy. Phys Med Biol 1999; 44: 967–81 33. Long FH, Nishioka NS, Deutsch TF. Measurement of optical and thermal properties of biliary calculi using
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pulsed photothermal radiometry. Lasers Surg Med 1987; 7: 461–6 Rosencwaig A. Photoacoustic spectroscopy of biological materials. Science 1973; 181: 657–8 Faber DJ, van der Meer FJ, Aalders MCG. Quantitive measurement of attenuation coefficients of weekly scattering media using optical coherence tomography. Opt Express 2004; 12: 4353–65 Van der Meer FJ, Faber DJ, Baraznji Sassoon DM, et al. Localized measurement of optical attenuation coefficients of atherosclerotic plaque constituents by quantitative optical coherence tomography. IEEE Trans Med Imaging 2005; 24: 1369–76 Schmitt JM, Knüttel A, Yadlowsky M, Eckhaus MA. Optical–coherence tomography of a dense tissue: statistics of attenuation and backscattering. Phys Med Biol 1994; 39: 1705–20 Kholodnykh AI, Petrova IY, Larin KV, et al. Precision of measurement of tissue optical properties with optical coherence tomography. Appl Opt 2003; 42: 3027–37 Turchin IV, Sergeeva EA, Dolin LS, et al. Novel algorithm of processing optical coherence tomography images for differentiation of biological tissue pathologies. J Biomed Opt 2005; 10: 064024 Wax A, Yang C, Dasari RR, Feld MS. Path-lengthresolved dynamic light scattering: modeling the transition from single to diffusive scattering. Appl Opt 2001; 40: 4222–7 Pan Y, Birngruber R, Engelhardt R. Contrast limits of coherence-gated imaging in scattered media. Appl Opt 1997; 36: 2979–83 Van Leeuwen TG, Faber DJ, Aalders MC. Measurement of the axial point spread function in scattering media using single-mode fiber-based optical coherence tomography. IEEE J Sel Top Quant 2003; 9; 227–33 Van der Meer FJ, Faber DJ, Perrée J, et al. Quantitative optical coherence tomography of arterial wall components. Lasers Med Sci 2005; 20: 45–51 Bolin FP, Preuss LE, Taylor RC, Ference RJ. Refractive index of some mammalian tissues using a fiber optic cladding method. Appl Opt 1989; 28: 2297–303 Tearney GT, Brezinski ME, Southern JF, et al. Determination of the refractive index of highly scattering human tissue by optical coherence tomography. Opt Lett 1995; 20: 2258–60 Knüttel A, Boehlau-Godau M. Spatially confined and temporally resolved refractive index and scattering evaluation in human skin performed with optical coherence tomography. J Biomed Opt 2000; 5: 83–92 Knüttel A, Bonev S, Knaak W. New method for evaluation of in vivo scattering and refractive index properties obtained with optical coherence tomography. J Biomed Opt 2004; 9: 232–73
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48. van der Meer FJ, Faber DJ, van Leeuwen TG. Measurements of refractive index and attenuation of individual vessel wall components with optical coherence tomography: effect of temperature. J Biomed Opt (accepted for publication). 49. Troy TL, Thennadil SN. Optical properties of human skin in the near infrared wavelength range of 1000 to 2200 nm. J Biomed Opt 2001; 6: 167–76 50. Simpson CR, Kohl M, Essenpreis M, Cope M. Nearinfrared optical properties of ex vivo human skin and subcutaneous tissues measured using Monte Carlo inversion technique. Phys Med Biol 1998; 43: 2465–78 51. Du Y, Hu XH, Cariveau M, et al. Optical properties of porcine skin dermis between 900 nm and 1500 nm. Phys Med Biol 2001; 46: 167–81 52. Cheong WF. Photo-thermal processes in tissue irradiated by Nd: YAG laser (1064 nm, 1320 nm). PhD thesis, University of Texas at Austin, TX, USA, 1990 53. Essenpreis M. Thermally induced changes in the optical properties of biological tissues. PhD thesis, University College London, London, UK, 1992 54. Schwarz J, Jacques SL, Vangsness CJ Jr. Optical properties of human meniscus. In: 13th Annual Meeting of the American Society of Lasers in Medical Surgery, New Orleans, April 1993 55. Hammer M, Roggan A, Schweitzer D, Müller G. Optical properties of ocular fundus tissues – an in vitro study using double-integrating sphere technique and inverse Monte Carlo simulation. Phys Med Biol 1995; 40: 963–78 56. Farrar SK, Roberts C, Johnston WM, Weber PA. Optical properties of human trabecular meshwork in the visible and near-infrared region. Lasers Surg Med 1999; 25: 348–62 57. Thüler P, Charvet I, Bevilacqua F, et al. In vivo endoscopic tissue diagnostics based on spectroscopic absorption, scattering, and phase function properties. J Biomed Opt 2003; 8: 495–503 58. McLeod JS, Blanc D, Colles MJ. Measurement of the optical absorption coefficient at 1.06 µm of various tissues using the photoacoustic effect. Lasers Surg Med 1988; 8: 143 59. Karagiannis JL, Zhang Z, Grossweiner B, Grossweiner LI. Applications of the 1-D diffusion approximation to the optics of tissue and tissue phantoms. Appl Opt 1989; 28: 2311–17 60. Splinter R, Svensen RH, Littman L, et al. Optical properties of normal, diseased, and photon coagulation at the Nd:Yag wavelength. Lasers Surg Med 1991; 11: 117–24
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CHAPTER 2 Principles of OCT James G Fujimoto, Joseph M Schmitt
INTRODUCTION
the assessment and guidance of microsurgical procedures such as vessel and nerve anastomoses or the guidance of procedures such as stent placement or atherectomy. Coupled with catheter, endoscopic, laparoscopic, or needle delivery, OCT promises to have a powerful impact on many medical applications ranging from the screening and diagnosis of neoplasia, to enabling new minimally invasive surgical procedures.
Optical coherence tomography (OCT) is a new type of optical imaging modality. OCT performs highresolution, cross-sectional tomographic imaging of the internal microstructure in materials and biological systems by measuring the echo time delay and magnitude of backscattered light1. Image resolutions of 1–15 µm can be achieved, one to two orders of magnitude finer than conventional ultrasound. Imaging of tissue pathology can be performed in situ and in real time. The unique features of this technology promise to enable a broad range of research and clinical applications. This chapters provides an overview of the basic principles of OCT technology, its background and early applications. OCT is a powerful imaging technology in medicine because it enables the real-time, in situ visualization of tissue microstructure without the need to remove and process specimens. The concept of OCT ‘optical biopsy’, the ability to visualize tissue morphology in situ and in real time, can be used for both diagnostic imaging and guiding intervention2. OCT can have applications in three general types of clinical situation: (1)
(2)
(3)
OCT performs cross-sectional imaging by measuring the magnitude and echo time delay of light. A crosssectional image is acquired by performing successive rapid axial measurements of echo time delay and scanning the incident optical beam transversely, as shown in Figure 2.1. The result is a two-dimensional data set that represents the optical back-reflection or backscattering in a cross-sectional plane through the material or tissue. OCT was first demonstrated by Huang et al. in 19911. In this study, imaging was performed ex vivo in the human retina and in atherosclerotic plaque as examples of imaging in nominally transparent tissues as well as in highly optically scattering tissues. Figures 2.2 and 2.3 show ex vivo OCT images of the human coronary artery and retina with corresponding histology from reference 1. OCT imaging was performed at a wavelength of 830 nm with 17 µm axial resolution in air, corresponding to 15 µm in tissue. The image is displayed using a log false color scale with a signal level ranging between 4×10−10 and 10−6, or between –94 dB and –60 dB of the incident light intensity. The OCT image of the coronary artery shows fibrocalcific plaque on the right three-quarters of the specimen and fibroatheromatous plaque on the left side. The plaque scatters light and therefore attenuates the OCT beam, limiting the image penetration depth. The OCT image of the retina shows the contour of the optic disc as well as retinal vasculature near the disc region. The retinal nerve fiber layer can also be visualized emanating
Where standard excisional biopsy is hazardous or impossible, such as in the eye, coronary arteries, or nervous tissues. Where standard excisional biopsy suffers from sampling errors. Excisional biopsy and histopathology are the standard for diagnosis of many diseases, including cancer. However, biopsy is a sampling procedure and, if the biopsy misses the lesion, then a false-negative result is obtained. OCT is being investigated to guide excisional biopsy in order to reduce the number of biopsies required and to improve sensitivity by reducing sampling errors. For guidance of interventional procedures. The ability to see beneath the tissue surface enables 19
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Transverse scanning
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Figure 2.1 OCT is analogous to ultrasound, except that it measures the magnitude and echo time delay of light rather than sound. Axial scans (A-scans) measure the back-reflection or backscattering versus depth. Cross-sectional images are generated by performing a series of axial scans at different transverse positions to generate a two-dimensional data set (Bscan) which is displayed as a gray-scale or false color image
Lumen
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Figure 2.2 OCT image of a human artery ex vivo. OCT imaging was performed at 830 nm wavelength with 17 µm axial resolution in air, corresponding to 15 µm in tissue. The image is displayed using a log false color scale with a signal level ranging between 4 × 10−10 and 10−6, or between –94 dB and –60 dB of the incident light intensity. The OCT image of the coronary artery shows fibrocalcific plaque on the right three-quarters and fibroatheromatous plaque on the left side of the specimen. The fatty calcified plaque scatters light and therefore attenuates the OCT beam, limiting the depth of the image penetration. (From reference 1, with permission)
Retina
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Figure 2.3 OCT image of the human retina ex vivo. OCT imaging was performed at 830 nm wavelength with 17µm axial resolution in air, corresponding to 15 µm in tissue. The OCT image of the retina shows the contour of the optic disc as well as retinal vasculature near the disc region. The retinal nerve fiber layer is also visualized. This image was obtained ex vivo and postmortem retinal detachment with subretinal fluid accumulation are evident. (From reference 1, with permission)
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Figure 2.4 OCT image of the normal human retina in vivo. The image was obtained at 800 nm and has a 10 µm axial resolution. The retinal pigment epithelium, choroid and retinal nerve fiber layers are visible as highly backscattering layers. OCT provides an unprecedented ability to visualize and quantitatively measure retinal pathology. It is rapidly becoming a standard of care in clinical ophthalmology. (From reference 3)
Reflectance
Figure 2.5 OCT image showing atherosclerotic plaque ex vivo and corresponding histology. The plaque is highly calcified with a low lipid content and a thin intimal cap. OCT can resolve small structures such as thin intimal layers associated with unstable plaques. The bar is 500 µm. (From reference 5)
from the optic nerve head. This image was obtained ex vivo, and postmortem retinal detachment with subretinal fluid accumulation are evident. Because of the ease of imaging the eye, the development of OCT in ophthalmology proceeded very rapidly. Figure 2.4 shows an example of an early in vivo OCT retinal image from a normal human subject, reproduced from reference 3. The retinal nerve fiber layer is evident as a highly backscattering layer which decreases in thickness away from the optic disc. The junction between the photoreceptor inner and outer segments and the retinal pigment
epithelium are evident as a thin, highly backscattering layer near the posterior of the retina. Imaging was performed at a wavelength of 800 nm with an axial image resolution of 10 µm. To enable clinical imaging studies in patients, a prototype OCT imaging instrument was developed based on a modified slit lamp biomicroscope that provided a simultaneous view of the retinal fundus for aiming and registering the OCT imaging beam as it was scanned across the retina3. This early device and the measurement protocols that were developed formed the basis for current OCT ophthalmic imaging instrumentation and clinical applications4. OCT is useful because it can perform ‘optical biopsy’, yielding cross-sectional images of pathology in situ and in real time. Although the first clinical applications of OCT were in ophthalmology, OCT also has applications in a wide range of other clinical specialties which involve imaging pathology in tissues that are non-transparent and optically scattering. Figure 2.5 shows an example of an early ex vivo OCT image of arterial plaque and corresponding histology reproduced from reference 5. The figure shows an unstable plaque characterized by a thin intimal cap layer adjacent to a heavily calcified plaque with low lipid content. The image has an axial resolution of ~15 µm in tissue. Imaging was performed at 1300 nm, a wavelength farther in the infrared than the 800 nm used in ophthalmology. Optical scattering decreases at longer wavelengths and therefore OCT image penetration depths improve (compare Figures 2.2 and 2.5). Depending upon the optical properties of the tissue, OCT image penetration depths at 1300 nm wavelength can be as high as 2–3 mm.
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1 mm Standard clinical ULTRASOUND 100 µm Resolution (log)
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Figure 2.6 Resolution and imaging depth of ultrasound, OCT and confocal microscopy. Standard clinical ultrasound imaging can image deep structures but has limited resolution. Higher frequencies yield finer resolution, but ultrasonic attenuation is increased, which limits image penetration. The axial image resolution in OCT ranges from 1 to 15 µm and is determined by the coherence length of the light source. In most biological tissues, image depth is limited to 2–3 mm by attenuation from optical scattering. Confocal microscopy has submicron resolution, but optical scattering limits the imaging depth to a few hundred microns in most tissues
OCT VERSUS OTHER IMAGING TECHNOLOGIES In order to understand the role of OCT imaging in clinical medicine, it is helpful to compare OCT with ultrasound and microscopy (Figure 2.6). The resolution of ultrasound imaging depends on the frequency or wavelength of the sound waves6–9. For clinical ultrasound systems, sound wave frequencies cover a wide range (3–40 MHz) and yield spatial resolutions of ~0.1–1 mm. Ultrasound has the advantage that sound waves at this frequency are readily transmitted into biological tissues and therefore it is possible to image tissues that are deep within the body. High-frequency ultrasound has been developed and investigated extensively in the laboratory as well as in several clinical applications which include intravascular imaging. Resolutions of 15–20 µm and finer have been achieved with frequencies of ~100 MHz. However, high-frequency ultrasound is strongly attenuated in biological tissues and this attenuation increases approximately in proportion to the frequency. Thus, high-frequency ultrasound has limited imaging depths of only a few millimeters. Sound frequency is an important
parameter in ultrasound imaging because it is possible to optimize image resolution for a given application, while trading off image penetration depth. The axial resolution in OCT is determined by the bandwidth of the light source used for imaging. Current OCT imaging technologies have axial resolutions ranging from 1 µm to 15 µm, approximately 10–100 times finer resolution than standard ultrasound imaging. The inherently high resolution provided by OCT imaging enables the visualization of tissue architectural morphology. OCT is ideally suited for ophthalmology, because of the ease of access to the eye and the lack of other methods for obtaining microstructural information. The principal disadvantage of OCT imaging is that light is highly scattered by most biological tissues. In tissues other than the eye, optical scattering limits image penetration depths to ~2 mm. However, because OCT is an optical technology, it can be integrated into a wide range of instruments such as endoscopes, catheters, laparoscopes, or needles, which enable the imaging of internal organ systems.
IMAGING USING LIGHT VERSUS SOUND OCT imaging is analogous to ultrasound B mode imaging, except that it uses light instead of sound. There are several different embodiments of OCT, but essentially OCT performs imaging by measuring the echo time delay and intensity of back-reflected or backscattered light from internal microstructures in materials or tissues. OCT images are two-dimensional or three-dimensional data sets which represent optical reflection or scattering in a cross-sectional plane or volume. When a beam of sound or light is directed onto tissue, it is back-reflected or backscattered from structures which have different acoustic or optical properties, as well as from boundaries between structures. The dimensions of these different structures can be determined by measuring the ‘echo’ time it takes for sound or light to return from structures at different axial distances. In ultrasound, the axial measurement of distance or range is called A-mode scanning, while crosssectional imaging is called B-mode scanning. The principal difference between ultrasound and optical imaging is that the speed of light is much faster than the speed of sound. The speed of sound in water is approximately 1500 meters per second, while the speed of light is approximately 3 × 108 meters per second. The measurement of distances with a 100-µm scale resolution, which would be typical for ultrasound, requires a time resolution of ~100 nanoseconds (100 × 10−9 seconds). This time resolution is well within the limits of electronic detection. Ultrasound technology has dramatically advanced in recent years with the availability of high-performance and lowcost analog-to-digital converters and digital signal
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Figure 2.7 Schematic diagram of the OCT system. Since the speed of light is much faster than that of sound, it is necessary to use correlation or interferometry techniques to detect the echo time delays of light. The echo time delay and magnitude of back-reflected or backscattering light can be measured using interferometry. The most common detection method is based upon a Michelson interferometer with a scanning reference delay arm. Back-reflected or backscattered light from the object being imaged is correlated with light that travels a known reference path delay. The light source for the interferometer can be either a broadband superluminescent diode or a narrow linewidth laser which is rapidly turned across a broadband of wavelengths. The system shown is configured for catheter/endoscope imaging. The fiberoptic implementation allows OCT to be integrated with a wide range of imaging devices
processing technology. Unlike sound, the echo time delays associated with light are extremely fast. The measurement of distances with a 10-µm resolution, which is typical in OCT imaging, requires a time resolution of ~30 femtoseconds (30 × 10−15 seconds). Direct electronic detection is not possible on this time scale and methods such as optical correlation or interferometery must be used.
OCT USES INTERFEROMETRY TO MEASURE ECHOES OCT measures the echo time delay of light using interferometric techniques which correlate the backreflected or backscattered light signal from the tissue, with light which has traveled a known reference path length or delay. Figure 2.7 shows a schematic diagram of a typical OCT interferometer system, built using fiberoptics. There are several possible detection methods which can be used to measure echo delays. These are explained in more technical detail in the Appendix at the end of this chapter. The light source for the interferometer can be either a broadband superluminescent diode or a narrow linewidth laser which is rapidly turned across a broadband of wavelengths. The interferometer uses a fiberoptic coupler which is analogous to an optical beamsplitter, dividing the input light into a measurement arm and a reference arm. The optical fiber in the interferometer measurement arm is connected to a catheter or other imaging device. The catheter focuses the OCT measurement beam onto the tissue being imaged and scans the
transverse position of the beam. Back-reflected or backscattered light echoes from the tissue are collected by the catheter and return back through the fiber in the interferometer measurement arm. The second arm of the interferometer consists of a retroreflecting mirror at a calibrated distance. The light echo from the reference arm returns with a calibrated delay. Echoes of light from the tissue and the reference arm are combined at the fiber-coupler, where they interfere and produce an output from the interferometer. The intensity of the interference is detected with a high-speed photodetector. The electronic signal is processed in order to extract a measurement of echo time delay, which is an axial scan or A-scan. Further technical details on detection techniques are provided in the Appendix at the end of this chapter.
HOW OCT IMAGES ARE GENERATED The simplest type of measurement that can be performed by OCT is an axial scan analogous to ultrasound A-mode scanning. The intensity of an optical back-reflection is a measure of the index of refraction discontinuity between different tissues. Different tissue types will also produce different amounts of optical backscattering depending upon their optical properties. This differential backscattering variation is also measurable in an OCT axial scan. The thickness of the tissue is calculated by multiplying the optical echo delay by the speed of light in the tissue. Thus, the measurement of physical thickness from OCT relies on knowing or assuming an index of
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refraction for the tissue. The magnitude of the light signal is extremely small, typically −50 dB to –90 dB of the incident light power. Thus, very high detection sensitivity to very weak light echoes is required. As shown previously in Figure 2.1, OCT cross-sectional imaging of the tissue is achieved by performing successive axial measurements of the tissue at different transverse positions, analogous to ultrasound B-mode imaging. The result is a two-dimensional array whose columns consist of axial scan data and represent the magnitude of back-reflection or backscattering of the optical beam as a function of depth in the tissue. Because the light beam can be focused to a small spot size, the transverse position of the beam is known with high precision. For purposes of visualization, this two-dimensional data set is displayed as a gray scale or a false color image. The OCT signal level typically ranges from approximately –50 dB, the maximum signal, to approximately –100 dB, the limit of detection sensitivity. Because the back-reflected or backscattered signal varies over five orders of magnitude, it is convenient to display the logarithm of the signal, as is typically done in ultrasound. This expands the dynamic range of the image, but results in compression of small relative variations in signal. The gray-scale display is used extensively in ultrasound imaging and has the advantage that it gives a correct intuitive interpretation of the image. Ultrasound images are typically compensated for signal attenuation with depth by increasing the display intensity for deeper structures. OCT images, however, are usually not depthcompensated. The disadvantage of the gray-scale display is that it is difficult to visualize differences in intensities as shades of gray. Computer monitors provide only 8 bits or 256 gray levels. In addition, the eye has a limited ability to differentiate gray levels, so that gray-scale images cannot represent the full dynamic range available in OCT images. In contrast to gray levels, color monitors typically have 24 bit or more color levels and the human eye can differentiate millions of distinct colors. To enhance the ability to differentiate subtle structures within the image, false color display is often used in OCT images, as shown in Figures 2.2, 2.3 and 2.4. In false color images, the logarithm of the intensity of the optical back-reflection or backscattering is mapped into different colors. The principal disadvantage of false color display is that it can produce artifacts in the image. If the signal intensity is changed, this can produce a color change of structures in the image and careful normalization of signal levels is required. False color display is extensively used in ophthalmic OCT imaging because differences in scattering properties of the different retinal layers can be visualized and the retina produces negligible optical attenuation that can lead to artifacts. OCT imaging is similar to ultrasound, in which image contrast is generated by acoustical properties
of tissues and corresponds to different color or gray levels in the image. In OCT images, tissue structures are visualized because they have different optical reflection and scattering properties. However, it is important to note that, although the OCT image represents the true tissue dimensions (correcting for index of refraction and beam refraction effects), in a false color image, the coloring of different structures represents different optical properties and not necessarily different tissue morphology. Care must be taken to avoid interpreting images in analogy with conventional histopathology. In histopathology, histological sections are selectively stained in order to produce contrast between different tissue structures. OCT must rely on the intrinsic differences in optical properties of different tissues in order to produce image contrast. If different tissues have identical optical properties, they will not be differentiable on OCT imaging.
CATHETER AND ENDOSCOPIC OCT IMAGING Because OCT technology is based on fiberoptics, it can be integrated with many standard optical diagnostic instruments. OCT has been integrated with slit lamps and fundus cameras for ophthalmic imaging of the retina3,4,10. In these instruments, the OCT beam is scanned using a pair of galvanometric beam steering mirrors and relay imaged onto the retina. The ophthalmic OCT imaging instrument requires a relatively complex design because it must allow the retinal fundus to be viewed en face, while showing the position registration of the OCT scanning beam as well as the OCT image that is acquired. Specific imaging protocols have been developed for obtaining diagnostic information on diseases such as macular edema in diabetic retinopathy or nerve fiber layer atrophy in glaucoma3,11,12. Another major class of OCT imaging probes includes flexible, miniature devices for internal body imaging. These devices are the cornerstone for intravascular OCT. Because OCT beam delivery is performed by fiberoptics, small-diameter endoscopes and catheters can be developed13. Figure 2.8 shows an early transverse scanning OCT catheter/endoscope that was a prototype to the current OCT intravascular imaging catheter. The catheter/endoscope has a singlemode optical fiber encased in a hollow rotating torque cable coupled to a distal lens and a microprism that directs the OCT beam radially outward from the catheter. The cable and distal optics are encased in a transparent housing. The OCT beam is scanned by rotating the cable to perform imaging in a radar-like pattern, transverse image through luminal structures or hollow organs. Imaging may also be performed in a longitudinal plane by push–pull movement of the
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Figure 2.8 Photograph of an early OCT catheter/endoscope for intraluminal imaging. A single-mode fiber lies within a rotating flexible speedometer cable enclosed in a protective plastic sheath. The distal end focuses the beam at 90° from the axis of the catheter. The diameter of the catheter is 2.9 French or about 1 mm. The catheter is shown on a United States coin for scale. OCT can be integrated with a wide range of diagnostic and interventional devices. (From reference 13)
fiberoptic cable assembly14. Figure 2.9 shows the first image of a human coronary artery ex vivo using an early prototype 2.9 French OCT catheter15. The figure shows a comparison of OCT with 30 MHz intravascular ultrasound (IVUS). The OCT image shows the excellent differentiation of the intima, media and adventitia which can be achieved with OCT. For comparison, Figure 2.10 shows a modern example of an OCT image and corresponding histology of a coronary artery ex vivo. The histology was prepared with Movat (pentachrome) stain and shows fibrofatty plaque, which is also visible in the corresponding OCT image. The development of endoscopic OCT imaging proceeded much more rapidly than intravascular OCT. The first demonstrations of in vivo endoscopic OCT imaging were performed in 199716,17. Figure 2.11 shows an example of OCT imaging of the rabbit esophagus in vivo and corresponding histology16. This image demonstrated visualization of the esophageal layers including the mucosa (m), the submucosa (sm), the inner muscularis (im), outer muscularis (om) and serosa (s). The first clinical studies of endoscopic OCT imaging in human subjects were reported in 1997 by Sergeev et al.17. OCT imaging was performed with a flexible forward scanning probe that could be introduced into the working channel of a standard endoscope, bronchoscope, or trocar. The OCT imaging device was a probe, 1.5–2 mm in diameter, which used a miniature magnetic scanner to produce a one-dimensional forward scanning beam. This study demonstrated the feasibility of performing clinical OCT imaging of the esophagus, larynx, stomach, urinary bladder and uterine cervix17. In vivo intravascular OCT imaging was challenging because of the need to develop suitable catheter imaging devices that could be used in animals and human subjects. In addition, since blood is highly optically scattering, it was necessary to develop
protocols to remove blood or to significantly reduce hematocrit in the imaging field. The first in vivo intravascular animal imaging studies were performed in a porcine model using saline flushing and were reported by Tearney et al. in 200018. This study performed OCT imaging and IVUS imaging of normal coronary arteries, intimal dissections, and stents with 10 µm resolution. OCT imaging in human patients was first reported by Jang et al. in 200119. This pioneering study used a 3.2 French OCT imaging catheter and demonstrated imaging of a tissue prolapse in a stent, comparing OCT with IVUS. Early studies compared OCT with IVUS and visualization of stenting20,21. Many other investigations subsequently followed. One of the advantages of OCT for intravascular imaging is that it enables the development of a wide range of catheter imaging devices that integrate intervention and visualization. The early catheter/endoscope shown in Figure 2.8 had a diameter of 2.9 French or 1mm, comparable to the size of a standard IVUS catheter. However, the development of even smallerdiameter OCT catheter/endoscopes is possible. Figure 2.12 shows examples of the LightLabs Imaging ImageWireTM and HeliosTM occlusion balloon catheters. These catheter devices use micro-optical fabrication methods to create lenses and beam-directing elements which have diameters of optical fibers (80–250µm), significantly smaller than can be achieved with IVUS catheters, which require active transducers. The development of catheter imaging devices is extremely challenging, because of the simultaneous mechanical, optical and biocompatibility requirements. Figure 2.13 shows a current example of OCT imaging of the human coronary artery in vivo using the LightLab OCT imaging system. The figure also shows an example of pullback imaging. The arrows indicate surface macrophages located at the edge of a fibrous cap that had ruptured earlier and healed, while ‘R’ denotes the probable site of the rupture.
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OCT
IVUS
Figure 2.9 Early OCT catheter image of a human artery ex vivo and comparison with intravascular ultrasound (IVUS). The OCT image has 15 µm axial resolution and enables the differentiation of the intima, media and adventitia of the artery. Intimal hyperplasia is evident. (From reference 15)
Fibrofatty plaque
Figure 2.10 State-of-the-art optical coherence tomography image of a human artery ex vivo with corresponding histology. The arrows show regions of fibrofatty plaque which corresponds to histology prepared with Movat (pentachrome) stain. (Courtesy of F. Kolodgie and R. Virmani, Department of Cardiovascular Pathology, Armed Forces Institute of Pathology, Washington (pathology) and S. Carlier, Cardiovascular Research Foundation (specimen))
a
m
c
b
sm im om s a
2x
v
v
2x
Figure 2.11 Endoscopic OCT imaging of the rabbit esophagus in vivo. (a) OCT enables visualization of the esophageal layers of the rabbit including the mucosa (m), the submucosa (sm), the inner muscular layer (im), the outer muscular layer (om), the serosa (s) and the adipose and vascular supportive tissues (A). (b) A blood vessel (v) can be seen within the submucosa of the esophagus. (c) Corresponding histology for (B). The scale bars are 500 µm. (From reference 16)
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Occlusion balloon ImageWire
Figure 2.12 Examples of intravascular OCT devices. Since OCT catheters do not require an active transducer, as in ultrasound, imaging can be performed with devices such as guidewires or integrated with interventional devices such as balloons. Imaging with a balloon can be used for stent placement as well as to remove blood from the field of view. The figure shows the LightLab Imaging ImageWireTM and HeliosTM occlusion balloon
R
1 mm 5
10
15
20
25
mm
30
0 mm
10% CD68 staining with 100% sensitivity and specificity. These findings show that OCT has the potential to define the inflammatory status of a given coronary segment and therefore addresses the impact of an important factor related to plaque instability. Other clinical studies are needed to confirm these preliminary findings.
OCT in acute coronary syndromes In three of the six patients with ST-segment elevation AMI, a successful thrombolysis had been performed prior to catheterization and OCT image wire positioning at the lesion site (Figures 7.1 and 7.2). In the remaining three patients with STsegment elevation AMI, a primary angioplasty was performed to recanalize the artery (Figures 7.3 and 7.4). In these cases OCT was performed after the artery had been opened by means of a gentle inflation with an undersized 1.5- or 2.0-mm balloon. In all patients with acute coronary syndrome (ten cases) a thrombus was revealed by OCT (Figure 7.5). In two of them the presence of a thick thrombotic burden hampered the visualization of the outer plaque and vessel wall. In the remaining eight patients a comprehensive morphological study was obtained. Signs of plaque rupture were clearly visible with fissurations detected at the shoulder site of the atherosclerotic plaque, connecting the arterial lumen with the lipid pool. In line with Jang et al.14 lipid pools were found at the lesion site in all cases. Thin fibrous caps, with a thickness less than 60 µm, were found in six lesions.
OCT in stable lesions PRELIMINARY HUMAN EXPERIENCE AT OUR INSTITUTION Our group performed OCT assessment in different clinical scenarios using the LightLab OCT system as described above. After cannulation of the coronary arteries with an 8 F guiding catheter 200 µg of intracardiac nitroglycerin was given; the target lesions were crossed by the image wire, using a frame rate of 15.6/s and at an axial resolution of 10 µm. The 0.019-inch OCT guidewire was first positioned distally to the target lesion and the Helios occlusion balloon was advanced over the image wire. After gentle balloon inflation at 0.3 atm, the arteries were flushed with intermittent saline solution through the Helios balloon in order to displace blood transiently. Finally, the imaging wire was pulled back at 2.0 mm/s, and the images were stored on a compact disc in non-compressed AVI format. We attempted to study 21 patients by OCT. In 18 (86%) the OCT imaging was successful; the image wire was positioned at the lesion site and images were deemed of sufficient quality. Of these 18 patients, six had ST-segment elevation acute myocardial infarction (AMI), four had non-ST-segment elevation AMI or unstable angina and eight had stable angina. The studied artery was the left anterior descending (LAD) in seven cases, the right coronary artery (RCA) in ten and the left circumflex (LCx) in one. No procedure-related complications occurred.
Eight stable lesions were imaged by OCT (Figures 7.6 to 7.9). Four plaques exhibited a homogeneous fibrotic composition and the remaining four displayed fibrotic tissue and calcific components. Lipid necrotic cores were found in two cases and plaque fissurations with thrombotic formations were never detected.
Combined use of OCT and IVUS In nine patients coronary lesions were imaged by either OCT or IVUS. Six patients were studied for acute coronary syndromes, three for stable angina. Table 7.2 and Figures 7.2 and 7.7–7.9 compare the morphological information obtained with the two techniques. Notably, apart from one case, having a marked thrombotic formation that hampered the visualization of the plaque and the vessel wall, OCT showed the specific features of unstable plaques (Table 7.2). In contrast, IVUS failed to depict plaque ulceration and lipid pools in five and three lesions, respectively. The limited penetration of the infrared light of the OCT does not permit, in all cases, the accurate assessment of plaque burden and vessel remodeling, a recognized minor criterion of plaque vulnerability11. In our preliminary experience a full plaquethickness OCT evaluation was obtained in four of nine lesions (Figure 7.6). Nonetheless, in the presence of an atherosclerotic plaque having a thickness
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IVUS
A
B
A B Calcium
A
B
Calcium Thr
LP
Figure 7.1 Patient with previous anterior ST-elevation acute myocardial infarction, treated with systemic thrombolysis. The angiogram (left panel) was obtained 4 weeks later and showed a 60% narrowing in the mid-segment (arrows). OCT revealed a mild plaque with a small lipid pool located at the 7 o’clock position (arrows) with a minimal thrombotic component encroaching upon the lumen (arrow). At the 2–3 o’clock position a calcific deposit is visible (arrow). The internal elastic lamina was clearly observed along three-quarters of the circumference (white line). The corresponding IVUS cross section showed the calcific rim (arrow) and missed the lipid pool
A
A
B
B
A B C
2.2 mm
C
C
Figure 7.2 The right coronary angiogram was obtained in a patient with a recent acute myocardial infarction. The OCT image wire was able to negotiate the severe and diffuse lesion in the mid-segment of the artery. At the site with most severe narrowing, OCT showed a lumen area of 0.22 mm2 and intracoronary thrombosis (arrows in lesion site A). More distally (lesion site B) signs of plaque ulceration were visualized at the 6 o’clock position (arrow). In the distal reference segment (panel C) OCT provided an accurate assessment of vessel dimension and morphology
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LP
A
FC Thr
Lesion site A
B IEL IEL LP FC
IEL Lesion site B
Figure 7.3 Patient with inferior ST-elevation acute myocardial infarction. The right coronary artery was totally occluded in the mid-segment. The artery was first opened with a balloon dilatation, using a small 1.5-mm balloon (angiogram in the left panel) and was then imaged with OCT. OCT was able to depict the morphological characteristic of a complicated lesion. At the lesion site (B) OCT revealed a thin fibrous cap (FC) with a small lipid core (LP) beneath. The internal elastic lamina (IEL) was observed along three-quarters of the circumference. At the lesion site A, 2 mm above site B, OCT showed a residual luminal thrombotic formation (Thr)
SB
Site C
C D
Media
Media
Media
Ref. site D
Figure 7.4 OCT images refer to the same cases as in Figure 7.3. Images were obtained distally to the culprit lesion. At the site C the take-off of a large side branch is visible. At site D a comprehensive assessment of reference vessel anatomy was obtained. An undiseased artery with a lumen of 2.2 mm and mild intimal hyperplasia (0.35 mm thickness) was revealed. Also, the medial layer, delimited by the internal and external lamina, was well depicted
of less than 1.5 mm, OCT can visualize the muscular layer and the internal and external lamina, and therefore enables a comprehensive evaluation of vessel anatomy. This eventuality is not rare because
many acute coronary syndromes are caused by fissuration of small atherosclerotic plaques or by erosion of minimally diseased arterial segments (Table 7.2). This latter event occurs frequently in
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OCT IVUS
LP
A
Figure 7.5 Patient with anterior non-ST elevation acute coronary syndrome. The angiogram showed a tight mid-left anterior descending lesion (arrowhead). OCT revealed a severe lumen narrowing with an area of 0.39 mm2 and a thrombotic formation (arrows), visible in the two lesion cross sections, which are spaced 0.5mm
Figure 7.6 Examples of fibrous lesion interrogated with either OCT or IVUS. OCT enabled an accurate assessment of the plaque along the entire circumference. Arrows and white line indicate the lumen border and the internal elastic lamina, respectively. The maximum thickness of the fibrotic lesion was 0.74 mm by OCT and 0.93 mm by IVUS. Of note, IVUS, by calculating the plaque plus media thickness, overestimated the plaque thickness
females and is responsible for up to 40% of acute coronary syndromes. Interestingly, in the cases in which OCT enabled a comprehensive plaque evaluation, measurements of plaque thickness by OCT were slightly smaller than those obtained by IVUS. In fact, because IVUS does not enable an accurate distinction between plaque and media, the thickness of atherosclerotic plaque is
A
Figure 7.7 Comparison between two OCT and IVUS corresponding cross sections. OCT reveals a lesion at the 7 o’clock position with lipidic components (arrow). The corresponding IVUS cross section shows a reduced echogenicity of the ultrasound signals, which is compatible with the presence of lipid pools (arrow). IVUS assessment confirmed that the hypoechoic plaque spot, appreciated by OCT and visible at 7 o’clock was due to a lipid pool instead of calcific components
Figure 7.8 Example of a calcific lesion imaged by IVUS and OCT. Both OCT and IVUS reveal three calcific spots, indicated by arrowheads. IVUS did not show the external elastic membrane at the calcific sites due to the shadowing induced by calcium. In contrast, OCT enabled an accurate evaluation of the circumferential extension and thickness of the calcific component, in all of the three cross sections, by revealing the external vessel contour (arrows)
conventionally measured as plaque plus media and, as a consequence, is overestimated. The OCT distinction between lipid pools and calcific deposits is uneasy, because the OCT appearance of the two components are rather similar. Unlike OCT, IVUS distinguishes between calcium and lipid pools with high accuracy; it is worth combining the two techniques to obtain a comprehensive study of atherosclerosis (Figure 7.7).
Quantitative assessment We compared measurements of luminal areas obtained by OCT and IVUS to assess the accuracy of
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Table 7.2
Figure 7.9 Example of a calcific lesion imaged by IVUS and OCT. Both OCT and IVUS reveal multiple calcific spots, indicated by arrows. The thickness of the calcific component, located from the 6 to 8 o’clock position, is well depicted by OCT
the former technique. In total we measured lumen areas in 16 cross sections evaluated by both OCT and IVUS. In nine cases lumen measurements were obtained at the target sites, while in the remaining seven cases measurements were obtained in reference segments. In three target lesions in which the minimal lumen area was less than 1.0 mm2, which is smaller than the IVUS transducer, the IVUS technique did not enable a reliable assessment of luminal dimensions. In contrast, in these three lesions the measurements obtained by OCT were accurate and smaller than those achieved by IVUS (0.63 + 0.44 mm2 vs. 1.0 + 0.01 mm2). In the remaining 13 lesions luminal measurements obtained by OCT and IVUS were similar (5.75 + 2.15 mm2 vs. 5.86 + 2.00 mm2; NS).
COMMENTS ON OUR FINDINGS Use of OCT in everyday practice will probably improve the assessment of culprit lesions in patients with acute coronary syndromes. In the presence of multiple severe narrowing or mild luminal impairments, angiography may not detect the culprit lesions for future acute coronary events15,16. Although use of IVUS improves the accuracy in the definition of the culprit lesion in uncertain cases, the technique may miss some morphological details such as signs of ulceration and thrombosis4,5,17–19. Based on published IVUS studies addressing the issue of plaque disruption, the incidence of disrupted lesions broadly ranged between 15.8 and 66%20–23. This extreme variability in the finding of plaque disruption is likely to be related to the resolution of the technique, which is around 200 µm. In contrast, the OCT, with its high axial resolution, promises to be a valuable tool for assessment of plaque vulnerability and rupture. Furthermore, OCT will offer considerable information on the pathophysiology of plaque rupture, by giving
Concomitant OCT and IVUS assessment
Thrombus Plaque ulceration Lipid pool Calcium Full plaque thickness
Unstable lesions (n=6)
Stable lesions (n=3)
OCT
IVUS
OCT
IVUS
6 5 5 1 3
5 2 3 1 6
0 0 1 2 1
0 0 0 2 3
more details on the composition of both culprit and stable lesions. Our findings differ from those recently published by Jang et al.14 in their first published in vivo OCT study. Also, in the 17 patients with stable angina, lipid-rich plaques were found in ten cases, thrombosis in six and signs of fissuration in two cases. There was only a trend toward a higher frequency of lipidrich plaques in patients with acute coronary syndromes compared with those with stable angina. The frequency of signs of plaque disruption did not differ in the two groups. In our experience morphological details indicative of plaque disruption (fissurations and thrombotic formations) were observed in all cases in which OCT enabled an accurate assessment of the lesion. Technical differences in the modality of OCT acquisition may explain these different findings. Importantly, the use of the thin LightLab image wire, having a thickness of 0.3 mm, probably improves the study of coronary lesions causing severe narrowing.
REFERENCES 1. Yabushita H, Bouma BE, Houser SL, et al. Characterization of human atherosclerosis by optical coherence tomography. Circulation 2002; 106: 1640–5 2. Jang I, Bouma B, Kang D, et al. Visualization of coronary atherosclerotic plaques in patients using optical coherence tomography: comparison with intravascular ultrasound. J Am Coll Cardiol 2002; 39: 604–49 3. Tearney G. Intravascular optical coherence tomography opens a window onto coronary artery disease. Opt Photonics News 21–5 4. Nissen SE, Gurley JC, Grines CL, et al. Intravascular ultrasound assessment of lumen size and wall morphology in normal subjects and patients with coronary artery disease. Circulation 1991; 84: 1087–99 5. Gussenhoven EJ, Essed CE, Lancee CT, et al. Arterial wall characteristic determined by intravascular ultrasound imaging. An in vitro study. J Am Coll Cardiol 1989; 14: 947–52 6. Prati F, Arbustini E, Labellarte A, et al. Correlation between high frequency intravascular ultrasound and histomorphology in human coronary arteries. Heart 2001; 85; 567–70
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7. Davies MJ, Richardson PD, Woolf N, et al. Risk of thrombosis in human atherosclerotic plaques: role of extracellular lipid, macrophage, and smooth muscle cell content. Br Heart J 1993; 69: 377–81 8. Moreno PR, Falk E, Palacios IF, et al. Macrophage infiltration in acute coronary syndromes: implications for plaque rupture. Circulation 1994; 90: 775–8 9. Nakamura N, Lee DP, Yeung AC. Identification and treatment of vulnerable plaque. Rev Cardiovasc Med 2004; 5(Suppl 2): S22–S33 10. Richardson PD, Davies MJ, Born GVR. Influence of plaque configuration and stress distribution on fissuring of coronary atherosclerotic plaques. Lancet 1989; 2: 941–4 11. Naghavi M, Libby P, Falk E, et al. From vulnerable plaque to vulnerable patient: a call for new definitions and risk assessment strategies: Part I. Circulation 2003; 108: 1664–72 12. Prati F, Arbustini E, Kwiatkowski P, et al. Does optical coherence tomography provide accurate measurements of atherosclerotic plaque components? (Abstract P614). Eur Heart J 2004; 25: 99 13. Tearney GJ, Yabushita H, Houser SL, et al. Quantification of macrophage content in atherosclerotic plaques by optical coherence tomography. Circulation 2003; 107: 113–19 14. Jang IK, Tearney GJ, MacNeill B. In vivo characterization of coronary atherosclerotic plaque by use of optical coherence tomography. Circulation 2005; 111: 1551–5 15. Tousoulis D, Davies G, Crake T, et al. Angiographic characteristics of infarct-related and non-infarctrelated stenosis in patients in whom stable angina progressed to acute myocardial infarction. Am Heart J 1998; 136: 382–8 16. Ambrose JA, Tannenbaum MA, Alexopoulos D, et al. Angiographic progression of coronary artery disease and the development of myocardial infarction. J Am Coll Cardiol 1988; 12: 56–62
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17. Mintz GS, Nissen SE, Anderson WD, et al. ACC Clinical Expert Consensus Document on Standards for the acquisition, measurement and reporting of intravascular ultrasound studies: a report of the American College of Cardiology Task Force on Clinical Expert Consensus Documents (Committee to Develop a Clinical Expert Consensus Document on Standards for Acquisition, Measurement and Reporting of Intravascular Ultrasound Studies (IVUS)). J Am Coll Cardiol 2001; 37: 1478–92 18. Rodriguez-Granillo GA, Garcia-Garcia HM, McFadden EP, et al. In vivo intravascular ultrasound-derived thin-cap fibroatheroma detection using ultrasound radiofrequency data analysis. J Am Coll Cardiol 2005; 46: 2038–42 19. Di Mario C, Gorge G, Peters R, et al., on behalf of the Study Group on Intracoronary Imaging of the Working Group of Coronary Circulation of the European Society of Cardiology. Clinical application and image interpretation in intracoronary ultrasound. Eur Heart J 1998; 19: 207–29 20. Kotani J, Mintz GS, Rai PB, et al. Intravascular ultrasound assessment of angiographic filling defects in native coronary arteries: do they always contain thrombi? J Am Coll Cardiol 2004; 44: 2087–9 21. Rioufol G, Finet G, Ginon I, et al. Multiple atherosclerotic plaque rupture in acute coronary syndrome. A three vessel intravascular ultrasound study. Circulation 2002; 106: 804–8 22. Sano T, Tanaka A, Namba M, et al. C-reactive protein and lesion morphology in patients with acute myocardial infarction. Circulation 2004; 108: 282–5 23. Hong MK, Mintz GS, Lee CW, et al. Comparison of coronary plaque rupture between stable angina and acute myocardial infarction: a three-vessel intravascular ultrasound study in 235 patients. Circulation 2004; 110: 928–33
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CHAPTER 8 OCT plaque characterization: comparison to angioscopy Yoshihiro Takeda, Jean-François Surmely, Takahiko Suzuki
Plaques that are prone to rupture, known as thin-cap fibroatheroma (TCFA), are assumed to be the most common lesions underlying acute coronary syndromes1. TCFA is reported to have the following pathological features: (1) a minimal fibrous cap thickness of less than 65 µm (some reports give a value of less than 150 µm); (2) a large lipid pool; and (3) macrophage infiltration of the thin fibrous cap1–3. If vulnerable plaque such as TCFA can be identified in vivo, it may be possible to alter the natural course of atherosclerosis before a clinical coronary event occurs. To do so, reliable, reproducible diagnostic methods with high predictive values are required. Numerous approaches to the imaging of TCFA are under development.
time of percutaneous coronary intervention, Thieme et al.7 performed a histopathological analysis of coronary atherectomy specimens which demonstrated an association between a yellow plaque and the presence of a lipid-rich atheroma. The analysis showed that glistening yellow and red–yellow lesions represented either lipid-rich atheroma (53%) or degenerated fibrous plaque with patchy necrosis (42%). White lesions, on the other hand, represented fibrous plaque without a large lipid pool (100%). In addition, several angioscopic studies6,8–10 have suggested that variations in yellow color intensity may reflect differences in the vulnerability of plaques. Uchida et al.6 performed a three-vessel angioscopic examination in 157 patients with stable angina, and followed the patients prospectively for 12 months. Acute coronary syndromes occurred more frequently in patients with yellow plaques than in those with white plaques (28.2% vs. 3.3%, p = 0.00021). Moreover, among patients with yellow plaques, acute coronary syndromes occurred more frequently in those with glistening yellow plaques than in those with non-glistening yellow plaques (68.4% vs. 7.6%; p = 0.00026). In a study by Ueda et al.8 including 843 patients, angioscopy of the culprit artery revealed a total of 1253 yellow plaques in the angiographically non-stenotic segments. The yellow color intensity was categorized as light yellow (n = 345), moderate yellow (n = 721) and glistening yellow (n = 187). The prevalence of thrombus detected by angioscopy was significantly higher according to the increased intensity of yellow color: 15% in light yellow, 26% in moderate yellow and 52% in glistening yellow, respectively; p < 0.0001. Takano et al.10 demonstrated that lipid-lowering therapy with atorvastatin resulted in the reduction of angioscopic yellow color intensity and complexity of coronary plaques such as surface irregularity, which implies a relationship between the yellow color intensity and differences in plaque stability.
LESSONS FROM ANGIOSCOPY Although angioscopy lacks the ability to identify many of the pathological features of TCFA, it allows direct full-color visualization of the plaque surface and is also useful for the assessment of surface irregularities and the presence of thrombus. Since its introduction into clinical practice, a number of angioscopic studies4–6 have described the appearance of culprit lesions in an attempt to understand the pathophysiology of acute coronary syndromes. Early angioscopic studies4,5 demonstrated a high frequency of ruptured yellow plaque with thrombus formation at the culprit site in patients with acute coronary syndromes. From the results of these studies, angioscopic yellow plaques are thought to correspond to a lipid-rich pool seen through a thin fibrous cap and are supposed to be associated with future development of acute coronary syndromes, whereas white plaques are thought to have a thick fibrous cap and thus are considered stable4–6. These findings were supported by a few histopathological studies. In patients undergoing angioscopic examination at the 79
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a
b
d
c
G
L G L L
Figure 8.1 Thin-cap fibroatheroma imaged by angioscopy, intravascular ultrasound (IVUS), and optical coherence tomography (OCT). (a) Angioscopy showing a yellow plaque with a smooth surface; (b) IVUS showing a hypoechoic plaque, extending circumferentially; (c) and (d) from the 5 o’clock to 9 o’clock positions (between arrows), OCT allows a clear visualization of the thin fibrous cap (arrowhead) overlying the lipid-rich plaque (L). This cannot be identified by IVUS. In the OCT images, a lipid pool is seen as a less-well-delineated region than calcification and has a decreased signal density as well as a more heterogeneous signal density than fibrous plaque (see references 11 and 12). In addition, OCT accurately measures both a lipid arc of 120° and cap thickness at the thinnest part of 35 µm. White bar = 1mm. G, guidewire location
IN VIVO DETECTION OF THIN-CAP FIBROATHEROMA BY OCT The high-resolution images (approximately 10 µm) of optical coherence tomography (OCT) allow an accurate identification of lipid-rich plaques11,12. OCT characteristics of various plaque types have been demonstrated to correlate well with ex vivo histopathological characteristics, yielding a sensitivity of 90% and a specificity of 90% for lipid-rich plaques; 95% and 97% for fibrocalcific plaques; and 71% and 97% for fibrous plaques, respectively12. OCT also allows an accurate detection of thin fibrous caps overlying lipid-rich plaques. Jang et al.11 have demonstrated that OCT is a feasible modality for identifying TCFA in living patients with various coronary syndromes. As shown in Figure 8.1 OCT identifies TCFA, and allows the accurate quantification of the cap thickness. On the other hand, the corresponding intravscular
ultrasound (IVUS) image shows a hypoechoic region (possibly representing lipid content), which appears to extend circumferentially, but cannot identify the fibrous cap. The corresponding glistening-yellow plaque on angioscopy is shown. Thrombus which is a feature of plaque instability is also readily imaged by OCT as well as by angioscopy (Figure 8.2). Another feature of plaque instability, such as plaque rupture, is shown in Figure 8.3. The angioscopy shows a yellow plaque with an irregular surface and no thrombus (Figure 8.3a). Minor degrees of plaque rupture are seen by OCT (Figure 8.3b).
CORONARY PLAQUE CHARACTERIZATION BY OCT AND ANGIOSCOPY: A COMPARATIVE STUDY As reviewed above, several angioscopic studies suggest that yellow plaques, particularly glistening ones,
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a
b
1.5
1.0
0.5
0.0
0.5
1.0
81
1.5 mm
1.5 1.0 0.5 0.0 0.5 1.0 1.5 Zoom:1.6x mm
Figure 8.2 Thrombus imaged by angioscopy (a) and optical coherence tomography (OCT) (b). (a) Angioscopy showing a protruding white thrombus (arrows) overlying a mural red thrombus (arrowhead). (b) OCT image of the same thrombus (arrows). A thrombus is defined as an irregular mass which is adherent to the vessel surface and protrudes into the lumen
a
b
1
0
1
2
mm
L
1 L 0
1 L 2 Zoom:1.3x
L
mm
Figure 8.3 (a) Angioscopy showing a yellow plaque with an irregular surface (arrow). (b) Optical coherence tomography (OCT) showing a lipid-rich plaque (L) with minor degrees of rupture of the thin fibrous cap (arrow). The cap thickness at its thinnest part (arrowhead) measures 60 µm. In accordance with the angioscopic findings, no thrombus was observed in the OCT image
may indicate thin-cap fibroatheroma. However, due to a limited resolution of the widely available imaging methods to examine the coronary arterial lumen, definite evidence in vivo is lacking. The aim of this study was to compare detailed in vivo plaque information obtained by angioscopy and OCT.
Methods Pre-interventional angioscopy and OCT images were obtained from 23 culprit lesions in 23 patients undergoing percutaneous coronary intervention for de novo native coronary artery diseases (13 with acute coronary syndromes and ten with stable angina pectoris). The angioscopic evaluation of plaques consisted of an assessment of the plaque color, surface irregularity and presence of thrombus6–10. Plaque color was classified as either white or yellow. Yellow color was
further subdivided into glistening and non-glistening yellow according to its intensity6–10. The presence of thrombus or surface irregularity including fissure, flap and ulceration was noted6–10. The OCT image wire (LightLabTM Imaging Corporation, Westford, MA) consists of a rotating optical fiber and microlens assembly encased in a 0.014-inch transparent sheath. The OCT image wire and a 4 F over-the-wire occlusion balloon were introduced into the coronary artery. Clear visualization of the coronary lumen was obtained after proximal vessel occlusion and distal continuous saline flush. Vessel occlusion was obtained with a low-pressure inflation (< 0.5 atm) of a compliant balloon; and distal saline flush was performed at a continuous rate of 0.5 ml/s through the irrigation channel of the occlusion balloon. An imaging run using an automated pullback at 1.0 mm/s was performed.
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Table 8.1 Angioscopic and optical coherence tomography (OCT) findings according to the intensity of yellow plaque color. Percentages are shown in parentheses
Angioscopy Surface irregularity Thrombus OCT Maximum lipid arc (degrees) Minimum fibrous cap thickness (µm) Mean fibrous cap thickness (µm) Thin-cap fibroatheroma* Plaque disruption Thrombus
Glistening yellow (n=8)
Non-glistening yellow (n=12)
p Value
5 (63) 7 (88)
0 (0) 4 (33)
< 0.05 < 0.05
211 ± 65
247 ± 86
0.34
67 ± 39
153 ± 92
< 0.05
98 ± 50
181 ± 101
< 0.05
7 (88) 6 (75) 7 (88)
3 (25) 5 (42) 5 (42)
< 0.05 0.19 < 0.05
*Thin-cap fibroatheroma is defined as a maximum lipid arc of ≥120 degrees and a minimum cap thickness of ≤ 65 µm
OCT images were analyzed according to previously validated criteria for plaque characterization11,12. For every successive image of a lesion with an OCTdetermined lipid, the fibrous cap thickness was measured at its thinnest part, and the arc of lipid was also measured11 (using National Institutes of Health Image, NIH public domain software, Bethesda, MD). For each lesion, the minimal cap thickness was defined as the smallest value of cap thickness within a lesion, and the mean cap thickness as the average of the values from all the measurable images within a lesion. The maximum value of lipid arc was also reported in each lesion. Lipid-rich plaque was defined as a plaque with a maximum lipid arc of ≥ 120°. Thin fibrous cap was defined as a minimum fibrous thickness of ≤ 65 µm. TCFA was defined as the combination of these two characteristics (lipid-rich plaque plus thin fibrous cap)11. The presence of plaque rupture or thrombus was noted.
Results Of the 23 patients enrolled, there were 16 men and seven women, and the mean age was 61 ± 12 years. Four patients (17%) presented with acute myocardial infarction, nine patients (40%) had unstable angina, and the remaining 10 patients (43%) had stable angina. The culprit lesions were analyzed by both angioscopy and OCT.
Angioscopy findings Angioscopic plaque color at the culprit sites consisted of 20 yellow and three white. The three white plaques were found in stable coronary artery diseases.
Surface irregularity was observed in 22% (n = 5) and thrombus in 48% (n = 11). All plaques with the presence of surface irregularity or thrombus were yellow plaques.
OCT findings All angioscopic yellow plaques (n = 20) were classified by OCT as lipid-rich plaques. On the other hand, the three angioscopic white plaques were classified as non-lipid-rich plaques. Correlations of the OCT finding with the intensity of yellow color are summarized in Table 8.1. When comparing the glistening and non-glistening yellow plaques, the maximum arcs of lipid were similar between the two groups. On the other hand, the minimum and mean values of fibrous cap thickness were significantly thinner in the glistening yellow plaques than in the non-glistening yellow plaques (Table 8.1). The distribution of the minimum cap thickness according to the yellow color intensity is represented in Figure 8.4. The frequency of TCFA determined by OCT was also significantly higher in the glistening yellow plaques than in the non-glistening yellow plaques (88% and 25%, p < 0.05) (Table 8.1, Figure 8.4). Figures 8.5–8.7 show two glistening yellow lesions at the culprit sites on angioscopy, which were diagnosed as TCFA by OCT. On the other hand, Figures 8.8 and 8.9 show 2 non-glistening yellow lesions at the culprit sites on angioscopy, which were not diagnosed as TCFA by OCT. Of the 20 lesions with angioscopic yellow color (including glistening and non-glistening yellow color), OCT and angioscopy show a similar frequency of
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Discussion
350 Minimum fibrous cap thickness (µm)
p < 0.05 300 250 200 150
Median 160 µm
100 Median 60 µm
50 0 Non-glistening yellow (n = 12)
Glistening yellow (n = 8)
Figure 8.4 Distribution of the minimum cap thickness for the glistening and non-glistening yellow plaques. The glistening yellow plaques have a significantly thinner minimum cap thickness compared to the non-glistening yellow plaques (p < 0.05). The frequency of thin-cap fibroatheroma determined by OCT was significantly higher in the glistening yellow plaques, compared to the non-glistening yellow plaques (88% vs. 25%, p < 0.05). Closed circles denote thin-cap fibroatheroma and open circles denote the absence of thin-cap fibroatheroma
a
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b
Figure 8.5 (a) Angiogram of the culprit lesion in the middle left anterior descending coronary artery of a patient with unstable angina pectoris. (b) Angioscopy of the culprit lesion showing a glistening yellow plaque (thick arrows) with protruding white thrombus (thin arrows)
thrombus (60% vs. 55%, respectively), but a higher frequency of plaque rupture (55% vs. 25%, respectively). When comparing glistening yellow and nonglistening yellow plaques, the incidence of plaque rupture detected by OCT was similar in both groups. On the other hand, the incidence of thrombus detected by OCT was significantly higher in the glistening plaques than in the non-glistening plaques (p < 0.05).
The major findings of our study were that (1) plaques with yellow appearances, independently of their yellow color intensity, correlated well with lipid-rich plaques diagnosed by OCT; and (2) variations in yellow color intensity reflected differences in the thickness of the fibrous cap overlying lipid-rich plaques as measured by OCT. As a result, the proportion of TCFA identified by OCT was significantly higher in the glistening yellow plaques, compared to the non-glistening yellow plaques. Our study confirms previous angioscopic findings that suggested that yellow plaques, particularly glistening ones, indicate TCFA6,7. It has been suggested that a cap thickness of < 300 µm overlying a lipid pool may be needed as a condition for yellow appearance to be visualized on angioscopy6,13. This was consistent with our OCT finding, where the upper value of the minimum cap thickness of yellow plaques measured 300 µm (Figure 8.4). Taking into consideration the good correlation in our study between OCT and angioscopy for the detection of lipid-rich plaques, the chosen OCT definition for lipid-rich plaque may be appropriate. Our findings of a relationship between the yellow color intensity and the cap thickness are supported by several histopathological or experimental studies6,7,13. Miyamoto et al. developed an in vitro model of lipid-rich plaques by injecting lipid into the bovine aorta at different depths. The per cent saturation of yellow color as observed with angioscopy increased inversely with the cap thickness, and for a cap thickness of < 300 µm, this relationship was linear with a correlation coefficient of 0.86 (p = 0.0001)13. An ex vivo histopathological analysis by Uchida et al.6 also revealed a good association between a glistening yellow color and the presence of a relatively thinner fibrous cap of < 100 µm overlying the atheroma. Both OCT and angioscopy are possibly equivalent for the detection of lipid-rich plaques and TCFA. However, the interpretation and classification of the angioscopic color is relatively subjective and observer dependent. It is of importance to emphasize that the fibrous cap thickness measured by OCT is more directly related to plaque stability in vivo. Our study demonstrates that angioscopic non-glistening yellow plaques had a wide range of cap thickness measured by OCT, ranging from 40 to 300 µm. Only 25% of non-glistening yellow plaques were diagnosed as TCFA by using OCT. It suggests that most non-glistening yellow plaques correspond to lipidrich plaques with the presence of a relatively thick fibrous cap, not indicative of TCFA. Furthermore, the high resolution of OCT facilitates the identification of minor degrees of rupture and thrombus that can be difficult to discern by angioscopy (Figures 8.5 and 8.6). Siegel et al.14 studied the sensitivity of angioscopy to detect plaque rupture and
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b
c
T G T
G
L
T
1mm
2x e
d G
L
T T L
G
Figure 8.6 Optical coherence tomography (OCT) images of the culprit lesion corresponding to the angioscopic image of Figure 8.5 (0.2 mm interval between each OCT image). (a and e) Thrombus protruding into the lumen (T) is suggestive of the white thrombus seen on angioscopy. (b–d) Lipid-rich plaque (L) covered by a thin fibrous cap, called thin fibrous cap atheroma. In (c) (magnified view of (d), minor degrees of plaque rupture and a small thrombus (T) were observed. The minimum cap thickness at this region measured 35 µm (arrow). G, guidewire location
b
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a G L T
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Figure 8.8 Corresponding angioscopy and optical coherence tomography (OCT) images of a thick fibrous cap atheroma. (a) Angioscopy showing non-glistening yellow plaque. (b) Lipid-rich plaque (L) extending from the 6 o’clock to the 11 o’clock positions covered by a thick fibrous cap measuring 250 µm at its thinnest part (thin arrow). Thick arrows indicates the adventitia
L Zoom:1.7x
Figure 8.7 (a) Angioscopy demonstrating the culprit lesion of a patient with acute myocardial infarction. Glistening yellow plaque with mural mixed thrombus (arrowheads) and protruding white thrombus (arrow) were observed at the culprit site. (b–d) Optical coherence tomography (OCT) images at 0.2-mm intervals at the culprit lesion. T, the thrombus covering the lipid-rich plaque (L), extending from the 12 o’clock to the 3 o’clock position. From the 6 o’clock to the 9 o’clock position, a thin fibrous cap overlying the lipid-rich plaque is also seen. G, guidewire location
thrombus in postmortem human arterial segments. The sensitivity of angioscopy was 73% for plaque rupture detection and 100% for thrombus detection, revealing that angioscopy underestimated the presence of plaque rupture. These results from the study by Siegel et al. are in line with our findings in which, in comparison with angioscopy, OCT showed a similar frequency for the detection of thrombus and a higher frequency for the detection of plaque rupture.
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G L C
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Figure 8.9 Corresponding angioscopy and optical coherence tomography (OCT) images of a thick fibrous cap atheroma. (a) Angioscopy showing non-glistening yellow plaque. (b) Lipid-rich plaque (L) extending from the 4 o’clock to the 11 o’clock positions covered by a thick fibrous cap measuring 300 µm at its thinnest part (arrow). C, calcification; G, guidewire location
An inherent limitation of OCT as well as angioscopy is the need to achieve a blood-free imaging zone. A further limitation of OCT is a relatively low axial penetration (2 mm), and thus it does not allow direct measurement of the whole size of the lipid pool. However, because the most important morphological characteristics of plaque vulnerability are superficial, the region of interest is within the imaging range of OCT.
CONCLUSION The unique capability of OCT to resolve micrometerscale features of coronary plaques allows a precise identification of TCFA, which is the commonest type of lesion underlying acute coronary syndromes. One of the advantages of OCT over angioscopy is an increased detection of plaque ruptures.
REFERENCES 1. Virmani R, Kolodgie FD, Burke AP, et al. Lessons from sudden coronary death: a comprehensive morphological classification scheme for atherosclerotic lesions. Arterioscler Thromb Vasc Biol 2000; 20: 1262–75 2. Burke AP, Virmani R, Galis Z, et al. 34th Bethesda Conference: Task force no. 2 – What is the pathologic basis for new atherosclerosis imaging techniques? J Am Coll Cardiol 2003; 41: 1874–86 3. Kolodgie FD, Virmani R, Burke AP, et al. Pathologic assessment of the vulnerable human coronary plaque. Heart 2004; 90: 1385–91 4. Mizuno K, Satomura K, Miyamoto A, et al. Angioscopic evaluation of coronary-artery thrombi in acute coronary syndromes. N Engl J Med 1992; 326: 287–91
5. de Feyter PJ, Ozaki Y, Baptista J, et al. Ischemiarelated lesion characteristics in patients with stable or unstable angina. A study with intracoronary angioscopy and ultrasound. Circulation 1995; 92: 1408–13 6. Uchida Y, Nakamura F, Tomaru T, et al. Prediction of acute coronary syndromes by percutaneous coronary angioscopy in patients with stable angina. Am Heart J 1995; 130: 195–203 7. Thieme T, Wernecke KD, Meyer R, et al. Angioscopic evaluation of atherosclerotic plaques: validation by histomorphologic analysis and association with stable and unstable coronary syndromes. J Am Coll Cardiol 1996; 28: 1–6 8. Ueda Y, Ohtani T, Shimizu M, et al. Assessment of plaque vulnerability by angioscopic classification of plaque color. Am Heart J 2004; 148: 333–5 9. Takano M, Mizuno K, Okamatsu K, et al. Mechanical and structural characteristics of vulnerable plaques: analysis by coronary angioscopy and intravascular ultrasound. J Am Coll Cardiol 2001; 38: 99–104 10. Takano M, Mizuno K, Yokoyama S, et al. Changes in coronary plaque color and morphology by lipid-lowering therapy with atorvastatin: serial evaluation by coronary angioscopy. J Am Coll Cardiol 2003; 42: 680–6 11. Jang IK, Tearney GJ, MacNeill B, et al. In vivo characterization of coronary atherosclerotic plaque by use of optical coherence tomography. Circulation 2005; 111: 1551–5 12. Yabushita H, Bouma BE, Houser SL, et al. Characterization of human atherosclerosis by optical coherence tomography. Circulation 2002; 106: 1640–5 13. Miyamoto A, Prieto AR, Friedl SE, et al. Atheromatous plaque cap thickness can be determined by quantitative color analysis during angioscopy: implications for identifying the vulnerable plaque. Clin Cardiol 2004; 27: 9–15 14. Siegel RJ, Ariani M, Fishbein MC, et al. Histopathologic validation of angioscopy and intravascular ultrasound. Circulation 1991; 84: 109–17
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CHAPTER 9 OCT plaque characterization: comparison to multislice computed tomography Carlos AG Van Mieghem, Nico R Mollet, Pim J de Feyter
a
INTRODUCTION Invasive coronary angiography remains the standard of reference for the assessment of advanced coronary artery disease, which is typically characterized by significant narrowing of the coronary lumen and provides the necessary road map for defining the type and extent of coronary revascularization (percutaneous coronary intervention or coronary artery bypass graft surgery). Unfortunately, ‘luminographic’ involvement is a late manifestation of coronary artery disease, since a large amount of atherosclerotic plaque accumulates in the vessel wall before affecting the coronary artery lumen1,2. Identification of the earlier stages of coronary artery disease is critically important, as it has been shown that the majority of acute coronary events, manifesting as acute myocardial infarction or sudden cardiac death, are initiated by sudden rupture and subsequent thrombosis of atherosclerotic plaques that are not causing significant stenosis3. In vivo visualization of the extent of the angiographically invisible coronary atherosclerotic process was initially demonstrated by intravascular ultrasound (IVUS), another invasive imaging technique4. Non-invasive alternatives for imaging of the coronary arteries became available one decade ago. Coronary imaging by magnetic resonance imaging as first demonstrated in the early 1990s looked very promising5. However, long acquisition times and insufficient spatial resolution limit its clinical usefulness for the assessment of coronary artery disease6. By contrast, rapid developments in computed tomography (CT) techniques have provided the necessary spatial and temporal resolution for coronary imaging. As a result, cardiac CT is emerging as the preferred non-invasive imaging tool that has the ability to visualize both the coronary artery lumen and the vessel wall. In this chapter we describe the potential of cardiac CT for coronary
X-ray tube
b
Patient X-ray beam
Gantry
Figure 9.1
Detectors
Frontal (a) and side (b) view of a CT scanner
plaque imaging and relate this feature to the current possibilities of optical coherence tomography (OCT).
CT TECHNOLOGY A CT scanner consists of an X-ray tube that rotates around the patient, with images collected by a row of detectors on the opposite side (Figure 9.1). The patient is continuously moved through the X-ray field while data are digitally recorded from the detectors. The resulting X-ray projection data form a helix or spiral, hence the description ‘spiral-CT’. While CT has been available for clinical use since the early 1970s, it has been poorly suited to cardiac imaging until a few years ago. Indeed, accurate noninvasive assessment of the coronary arteries poses several difficulties. First, high spatial resolution is required for accurate visualization of their small size and tortuous three-dimensional anatomy. Second, sufficient temporal resolution is needed to minimize artifacts related to rapid coronary motion. Third, the scan must be performed within a single short breath hold to avoid respiratory motion artifacts. To obtain motion-free images data are typically obtained 87
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a
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CX
CX
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Figure 9.2 Patient with acute coronary syndrome showing a thrombotic subocclusive lesion of the circumflex coronary artery (CX) on conventional coronary angiography (a, arrow). Volume-rendered (b) and multiplanar reformatted (d) CT image confirm the presence of a subocclusive non-calcified plaque (arrows). On intravascular ultrasound (c) this lesion is hyperechogenic in composition. The speckled appearance within the lumen is compatible with thrombus
during the diastolic phase of the cardiac cycle. Image acquisition must therefore be synchronized to the cardiac cycle using electrocardiogram (ECG) gating. In most instances retrospective ECG gating is used. This means that data are acquired during the whole cardiac cycle. After data acquisition, different reconstruction windows during the diastolic phase of the heart can be explored to obtain images with the lowest number of motion artifacts. Heart-rate-lowering drugs are generally recommended for patients with a resting heart rate above 70 beats/minute7. Two types of CT scanner, electron-beam CT (EBCT) and multislice CT (MSCT), exist for noninvasive cardiac imaging. EBCT offers better temporal resolution, but MSCT scanners provide higher spatial resolution, shorter scan time and higher signal-to-noise ratio and are currently the preferred CT imaging modality. MSCT scanners enable the simultaneous acquisition of multiple sections per rotation of the X-ray tube, hence the terminology ‘multislice’ spiral CT. Current 64-slice CT scanners allow data acquisition by up to 64 detector rows in one rotation. They feature a temporal resolution of 165 ms and isotropic (i.e. equal voxel dimensions in x, y and z axes) through plane spatial resolution of 0.4 mm8.
PLAQUE EVALUATION AND CHARACTERIZATION Based on clinical grounds the presence of coronary artery disease in a patient is usually noted if there is a history of angina pectoris, myocardial infarction or coronary revascularization. Current diagnostic workup of patients with suspicion of coronary artery disease targets the detection of myocardial ischemia due to flow-limiting coronary stenoses. However, this approach only identifies coronary atherosclerosis in an advanced development stage and falls short in identifying a large group of patients who present with acute coronary syndromes, such as unstable angina, myocardial infarction or sudden cardiac death, that are often the first clinical manifestation of coronary artery disease. Rupture or erosion with concomitant thrombosis of non-obstructive coronary plaques is the most frequent cause of these acute cardiac events (Figure 9.2)9. Pathological studies have identified several characteristics of plaques at increased risk for thrombosis, the so-called vulnerable plaque: they typically present eccentric remodeling, contain a large lipid core and are covered by a very thin fibrous cap showing an abundance of macrophages in the
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shoulder regions of the plaque10. Once vulnerable plaques have ruptured and resulted in an acute coronary syndrome, survival is diminished. Early identification and prophylactic stabilization of vulnerable plaque(s) may therefore play an important role in future strategies to prevent acute coronary syndromes. Coronary angiography, the reference standard for the identification of obstructive coronary artery disease, provides little or no information on the actual extent of the atherosclerotic process or on the composition of the atherosclerotic plaque11. As a result, coronary angiography cannot be used for the detection of vulnerable plaque(s). Unlike conventional angiography, coronary CT has the ability to depict the vessel wall and coronary plaque irrespective of the lumen obstruction, thus allowing assessment of both non-flow-limiting and flow-limiting plaques12.
CT STENOSIS DETECTION MSCT and to a lesser extent EBCT provide the necessary spatial and temporal resolution for accurate detection of significant obstructive coronary artery disease. Early reports using four-slice MSCT showed promising results. However, this technique was not robust enough, owing to its relatively long scan time and limited resolution, resulting in a number of non-evaluable coronary segments as high as 43%13. The diagnostic accuracy of CT coronary angiography significantly improved with 16-slice and the latest 64-slice MSCT, allowing reliable evaluation of coronary artery segments up to 1.5 mm in diameter8,14–16. The reported results depend largely on patient selection and several factors including elevated heart rates or severe coronary calcification cause suboptimal image quality, making it sometimes difficult to correctly evaluate the coronary arteries. Consequently, patients with arrhythmias are generally excluded and heart-rate-lowering medication is routinely administered for heart rates above 65–70 bpm.
89
Score for predicting future cardiac events, and is therefore suggested as an adjunctive risk-stratification tool in intermediate-risk patients20,21. However, up to 20% of patients presenting an acute coronary event may have mildly calcified or non-calcified culprit lesions and thus are not identified by coronary calcium scoring (Figure 9.4)22. Contrast-enhanced MSCT is able to detect calcified and non-calcified coronary plaques. Based on the tissue-specific X-ray attenuation characteristics, it is possible to differentiate between fibrous tissue, lipid and calcium23–27. CT also has the ability to detect coronary segments with positive remodeling that can harbor substantial amounts of plaque without significant luminal stenosis12. The clinical relevance of this information is unclear and is the subject of ongoing studies. In addition, several limitations have to be taken into account: reported data were exclusively obtained in patients with high CT-image quality and are restricted to larger advanced plaques in sufficiently large coronary artery segments (at least 2 mm in diameter). Furthermore, lipid-rich and fibrous plaque shows overlapping CT attenuation characteristics and makes accurate subclassification of non-calcified plaques difficult.
OCT PLAQUE CHARACTERIZATION OCT is an invasive imaging modality that can visualize the structure of normal and atherosclerotic coronary arteries with ultrahigh resolution (Figure 9.5)28. Compared to MSCT, OCT allows much more detailed analysis of different coronary structures such as presence of in-stent intimal hyperplasia or advanced plaque characterization (Figure 9.6)29. Indeed, lipidrich plaques have some unique differing OCT features compared to fibrous plaques (Figure 9.7). The high-resolution capacity of OCT offers the potential to detect some of the key features of the vulnerable plaque in vivo: a large lipid pool, a thin fibrous cap and the accumulation of macrophages near the fibrous cap30.
CT PLAQUE IMAGING EBCT and non-enhanced MSCT can accurately detect coronary calcium17,18. Because arterial calcification almost always represents atherosclerosis, detection of coronary artery calcium by means of CT is a reliable, non-invasive tool for determining the presence of coronary atherosclerosis. The amount of coronary calcium is closely correlated with the total atherosclerotic plaque burden and strongly predicts future cardiac events (Figure 9.3)19. Higher amounts of calcium are associated with a higher likelihood of adverse coronary events. At a population level, the coronary calcium score provides incremental value compared with traditional risk factors such as the Framingham Risk
CONCLUSIONS MSCT of the heart is a rapidly developing technology. In a selected patient population, current 64-slice CT scanners allow comprehensive non-invasive coronary imaging, including assessment of luminal stenosis and detection of non-obstructive atherosclerotic plaque. The high negative predictive value makes MSCT most suitable for exclusion of obstructive coronary plaques in patients with a low or intermediate likelihood of coronary artery disease. Beyond the detection of stenosis, MSCT may prove useful to identify high-risk coronary plaque characteristics. The possibility that MSCT
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Figure 9.3 A 51-year-old man presented with an acute coronary syndrome with transient ST-segment depression in the anterior ECG leads. On conventional coronary angiography mild wall irregularities in the proximal left anterior descending coronary artery (LAD) were noted (a). Coronary artery calcifications in the proximal part of the left coronary artery (c) and right coronary artery (d) were detected on non-enhanced MSCT confirming the presence of coronary atherosclerosis. Total Agatston calcification score was 292. The calcified plaque in the LAD was non-obstructive as illustrated on MSCT coronary angiography (white arrow, e). Volume-rendered CT image (white arrow, b) confirms the presence of a calcified plaque in the proximal LAD. CX, circumflex coronary artery
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Figure 9.4 A 40-year-old patient presenting with two episodes of non-ST-segment elevation myocardial infarction in a 1-month time interval. Conventional coronary angiography (a) and non-enhanced MSCT (b) were normal. However, MSCT coronary angiography revealed a large non-calcified plaque without lumen obstruction in the proximal left anterior descending coronary artery (LAD) (c, black arrowheads). (d) Cross-sectional CT image with arrowheads indicating the non-calcified plaque occupying the vessel wall. CX, circumflex coronary artery
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Figure 9.5 Comparison of intravascular OCT with findings on conventional angiography, MSCT and intravascular ultrasound (IVUS). OCT was performed on the left anterior descending coronary artery (LAD) proximal to the first septal branch (S1). Angiographically (a) and on multislice computed tomography coronary angiography (b with inset, c) this part of the vessel has a normal appearance. The cross-sectional IVUS (d) and OCT images (e, f) show a normal appearance of the vessel wall except for some minimal thickening of the intima. CX, circumflex coronary artery
B
B A
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Figure 9.6 Normal appearance of the right coronary artery (RCA) on conventional coronary angiography (CCA) 6 months after implantation of three overlapping stents. Hypoattenuating circular tissue is clearly present on the cross-sectional computed tomography image of the proximal part of the RCA (cross section, B). Neointimal tissue growth within the stent is much better appreciated on intravascular OCT, also in the more distal part of the RCA (A and B). Arrowheads indicate the stent struts. Some minor stent malapposition can be appreciated at the 5 o’clock position (OCT cross-sectional image, A, arrowheads)
can be used as an initial non-invasive technique to establish, in a high-risk individual, the presence and location of a possible high-risk lesion is currently investigated in a prospective study that will correlate MSCT coronary plaque characteristics with the propensity
for future adverse coronary events. Ultimately, noninvasive imaging techniques such as MSCT might help target the use of invasive imaging modalities such as OCT in the search of patients at risk for acute coronary syndromes and sudden cardiac death31,32.
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a
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Figure 9.7 Non-calcified plaque as visualized with MSCT in the proximal part of the left anterior descending coronary artery (LAD) (a and c, arrowheads). The optical coherence tomography (OCT) cross-sectional image (b, arrowheads) with uniform highly reflective tissue is compatible with fibrous plaque. By contrast, lipid-rich plaques are visualized as low reflective structures within the arterial wall. This distinction in plaque appearance cannot be appreciated on MSCT, which uniformly depicts a non-calcified plaque as a structure within the range of 10–100 Hounsfield units. Also, the appearance of the plaque has much less image resolution as compared with OCT (see cross-sectional CT image in panel d, arrowheads)
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10. Virmani R, Kolodgie FD, Burke AP, et al. Lessons from sudden coronary death: a comprehensive morphological classification scheme for atherosclerotic lesions. Arterioscler Thromb Vasc Biol 2000; 20: 1262–75 11. Monroe VS, Parilak LD, Kerensky RA. Angiographic patterns and the natural history of the vulnerable plaque. Prog Cardiovasc Dis 2002; 44: 339–47 12. Achenbach S, Ropers D, Hoffmann U, et al. Assessment of coronary remodeling in stenotic and nonstenotic coronary atherosclerotic lesions by multidetector spiral computed tomography. J Am Coll Cardiol 2004; 43: 842–7 13. Kuettner A, Kopp AF, Schroeder S, et al. Diagnostic accuracy of multidetector computed tomography coronary angiography in patients with angiographically proven coronary artery disease. J Am Coll Cardiol 2004; 43: 831–9 14. Leschka S, Alkadhi H, Plass A, et al. Accuracy of MSCT coronary angiography with 64-slice technology: first experience. Eur Heart J 2005; 26: 1482–7 15. Leber AW, Knez A, von Ziegler F, et al. Quantification of obstructive and nonobstructive coronary lesions by 64-slice computed tomography: a comparative study with quantitative coronary angiography and intravascular ultrasound. J Am Coll Cardiol 2005; 46: 147–54 16. Raff GL, Gallagher MJ, O’Neill WW, Goldstein JA. Diagnostic accuracy of noninvasive coronary angiography using 64-slice spiral computed tomography. J Am Coll Cardiol 2005; 46: 552–7 17. Agatston AS, Janowitz WR, Hildner FJ, et al. Quantification of coronary artery calcium using ultrafast
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19.
20.
21.
22.
23.
24.
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25.
26.
27.
28.
29.
30.
31.
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atherosclerotic plaques: a comparative study with intracoronary ultrasound. J Am Coll Cardiol 2004; 43: 1241–7 Achenbach S, Moselewski F, Ropers D, et al. Detection of calcified and noncalcified coronary atherosclerotic plaque by contrast-enhanced, submillimeter multidetector spiral computed tomography: a segment-based comparison with intravascular ultrasound. Circulation 2004; 109: 14–17 Schroeder S, Kuettner A, Leitritz M, et al. Reliability of differentiating human coronary plaque morphology using contrast-enhanced multislice spiral computed tomography: a comparison with histology. J Comput Assist Tomogr 2004; 28: 449–54 Becker CR, Nikolaou K, Muders M, et al. Ex vivo coronary atherosclerotic plaque characterization with multi-detector-row CT. Eur Radiol 2003; 13: 2094–8 Jang IK, Tearney GJ, MacNeill B, et al. In vivo characterization of coronary atherosclerotic plaque by use of optical coherence tomography. Circulation 2005; 111: 1551–5 Yabushita H, Bouma BE, Houser SL, et al. Characterization of human atherosclerosis by optical coherence tomography. Circulation 2002; 106: 1640–5 Tearney GJ, Yabushita H, Houser SL, et al. Quantification of macrophage content in atherosclerotic plaques by optical coherence tomography. Circulation 2003; 107: 113–19 Naghavi M, Libby P, Falk E, et al. From vulnerable plaque to vulnerable patient: a call for new definitions and risk assessment strategies: Part I. Circulation 2003; 108: 1664–72 Naghavi M, Libby P, Falk E, et al. From vulnerable plaque to vulnerable patient: a call for new definitions and risk assessment strategies: Part II. Circulation 2003; 108: 1772–8
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CHAPTER 10 OCT plaque characterization: comparison to IVUS-VH Gastón A Rodríguez-Granillo, Patrick W Serruys
For decades, angiography has been the gold standard for assessing the morphology and severity of atherosclerotic lesions in the coronary tree. Nevertheless, quantitative angiographic measurements can be deceptive, since this technique allows the assessment of only the shape of the lumen1. In turn, atherosclerosis is a disease of the vessel wall and, due to the compensatory expansive remodeling effect, the lumen area remains unaffected until final stages of the disease2. It has been established that unheralded acute coronary syndromes are common initial manifestations of coronary atherosclerosis and that most such events arise from sites with non-flow-limiting coronary atherosclerosis3,4. Postmortem studies have suggested that plaque composition is a crucial determinant of the propensity of atherosclerotic lesions to rupture. Recently, a study including a large series of victims of sudden cardiac death suggested that ruptured thin-cap fibroatheroma (TCFA) lesions were the precipitating factor of 60% of acute coronary thrombi. Furthermore, 70% of those patients had other TCFAs in their coronary tree that had not ruptured5. A large (avascular, hypocellular, lipid-rich) necrotic core, a thin fibrous cap with inflammatory infiltration and paucity of smooth
Table 10.1
OCT IVUS-VH
muscle cells, and the presence of expansive (positive) remodeling have been identified as the major criteria to define TCFA lesions6,7–10. In vivo detection of TCFA lesions may have an important impact on the prevention of acute myocardial infarction and sudden death. Currently, a variety of intravascular imaging tools is under preclinical or clinical investigation that might be able to detect specific features of TCFA. This chapter focuses on the current status of spectral analysis of the radiofrequency (RF) intravascular ultrasound data (IVUS-VH) and the potential of combining the information with data obtained from optical coherence tomography OCT imaging to enhance the prognostic value of invasive plaque characterization (Table 10.1). Spectral analysis of the radiofrequency intravascular ultrasound data (IVUS-VH) offers the potential to detect a lipid-rich necrotic core in coronary arteries with higher accuracy than standard IVUS, and recent in vitro research has suggested that TCFA might express a typical IVUS-VH pattern. The high resolution of OCT offers the unique potential of directly visualizing the thin fibrous cap that covers the necrotic core (Tables 10.1 and 2).
Technical specifications of OCT and IVUS-VH Axial resolution,
Penetration depth
Catheter diameter
Guiding Catheter size
10 µm 100–150 µm
2 mm 10 mm
0.019 inch 2.9F
7F 5F
Requires flushing/vessel occlusion during imaging Yes No
OCT, optical coherence tomography; IVUS-VH, spectral analysis of intravascular ultrasound radiofrequency data; F, French Charrière
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Table 10.2
OCT IVUS-VH
Ability to detect the major criteria of vulnerability (from reference 6) Thickness of the cap
Quantification of necrotic core
Macrophages
Remodeling
+++ +
NA +++
++ NA
+ +++
OCT, optical coherence tomography; IVUS-VH, spectral analysis of intravascular ultrasound radiofrequency data; NA, not available
Gray-scale IVUS: Amplitude (envelope) detection
IVUS-VH uses power and frequency
Radiofrequency data
Different tissues: same amplitude but different frequency
Figure 10.1 IVUS gray-scale imaging is formed by the envelope (amplitude) of the radiofrequency (RF) signal, discarding a considerable amount of information lying beneath and between the peaks of the RF signal. The frequency of a tissue may differ despite having the same amplitude
TECHNICAL ASPECTS IVUS-VH (virtual histology) Intravascular ultrasound (IVUS) is the gold standard for in vivo evaluation of coronary plaque, lumen and vessel dimensions by providing accurate, reproducible, real-time, tomographic information on the vessel wall11–13. However, although visual interpretation of gray-scale IVUS can identify calcification within plaques, it cannot reliably differentiate lipid-rich from fibrous plaque12. IVUS gray-scale imaging is formed by the envelope (amplitude) of the RF signal, discarding a considerable amount of information lying beneath and between the peaks of the RF signal. The amplitude of the RF data might sometimes be similar between different tissues, leading to misinterpretation of gray-scale imaging. Nevertheless, the frequency and power of the RF signal commonly differs between tissues, regardless of eventual similarities on the amplitude (Figure 10.1). Spectral analysis of the RF data (IVUS-VH, Volcano, Rancho Cordova, CA) evaluates different spectral parameters of the RF data (including Y-intercept, minimum power, maximum power, mid-band power, frequency at minimum power, frequency at maximum power, slope) to construct tissue maps that classify plaque into four major components. In preliminary in vitro
studies, four histological plaque components (fibrous, fibrolipidic, necrotic core and calcium) were correlated with a specific spectrum of the radiofrequency signal 14. These different plaque components were assigned color codes. Calcified, fibrous, fibrolipidic and necrotic core regions were labeled white, green, greenish-yellow and red, respectively (Figure 10.2). This approach has led to a significant increase in the sensitivity and specificity of IVUS to characterize plaque, particularly of lipid deposits. The sensitivity of gray-scale IVUS to detect lipid deposits was reported as low as 46%, whereas the predictive accuracy of IVUS-VH to detect necrotic core can be as high as 86%14,15. Recent improvements in the classification tree have led to a further enhancement in the accuracy of the technique. A sensitivity and specificity higher than 90% for detecting necrotic core has been demonstrated using atherectomy samples16. IVUS-VH has an axial, spatial and longitudinal resolution of 150, 240 and 300 µm, respectively.
Clinical IVUS-VH data acquisition IVUS-VH data in coronary arteries is currently acquired with a commercially available phasedarray (64 elements) catheter (Eagle EyeTM 20-MHz catheter, Volcano). The IVUS catheter is positioned in standard rapid-exchange technique distally into
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Figure 10.2 Left, an IVUS cross-sectional area reconstructed from backscattered signals. Right, the corresponding tissue map depicting where the different plaque components are assigned color codes. Calcified, fibrous, fibrolipidic and necrotic core regions are labeled white, green, greenish-yellow and red, respectively
the coronary artery and then withdrawn at a continuous speed of 0.5 mm/s until the ostium is reached, using an automated pullback device. Angiographic cine runs, before and during contrast injection, are performed to define the position of the imaging catheter within the coronary artery before the pullback is started. IVUS-VH acquisition is electrocardiogram (ECG)-gated at the R-tops using a dedicated console (Volcano).
IVUS-VH analysis IVUS B-mode images are reconstructed from the RF data by customized software. Contour detection is performed using cross-sectional gray-scale views with semi-automatic contour detection software to provide a quantitative geometrical and compositional output (IvusLab 4.4, Volcano). Owing to the unreliability of manual calibration17, the RF data are normalized using a technique known as ‘blind deconvolution’, an iterative algorithm that deconvolves the catheter transfer function from the backscatter, thus accounting for catheter-to-catheter variability18.
OPTICAL COHERENCE TOMOGRAPHY Plaque characterization and vulnerable plaque detection with OCT and IVUS-VH OCT can qualitatively discriminate between different plaque types (fibrous, fibrocalcific and lipidrich) with a sensitivity and specificity ranging between 71 and 98% 19. In their study, Yabushita et al.20 found that histologically confirmed fibrous
plaques exhibited homogeneous, highly backscattering (signal-rich) qualities devoid of OCT signal-poor regions. Fibrocalcific plaques were signal-poor with sharply delineated upper and/or lower borders (lipidrich plaques revealed diffusely bordered, signal-poor regions). Clinical examples for different plaque types by OCT are illustrated in Figure 10.3. Although OCT has demonstrated a high accuracy in characterizing coronary plaques, imaging of the entire external elastic membrane is rarely achieved, owing to the shallow penetration of the technique (2 mm), precluding the quantitative analysis of plaque burden. Since the risk of rupture and subsequent thrombosis is strongly related to the relative lipid content within an atherosclerotic plaque, this might represent a limitation of OCT for the imaging of vulnerable plaque7. The capacity of IVUS-VH for identifying different tissue components has been validated ex vivo. The predictive accuracy was 79.7%, 81.2%, 92.8% and 85.5% for detecting fibrous tissue (areas of densely packed collagen), fibrolipidic tissue (areas with significant lipid interspersed in collagen), calcified tissue (areas with dense calcium deposits without adjacent necrosis) and necrotic core tissue (areas comprising cholesterol clefts, foam cells and microcalcifications), respectively. There were significant differences in plaque composition of patients who presented with acute coronary syndromes and those who presented with stable angina. In the former group, the per cent necrotic core was significantly greater than in stable patients, whereas an opposite trend was observed for fibrotic content. In addition, we found a significant relationship between the necrotic core percentage and vessel area obstruction, suggesting that the necrotic core increases linearly with further increase in the
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A
B
C
m C l
i
Figure 10.3 Examples of OCT cross-sections of fibrous concentric intimal thickening exhibiting a homogeneous, highly backscattering (signal-rich) intima (i) devoid of OCT signal-poor regions (A); a calcified (B) plaque (b, signal-poor with sharply delineated borders) with overlying fibrous cap; and a lipid-rich (l) plaque depicting diffusely bordered, signal-poor regions (C). m, media
degree of stenosis. Finally, we found a significant, albeit weak, relationship between relative necrotic core content and C-reactive protein (CRP) levels21.
Thin fibrous cap detection Definition of thin cap It is worth mentioning that, although the most accepted threshold for defining a cap as ‘thin’ has previously been set at < 65 µm, this was based on postmortem studies22. Extrapolation of such criteria to in vivo studies requires caution. It is well established that tissue shrinkage occurs during tissue fixation23. Shrinkage (particularly of collagen tissue, the main component of fibrous caps) of up to 60%, 15% and 80% can occur during critical-point drying, freeze drying and air drying, respectively23,24. Furthermore, postmortem contraction of arteries is an additional confounding factor25. It is likely, therefore, that the threshold used to define a thin cap in vivo should be higher than 65 µm. Since the axial resolution of IVUS-VH is 100–150 µm, we assumed that the absence of visible fibrous tissue overlying a necrotic core suggested a cap thickness of below 100–150 µm and used the absence of such tissue to define a thin fibrous cap26. Finally, it is noteworthy that a number of important ex vivo studies have used a higher (> 200 µm) threshold9,27,28. Indeed, one of these studies identified a mean cap thickness of 260 µm and 360 µm for ‘vulnerable’ and ‘non-vulnerable’ plaques, respectively28 (Table 10.2). As a result of its extremely high axial resolution (10 µm), there is no doubt that OCT is the in vivo gold standard for identifying and measuring the thickness of the fibrous cap. In their study, Jang et al.29 identified a significant difference in minimal cap thickness between acute myocardial infarction
(AMI) and stable angina patients, with median (interquartile range) values of 47.0 (25.3–184.3) µm and 102.6 (22.0–291.1) µm in AMI and stable angina patients, respectively (p = 0.02). We recently evaluated the incidence of IVUSderived thin-cap fibroatheroma (IDTCFA) in coronary artery segments with non-significant lesions on angiography using IVUS-VH30. In this study, two experienced, independent IVUS analysts defined IDTCFA as a lesion fulfilling the following criteria in at least three consecutive cross-sectional areas: (1) necrotic core ≥ 10% without evident overlying fibrous tissue; (2) per cent obstruction of ≥ 40%. In this study, 62% of patients had at least one IDTCFA in the interrogated vessels. Patients with acute coronary syndromes had a significantly higher incidence of IDTCFA than stable patients (3.0 (interquartile range 0.0, 5.0) IDTCFA/coronary vs. 1.0 (interquartile range 0.0, 2.8) IDTCFA/coronary, p = 0.018). Of note, no relation was found between patient’s characteristics and the presence of IDTCFA. Finally, a clear clustering pattern was seen along the coronaries, with 66 (66.7%) IDTCFA located in the first 20 mm whereas further along the vessels the incidence was significantly lower (33, 33.3%, p = 0.008)30. Such distribution of the IDTCFA in the coronaries was in line with previous ex vivo and clinical studies, with a clear clustering pattern from the ostium, thus supporting the non-uniform distribution of vulnerable plaques along the coronary tree31,32. The significantly higher prevalence of IDTCFA in non-culprit coronaries of patients presenting with an acute coronary syndrome supports the theory that holds acute coronary syndromes as multifocal processes. Of note, the mean positive atheroma volume and the mean necrotic core areas of the IDTCFAs detected by IVUS-VH were also similar to previously reported histopathological data (55.9% vs. 59.6% and 19% vs. 23%, respectively)33.
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Figure 10.4 Matching of OCT and IVUS-VH is feasible using side-branches as landmarks and longitudinal and crosssectional views
Positive remodeling detection Expansive remodeling of coronary vessels was originally deemed a beneficial compensatory effect that counterbalanced the axial progressive growth of the vessel wall to preserve the lumen dimensions2. However, several studies have shown increased levels of inflammatory markers, larger necrotic cores and pronounced medial thinning in positive remodeled vessels – all factors related to the tendency of plaques to undergo rupture34–37. Overall, this has led the experts to confer positive remodeling a major importance in the vulnerability triad6. Precise contour detection of the external elastic membrane (vessel area) is pivotal for estimation of the presence and pattern of remodeling. Owing to the high penetration of 20-MHz catheters, IVUSVH can accurately assess vessel size and, therefore, provided that plaques are not heavily calcified, estimate the degree and type of remodeling. This was recently demonstrated in vivo, where we found a significant positive relationship between relative necrotic core content and the remodeling index (r = 0.83, p < 0.0001). Moreover, fibrous tissue was inversely correlated with the remodeling index (r = –0.45, p = 0.003, Figure 10.3)37. Likewise, lesions with positive remodeling presented significantly larger necrotic core percentages than lesions with no remodeling or negative remodeling (22.1 ± 6.3 vs. 15.1 ± 7.6 vs. 6.6 ± 6.9%, p < 0.0001). Conversely, negative remodeling lesions
tend to show larger fibrous tissue percentages than lesions with no remodeling and positive remodeling (68.6 ± 13.7 vs. 62.9 ± 9.5 vs. 58.1 ± 12.9%, p = 0.13). In contrast, and as aforementioned, OCT imaging of the entire media– adventitia interface is rarely achieved, precluding the use of OCT to assess the remodeling pattern of coronaries.
Combining OCT and IVUS-VH It is clear that the finest tool to assess the presence of the major criteria that define TCFA would be a tool that combines the optimal axial resolution of OCT with the accurate plaque characterization and deep penetration of IVUS-VH. Unfortunately, such a tool has not yet been developed. Instead, intensive efforts are being made to overcome the weaknesses of both techniques. In the meantime, we are currently assessing in vivo the agreement between both techniques to characterize plaques. Using side-branches as landmarks and with the aid of longitudinal and cross-sectional views, matching of cross sections is feasible (Figure 10.4). Our preliminary experience shows a high agreement between techniques towards the detection of fibrous, fibrocalcific and necrotic core regions (Figures 10.5, 10.6 and 10.7). Both OCT and IVUS-VH have demonstrated the ability in vivo to identify surrogates of TCFA. Nevertheless, prospective studies are needed in order to evaluate the prognostic value of such findings in natural history studies.
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Figure 10.5 Matched imaging immediately distal to a stent, showing an eccentric fibrotic plaque with both techniques, optical coherence tomography and radiofrequency intravascular ultrasound
Figure 10.6 Matched imaging distal to a sidebranch (*) showing fibrotic tissue at the 12 o’clock and 7 o’clock positions, whereas a heterogeneous, signal-poor region is located at the 9 o’clock position and correlated well with a necrotic-core-rich region with radiofrequency intravascular ultrasound
Figure 10.7 From left to right: IVUS cross-sectional area reconstructed from backscattered signals, showing a small calcified region at the 6 o’clock position. IVUS-VH shows a necrotic core-rich tissue with underlying calcified tissue. A lipid-rich region with underlying calcified tissue can be appreciated with OCT imaging
REFERENCES 1. Topol EJ, Nissen SE. Our preoccupation with coronary luminology. The dissociation between clinical and angiographic findings in ischemic heart disease. Circulation 1995; 92: 2333–42 2. Glagov S, Weisenberg E, Zarins CK, et al. Compensatory enlargement of human atherosclerotic coronary arteries. N Engl J Med 1987; 316: 1371–5
3. Ambrose JA, Tannenbaum MA, Alexopoulos D, et al. Angiographic progression of coronary artery disease and the development of myocardial infarction. J Am Coll Cardiol 1988; 12: 56–62 4. Little WC, Constantinescu M, Applegate RJ, et al. Can coronary angiography predict the site of a subsequent myocardial infarction in patients with mild-tomoderate coronary artery disease? Circulation 1988; 78: 1157–66
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5. Farb A, Burke AP, Tang AL, et al. Coronary plaque erosion without rupture into a lipid core. A frequent cause of coronary thrombosis in sudden coronary death. Circulation 1996; 93: 1354–63 6. Schaar JA, Muller JE, Falk E, et al. Terminology for high-risk and vulnerable coronary artery plaques. Report of a Meeting on the Vulnerable Plaque, 17–18 June 2003, Santorini, Greece. Eur Heart J 2004; 25: 1077–82 7. Davies MJ, Richardson PD, Woolf N, et al. Risk of thrombosis in human atherosclerotic plaques: role of extracellular lipid, macrophage, and smooth muscle cell content. Br Heart J 1993; 69: 377–81 8. Gertz SD, Roberts WC. Hemodynamic shear force in rupture of coronary arterial atherosclerotic plaques. Am J Cardiol 1990; 66: 1368–72 9. Felton CV, Crook D, Davies MJ, Oliver MF. Relation of plaque lipid composition and morphology to the stability of human aortic plaques. Arterioscler Thromb Vasc Biol 1997; 17: 1337–45 10. Virmani R, Kolodgie FD, Burke AP, et al. Lessons from sudden coronary death: a comprehensive morphological classification scheme for atherosclerotic lesions. Arterioscler Thromb Vasc Biol 2000; 20: 1262–75 11. Tobis JM, Mallery JA, Gessert J, et al. Intravascular ultrasound cross-sectional arterial imaging before and after balloon angioplasty in vitro. Circulation 1989; 80: 873–82 12. Potkin BN, Bartorelli AL, Gessert JM, et al. Coronary artery imaging with intravascular high-frequency ultrasound. Circulation 1990; 81: 1575–85 13. Nishimura RA, Edwards WD, Warnes CA, et al. Intravascular ultrasound imaging: in vitro validation and pathologic correlation. J Am Coll Cardiol 1990; 16: 145–54 14. Nair A, Kuban BD, Tuzcu EM, et al. Coronary plaque classification with intravascular ultrasound radiofrequency data analysis. Circulation 2002; 106: 2200–6 15. Peters RJ, Kok WE, Havenith MG, et al. Histopathologic validation of intracoronary ultrasound imaging. J Am Soc Echocardiogr 1994; 7: 230–41 16. Nasu K, Katoh O, Margolis P, et al. Correlation of In Vivo Intravascular Ultrasound Radiofrequency Data Analysis with In Vitro Histopathology in Human Coronary Atherosclerotic Plaques (VH-DCA Japan trial). Stockholm: European Society of Cardiology, 2005 17. Rodriguez-Granillo GA, Aoki J, Ong AT, et al. Methodological considerations and approach to cross-technique comparisons using in vivo coronary plaque characterization based on intravascular ultrasound radiofrequency data analysis: insights from the Integrated Biomarker and Imaging Study (IBIS). Int J Cardiovasc Intervent 2005; 7: 52–8 18. Kåresen K. Deconvolution of sparse spike trains by iterated window maximization. IEEE Trans Signal Process 1997; 45: 1173–83 19. Regar E, Rodriguez-Granillo G, Bruining N, et al. Intracoronary optical coherence tomography (OCT) – a novel approach for three-dimensional quantitative analysis. Z Kardiol 2005; 94 (1 Suppl): 402 20. Yabushita H, Bouma BE, Houser SL, et al. Characterization of human atherosclerosis by optical coherence tomography. Circulation 2002; 106: 1640–5
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21. Rodriguez-Granillo GA, McFadden E, Valgimigli M, et al. Coronary plaque composition of non-culprit lesions assessed by in vivo intracoronary ultrasound radio frequency data analysis, is related to clinical presentation. Am Heart J 2006; 151: 1027–31 22. Burke AP, Farb A, Malcom GT, et al. Coronary risk factors and plaque morphology in men with coronary disease who died suddenly. N Engl J Med 1997; 336: 1276–82 23. Lee RM. A critical appraise of the effects of fixation, dehydration and embedding of cell volume. In: Revel JP, Barnard T, Haggis GH, eds. The Science of Biological Specimen Preparation for Microscopy and Microanalysis. Scanning Electron Microscopy, AMF O’Hare, Chicago, IL, 1984: 61–70 24. Boyde A, Jones SJ, Tamarin A. Dimensional changes during specimen preparation for scanning electron microscopy. Scan Electron Microsc 1977; I: 507–18 25. Fishbein MC, Siegel RJ. How big are coronary atherosclerotic plaques that rupture? Circulation 1996; 94: 2662–6 26. Nair A, Vince DG. Regularized autoregressive analysis of intravascular ultrasound data: improvement in spatial accuracy of plaque tissue maps. IEEE Trans Ultrason Ferroelect Frequency Control 2004; 51: 420–31 27. Mann JM, Davies MJ. Vulnerable plaque. Relation of characteristics to degree of stenosis in human coronary arteries. Circulation 1996; 94: 928–31 28. Schaar JA, De Korte CL, Mastik F, et al. Characterizing vulnerable plaque features with intravascular elastography. Circulation 2003; 108: 2636–41 29. Jang IK, Tearney GJ, MacNeill B, et al. In vivo characterization of coronary atherosclerotic plaque by use of optical coherence tomography. Circulation 2005; 111: 1551–5 30. Rodriguez-Granillo GA, Garcia-Garcia HM, McFadden E, et al. In vivo intravascular ultrasound-derived thin-cap fibroatheroma detection using ultrasound radiofrequency data analysis. J Am Coll Cardiol 2005; 46: 2038–42 31. Kolodgie FD, Burke AP, Farb A, et al. The thin-cap fibroatheroma: a type of vulnerable plaque: the major precursor lesion to acute coronary syndromes. Curr Opin Cardiol 2001; 16: 285–92 32. Wang JC, Normand SL, Mauri L, Kuntz RE. Coronary artery spatial distribution of acute myocardial infarction occlusions. Circulation 2004; 110: 278–84 33. Virmani R, Burke AP, Kolodgie FD, Farb A. Vulnerable plaque: the pathology of unstable coronary lesions. J Interv Cardiol 2002; 15: 439–46 34. Pasterkamp G, Schoneveld AH, van der Wal AC, et al. Relation of arterial geometry to luminal narrowing and histologic markers for plaque vulnerability: the remodeling paradox. J Am Coll Cardiol 1998; 32: 655–62 35. Varnava AM, Mills PG, Davies MJ. Relationship between coronary artery remodeling and plaque vulnerability. Circulation 2002; 105: 939–43 36. Burke AP, Kolodgie FD, Farb A, et al. Morphological predictors of arterial remodeling in coronary atherosclerosis. Circulation 2002; 105: 297–303 37. Rodriguez-Granillo GA, Serruys PW, Garcia-Garcia HM, et al. Coronary artery remodelling is related to plaque composition. Heart 2006; 92: 388–91
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CHAPTER 11 How to match different imaging technologies Nico Bruining, Ronald Hamers
INTRODUCTION
progression–regression, testing new pharmaceutical or interventional treatments. Furthermore, none of them is currently capable of identifying all characteristic features of the so-called vulnerable plaque. A possibility of simultaneously displaying image information of different modalities for an individual coronary segment onto one single computer screen could help to integrate multimodal complex information on coronary plaques. This chapter is focused on a combined visualization of ICUS and OCT, although every other coronary imaging technique could be added in a similar way.
Since the introduction of coronary angiography, numerous other coronary imaging techniques have been developed or are under development. During the past decade intravascular (IVUS) or intracoronary ultrasound (ICUS) has become a successful additional and widely accepted imaging tool to visualize coronary plaques in a way that is not possible with angiography. ICUS has the capability to penetrate the plaque and to show the tissue within. This has made ICUS the standard tool for trials of progression or regression of atherosclerosis or to evaluate neointima hyperplasia (NIH) after coronary stenting, with either bare or drug-eluting stents. However, ICUS has its limitations too: for instance, not the whole coronary tree can be imaged, it has limited image resolution (axial and spatial), it cannot penetrate severe calcified plaque regions and it shows plaque components as shades of gray-values, which cannot easily be translated 1-to-1 to separate tissue components as identified by histopathology. Therefore, the search for additional coronary plaque imaging techniques is still ongoing. Currently, a variety of invasive imaging techniques are being used clinically or for research purposes, e.g. ICUS, available at 20-, 30- and 40-MHz frequencies (the higher the frequency the better the image resolution; 50-MHz catheters are under development); optical coherence tomography (OCT); intravascular magnetic resonance imaging (MRI); and angioscopy. Of course a non-invasive diagnostic imaging tool would be the ultimate goal for coronary imaging. Multislice computed tomography (MSCT) is making rapid progress and could become one of the standard imaging tools in the near future. All these different coronary imaging modalities have advantages and disadvantages and none of them is capable by itself of answering all the research and clinical questions at the moment, especially to followup patients participating in trials of natural history or
HANDLING OF THE IMAGING DATA For later image inspection, synchronization of the images derived from the different imaging techniques, with respect to the location within the coronary vessel, is mandatory. To be able to do this it would be very helpful if the images were acquired and processed in a similar fashion at the catheterization laboratory (cathlab). Of course, this is only of importance for more or less similarly acquired image data from intravascular imaging techniques.
Image acquisition ICUS imaging is a routine cathlab procedure that has been well documented in the past1,2. Briefly, after introduction of the ICUS catheter in the coronary artery, a pullback device pulls the catheter back at a constant speed (typically 0.5 or 1.0 mm/s). Images are being generated at 25 frames/s and stored, on videotape, on CD-, or on DVD-ROM. Networking capabilities built into the ultrasound consoles can also be found on some newer-generation ultrasound consoles. This allows direct upload of the images after the examination onto a picture archiving system. These constant-speed pullback procedures have one problem, which is caused by the motion of 103
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Figure 11.1 Left, a single OCT cross section; right, two perpendicular reconstructed longitudinal planes of an OCT pullback. The two thin lines in the cross section indicate the cut-planes of the longitudinal views at the right side. In the longitudinal planes it can be observed that the vessel wall appears to have a saw-tooth shape. The motion of the catheter during the pullback causes this. It is obvious that if this motion-induced artifact could be overcome, qualitative and quantitative analysis would be much easier
the heart during the cardiac cycle, and that is that during the pullback the catheter not only is pulled by the slow constant speed but is also rapidly moving around in the coronary in all directions, causing artifacts which hamper later qualitative and quantitative analysis 3,4. Figure 11.1 illustrates this problem with OCT. Motion-induced artifacts can be avoided either by electrocardiogram (ECG)-gated image acquisition in the cathlab3, or by off-line processing with retrospective gating. For ICUS this retrospective gating is possible with our Intelligate® software, developed in-house, without needing additional ECG recordings at the cathlab during the procedure3,4. Figure 11.2 illustrates an in vivo ICUS pullback within a coronary segment being performed without ECG gating, and with ECG gating at the time of image acquisition in the cathlab and with retrospective gating. The image acquisition procedure with a commercially available intravascular OCT system (LightLab Imaging, Boston, MA) is similar to an ICUS procedure. Here too, a pullback procedure can be performed to investigate a coronary segment of interest. Hence, OCT also suffers from the cardiac motion. Current clinically available OCT systems do not allow for ECG-gated pullback. An image-based retrospective gating technique could possibly overcome these motion-induced visualization artifacts. This should be investigated. However, to obtain good gating results and a high likelihood of being able to select images created near the end-diastolic phase, a frame-rate of a minimum of 25 frames/s, or preferably even higher, is mandatory (this results in 40 ms intervals between individual frames). The current OCT frame rate of ≤ 15 frames/s is just borderline of what could be used for an image-based gating technique4.
Image storage and processing As described above, ICUS images are still mostly stored on videotape, and those need to be digitized
first before they can be used for computerized viewing and further quantitative analysis. Dedicated software for this purpose is widely commercially available. However, the newer-generation ultrasound consoles are completely digital, as are their image storage. Digital image transfer overcomes the timeconsuming nature and quality degeneration of the digital– analog conversion process at the ultrasound console and the analog–digital conversion process at the analysis station. It is, however, mandatory that the digitized ICUS images are stored in the standardized medical image format DICOM5. For ICUS there is a chapter created in the DICOM standard, which describes how the images can be stored unambiguously and could thus be interpreted with any kind of third-party software. The current available intravascular OCT system (LightLab Imaging, Boston, MA) does, unfortunately, not have a description in the DICOM format. The images are digitally stored on the OCT console in the AVI format, which is mostly used for exchange of movies on the Internet. It is highly recommended that the OCT images be stored in the standardized open DICOM format so that every researcher can use the images for viewing and further quantitative analysis. At the Thoraxcenter we developed a software package that translates the OCT AVI files to a DICOM image file. This allows the OCT files to be read by the same software that has been developed for quantitative ICUS analysis (CURAD BV, Wijk bij Duurstede, The Netherlands).
Why combined image analysis? As described also in other chapters in this book, OCT has a unique high lateral and axial image resolution. These high image resolution capabilities of OCT could be helpful in identifying thin-cap fibrous atheroma (TCFA). However, the penetration depth of OCT is lower than that of ICUS. In consequence, OCT cannot completely visualize large plaque areas. ICUS,
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a
b
c
Figure 11.2 In vivo ICUS pullback within a coronary segment being performed without electrocardiogram (ECG) gating (a), with ECG gating at the time of image acquisition in the cathlab (b) and with retrospective ECG-gating (c). The yellow lines indicate the distal and the proximal ends of a stent. *, side-branch
on the other hand, has a lower resolution making it difficult to visualize TCFA, but a far greater penetration depth and is able, in most cases, to show the plaque up to the external elastic membrane, as far as it is not heavily calcified. The combination of these two imaging modalities could reveal much more detail about the coronary plaque than they are able to show individually. In vitro research of explanted human coronary arteries illustrates the increased value of multimodality imaging (Figure 11.3). This approach is of interest for the validation of newly developed imaging techniques, whether they are invasive or non-invasive. Validation with respect to the gold standard histopathology requires exact identification of the same cross-sectional slice of the coronary artery. Figure 11.4 illustrates the possibility of retrieving cross-sectional images derived from synchronized imaging data sets acquired with OCT, ICUS and coronary multislice
computer tomography angiogram (MSCT-A) of an explanted human coronary artery.
One platform for analysis The approach we took at the Thoraxcenter was to bring the different imaging modalities together into one single viewing and quantitative analysis environment (CURAD). Figure 11.5 illustrates that, if both OCT and ICUS are three-dimensionally reconstructed and visualized in longitudinal views (L-views)1, it becomes possible easily to identify corresponding regions of interest in both modalities by using landmarks such as side-branches or implanted devices (e.g. stents). The quantitative software can be used to check whether the target regions selected in both OCT and ICUS have similar lengths. However, these lengths will not be exactly the same, due to a probably different path followed by the catheters
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a
b
d
c
Figure 11.3 (a) A reconstructed longitudinal OCT pullback of an explanted human coronary LAD imaged in a saline bath. At the right side of the pullback (indicated by the thin yellow line), also on the left side, a sheath is visible. The asterisk indicates a side-branch. This side-branch is also visible in the photograph of the specimen (d). (b) The same specimen but now imaged with 40 MHz ICUS. (c) MSCT-A reconstruction. In the MSCT-A reconstruction, the sheaths are more prominently visible. By using the sheaths as geometrical landmarks (e.g. in vivo you can use side-branches or an implanted device), the image data sets can be synchronized. The software automatically takes into account the differences in longitudinal resolution, when going through the data manually for closer inspection of the individual cross sections
a
b
c
d
Figure 11.4 (a) A histopathological image stained with tri-chrome Masson, where white areas are calcified and/or calcified/ necrotic areas, purple are smooth muscle cells and inner green are collagen and outer green are the adventitia. (b) The same cross section imaged with MSCT-A. (c) The OCT-derived image; (d) the ICUS cross section. It can be appreciated that the lumen morphology appears similar on both histopathology and OCT (a and c). It can also be observed that ICUS representation of the lumen morphology is slightly different from both histopathology and OCT. This is caused by the size of the catheter, which is pushing open the lumen in this case (e.g. deforming it slightly) and the lower resolution of ICUS, which shows a ‘block’ of 20–30 histology slices. For research aiming at plaque compositional imaging it is key that this image synchronization is without any errors
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b a
d c
Figure 11.5 Both the pullback of an implanted stent made with OCT (a and b) and ICUS (c and d) in vivo. The thick dotted lines in the longitudinal views indicate the same cross sections in both the OCT (a) and ICUS (b). The thin lines, left and right of the thick dotted line, in both b and d, indicate the beginning and end of the stented area. The ICUS data set has been retrospectively gated, which causes the smooth appearance of the vessel wall. It would be very helpful if this could also be achieved in the future for OCT. However, even without this gating, it is possible to synchronize both data sets; that is, one of the data sets is master and the other is slave. In this way it is possible to view similar locations easily in both data sets, as is highlighted in a and c, where the same cross section of the coronary vessel can be appreciated. The asterisks in d, the ICUS longitudinal view, indicate acoustic shadowed areas caused by calcification. This is a limitation of ICUS, which is not encountered in other techniques such as OCT
through the curved coronary artery and the current non- ECG-gated status of OCT data sets. The dedicated software we use is capable of synchronizing the data sets. Corresponding individual single cross sections can be observed in both data sets, simultaneously taking into account possible differences in distances between the individual images in both data sets. Figure 11.6 shows an in vivo example of image matching between images derived within one patient of, in this case, a non-invasive coronary MSCT-A and an ICUS examination. The possibility of showing both modalities synchronized onto one single computer screen creates the optimum environment to evaluate both modalities. ICUS is an excellent technique to image coronary lumen and plaque, but is limited in calcified regions. Coronary MSCT-A must be evaluated for its lumen and plaque quantitative capabilities, but is without question extremely good at showing calcifications which can be observed in Figure 11.6 (a).
FUTURE WORK As has been described, this multimodality imaging approach could also be used for every other emerging imaging modality. For OCT it will become important, as can been observed in Figures 11.1 and 11.5, that gating becomes possible. An ECG-gated pullback approach in the cathlab will probably be difficult, since this prolongs the examination time. Since the coronary must be flushed with saline, this will cause discomfort for the patient. An image-based gating approach such as Intelligate4 successfully applied to ICUS could perhaps solve this problem. However, for this, the image generation speed of OCT must be increased to, if possible, 25 frames/s. This is necessary to generate, and thus select images, close to the end-diastolic phase.
SUMMARY Numerous imaging techniques are being used or are under development to image coronary plaques and to
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CA a
SB
SB
SB
b
SB
CA
Figure 11.6 Another example of in vivo acquired data (a), a reconstructed MSCT-A (b) and an ICUS-derived view of the same coronary segment. The two side-branches (SB), as indicated in the bottom half of (a), define the segment, which can be analyzed in both modalities. In (b), in both the bottom and the top halves of the reconstructed longitudinal view, these two side-branches are also indicated. The calcium present in this coronary segment is clearly visible as bright white spots in the MSCT-A views. In the ICUS views (b), these calcified areas can be identified by the so-called acoustic shadowing (there is no ultrasound signal received from this area). CA, calcification
investigate the behavior of these plaques over time. In addition, these are used to identify those which could be possibly vulnerable. However, there still is not one individual imaging technique that can deliver all the answers to the current research questions. At the moment a combination of imaging techniques, e.g. multimodality imaging, could boost this research. It is possible to present synchronized images of a coronary segment under investigation acquired with different imaging modalities, whether invasive or non-invasive. However, this is currently only possible by dedicated developed software. Further standardization of image acquisition, processing and storage could improve the multimodality approach.
2.
3.
4.
REFERENCES 5. 1. American College of Cardiology Clinical Expert Consensus Document on Standards for Acquisition,
Measurement and Reporting of Intravascular Ultrasound Studies (ICUS). A report of the American College of Cardiology Task Force on Clinical Expert Consensus Documents developed in collaboration with the European Society of Cardiology endorsed by the Society of Cardiac Angiography and Interventions. Eur J Echocardiogr 2001; 2: 299–313 Fitzgerald PJ, St. Goar FG, Connolly AJ, et al. Intravascular ultrasound imaging of coronary arteries. Is three layers the norm? Circulation 1992; 86: 154–8 Bruining N, von Birgelen C, de Feyter PJ, et al. ECGgated versus nongated three-dimensional intracoronary ultrasound analysis: implications for volumetric measurements. Cathet Cardiovasc Diagn 1998; 43: 254–60 De Winter SA, Hamers R, Degertekin M, et al. Retrospective image-based gating of intracoronary ultrasound images for improved quantitative analysis: the intelligate method. Cathet Cardiovasc Interv 2004; 61: 84–94 Parisot C. The DICOM standard. A breakthrough for digital information exchange in cardiology. Int J Card Imaging 1995; 11 (Suppl 3): 171–7
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CHAPTER 12 Vulnerable plaque: the present and the future John A Ambrose, Ralph J Wessel
INTRODUCTION
that the plaque is a definitive precursor to an acute clinical event.
The concept of the vulnerable plaque is based on the premise that there are certain plaques prone to coronary thrombosis leading to acute coronary events. Autopsy studies in patients dying after myocardial infarction or with sudden coronary death identified those plaques responsible. As thrombosis had been established as the cause of most myocardial infarctions and a majority of episodes of acute coronary syndromes (ACS) and sudden coronary death, it was natural to extend these data to search for susceptible (or vulnerable) plaque presumably responsible for an acute coronary event, but before the event occurred. A vulnerable plaque has simply been defined as that plaque prone to thrombosis and rapid progression. Subsequently, the term ‘high-risk’ plaque was used interchangeably with ‘vulnerable’ plaque and both are currently considered acceptable terms1. As an analogy that has been frequently utilized, the vulnerable plaque is similar to a quiescent volcano. It has the potential to erupt (based on geological principles of instability) and, once it does so, it is not unlike the thrombosed or unstable plaque that has caused an acute clinical event such as myocardial infarction or sudden death by formation of an intraluminal thrombus and rapidly occluding the lumen of the coronary artery. It is important, however, in any discussion of vulnerable plaque to understand that there are limitations to this concept, given the lack of prospective data to identify such plaque. We presume that the characteristics of a vulnerable plaque are similar to the characteristics of plaque that has thrombosed and already caused the clinical event. Thus, in discussing such plaques, it is presently preferable to presume that the plaque is ‘vulnerable’ as opposed to assuming with certainty
ADDITIONAL TERMINOLOGY The vulnerable patient Patients who are prone to coronary thrombosis and/or sudden coronary death are often termed vulnerable patients2. This may be related to several factors including an increased atherosclerotic burden, a pro-inflammatory or pro-coagulant state (thrombogenic blood), the presence of multiple presumed vulnerable plaques or the presence of a vulnerable myocardium prone to the development of a malignant arrhythmia.
Atherothrombosis In the pathogeneses of vulnerable plaque, the term atherothrombosis is often used to describe the underlying vascular process of atherosclerosis superimposed on thrombosis leading to adverse clinical events3. This term also includes the vascular processes underlying stroke and acute limb ischemia, as well as an acute coronary event.
Thrombosed or unstable plaque This is the term given to that plaque that has disrupted or eroded leading to intraluminal coronary thrombosis. We now recognize that not all thrombosed plaques lead to acute clinical events. A plaque may form a mural thrombus, and yet the patient remains asymptomatic. This thrombus may be incorporated into the vessel wall and lead to atherosclerotic progression. In other cases, the thrombosed plaque narrows the lumen enough to lead to a stable anginal syndrome4.
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VULNERABLE PLAQUE: THE PRESENT Features of the vulnerable plaque Pathology The only reliable evidence concerning plaques that cause coronary thrombosis comes from autopsy studies. As mentioned previously, these studies were not prospective but represented retrospective analysis of plaques that had already ruptured and thrombosed. However, this is, at present, our best method for determining which plaques might be susceptible to subsequent intraluminal thrombus formation. The most common plaque type presumed to be vulnerable is the so-called thin-cap fibroatheroma (TCFA)5. The TCFA has a lipid-rich core covered by a thin, fibrous cap. Based on autopsy studies6 from ruptured plaques, the cap thickness is < 100 µm and may even be < 65 µm. The cap has few smooth muscle cells and is inflamed. The inflammation involves predominantly both macrophages (macrophage-rich foam cells) and lymphocytes. Extravasated erythrocytes may be seen within the lipid or necrotic core. Inflammatory cells tend to congregate in the cap at the shoulders of the plaque, which is a common site of rupture. These plaques also generally show positive (outward) vessel remodeling and have extensive networks of vasa vasorum. This plaque is the substrate for a majority of symptomatic coronary thromboses and is detected in 60–70% of cases. The second type of plaque associated with coronary thromboses from pathological studies is the socalled superficial plaque erosion7. There is no plaque disruption here but a superficial erosion over which the thrombus forms. This plaque is often rich in proteoglycans and may lack a lipid pool or necrotic core. The fibrous cap is usually thick and contains many smooth muscle cells. These plaques usually demonstrate so-called constrictive (inward) vessel remodeling. As there may be no lipid core, inflammatory cells or a thin fibrous cap, it may be more difficult to identify this plaque prior to the event than a TCFA. The third plaque type, that has been associated with thrombus, albeit the least frequent, is the socalled calcified nodule. In this case, the plaque is calcified and the nodule protrudes into the lumen. It is also conceivable that a vulnerable plaque may be one that has already disrupted or eroded and formed a small intraluminal thrombus even though the lumen is not completely occluded, and initially, the patient remains clinically asymptomatic. In patients dying from natural causes, asymptomatic plaque disruptions were seen in 22% of cases, particularly in patients with multiple risk factors for coronary artery disease8. Although unproven, these partially thrombosed, but asymptomatic, plaques could represent the site of a new clinical coronary event when the thrombus
suddenly grows and acutely encroaches on the arterial lumen. However, this is hypothetical at present and would require prospective validation. In a very recent study, it was found that in at least 50% of patients with acute ST-elevation myocardial infarction, the thrombi were days or weeks old, which indicates that the acute thrombotic event was preceded by one or more successive thrombotic events9.
Imaging studies Angiography suggests that the site of acute clinical coronary events such as myocardial infarction frequently occur from plaques that, prior to the event, were not severely stenotic. Based on angiographic studies by our group and others and recently supported by Glaser et al., non-severely stenotic plaques (< 70% stenosis) are the site of a majority of ST elevation infarctions and other ACS10,11. Because of positive vessel remodeling, these plaques are not small, but the outward expansion of the plaque maintains the lumen. Intravascular ultrasound studies indicate that, in ACS such as unstable angina or myocardial infarction, the culprit plaque often shows positive vessel remodeling12. Once these vessels thrombose, the nonstenotic plaque becomes acutely obstructive and collaterals are not immediately recruitable to limit the amount of myocardial necrosis13. Thus, the vulnerable plaque is usually a non-hemodynamically significant stenosis which, prior to an acute coronary event, would ordinarily not necessitate an invasive approach based on the degree of luminal stenosis.
Diagnosis of the vulnerable plaque At present, there is no method to diagnose a vulnerable plaque in vivo. This is mainly related to the absence of prospective natural history studies. The lesion that caused the infarction was presumably ‘vulnerable’ prior to the event, but how many plaques with similar characteristics remain quiescent on follow-up? On the other hand, it is recognized that there are high-risk (vulnerable) patients who are susceptible to coronary events. These include patients who recently had an acute coronary event as well as patients with multiple risk factors for coronary artery disease. Thus, patients with a recent acute coronary event may have an incidence of recurrence of 15–20% in the first few months after the initial event with a subsequent prevalence of 5–7%11,14.
Present approaches for therapy of the vulnerable plaque Therapy for the vulnerable plaque can be divided into local/regional therapy and systemic therapy.
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Local/regional therapy It is the author’s belief that there are four prerequisites for a local or regional approach to the vulnerable plaque (presuming that the plaque is not hemodynamically significant at the time of initial diagnosis): (1)
(2)
(3)
(4)
The identification of a presumed vulnerable plaque with non- invasive or invasive technology. The number of vulnerable plaques in a given individual is finite, allowing for a local/ regional approach. The natural history of the vulnerable plaque has been documented in comparison to best medical approaches (systemic therapy). An interventional approach is proven to alter the natural history relative to systemic therapy alone.
In this book the possibilities for vulnerable plaque detection by optical coherence tomography (OCT) are discussed. This technique has great potential to fulfill the first prerequisite. Concerning prerequisite 2, there is no consensus on the number of vulnerable coronary plaques present in a given individual. Intravascular ultrasound15 and pathological studies16 suggest that, in a large percentage of patients studied at the time of an acute coronary event, one or more ruptured plaques in other vessels will be present. These asymptomatic ruptured plaques may or may not be the substrate for a future clinical event, as previously discussed. There are probably more vulnerable plaques by the usual definition than asymptomatic ruptured plaques. Prerequisites 3 and 4 still require validation. Thus, it is the author’s current opinion that a local or regional approach to an asymptomatic, presumed vulnerable, coronary plaque which is not severely stenotic and not hemodynamically flow limiting is premature. Ongoing natural history studies must be completed and therapeutic interventional studies must show benefit before a local or regional approach becomes an acceptable, evidence-based strategy.
Systemic therapy Because local and regional therapy has not been established at present as an option for vulnerable plaque, how might one therapeutically modify a presumed vulnerable plaque to prevent new adverse clinical events? This necessitates a short discussion of plaque stabilization. The concept of plaque stabilization was developed to explain how systemic therapy such as the use of statins reduced subsequent clinical events without significantly changing luminal narrowing on angiographic follow-up17. It was postulated that these
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drugs significantly reduced clinical events by stabilizing potentially vulnerable plaques. However, how does one characterize a drug or therapy as plaque stabilizing? As there is no universally accepted animal model of the vulnerable plaque to test which systemic therapies might be plaque-stabilizing, and there is no direct approach in man, an indirect method has been suggested18. This method, proposed by our group, is based on two premises: biological plausibility and clinical evidence. The clinical evidence comes from randomized, placebo-controlled clinical trials of different drug strategies tested in either primary or secondary prevention. Of the clinical events that have been monitored in these trials, only a significant reduction in fatal or non-fatal myocardial infarction approaches an endpoint for which a plaque was potentially stabilized and/or a thrombus prevented. Other endpoints such as preventing either cardiovascular death or the need for emergency revascularization are too non-specific. Considering cardiovascular mortality as an endpoint, this could be related to plaque stabilization effects, but there are other mechanisms such as reducing arrhythmic death or congestive heart failure. Therefore, in order to establish whether or not a therapy potentially possesses a plaque-stabilizing effect, the therapy must have significantly reduced myocardial infarction on follow-up in a placebo-controlled randomized clinical trial. Concerning biological plausibility, there must be one or more mechanisms by which the therapy stabilized the plaque. There may be a favorable effect on the vessel wall, such as an anti-inflammatory or antithrombotic effect, a reduction in plaque lipid content, a reduction in the number of vasa vasorum or an improvement in endothelial function19. Other effects may be extrinsic to the vessel wall such as reducing stress on the plaque or reducing the thrombogenicity of the blood. Based on biological plausibility with or without clinical evidence, it is possible to divide therapies into four categories (Table 12.1). Of the therapies listed, only group 1 therapies have clearly been shown to reduce subsequent myocardial infarction in primary and/or secondary prevention trials. The best data came from the use of cholesterol-lowering agents such as the statins and from angiotensin converting enzyme (ACE) inhibitors. Group 2 therapies are those in which the clinical evidence, so far, has been negative. Group 3 therapies are therapies for which the clinical evidence has been inconclusive. Group 4 therapies are therapies that possess biological possibility; however, clinical data have not yet been generated. A ‘shotgun’ approach is currently employed to treat patients we deem at high risk for cardiovascular events. Thus, the vulnerable patient is treated, for the most part, with all group 1 therapies. Selected patients will also be treated with group 3 therapies, particularly with those therapies that reduce blood
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Table 12.1 A classification of systemic therapies for potential plaque stabilization Group 1 (+) biological plausibility, (+) clinical evidence 1. Cholesterol-lowering agents, particularly the statins 2. ACE inhibitors 3. β-Blockers 4. Aspirin Group 2 (+) biological plausibility, (–) clinical evidence 1. Antibiotics 2. Antioxidants 3. Folic acid Group 3 (+) biological plausibility, (+) clinical evidence 1. Angiotensin receptor blockers 2. Other blood pressure lowering agents 3. Omega-3 fatty acids 4. Influenza vaccine 5. Clopidogrel Group 4 (+) biological plausibility, (–) clinical evidence HDL increasing therapies – CETP inhibitors – Apo A1 mimetics PPAR gamma agonists MMP inhibitors ACE, angiotensin converting enzyme; HDL, high-density lipoprotein; CETP, cholesterol ester transfer protein; Apo, apolipoprotein; PPAR, peroxisome proliferator-activator protein; MMP, matrix metalloproteinase
pressure and clopidogral is used for ACS patients and/or for those in whom one or more stents were recently implanted. For a more detailed discussion of this subject, it is suggested that the original reference18 is consulted.
FUTURE APPROACHES FOR THERAPY FOR VULNERABLE PLAQUE In the future, will a local or regional approach be an acceptable strategy for vulnerable plaque detection and therapy? Will our present strategy of therapy for the vulnerable patient be changed? As a practicing interventional cardiologist, I believe that a local/regional approach is possible. However, the aforementioned prerequisites should be fulfilled before such an evidence-based approach can be encouraged. I am convinced, however, that future innovations in technology will allow for identification of atherosclerotic plaques which are subsequently proven to be vulnerable. Techniques such as OCT, intravascular ultrasound, virtual histology and palpography hold great promise for identifying the ‘vulnerable’ TCFA. I am less hopeful that the
site of future plaque erosions may be identifiable with technology. Natural history studies such as the PROSPECT trial should help determine which plaques are truly vulnerable. Will future systemic therapy improve? Additional studies utilizing new and improved agents will be published increasing our armamentarium of approaches. Hopefully, a universally acceptable animal model of the vulnerable plaque will have been developed which will facilitate much of this research. Therapies that increase high-density lipoprotein cholesterol, including those listed in Table 12.1, have the potential for reversing atherosclerosis. Ultimately, the field of pharmacogenomics may tailor therapy to an individual genetic profile, thus eliminating the need for treating all patients at risk with all therapies20. I can foresee a time when certain individuals might be treated by both approaches: the local/regional approach for a non-hemodynamically significant plaque with a high likelihood of thrombosing based on certain characteristics (anatomic, mechanical or biological). The patient will also receive new as well as established systemic therapies based on the latest clinical studies. Given the interest in the field of vulnerable plaque and the support of industry, significant advances will be made over the next 5–10 years. Considering the remarkable improvements in the diagnosis and therapy for acute and chronic coronary artery disease over the past 20 years, it may be impossible to predict just what the future holds.
REFERENCES 1. Schaar JA, Muller JE, Falk E, et al. Terminology for high-risk and vulnerable coronary artery plaques. Report of a Meeting on the Vulnerable Plaque. June 17 & 18, 2003, Santorini, Greece. Eur Heart J 2004; 25: 1077–82 2. Naghair M, Libby P, Falk E, et al. From vulnerable plaque to vulnerable patient. A call for definitions & risk assessment strategies: Part I. Circulation 2003; 108: 1664–78 3. Fuster V, Moreno PR, Fayad ZA, et al. Atherothrombosis and high risk plaque Part 1: Evolving concepts. J Am Coll Cardiol 2005; 46: 937–54 4. Mann J, Davies MJ. Mechanisms of progression in native coronary artery disease: role of healed plaque disruption. Heart 1999; 82: 265–8 5. Virmani R, Kolodgie FD, Burke AP, et al. Lessons from sudden coronary death: a comprehensive morphologic classification scheme for atherosclerotic lesions. Arterioscler Thromb Biol 2000; 20: 1262–75 6. Burke AP, Farb A, Malcom GT, et al. Coronary risk factors and plaque morphology in men with
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7.
8.
9.
10.
11.
12.
coronary disease who die suddenly. N Engl J Med 1997; 336: 1276–82 Farb A, Burke AP, Tang AL, et al. Coronary plaque erosion without rupture into a lipid core. A frequent cause of coronary thromboses in sudden coronary death. Circulation 1996; 93: 1354–65 Davies MJ, Bland JM, Hangartner JRW, et al. Factors influencing the presence or absence of acute coronary artery thrombi in sudden ischemic death. Eur Heart J 1989; 10: 203–8 Rittersma SZ, van der Wal AC, Koch KT, et al. Plaque instability frequently occurs days or weeks before occlusive coronary thrombosis: a pathological thrombectomy study in primary percutaneous coronary intervention. Circulation 2005; 111: 1160–5 Ambrose JA, Tannenbaum M, Alexopoulous D, et al. Angiographic progression of coronary artery disease and the development of myocardial infarction. J Am Coll Cardiol 1988; 12: 56–63 Glaser R, Selzer F, Faxon DP, et al. Clinical progression of incidental asymptomatic lesions discovered during culprit vessel coronary intervention. Circulation 2005; 111: 143–9 Schoenhagen P, Ziada KM, Kapodia SR, et al. Extent and direction of arterial remodeling in stable versus unstable coronary syndromes. Circulation 2000; 101: 598–603
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13. Ambrose JA. Plaque disruption and the acute coronary syndromes of unstable angina and myocardial infarction. If the substrate is similar why is the clinical presentation different? J Am Coll Cardiol 1992; 19: 1653–68 14. Mahaffey KW, Cohen M, Garg J, et al. High-risk patients with acute coronary syndromes treated with low-molecular-weight or unfractionated heparin: outcomes at 6 month & 1 year in the synergy trial. J Am Med Assoc 2005; 294: 2594–600 15. Rioufol G, Finet G, Ginon I, et al. Multiple atherosclerotic plaque rupture in acute coronary syndrome: a 3-vessel intravascular ultrasound approach. Circulation 2003; 106: 801–8 16. Falkp E, Shah PK, Fuster V. Coronary plaque disruption. Circulation 1995; 92: 657–71 17. Brown BG, Zhao XQ, Sacco DE, Albero JJ. Lipidlowering and plaque disruption. New insights into prevention of plaque disruption and clinical events. Circulation 1993; 87: 1781–91 18. Ambrose JA, D’ Agate DJ. A new classification of potential therapies for plaque stabilization. Am J Cardiol 2005; 95: 379–82 19. Libby P, Theroux P. Pathophysiology of coronary disease. Circulation 2005; 111: 3481–8 20. Roden DN. Cardiovascular pharmacogenomics. Circulation 2003; 108: 3071–74
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CHAPTER 13 Resolution versus imaging depth: every advantage has its disadvantage Freek J van der Meer, Ton G van Leeuwen
INTRODUCTION
both apoptotic and necrotic cells and cellular debris, and have a thin fibrous cap, are prone to rupture. Further lipid deposition and necrosis of foam cells result in a lipid pool, whose thrombogenic contents cause thrombus formation when cap rupture occurs (Figure 13.1c). Thrombus formation may also occur when the endothelial lining is damaged (erosion) (Figure 13.1d). The thrombus can embolize in other vessels causing symptoms of acute syndromes, such as the abrupt reduction in flow to a region of the myocardium (myocardial infarction), or strokes. Further advanced plaques show calcifications (Figure 13.1e), which can result in thrombus formation by protruding into the lumen through a disrupted thin fibrous cap. Healing of cap rupture and further accumulation of lipid, calcifications, SMCs and fibrous tissue can eventually compromise the vascular lumen (Figure 13.1f).
In this chapter, we discuss the different imaging techniques currently available to image the arterial wall. The first question that has to be answered is ‘What do we want to image?’. The normal arterial wall (Figure 13.1a) is composed of three layers, which are separated by elastic laminae. The innermost layer, the intima, is separated from the bloodstream by endothelial cells and, in normal condition, is a thin layer of extracellular matrix and incidental smooth muscle cells (SMCs). The intima is separated from the media by the internal elastic lamina. The media consists of SMCs, bundles of collagen fibers and elastic fibrils embedded in an extracellular matrix. It is separated from the outermost adventitia by the external elastic lamina. The adventitia is a layer of connective tissue, collagen and elastic fibers embedding the entire vessel within its surroundings. High-resolution imaging techniques should be able to distinguish these ‘normal’ arterial wall layers. Furthermore, atherosclerotic lesions, ideally in all their stages, should be visualized. In general, atherosclerosis starts with lipid deposition in the intima, the so-called ‘fatty streak’. The lipid deposition gradually increases and the intima thickens, owing to migrating SMCs from the media and monocytes that enter from the blood. The monocytes differentiate into macrophages that internalize the lipid deposition, becoming lipidladen foam cells, whereas SMCs change into a secreting phenotype, producing collagen to form a protective cap (Figure 13.1b). Due to compensatory enlargement of the vessel, early lesions can continue to develop without compromising the lumen (‘remodeling’)1. However, some plaques with a large lipid core, which by now contain
VULNERABLE PLAQUE Vulnerable plaques have been defined as precursors to lesions that rupture. A large number of vulnerable plaques are relatively uncalcified, relatively non-stenotic and similar to type IV atherosclerotic lesions described in the American Heart Association classification2. They are morphologically characterized by a lipid core covered by a thin fibrous cap (thickness < 65 µm)3–6. These unstable plaques are very prone to rupture or fissure, especially in the shoulders of the fibrous cap, with subsequent exposure of the thrombogenic lipid core to the flowing blood, resulting in thrombosis. However, different types of vulnerable plaque exist. Coronary thrombosis may occur from other lesions such as plaque erosion and calcified 115
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a
b
adventitia calcification
c
d
media
e
intima
thrombus
f
lipid pool macrophages
Figure 13.1 Schematic drawing of the normal arterial wall (a) and of atherosclerotic lesions, showing lipid accumulation (b), ruptured lipid-rich plaque with non-occlusive thrombus (c), thrombosis due to erosion/endothelial denudation (d), calcification (e) and chronic occlusion (f)
Table 13.1 Overview of markers for plaque vulnerability, categorized as morphological or functional. Adapted from reference 7 Morphology/structure
Activity/function
Cap thickness Lipid core size Stenosis Remodeling Color Collagen content versus lipid content, mechanical stability Calcification burden and pattern Shear stress
Inflammation (macrophage density) Endothelial denudation or dysfunction Plaque oxidative stress Superficial platelet aggregation/fibrin deposition Rate of apoptosis Angiogenesis, intraplaque hemorrhage, leaking vasa vasorum Matrix-digesting enzyme activity (MMP) Certain microbial agents (HSP60, Chlamydia pneumoniae)
nodules, although at a lesser frequency than cap rupture. In a recent publication, Naghavi et al. summarized the characteristics of atherosclerotic lesions that resulted in vascular occlusion and other clinical symptoms (Table 13.1)7.
IMAGING OF THE VULNERABLE PLAQUE Several imaging techniques are currently available for the detection of stenosis and plaques, ranging from non-invasive to catheter-based invasive systems, using electromagnetic or ultrasound waves4,8–10. For non-invasive imaging, the radiation that is minimally absorbed by tissue can be utilized. In Figure 13.2, the absorption spectrum of water for electromagnetic waves is plotted. From this graph it
is clear that, for X-rays, γ-rays and radio waves, the absorption coefficient is smaller than 1 cm−1 and therefore these are very suitable for imaging through centimeters of tissue. X-rays have been utilized for more than 100 years for non-invasive imaging. The contrast is based on differences in absorption for the X-rays by the tissue. However, due to the low absorption differences between blood and vascular wall components, highly absorbing contrast agents have to be injected to visualize the lumen. Consequently, angiography visualizes the vascular lumen, albeit with a good resolution, but is not able to image the vascular wall and its contents. Due to the fact that vulnerable plaques are often hemodynamically insignificant, they are difficult to detect with angiography4,11. Nevertheless, angiography was the
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(visible) light
105 Absorption coefficient [cm−1]
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radio waves
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10 1
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107
1010
1013
1016
Frequency [Hz]
gold standard for coronary imaging for decades. Computed tomography (CT) of multidirectional X-ray projections allows three-dimensional visualization of morphological structures. However, due to limited resolution (up to 0.6 × 0.75 mm) and limited contrast, although much better than for X-ray projection imaging, only calcifications can be clearly detected12. For the more energetic part of the electromagnetic spectrum, the imaging techniques such as positron emission tomography (PET) and single photon emission computed tomography (SPECT) are hampered by their low resolution (approximately 3–10 mm)13. At the other side of the spectrum (left side of Figure 13.2), radio waves in combination with a high magnetic field are used for non-invasive imaging of tissues. In this so-called nuclear magnetic resonance imaging (MRI), radio waves are used to excite the magnetic field-induced split ground state of hydrogen atoms in the tissue. After excitation, radio waves are emitted which can be characterized according to three parameters: the signal strength, which depends on the density of the protons; the time T1 needed for recovery of the excited spins to the equilibrium, which depends on the spin-lattice interaction; and the decay time T2 of the radiofrequency signal, which depends on the spin–spin interaction. These parameters are tissue specific and therefore can be used to differentiate the tissue components. Thus, MRI has the potential to distinguish atherosclerotic plaque and to determine its composition and microanatomy14. In patients, MRI is able to identify unstable plaques in the aorta11. However, the resolution of MRI, which is approximately 0.4 mm in-plane resolution with a 3-mm slice thickness15,16, and the
1019
1022
Figure 13.2 The absorption coefficient of water as a function of the frequency of the electromagnetic waves. Note the logarithmic scales and the regions in which the absorption coefficient is less than 1 cm−1: radio waves, visible light, X- and γ-rays
imaging time (several minutes) limit its application for the detection of the specific morphological characteristics of unstable plaques in coronary arteries8. New developments, using intravascular MRI probes, allow imaging at a resolution of 150 µm within a slice. In the visible part of the electromagnetic spectrum, the absorption by water is also low (Figure 13.2). However, in this part the scattering of the light by the tissue constituents hampers the utilization of these electromagnetic waves for non-invasive imaging. Using fiberoptics, light can be used in catheter-based systems for intravascular imaging. In angioscopy, via a coherent bundle of optical fibers, an intraluminal image is obtained while the blood is removed with flushing saline or CO2 gas17. Angioscopy is a straightforward imaging technique that only provides information on the morphology of the endoluminal surface and is therefore, like angiography, unable to identify the extent of an atherosclerotic plaque into the vessel wall. In some cases, angioscopy can indirectly detect the position of a fibroatheromatous plaque4. The yellow color intensity of plaque determined by angioscopy can indicate the prevalence of thrombosis on the plaque and thus be a marker of plaque vulnerability18. Finally, the plaque cap thickness is a determinant of plaque color, and quantitative colorimetry might be useful for the detection of vulnerable plaques19. Instead of electromagnetic waves, acoustic waves can also be used for medical imaging. In ultrasound (US) imaging, the intensity of back-reflected acoustic pulses is depicted as a function of the time of flight. The contrast of US imaging is based on differences in the acoustic impedance of the different tissue layers. Both the axial resolution and the
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10 Tomography: SPECT & PET 1
US: 7.5–20 MHz 0.1
M
RI
US: 3–5 MHz CT
Depth resolution [mm]
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20–40 MHz 40–70 MHz Depth resolution [µm]
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1
Depth [mm]
Figure 13.3 Axial (depth) resolution and obtainable imaging depth for US imaging devices with different frequencies compared with other imaging techniques, such as single photon emission computed tomography (SPECT), PET, CT, MRI, OCT and (confocal) microscopy ((C)M)
attenuation of the US in tissue are proportional to the frequency of the US waves (Figure 13.3). Therefore, for high-resolution imaging of the arterial wall, the US signals of frequencies around 30 MHz have to be delivered and detected intravascularly. This intravascular ultrasound (IVUS) imaging, which has an axial resolution of approximately 100 µm, currently represents the gold standard in the assessment of atherosclerotic disease. IVUS facilitated indepth understanding of coronary artery disease, by arterial remodeling and therapeutic strategies such as stent implantation and coronary brachytherapy20. IVUS imaging, although being able to image the vascular wall, is limited in specifically identifying lipid-rich plaques4; therefore, the contrast and the resolution are not suitable for directly detecting the vulnerable plaque. Using a sophisticated analysis of the US signals obtained during systole, the local mechanical properties can be assessed. This so-called elastography can distinguish the weaker and stiffer regions in the arterial wall and therefore can identify the vulnerable plaque. Intravascular elastography is a unique tool to assess lesion composition and vulnerability21–23, which has been able to detect vulnerable plaques in vitro24. With the development of threedimensional elastography and palpography, in vivo identification of weak spots over the full length of human coronary arteries has become possible25. An entirely different approach to detect plaque vulnerability is the measurement of the temperature of the arterial wall, which may be increased by local inflammation. With a precise thermography catheter, the heat or metabolic activity can be localized and correlated with plaques at high risk of rupture or thrombosis26. Indeed, an increased
thermal heterogeneity within human atherosclerotic coronary arteries was observed in patients with unstable angina and acute myocardial infarction, suggesting that it may be related to the pathogenesis27. However, the spatial resolution (approximately 0.5 mm) and the potential in vivo underestimation of heat production locally in human atherosclerotic plaque due to the ‘cooling effect’ of coronary blood flow28 currently limits the applicability of this technique. There are two factors that hamper the detection of the vulnerable plaque using the above-described techniques: they either (1) are en face imaging techniques, which are not able to show the depthresolved morphology (angioscopy, thermography); or (2) have a resolution that does not permit detailed imaging (IVUS, MRI and CT). The need for a highresolution imaging technique that can detect unstable coronary atherosclerotic plaques before they become clinically significant is paramount. This imaging lacuna could be filled by optical coherence tomography (OCT). Intravascular OCT may play an important role in guiding therapeutic interventions, diagnosing atherosclerosis and researching the causes of coronary artery disease. Since its introduction in the early 1990s, OCT has become a powerful method for imaging the internal structure of biological systems and materials29. OCT is analogous to B-mode ultrasound, except that it uses light rather than sound. Whereas in ultrasound the location of the reflected object is determined by measuring echo delay times, in OCT depth-resolved measurement of the backscattered light is achieved through low-coherence interferometry. This phenomenon allows the backscattered light to be determined from different positions within the sample
0.1 1
10
100
Depth [µm]
1
10
100
1000
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Table 13.2 Overview of light sources and their specifications. The center wavelength (λ) is proportional to the imaging depth (di). The bandwidth of the light source (∆λ) is inversely proportional to the coherence length (lc). The maximal power (Pmax) is also given. SLD, super luminescent diode; AF, autofluorescent fiber; Ti:Al2O3, titanium sapphire laser Light source SLD SLD AF Ti:Al2O3
λ (nm)
∆λ (nm)
Pmax (mW)
lc (µm)
di (mm)
825 1300 1300 800
~ 25 ~ 50 ~ 60 ~ 100–250
~5 ~5 ~ 20 ~ 1000
~ 12 ~ 15 ~ 12 ~ 1–3
0.5–1.0 1.0–2.0 1.0–2.0 0.5–1.5
(i.e. in-depth, ‘coherence gating’). Consequently, the axial resolution is directly related to the coherence length of the light source, which is inversely related to the bandwidth of the light source. The transverse resolution for OCT imaging is determined by the focused spot size, as in microscopy. In contrast to conventional microscopy, the lateral resolution is decoupled from the axial resolution. Furthermore, OCT provides cross-sectional images of structure below the tissue surface in analogy to histopathology. Standard-resolution OCT can achieve axial resolutions of 10–15 µm. The contrast of an OCT image is determined by differences in the optical properties (e.g. scattering and absorption) of different tissue layers and their components. The imaging depth is also determined by the optical properties of the tissue. Using wavelengths in the near infrared, where hemoglobin and melanin absorption are low and scattering is reduced, permits imaging depths of up to 2 mm in tissues30,31. Although this depth is shallow compared with other clinical imaging techniques such as US (Figure 13.3), the image resolution of OCT is one to two orders of magnitude better than conventional US imaging, MRI or CT. Recently, using stateof-the-art lasers as light sources, ultrahigh-resolution imaging with axial resolutions as fine as 1–2 µm has been demonstrated (Table 13.2)32. In conclusion, all imaging techniques have an inverse relation between imaging depth and possible resolution. Furthermore, with their different types of radiation, every imaging technique has its own contrast mechanism. In cardiology, OCT could be used to detect and analyze atherosclerotic lesions, due to its capacity for high-resolution imaging of superficial structures and its capability of distinguishing the different layers from the normal as well as diseased arterial walls. Furthermore, OCT is the only imaging modality capable of measuring this cap thickness33 and therefore could be a tool in detection of rupture-prone vulnerable plaques34. Finally, using quantitative analysis on the OCT signal, virtual histology-like images can be produced (see Chapter 25).
REFERENCES 1. Glagov S, Weisenberg E, Zarins CK, et al. Compensatory enlargement of human atherosclerotic coronary arteries. N Engl J Med 1987; 316: 1371–5 2. Stary HC, Chandler AB, Dinsmore RE, et al. A definition of advanced types of atherosclerotic lesions and a histological classification of atherosclerosis. A report from the Committee on Vascular Lesions of the Council on Arteriosclerosis, American Heart Association. Circulation 1995; 92: 1355–74 3. Fuster V, Fayad ZA, Badimon JJ. Acute coronary syndromes: biology. Lancet 1999; 353(Suppl 2): SII5–SII9 4. Kleber FX, Dopfmer S, Thieme T. Invasive strategies to discriminate stable and unstable coronary plaques. Eur Heart J 1998; 19(Suppl C): C44–C49 5. Kolodgie FD, Burke AP, Farb A, et al. The thin-cap fibroatheroma: a type of vulnerable plaque – the major precursor lesion to acute coronary syndromes. Curr Opin Cardiol 2001; 16: 285–92 6. Virmani R, Burke AP, Kolodgie FD, Farb A. Vulnerable plaque: the pathology of unstable coronary lesions. J Interv Cardiol 2002; 15: 439–46 7. Naghavi, M, Libby P, Falk E, et al. From vulnerable plaque to vulnerable patient: a call for new definitions and risk assessment strategies: Part I. Circulation 2003; 108: 1664–72 8. MacNeill BD, Lowe HC, Takano M, et al. Intravascular modalities for detection of vulnerable plaque: current status. Arterioscler Thromb Vasc Biol 2003; 23: 1333–42 9. Nemirovsky D, Imaging of high-risk plaque. Cardiology 2003; 100: 160–75 10. Naghavi M, Madjid M, Khan MR, et al. New developments in the detection of vulnerable plaque. Curr Atheroscler Rep 2001; 3: 125–35 11. Fuster V. Mechanisms leading to myocardial infarction: insights from studies of vascular biology. Circulation 1994; 90: 2126–46 12. Kopp AF, Schroeder S, Baumbach A, et al. Non-invasive characterisation of coronary lesion morphology and composition by multislice CT: first results in comparison with intracoronary ultrasound. Eur Radiol 2001; 11: 1607–11 13. Sanchez-Crespo A, Andreo P, Larsson SA. Positron flight in human tissues and its influence on PET image spatial resolution. Eur J Nucl Med Mol Imaging 2004; 31: 44–51 14. Choudhury RP, Fuster V, Badimon JJ, et al. MRI and characterization of atherosclerotic plaque: emerging
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applications and molecular imaging. Arterioscler Thromb Vasc Biol 2002; 22: 1065–74 Fayad ZA, Fuster V, Fallon JT, et al. Noninvasive in vivo human coronary artery lumen and wall imaging using black-blood magnetic resonance imaging. Circulation 2000; 102: 506–10 Fayad ZA. MR imaging for the noninvasive assessment of atherothrombotic plaques. Magn Reson Imaging Clin North Am 2003; 11: 101–13 Smits PC, Post MJ, Velema E, et al. Percutaneous coronary and peripheral angioscopy with saline solution and carbon-dioxide gas in porcine and canine arteries. Am Heart J 1991; 122: 1315–22 Ueda Y, Ohtani T, Shimizu M, et al. Assessment of plaque vulnerability by angioscopic classification of plaque color. Am Heart J 2004; 148: 333–5 Miyamoto A, Prieto AR, Friedl SE, et al. Atheromatous plaque cap thickness can be determined by quantitative color analysis during angioscopy: implications for identifying the vulnerable plaque. Clin Cardiol 2004; 27: 9–15 Regar E, Serruys PW. Ten years after introduction of intravascular ultrasound in the catheterization laboratory: tool or toy? Z Kardiol 2002; 91(Suppl 3): 89–97 de Korte CL, Schaar JA, Mastik F, et al. Intravascular elastography: from bench to bedside. J Interv Cardiol 2003; 16: 253–9 de Korte CL, Sierevogel MJ, Mastik F, et al. Identification of atherosclerotic plaque components with intravascular ultrasound elastography in vivo: a Yucatan pig study. Circulation 2002; 105: 1627–30 de Korte CL, Cespedes EI, van der Steen AF, et al. Intravascular ultrasound elastography: assessment and imaging of elastic properties of diseased arteries and vulnerable plaque. Eur J Ultrasound 1998; 7: 219–24 Schaar JA, de Korte CL, Mastik F, et al. Characterizing vulnerable plaque features with intravascular elastography. Circulation 2003; 108: 2636–41
25. Schaar JA, Regar E, Mastik F, et al. Incidence of high-strain patterns in human coronary arteries: assessment with three-dimensional intravascular palpography and correlation with clinical presentation. Circulation 2004; 109: 2716–19 26. Casscells W, Hathorn B, David M, et al. Thermal detection of cellular infiltrates in living atherosclerotic plaques: possible implications for plaque rupture and thrombosis. Lancet 1996; 347: 1447–51 27. Stefanadis C, Diamantopoulos L, Vlachopoulos C, et al. Thermal heterogeneity within human atherosclerotic coronary arteries detected in vivo: a new method of detection by application of a special thermography catheter. Circulation 1999; 99: 1965–71 28. Stefanadis C, Toutouzas K, Tsiamis E, et al. Thermal heterogeneity in stable human coronary atherosclerotic plaques is underestimated in vivo: the cooling effect of blood flow. J Am Coll Cardiol 2003; 41: 403–8 29. Huang D, Swanson EA, Lin CP, et al. Optical coherence tomography. Science 1991; 254: 1178–81 30. Fujimoto JG, Brezinski ME, Tearney GJ, et al. Optical biopsy and imaging using optical coherence tomography. Nat Med 1995; 1: 970–2 31. Schmitt JM, Knuttel A, Yadlowsky M, Eckhaus MA. Optical-coherence tomography of a dense tissue: statistics of attenuation and backscattering. Phys Med Biol 1994; 39: 1705–20 32. Drexler W, Morgner U, Kartner FX, et al. In vivo ultrahigh-resolution optical coherence tomography. Opt Lett 1999; 24: 1221–3 33. Pasterkamp G, Falk E, Woutman H, Borst C. Techniques characterizing the coronary atherosclerotic plaque: influence on clinical decision making. J Am Coll Cardiol 2000; 36: 13–21 34. Brezinski ME, Tearney GJ, Bouma BE, et al. Optical coherence tomography for optical biopsy – properties and demonstration of vascular pathology. Circulation 1996; 93: 1206–13
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CHAPTER 14 OCT imaging of vulnerable plaque: the Massachusetts General Hospital experience Christopher Raffel, Guillermo J Tearney, Brett E Bouma, Ik-Kyung Jang
INTRODUCTION
are used to produce a focused output beam that propagates transversely to the catheter axis toward the vessel wall. Details of the OCT system have been previously described3. The typical penetration depth in non-transparent tissue is about 2–3 mm permitting high-resolution imaging without motion artifact. At present, a sampling rate of 4–8 frames per second is typically achieved. Since blood reflects the optical signal, causing significant artifact, images of the vessel lumen and surrounding vessel wall can be acquired only in the absence of red blood cells. To create a blood-free zone, 8–10 ml of normal saline is flushed through the guiding catheter, which allows an image acquisition for approximately 2–3 seconds. This allows sampling of only discrete transverse segments, precluding continuous imaging of longer sections of arterial wall. We note that second-generation OCT technology promises to overcome this limitation, as described at the end of this chapter.
With the dramatic reduction in restenosis rates following the introduction of drug-eluting stents for the treatment of established obstructive coronary artery disease (CAD), identifying and treating the patient with vulnerable plaque in an effort to prevent acute coronary events has become the new ‘holy grail’ of CAD research1. Optical coherence tomography (OCT) promises to be an important diagnostic modality to help achieve this goal. Over the past decade considerable progress has been made in the development of intravascular OCT, and research at the Massachusetts General Hospital (MGH) has been at the forefront of efforts to investigate the ability of this technology in imaging atherosclerotic disease. Following on from the early feasibility studies in vitro2, using a catheter-based OCT system developed in the laboratories at the Wellman Center for Photomedicine, MGH, our group has pursued an active research program to assess the feasibility and validity of cardiac OCT (Table 14.1). This chapter will review some of these significant developments in particular as it pertains to the ability of OCT to evaluate vulnerable plaque in humans.
EARLY FEASIBILITY STUDIES Early ex vivo studies on explanted human aorta and coronary arteries established the feasibility of OCT to image atherosclerotic plaque morphology at a micrometer scale2,4 and clearly documented its superior resolution and contrast when compared to intravascular ultrasound (IVUS) imaging5. Subsequently, the feasibility of intravascular imaging with OCT in vivo was assessed in the aorta of NZ white rabbits. This work showed that high-resolution intravascular imaging could be performed in vivo using OCT6.
THE MASSACHUSETTS GENERAL HOSPITAL OCT PLATFORM The current intracoronary OCT catheter is made by replacing the core of an intravascular ultrasound catheter with a single optical fiber. At the distal tip of the fiber, a gradient index lens and a microprism
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Table 14.1
Development of cardiac OCT at the Massachusetts General Hospital (MGH)
Milestones in the development of cardiac OCT at MGH
Reference
First systematic characterization of human atherosclerotic plaque with OCT Demonstration of the ability of OCT to detect and quantify the amount of macrophages in atherosclerotic plaque First in-animal application of OCT imaging of coronary arteries in vivo First in-human application of OCT imaging of coronary arteries in vivo and comparison of OCT to IVUS in imaging in vivo atherosclerotic plaque in humans First systematic published studies evaluating OCT during PCI in humans In vivo evaluation of plaque morphology and macrophage content in patients with various coronary syndromes
7 20 30 22 23, 24 25, 26
PCI, percutaneous coronary intervention
CHARACTERIZATION OF HUMAN ATHEROSCLEROTIC PLAQUE WITH OCT EX VIVO Plaque morphology The first step towards the development of OCT as a tool in vulnerable plaque assessment was taken with the characterization of human atherosclerotic plaque using OCT. Using 357 postmortem atherosclerotic segments from 90 cadavers7, OCT criteria for the various plaque components were established using 50 segments as a training set. This enabled the identification of three types of histological plaque (fibrous, fibrocalcific and lipid-rich). Fibrous plaques were characterized by homogeneous highly backscattering, signal-rich (echodense) regions. Calcified plaques appeared as signal-poor regions with sharply defined borders. Lipid pools were similarly signal-poor, but were poorly delineated with respect to the surrounding tissue, except on the luminal side where they were often separated by a high-signal, thin, homogeneous band of tissue, which represented the fibrous cap (Figure 14.1). Using these criteria, the remaining 307 segments were interpreted by investigators blinded to histology. High sensitivity and specificity were obtained for the detection of both calcific and lipidrich plaques with OCT as compared with histology (96% and 97%; 90% and 92%, respectively). Sensitivity and specificity for fibrous plaque was 79% and 97%, respectively. The inter- and intraobserver agreements for characterization of plaque type by use of the OCT image criteria were high (κ = 0.88 and 0.91, respectively).
Fibrous cap thickness Of the morphological features associated with rupture of a vulnerable plaque, fibrous cap attenuation appears to be the most critical8. Autopsy studies of
ruptured plaques have shown a fibrous cap thickness near the rupture site of 23 ± 19 µm (mean ± SD) with 95% of ruptured caps measuring < 65 µm9,10. Therefore, the use of cap thickness as a criterion for vulnerable plaque detection and the investigation of the significance of cap thickness in acute syndromes would best be facilitated by an imaging modality with resolution well below 65 µm. OCT is currently the only modality with this degree of resolution (Figure 14.2). To evaluate this potential capability of OCT we compared the cap thickness measured by OCT and histology in 29 lipid-rich plaques. Plaques with a cap thickness of > 500 µm were excluded. There was a close correlation between the two methods (r = 0.89, p < 0.0001; unpublished data) confirming the ability of OCT to measure plaque cap thickness precisely.
DETECTION OF MACROPHAGE ACCUMULATION IN ATHEROSCLEROTIC PLAQUE Interstitial collagen confers stability and tensile strength to the fibrous cap in thin-cap fibroatheroma (TCFA). There appears to be a dynamic balance between collagen synthesis and breakdown in atherosclerotic plaques maintained by the inflammatory state of the lesion11. Macrophages, in response to stimulation by T cells, can produce matrix metalloproteinases (MMPs), which actively break down collagen and other extracellular matrix proteins in the fibrous cap, compromising its integrity12–15. There is extensive evidence linking macrophage infiltration with plaque instability16–18. Histologically, disrupted plaques demonstrate intense macrophage infiltration localized within the thin fibrous cap. Macrophages are more frequently demonstrated in coronary plaque obtained from patients with acute coronary syndromes (ACS) compared with patients with stable angina. Therefore, these cells are
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a
c
123
e
C
b
d
f
Figure 14.1 OCT images and corresponding histological analysis for fibrous (a), calcific (c) and lipid-rich (e) plaque types. In fibrous plaques the OCT signal (F) is observed to be strong and homogeneous. In comparison, both calcific (C) and lipidrich regions (arrows) appear as a signal-poor region within the vessel wall. Lipid-rich plaques have diffuse or poorly demarcated borders, whereas the borders of calcific nodules are sharply delineated. Images b, d and f depict corresponding histological preparations. (b, Movat pentachrome stain; d, hematoxylin–eosin stain; f, Masson trichrome stain; original magnification × 40). Scale bars, 500 µm. (From reference 31)
a
L
b
L Figure 14.2 (a) OCT image of a thin-cap fibroatheroma with corresponding histology (b). (Movat’s pentachrome; magnification × 20). Bar, 250 µm. Cap thickness was 44.1 µm by OCT and 40.4 µm by histology. (From reference 7)
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both an integral functional component and a marker of the inflammatory process that is fundamental to the atherosclerotic process in humans. The ability to identify macrophages in vivo would provide invaluable information in assessing the inflammatory state of a plaque and its vulnerability to rupture.
Methodology of quantitative macrophage analysis using OCT Since OCT measures the intensity of light returning from within a sample, specimens that have a high heterogeneity of optical index of refraction will exhibit stronger optical scattering and thus a stronger OCT signal. If the size scale of the index of refraction heterogeneity is larger than the resolution, the OCT signal will have a larger variance. Given the size scale of plaque macrophages (20–50 µm), the resolution of OCT (~10 µm), and the significant variation of optical index of refraction between lipid and collagen, we hypothesized that plaques rich in macrophages should exhibit a high OCT signal variance. With standard image processing methods19 to remove background noise and speckle noise, the variance, σ, within the region of interest (ROI) of an OCT image can be represented by the following: σ2 =
1 ¯ 2 ROIwidth ROIheight (S(x, y) − S) N −1
where N = number of pixels in the ROI, ROIwidth = width of the ROI, ROIheight = height of the ROI, S(x,y) = OCT signal as a function of x and y – locations within the ROI, and S = average OCT signal within the ROI. To correct the data for variations in the OCT system settings, σ was normalized by the maximum and minimum OCT signal present in the OCT image, as follows: NSD =
σ Smax − Smin
where NSD = normalized standard deviation of the OCT signal, Smax = maximum OCT image value, and Smin = minimum OCT image value. It was hypothesized that, by determining the OCT NSD in the ROI within the fibrous cap, it would be possible to quantify the macrophage content within that region.
Ex vivo validation Twenty-six lipid-rich atherosclerotic plaques (19 aortas and seven carotid bulbs) were investigated ex vivo20. OCT imaging for macrophage density was performed within 72 hours postmortem and compared to macrophage density quantified histomorphometrically by immunoperoxidase staining with
CD68 of the corresponding fibrous cap ROI. Fibrous cap smooth-muscle cell density was also determined by immunohistochemistry using α-actin monoclonal antibody used as a negative control. Raw and logarithmic-based (log10) OCT NSD imaging data were compared quantitatively to immunohistological results. A relatively homogeneous OCT signal corresponded to a low macrophage density while a heterogeneous OCT signal with punctate, highly reflecting regions corresponded to a high macrophage density within the plaque (Figure 14.3). For the raw OCT data, a strong correlation (r = 0.84, p < 0.0001) was found between OCT NSD signal intensity and macrophage content as determined by histology (Figure 14.4). It has been shown that plaques with a macrophage content of 10–20% are more likely to be associated with unstable coronary syndromes19. When using a threshold of macrophage content of 10% of the area, the sensitivity and specificity of raw OCT data to detect plaque rich in macrophages reached 100%. The correlation of raw OCT NSD and CD68 per cent staining, controlling for cap thickness, was r = 0.80 (p < 0.0001), indicating that OCT measurement of macrophage density was independent of cap thickness. Although the logarithm of the OCT signal provided an increased dynamic range for image display, it was not as robust as the raw OCT signal in determining macrophage content (Table 14.2), probably due to a decreased contrast between macrophages and surrounding matrix. A negative correlation was found between CD68 and smooth-muscle actin per cent area staining (r = –0.44, p < 0.05). In turn, a statistically significant negative relationship between smooth-muscle cell density and raw OCT NSD data was observed (r = –0.56, p < 0.005), reflecting in part the inverse relationship between macrophages and smooth muscle cells described in previous histological studies17, 21. These results demonstrated the capability of OCT in evaluating macrophage content within fibrous caps of atheroma with a high degree of accuracy.
EVALUATION OF OCT IN HUMANS Feasibility study The first in-man application of OCT was published in 200222. Seventeen mild-to-moderately stenotic coronary lesions in ten patients undergoing percutaneous coronary intervention were investigated using OCT and IVUS. Corresponding OCT and IVUS images of the same lesion were compared for detection of components of the vessel wall. Lipid-rich, fibrous and calcific plaques were readily identified. OCT detected all echolucent regions identified with IVUS imaging and, moreover, identified two additional echolucent areas, probably corresponding to lipid pools. Similarly, calcifications detected with IVUS were also seen with OCT, with the latter providing higher contrast
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b
250µm a
c
d
Figure 14.3 (a) OCT image of a fibroatheroma with a relatively homogeneous OCT signal, signifying a low density of macrophages within the fibrous cap. (b) Corresponding histology (CD68 immunoperoxidase; original magnification × 100). (c) OCT image of a fibroatheroma with a heterogeneous OCT signal showing punctate, highly reflecting regions, signifying a high density of macrophages within the fibrous cap. (d) Corresponding histology. (CD68 immunoperoxidase; original magnification × 100). (Adapted from reference 20) 35
25
Table 14.2 Macrophage content of plaque CD68 per cent cutoff, 10%. Data in parentheses represent 95% confidence intervals
20
Finding
Raw OCT signal
Logarithm OCT signal
Correlation Sensitivity Specificity PPV NPV
0.84 (p < 0.0001) 1.0 (0.69–1.0) 1.0 (0.8–1.0) 1.0 (0.69–1.0) 1.0 (0.8–1.0)
0.47 (p < 0.05) 0.70 (0.35–0.93) 0.75 (0.48–0.93) 0.64 (0.3–0.89) 0.80 (0.52–0.96)
% CD68 staining
30
15 10 5 0 0.02 −5 −10
0.03
0.04
0.05
0.06
0.07
0.08
0.09
0.1
PPV, positive predictive value; NPV, negative predictive value OCT NSD (raw signal)
Figure 14.4 Correlation between the raw OCT normalized standard deviation (NSD) data and CD68 per cent area staining. (Adapted from reference 20)
between calcified areas and the surrounding tissue. The characteristic acoustic shadow cast by calcified tissue in IVUS images prevented an exact assessment of the size and depth of coronary calcifications, while in OCT no such artifact was noted. The overall safety of OCT was excellent, with all patients tolerating the procedure well and without complication. This study demonstrated that OCT imaging could be reliably and
safely performed in the human coronary tree and that, in comparison to IVUS, OCT provided additional information on plaque morphology, which may be used to improve plaque characterization.
Use of OCT in percutaneous coronary intervention While IVUS imaging is traditionally used to assess the outcome of coronary stenting, detailed information is frequently not possible, because the metal struts impair image quality. In 43 imaged stents, OCT consistently
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detected more incidences of dissection, tissue prolapse and incomplete stent deployment than IVUS23. The feasibility of OCT as a tool in percutaneous coronary intervention in comparison to IVUS was also evaluated in ten patients before and after coronary intervention. Balloon-induced dissection, intraluminal thrombus, the number of cuts made by a cutting balloon, tissue prolapse and suboptimally deployed stents were all well defined with OCT. In contrast, the limited resolution in IVUS failed to define the cuts made by a cutting balloon and also missed underdeployed stent struts24. This subject is dealt with in more detail in Chapter 17.
PLAQUE CHARACTERIZATION WITH OCT IN PATIENTS WITH ACUTE AND STABLE CORONARY SYNDROMES The next logical step in the developmental process of cardiac OCT was undertaken with studies to characterize plaque morphology and plaque macrophage content and distribution in patients presenting with various coronary syndromes25,26. A total of 57 patients were enrolled at the MGH: 20 patients following acute ST-elevation myocardial infarction (STEMI), 20 patients with non-ST-elevation ACS (NSTEACS), (non-ST-elevation myocardial infarction (NSTEMI) and unstable angina) and 17 with stable angina pectoris (SAP). All subjects underwent coronary angiography and concurrent OCT imaging of culprit and non-culprit plaque prior to any intervention. OCT imaging of the plaque was performed at its center and the proximal and distal shoulder regions. No significant differences were found in the baseline characteristics and the distributions of the culprit lesions between the groups. The time from symptom onset (mean ± SD) to imaging was 4.6 ± 5.3 days for STEMI patients and 3.3 ± 1.7 days for NSTEACS patients.
Culprit and remote plaque morphology Lipid-rich plaque was defined as lipid occupying ≥ 2 quadrants of the cross- sectional area and TCFA by lipid-rich plaque with a fibrous cap thickness of ≤ 65µm. Lipid-rich plaque was observed in 90% of STEMI patients, 75% of NSTEACS patients and 59% of SAP patients (p = 0.09) An example of a patient with STEMI and a patient with SAP is shown in Figure 14.5. The frequency of TCFA was significantly different between the groups and, similarly, the median fibrous cap thickness differed significantly between the groups (Table 14.3). There were no significant differences between the frequency of TCFA (p = 0.172) or the fibrous cap thickness (p = 0.38) between subjects with STEMI and NSTEACS. The incidence of plaque calcification differed significantly
between the groups. The frequency of plaque rupture or the presence of thrombus, however, was not significantly different (Table 14.3). The significant association between TCFA, fibrous cap attenuation and the patients who presented with unstable coronary syndromes is in keeping with the findings of the autopsy studies which have provided the foundation for our current understanding of the role of plaque disruption in acute coronary events8,10,27,28. This study reinforces the importance of fibrous cap thickness as a measure of plaque vulnerability and demonstrates the advantage of the high resolution provided by OCT. The frequency of detection of fibrous cap rupture by OCT was lower than expected (Table 14.3) in comparison with histopathological and IVUS studies. Since OCT imaging was performed only at the center and the proximal and distal shoulder region of culprit lesions, however, it is probable that sites of cap disruption were not imaged with this discrete sampling method. This result highlights the significant shortcoming of first-generation OCT technology and the need for comprehensive intravascular imaging, as will be provided by new second-generation systems. Finally, the feasibility and safety of OCT for in vivo imaging of coronary plaque is reiterated.
Macrophage concentration and distribution Macrophage concentration was evaluated in 49 patients (19 STEMI, 19 NSTEACS and 11 SAP) both within the culprit plaque and within non-culprit lesions in the same coronary artery26. A total of 119 lipid-rich plaques (76 culprit, 43 remote) and 41 fibrous plaques were evaluated. Analysis to quantify macrophage infiltration was performed within segmented ROI in fibrous caps of lipid-rich plaques and fibrous plaques and additionally within the most superficial portion (< 50 µm from the lumen) of the fibrous cap compared with that of the subsurface regions (> 50 µm from the lumen). Measurement of macrophage content on raw OCT data within the ROI was performed as described above. The NSD for each pixel within the ROI of each cap was measured for each image, and macrophage density was assessed by obtaining the average of the NSD values (mean NSD) within that ROI (Figure 14.6). Macrophage densities in lipid-rich plaques varied significantly among the three clinical groups (p < 0.001) with a higher macrophage concentration in the ACS patients compared to the patients with SAP (p = 0.002, Table 14.4). Consistent with their common pathophysiology, there was no difference in the macrophage densities between the two groups of ACS patients (p = 0.38). In line with the concept of pan-arterial inflammation, macrophage densities at both culprit and remote sites within each clinical group were similar (Figure 14.7a). Similarly, within the same patient (n = 15), macrophage density at
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a
238 b
c
d
L
127
T
* L
500 µm Figure 14.5 (a) Coronary angiogram showing evidence of significant stenosis in the mid-right coronary artery (arrow) in a patient with stable angina. (b) The corresponding OCT image clearly demonstrates a lipid-rich plaque (L, lipid pools) with disruption of the fibrous cap (arrows) and associated thrombus (T). There is a high OCT signal and significant signal heterogeneity within the tissue of the fibrous cap, consistent with a high macrophage density (see Figure 14.2). The raw OCT signal normalized standard deviation for the cap region marked by an asterisk was 9.0%. (c) Coronary angiogram showing evidence of a ruptured plaque in the left circumflex artery (arrow) in a patient who had a recent ST-elevation myocardial infarction. (d) The corresponding OCT image demonstrating a fibrous plaque with a thick fibrous cap.
remote sites correlated significantly with that of culprit sites (Figure 14.7b). Macrophage infiltration was not confined only to lipid-rich plaque; a significantly higher macrophage density was seen in fibrous plaques in the unstable coronary group. In patients with STEMI and NSTEACS, the relationship of focal macrophage density in the fibrous cap to the site of plaque cap rupture was evaluated by analyzing culprit lipid-rich plaques that demonstrated clear evidence of plaque rupture (n = 6; two STEMI, four NSTEACS). For each rupture site, the macrophage density within a 250-µm segment at the point of disruption was analyzed and compared to that of the remainder of the fibrous cap (Figure 14.8a). There was a significantly higher macrophage density at the rupture site than at the adjacent non-ruptured
cap within the same image (Figure 8b). Furthermore, macrophage density at rupture sites was significantly greater than that of all non-ruptured culprit sites in the combined STEMI and ACS groups (6.95 ± 1.6%, 5.75 ± 1.8%; p = 0.04). To further evaluate the relevance of focal macrophage infiltration of the fibrous cap, the spatial location of macrophages within the cap of lipid-rich plaques was examined to determine whether proximity to the endothelial surface was related to the clinical syndrome. Receiver operating curves for the prediction of an unstable coronary syndrome were constructed for the macrophage densities in the superficial and subsurface layers of the fibrous cap. At culprit sites, the area under the curve (AUC) for superficial macrophage density was significantly better than subsurface macrophage
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Table 14.3
OCT findings in patients with unstable and stable coronary syndromes
Finding Lipid-rich plaque Fibrous cap thickness (median, µm) TCFA Plaque disruption Calcification Thrombus
STEMI (n = 20)
NSTEACS (n = 20)
SAP (n = 17)
p Value
18 47.0 (n = 18)
15 53.8 (n = 18)
10 102.6 (n = 15)
0.090 0.034
13 (n = 18) 5 (25%) 2 4
9 (n = 18) 3 (15%) 3 5
3 (n = 15) 2 (12%) 7 6
0.012 0.053 0.049 NS
STEMI, ST-elevation myocardial infarction; NSTEACS, non-ST-elevation acute coronary syndrome; SAP, stable angina pectoris; TCFA, thin-cap fibroatheroma
a
b
c
*
LP LP LP
0%
10%
Figure 14.6 (a) OCT image of a lipid-rich plaque. The asterisk represents guidewire shadow artifact. LP, lipid pool. A 500-µm scale bar is shown in the top right-hand corner. (b) Outline (red) of the segmented fibrous cap of the OCT image depicted in (a). (c) Normalized standard deviation image superimposed over a standard intensity image, showing locations (blue→red) corresponding to increased macrophage density. The color scale bar represents the color mapping of the normalized standard deviation parameter. (Adapted from reference 26)
Table 14.4
Macrophage densities in lipid-rich plaques
Macrophage density (mean ± SD%)
STEMI (n = 19)
NSTEACS (n = 19)
SAP (n = 11)
p Value
5.54 ± 1.48
5.86 ± 2.01
4.14 ± 1.81
culprit
5.66 ± 1.44
5.91 ± 2.06
4.21 ± 1.74
remote
5.38 ± 1.56
5.76 ± 1.95
4.02 ± 2.02
5.41 ± 1.10
5.41 ± 1.10
4.43 ± 1.46
< 0.001d (0.002a, < 0.001b, 0.38c) 0.011 (0.013a, 0.003b) 0.076 (All ACS vs.SAP, 0.03) 0.025d
Lipid-rich plaques all
Fibrous plaques
STEMI, ST-elevation myocardial infarction; NSTEACS, non-ST-elevation acute coronary syndrome; SAP, stable angina pectoris; ACS, acute coronary syndromes; UAP, unstable angina pectoris. Values are presented as mean ± SD. Differences between groups determined using analysis of variance. p Values for comparisons between groups: aSTEMI vs. UAP; bNSTEACS vs. SAP; cSTEMI vs. NSTEACS; dAll ACS (STEMI + NSTEACS) vs. SAP
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a
b
7
y = 0.884x + 0.43 r = 0.67; p = 0.006
9
6 Remote lesion (%)
Macrophage density (%)
129
5 4 3 2 1 0 STEMI
ACS
7 6 5 4 3 2
SAP
Culprit
8
2
Remote
3
4
5
6
7
8
Culprit lesion (%)
Figure 14.7 Multifocal macrophage density results. (a) Bar graph showing macrophage density at culprit and remote sites for each clinical syndrome. Standard error bars are represented. (b) Correlation of macrophage density in culprit lesions relative to that at remote sites within the same patient. (Pearson’s correlation coefficient (r), and p value are depicted in the insert). ACS, acute coronary syndrome; SAP, stable angina pectoris; STEMI, ST-segment elevation myocardial infarction. (Adapted from reference 26)
a
b
p = 0.002
LP 8
6.95±1.6
7 5.29±1.17
Density (%)
6
LP
5 4 3 2 1 0
*
LP
Rupture
Non-rupture
Figure 14.8 Focal macrophage density results. (a) OCT image of a rupture site (outlined in red) overlying a lipid-rich plaque (LP). The asterisk represents guidewire shadow. A 500-µm scale bar is seen in the top right-hand corner. (b) Bar graph representing mean macrophage density at sites of rupture (mean ± SD), corresponding to outlined segment in (a), compared with the rest of the plaque. Standard error bars are represented. (Adapted from reference 26)
density at predicting an unstable presentation (AUC 0.79 ± 0.06 vs. 0.69 ± 0.07, respectively; p = 0.035). This was not found to be the case in plaques at remote sites, suggesting that macrophage content at the surface of culprit but not remote lesions was predictive of an acute coronary event. It is possible, therefore, that OCT-based evaluation of superficial macrophage content could provide us with a new parameter for assessing individual plaque vulnerability.
Taken collectively the results of this study demonstrate the focal and multifocal nature of the inflammatory process that occurs in subjects with unstable coronary syndromes as compared to stable patients. Importantly, by accurately measuring macrophage concentration, it establishes the ability of OCT to assess the biological activity of plaque in vivo, a significant addition to the capability of any diagnostic modality in vulnerable plaque evaluation.
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FUTURE DEVELOPMENT AND RESEARCH OF OCT AT THE MASSACHUSETTS GENERAL HOSPITAL A second-generation OCT technology, optical frequency domain imaging (OFDI)29 is currently undergoing development and animal testing at MGH. OFDI can provide more than two orders of magnitude improvement in image acquisition speed while not compromising resolution or image contrast. This development will facilitate continuous imaging of long segments of vessel wall, overcoming an important limitation of the first-generation OCT technology. On completion of animal studies, in-human testing of the new system will begin with the goal of performing a long-term follow-up study to evaluate the natural history of vulnerable plaque.
CONCLUSION The study of the morphological, cellular and biological features of plaque has formed the basis of our current paradigm of the pathophysiology of ACS. Most of the evidence to support this concept has been obtained from ex vivo and in vitro histopathological and biochemical studies and indirectly from clinical studies such as those measuring levels of circulating proinflammatory biomarkers. The MGH OCT program has demonstrated the potential capability of OCT for detecting these important features of atheroma in vivo, bringing the study of plaque vulnerability from bench to bedside. With the next generation of OCT systems and improved imaging techniques we hope that evaluation of vulnerable plaque will become a clinical reality moving closer toward attaining the new ‘holy grail’.
REFERENCES 1. Narula J, Finn AV, Demaria AN. Picking plaques that pop. J Am Coll Cardiol 2005; 45: 1970–3 2. Brezinski ME, Tearney GJ, Bouma BE, et al. Optical coherence tomography for optical biopsy. Properties and demonstration of vascular pathology. Circulation 1996; 93: 1206–13 3. Bouma BE, Tearney GJ. Clinical imaging with optical coherence tomography. Acad Radiol 2002; 9: 942–53 4. Tearney GJ, Brezinski ME, Boppart SA, et al. Images in cardiovascular medicine. Catheter-based optical imaging of a human coronary artery. Circulation 1996; 94: 3013 5. Brezinski ME, Tearney GJ, Weissman NJ, et al. Assessing atherosclerotic plaque morphology: comparison of optical coherence tomography and high frequency intravascular ultrasound. Heart 1997; 77: 397–403 6. Fujimoto JG, Boppart SA, Tearney GJ, et al. High resolution in vivo intra-arterial imaging with optical coherence tomography. Heart 1999; 82: 128–33
7. Yabushita H, Bouma BE, Houser SL, et al. Characterization of human atherosclerosis by optical coherence tomography. Circulation 2002; 106: 1640–5 8. Kolodgie FD, Burke AP, Farb A, et al. The thin-cap fibroatheroma: a type of vulnerable plaque: the major precursor lesion to acute coronary syndromes. Curr Opin Cardiol 2001; 16: 285–92 9. Davies MJ, Richardson PD, Woolf N, et al. Risk of thrombosis in human atherosclerotic plaques: role of extracellular lipid, macrophage, and smooth muscle cell content. Br Heart J 1993; 69: 377–81 10. Burke AP, Farb A, Malcom GT, et al. Effect of risk factors on the mechanism of acute thrombosis and sudden coronary death in women. Circulation 1998; 97: 2110–16 11. Fuster V, Moreno PR, Fayad ZA, et al. Atherothrombosis and high-risk plaque: part I: evolving concepts. J Am Coll Cardiol 2005; 46: 937–54 12. Shah PK, Falk E, Badimon JJ, et al. Human monocytederived macrophages induce collagen breakdown in fibrous caps of atherosclerotic plaques. Potential role of matrix-degrading metalloproteinases and implications for plaque rupture. Circulation 1995; 92: 1565–9 13. Sukhova GK, Schonbeck U, Rabkin E, et al. Evidence for increased collagenolysis by interstitial collagenases-1 and -3 in vulnerable human atheromatous plaques. Circulation 1999; 99: 2503–9 14. Shah PK, Galis ZS. Matrix metalloproteinase hypothesis of plaque rupture: players keep piling up but questions remain. Circulation 2001; 104: 1878–80 15. Galis ZS, Khatri JJ. Matrix metalloproteinases in vascular remodeling and atherogenesis: the good, the bad, and the ugly. Circ Res 2002; 90: 251–62 16. Lendon CL, Davies MJ, Born GV, Richardson PD. Atherosclerotic plaque caps are locally weakened when macrophages density is increased. Atherosclerosis 1991; 87: 87–90 17. van der Wal AC, Becker AE, van der Loos CM, Das PK. Site of intimal rupture or erosion of thrombosed coronary atherosclerotic plaques is characterized by an inflammatory process irrespective of the dominant plaque morphology. Circulation 1994; 89: 36–44 18. Moreno PR, Falk E, Palacios IF, et al. Macrophage infiltration in acute coronary syndromes. Implications for plaque rupture. Circulation 1994; 90: 775–8 19. Pratt WK. Digital Image Processing. New York: John Wiley and Sons, 1991 20. Tearney GJ, Yabushita H, Houser SL, et al. Quantification of macrophage content in atherosclerotic plaques by optical coherence tomography. Circulation 2003; 107: 113–19 21. Bauriedel G, Hutter R, Welsch U, et al. Role of smooth muscle cell death in advanced coronary primary lesions: implications for plaque instability. Cardiovasc Res 1999; 41: 480–8 22. Jang IK, Bouma BE, Kang DH, et al. Visualization of coronary atherosclerotic plaques in patients using optical coherence tomography: comparison with intravascular ultrasound. J Am Coll Cardiol 2002; 39: 604–9 23. Bouma BE, Tearney GJ, Yabushita H, et al. Evaluation of intracoronary stenting by intravascular optical coherence tomography. Heart 2003; 89: 317–20 24. Diaz-Sandoval LJ, Bouma BE, Tearney GJ, Jang I. Optical coherence tomography as a tool for percutaneous
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coronary interventions. Cathet Cardiovasc Intervent 2005; 65: 492–6 25. Jang IK, Tearney GJ, MacNeill B, et al. In vivo characterization of coronary atherosclerotic plaque by use of optical coherence tomography. Circulation 2005; 111: 1551–5 26. MacNeill BD, Jang IK, Bouma BE, et al. Focal and multifocal plaque macrophage distributions in patients with acute and stable presentations of coronary artery disease. J Am Coll Cardiol 2004; 44: 972–9 27. Burke AP, Farb A, Malcom GT, et al. Coronary risk factors and plaque morphology in men with coronary disease who died suddenly. N Engl J Med 1997; 336: 1276–82
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28. Virmani R, Kolodgie FD, Burke AP, et al. Lessons rom sudden coronary death: a comprehensive morphological classification scheme for atherosclerotic lesions. Arterioscler Thromb Vasc Biol 2000; 20: 1262–75 29. Yun SH, Tearney GJ, de Boer JF, et al. High-speed optical frequency-domain imaging. Opt Express 2003; 11: 2953–63 30. Tearney GJ, Jang IK, Kang DH, et al. Porcine coronary imaging in vivo by optical coherence tomography. Acta Cardiol 2000; 55: 233–7 31. MacNeill BD, Bouma BE, Yabushita H, et al. Intravascular optical coherence tomography: cellular imaging. J Nucl Cardiol 2005; 12: 460–5
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CHAPTER 15 Clinical lessons from OCT imaging of Apo E knockout mice Mehmet Cilingiroglu, Jung Hwan Oh, Pramod K Sanghi, Nate J Kemp, Sharon Thomsen, Thomas E Milner, Marc D Feldman
Spontaneous rupture of atherosclerotic plaque with superimposed thrombus is the leading cause of acute coronary syndromes and sudden death in humans1–3. The histopathological features that predict which atherosclerotic plaques are more likely to rupture have been well described and include thinning of the overlying fibrous cap to < 65µm, associated with a large lipid core beneath and a large percentage of the vessel wall being composed of lipid1–6. Ulcerated plaques were further characterized as having increased infiltration of macrophages, particularly at the shoulders of the fibrous cap7. Finally, those fibrous caps that have gradients in collagen content in the cap itself have also been identified as being more prone to rupture8. Despite knowledge of these classical pathological features, no technologies are available today that can image the plaque in situ to identify these vulnerable characteristics. Several imaging modalities such as intravascular ultrasound (IVUS), multislice computed tomography (CT) scanning and magnetic resonance imaging (MRI) are being investigated to detect features of vulnerable plaque for early diagnosis and provide better risk stratification of patients. Unfortunately, these imaging technologies are limited by a resolution of 100 µm, and are unable to detect the thin fibrous cap with a mean thickness of 23 µm that is at risk for rupture. Further, optical coherence tomography (OCT) is capable of performing molecular imaging, as will be demonstrated in this chapter; it is a technique that is only being explored with MRI9. OCT is an optical imaging modality with a resolution of 2–4 µm, greater than any competing technology10,11. OCT can penetrate 2 mm into tissue, sufficient to observe features of vulnerable plaque. Thus, OCT has the resolution to discern clinically
relevant thin fibrous caps as well as the lipid content of the vessel wall. Finally, OCT has the capability of imaging tissue-based macrophages, as well as gradients in collagen content within the fibrous cap. If the characteristic features that lead to unstable plaque could be recognized and then stabilized, a dramatic decrease may be achieved not only in acute myocardial infarction and unstable angina syndromes, but also in the progression of coronary artery disease. The development of a technology to image vulnerable plaques offers a promising long-term strategy to solve this problem. However, studies to understand the information encoded in reflected light from plaque are limited in the clinical setting. A gold standard for in vivo comparison does not exist. Comparison of in vivo OCT imaging to histology is very difficult, as the only possibilities are atherectomy specimens, that are limited by a very small sample size and the loss of tissue orientation. As an alternative, investigators have turned to human autopsy tissue12–16. However, the optical properties of tissue change after being removed from the body. OCT light reflection is in part dependent upon differences in the index of refraction, and this optical property changes from the in vivo to the ex vivo condition. As a result, we have focused our studies on in vivo atherosclerotic tissues. The apoE knockout murine model of atherosclerosis is well established and mimics many features of human coronary plaque progression17–21. These genealtered mice contain the entire spectrum of lesions observed during the progression of human atherosclerosis. The apoE knockout mouse is the only animal model that develops spontaneous plaque rupture and thus it can be argued that, as a model, it is superior to any larger mammal. Specific features 133
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that these mice develop which are known to be present in human atherosclerosis include: (1)
(2)
(3) (4) (5) (6)
(7)
(8) (9) (10) (11) (12) (13) (14)
(15)
Elevated cholesterol, slightly elevated triglyceride (TG), and reduced high-density lipoprotein (HDL); Large accumulation of particles in the very low-density lipoprotein (VLDL) and IDL size ranges; Arterial remodeling to accommodate plaque expansion; Monocyte attachment to vascular endothelial cells; Development of foam cells and macrophage infiltration; Fibrous caps containing smooth muscle cells surrounded by connective tissue matrix composed of collagen and elastin; Lipid cores underneath the fibrous caps containing necrotic tissue with cholesterol esters and foam cells; evidence of spontaneous plaque rupture associated with luminal thrombus; Intraplaque hemorrhage as a mechanism to produce plaque expansion; Plaque calcification; Predominance of plaque occurrence at arterial branch points; Substantial narrowing of the arterial lumen including total occlusion; Atherosclerosis accelerated by a high-fat diet; T-cell lymphocyte involvement implying a direct link between atherosclerosis and the immune response; Presence of matrix metalloproteinase in the shoulder of the plaque.
Despite its being an ideal model to examine vulnerable plaque, no previous studies of atherosclerosis in the apoE knockout murine model using OCT have been described. The purpose of our studies was to develop techniques and criteria to identify and measure the thin fibrous cap, identify large lipid cores and vessel walls composed of a large percentage of lipid, identify the presence of tissue-based macrophages and determine gradients in the collagen content of the fibrous cap.
PROTOCOLS UTILIZED The experimental protocol was approved by the Institutional Animal Care and Use Committee at the University of Texas and conformed with ‘Guidelines for the Care and Use of Laboratory Animals’ (NIH publication No. 86-23) and ‘Principles of Laboratory Animal Care’ (published by the National Society for Medical Research). C57BL/6J strain male and female
Mirror OCT light source Photoreceiver Imaging lens Stabilizing probe
Innominate artery
Heating pad
Respirator
XYZ micropositioner
Figure 15.1 Experimental preparation demonstrating that the innominate artery being imaged by OCT is via the outside of the artery in an intact apoE knockout mouse
apoE knockout mice were purchased from the Jackson Laboratory (Bar Harbor, ME) as retired breeders (ApoEtm1Unc). We performed the experiments on 19 (n = 7 in vivo and n = 12 ex vivo) apoE knockout mice between ages 5 to 9 months that were fed a rodent chow diet.
In vivo studies Mice were sedated with intraperitoneal ketamine and xylazine (0.2 ml/25 g bodyweight of mouse cocktail – formula is 3 cc ketamine (100 mg/ml) mixed with 2 cc xylazine (20 mg/ml) in 0.9% NaCl), and supplemented with additional sedation as needed. They were intubated and received 100% FIO2 via a rodent respirator at 150 breaths/min (Harvard Apparatus, MA). The murine heart rates were 250–350 beats/min. An anterior thoracotomy was performed (Figure 15.1). Since the innominate artery is an anterior structure, little additional dissection was required to isolate and focus the studies on this structure. The innominate artery was stabilized with two custom stainless steel probes placed 3 mm apart underneath the intact artery in order to reduce motion artifact. The probes were elevated with little applied pressure so that blood flow continued through the innominate artery. OCT light was focused on the surface of the innominate artery of the open-chest mouse. Since the OCT source light (λo = 830 nm) is not visible to the unaided eye, a visible red light (λ = 660 nm) was combined with the OCT source beam.
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To minimize light scattering by red blood cells, saline flushes were administered via a 23-gauge catheter placed via the apex into the beating left ventricle during OCT image acquisition. B-scan images were acquired across an imaginary ‘line’ orthogonal to the lumen of the innominate artery. To register OCT and histology images, India ink was used to mark the imaged surface of the artery. Following OCT imaging, the innominate artery was pressure perfused via the left ventricle with 10% buffered formalin. The segment of the artery that was imaged together with a portion of the aorta were surgically isolated and sectioned for routine histology.
Ex vivo studies ApoE knockout murine measurements of fibrous cap thickness, lipid core size and the percentage of the vessel wall composed of lipid were compared between OCT images and histology using an ex vivo preparation to eliminate motion artifact in the live preparation. Mice were sedated using the previously described protocol. An anterior thoracotomy was performed and a razor blade was used to cut the segment of the innominate artery out of the mouse, beginning at the aortic side of the aorta–innominate junction to immediately above the areas that were imaged. The distal end of the innominate artery was sutured and immediately placed on a wax-filled Petri dish. Saline injection under physiological pressure was used to maintain the circular cross section of the vessel. India ink was used to identify the arterial location where OCT B-scan cross-sectional images were recorded. Upon completion of imaging, saline was substituted with 10% formalin under pressure for tissue fixation for 5 minutes. The artery was then submerged in a solution of 10% formalin for 24 hours, and subsequently processed for histology.
OCT instrumentation The OCT instrument utilized a mode-locked Ti:Al2O3 femtosecond laser source operating at λ0 = 830 nm with bandwidth ∆λ = 55 nm full width at half maximum (FWHM). Light injected into the interferometer was linearly polarized at 45° and divided into reference and sample paths with a non-polarizing 50/50 beam-splitter. A mirror mounted to a loudspeaker diaphragm in the reference path varies optical path-length in the longitudinal direction with a 15-Hz sinusoid. Sample path optics included scanning lenses and scanning galvanometers for creation of user-specified lateral scan patterns in the x and y directions. A corrected achromatic triplet lens (f = 8mm) focused light onto the artery. Light reflected in the reference path, was recombined with light reflected from the artery and was diverted to the detection path, where dual photoreceivers measured interference fringes versus depth (z) for both
135
horizontal [H(z)] and vertical [V(z)] polarization components. Pre-filtering, analog-to-digital conversion, and digital demodulation of detected interference fringe intensities provided the polarization-insensitive intensity [I(z) = H(z)2 + V(z)2] of light backscattered from each depth (z) deep in the artery. Multiple acquisitions of I(z) while scanning the beam laterally across the artery surface allowed recording of OCT B-scan images.
Histology and OCT image analysis The gold standard for identification of vulnerable plaque in our studies was histology. Innominate artery segments were pressure perfused with 10% formalin and then processed for standard paraffin embedding. The specimens were oriented by having a segment of the aorta remain attached to the innominate artery, and India ink on the anterior surface to define the site of OCT imaging. Sections 5 µm thick were cut and stained with hematoxylin and eosin (H&E) and trichrome Masson for the ex vivo studies to define fibrous cap thickness, lipid core size and per cent lipid content. Von Kossa stain was used for in vivo studies to identify calcium hydroxyapatite. Image Pro Plus (version 5.5) software was used to measure individual fibrous cap thickness, lipid core and total lipid and vessel areas. For fibrous cap thickness, five separate measurements were taken for each individual fibrous cap and the mean value was calculated for the best estimate. To calculate per cent lipid content for each innominate artery, the sum of areas of all lipid cores was divided by the area of the vessel analyzed. Since OCT light did not fully penetrate both walls of the innominate artery, regions of the OCT image with sufficient signal-to-noise ratio defined the areas to be analyzed in the histological image. Longitudinal (depth) dimension in OCT images was determined by dividing optical path-length by a refractive index (RI) = 1.42. Although the refractive index is known to vary between arterial constituents (e.g. lipid and calcium), RI = 1.42 is believed to represent a mean value. Longitudinal resolution of OCT images was limited by the spectral width (∆λ = 55 nm) of incident source light to approximately 5 µm. Lateral dimension in OCT images was determined by imaging a metal-on-glass calibration line pattern. Lateral resolution was limited by the numerical aperture (0.25) of the light incident on the artery to 15 µm. Image Pro Plus (version 5.5) software was also used to make the identical analysis of all OCT images as described above.
Correlation between OCT images and histology We focused the analysis on one match per mouse between OCT B-scan images and corresponding histological sections for ex vivo (n = 12) and in vivo
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100 Fibrous cap thickness by OCT and histology (µm)
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60
40
20 0
1
2
3
4
5
6
7
8
9
10 11 12
Sample number OCT
Histology
Figure 15.2 Comparison of fibrous cap thickness measurements by OCT and histology. Data are the mean of five measurements along the entire length of the fibrous cap. Each open triangle and closed circle corresponds to histology and OCT measurements for the fibrous cap thickness for each sample, respectively. There was consistent 50% shrinkage in fibrous cap thickness due to the loss of water during tissue processing and fixation of the samples
(n = 7) studies. Each OCT/histology match was assigned an identification number. The site of OCT light entry in histology images was identified by the presence of India ink. The aortic side of the innominate artery was defined by residual aortic tissue. A pathologist reviewed the histology in a blinded fashion. The OCT B-scan images were read by two independent observers in a blinded fashion. Both pathologist and OCT readers were instructed to identify the fibrous cap, lipid core(s) and calcification, and correlated their size, shape and location. Morphometry of fibrous cap thickness and lipid core areas was performed using a Zeiss Axiophot light microscope fitted with a Spot digitizing camera coupled to a computer with Image Pro Plus 5 software.
Tissue shrinkage (50±1.5%) of the formalin-fixed, paraffin-embedded histology specimens was observed. The amount of shrinkage in histology specimens was reproducible across innominate arteries from different mice. The correlation between the OCT and histology measurements for fibrous cap thickness was tested with Pearson correlation coefficient. The results indicate that the two measurements correlate with each other with r = 0.97 and p < 0.0001. The coefficient of determination with regression analysis indicated that 94% of the variability in OCT could be accounted for by histology. Lipid core size and per cent lipid content were measured in matching OCT and histology images (n = 6). The results are shown in Table 15.1. The lipid cores ranged in size from 7417 to 70 521 µm2 by OCT and 3383 to 37 772 µm2 by histology. The number of lipid cores per innominate artery in those specimens ranged from one to six. Similar to the fibrous caps, the amount of shrinkage in lipid core size was also reproducible across mice with an average of 50 ± 12%. Large lipid cores are shown in Figures 15.3 and 15.4. Small lipid cores are shown in Figure 15.5. The per cent lipid content ranged from 5.9 to 36.5% by OCT and from 5.6 to 38.4% by histology. The results indicate the equality of variances is not statistically different (p = 0.89), and there were no differences between the per cent lipid content between OCT and histology (p = 0.96). However, these studies were confined to a portion of the vessel identified by OCT, since the OCT light could not fully penetrate both walls of the artery. Results of our experiments demonstrate the ability of OCT accurately to measure fibrous caps thicknesses that span the range observed in patients at risk for acute coronary syndromes6. We also showed the ability of OCT accurately to measure lipid core size and quantify the percentage of the vessel wall composed of lipid. Vessels composed of more than 25% lipid are thought to be vulnerable for future rupture23, and OCT was able to identify vessels whose per cent lipid content spanned this critical range.
RESULTS OCT can accurately determine fibrous cap thickness, lipid core size and per cent lipid content Individual fibrous cap thickness for OCT and histology are displayed in Figure 15.2 and have been in part previously published22. Fibrous cap thickness ranged from 97 µm (thickest) to 21 µm (thinnest) by OCT and from 47 µm (thickest) to 11 µm (thinnest) by histology. An example of a thin fibrous cap is shown in the OCT B-scan and the histology image in Figure 15.3 (21 µm by OCT) and Figure 15.4 (52 µm by OCT). An example of thick fibrous cap is shown in Figure 15.5 (108 µm by OCT).
OCT can identify and penetrate calcium One of the major limitations of IVUS is an inability to image structures that lie behind calcium. Unlike ultrasound, OCT can penetrate calcified structures15. During in vivo studies, OCT B-scan images demonstrated that the brightest light reflection occurred in arteries with calcium hydroxyapatite and cholesterol esters mixed together. This result was anticipated because calcium mixed with lipid has a large index of refraction gradient, and therefore would appear as bright features in the OCT B-scan images. The pixel brightness of calcium mixed with lipid, fibrous tissue and pure lipid all differed and were 177 ± 15, 69 ± 6 and 32 ± 7 gray-scale arbitrary units,
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Figure 15.3 Unstable plaque. An example of the analysis technique for the OCT image (a), and histology image (b). There are two lipid areas which were measured, five measurement locations for the fibrous cap thickness (F1–F5), and the area of the vessel analyzed defined by the OCT area of visualization. In this example, the thinnest location of the fibrous cap was 21 µm by OCT and 11 µm by histology; the largest lipid core was 43 267 µm2 by OCT and 21 952 µm2 by histology, and the percentage of the plaque composed of lipid was 21.4% by OCT and 19.7% by histology. The echo-bright region immediately superior to the larger lipid core is due to calcium (von Kossa stain not shown)
respectively (one-way ANOVA, p < 0.001). An example of the bright features in OCT B-scans and the corresponding histological images are shown in Figure 15.6. Von Kossa stain was used to confirm that the bright speckles (Figure 15.6a) and bright linear regions (Figure 15.6b) correspond to areas of calcium hydroxyapatite. The appearance of calcified structures with sound (IVUS) has always been plagued by not only calcium shadowing, but a discrepancy between histological images of speckled calcium in lipid, and sound-based
images of large bright regions that never seem to capture the individual calcium hydroxyapatite structures. Because resolution of IVUS and OCT scale respectively with the wavelengths of sound (200 µm) and light (1 µm), OCT can provide one to two orders of magnitude greater resolution than IVUS. The ability of OCT in Figure 15.6a to identify the individual speckles of calcium hydroxyapatite that are apparent in the von Kossa-stained histology, is consistent with the light penetration and resolution capabilities of OCT.
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Figure 15.4 Unstable plaque. An example of raw images without overlay of the measurement tool. (a) The OCT and (b) the histology. The mean fibrous cap thickness was 52 µm by OCT and 26 µm by histology; the lipid core size was 10 849 µm2 by OCT and 5393 µm2 by histology; and the percentage of the plaque composed of lipid was 23.8% by OCT and 25.6% by histology
Polarization-sensitive OCT to detect collagen in the fibrous cap Fibrous caps contain a high content of birefringent material, in particular collagen. Collagen concentration in vulnerable fibrous caps is known to be deposited unevenly8. In contrast, stable fibrous caps have collagen that is evenly distributed. By measuring both the distribution of collagen as well as the
fibrous cap thickness, polarization-sensitive OCT (PS-OCT) may offer detailed information regarding the stability of the fibrous cap not available to other imaging modalities. The apoE knockout mouse used in this project has birefringent material (collagen) in the fibrous cap. Evidence of birefringence is presented in Figure 15.7, where polarization microscopy images of a thin fibrous cap are presented at 0°, 45° and 90°
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Table 15.1 Lipid core measurements by OCT and histology (µm2) Sample number
Lipid core by OCT
Lipid core by histology
Mouse 1 Mouse 2 Mouse 3 Mouse 4 Mouse 5 Mouse 6
7417 30 780 15 390 70 521 43 267 15 853
3383 16 222 7708 37 772 28 148 7646
Corresponding area measurements demonstrate consistent shrinkage of histology compared to OCT
orientations. The maximum polarization microscopy contrast is present at a 0° incident polarization for this example. PS-OCT detects transformations in the backscattered polarization state due to tissue birefringence. The magnitude of this transformation is displayed as the length of arcs on the Poincaré sphere (Figure 15.8). Imaging was performed ex vivo on an innominate artery cut along the long axis and pinned open on a wax block to access the luminal surface similar to the perspective of a catheter used in a patient. The luminal surface was scanned with both OCT and PS-OCT. Uneven distribution of collagen is evident in the fibrous cap shown in Figure 15.8. The shoulder exhibits well-organized collagen as demonstrated by the longer arcs on the Poincaré sphere in this region, while the peak of the fibrous cap exhibits less-organized collagen, shorter arcs on the Poincaré sphere, and thus a greater likelihood of vulnerability in this fibrous cap. Efforts are underway to quantitatively characterize collagen density and collagen orientation in the fibrous cap with enhanced PSOCT algorithms and instrumentation.
Iron oxide to detect tissue-based macrophages Atherosclerosis is an inflammatory process which is characterized by increased endothelial permeability and subsequent entry of macrophages and T lymphocytes into the vessel wall24. Plaques which are felt to be vulnerable have a preponderance of macrophages in the shoulder region of the fibrous cap. The macrophages release matrix metalloproteinases which degrade collagen and weaken the fibrous cap25. The identification of these macrophages by OCT would be another important measure of the vulnerability of a fibrous cap. Some investigators have utilized the OCT signal intensity as an indirect means to identify the presence of macrophages16. A more precise alternative would be to use micrometer-sized metal particles that are selectively taken up by the
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reticuloendothelial system in order specifically to identify tissue-based macrophages26 and enhance the reflection of OCT light. We tested the hypothesis that the intravenous injection of iron oxide nanoparticles (Feridex®, Berlex Lab., Montville, NJ) into apoE knockout mice would be taken up at the shoulders of plaques infiltrated with macrophages. The physiological distribution of the iron oxide particles depends primarily on the size of the particles and the physical properties of their coating. Feridex, a superparamagnetic iron oxide particle (SPIO), is clinically used in identifying cancerous lesions during MRI imaging, and consists of particles ranging from 80 to 100 nm in size. An example of this successful uptake is shown in Figure 15.9. The iron oxide particles have been stained blue with the Prussian Blue stain and can be seen inside macrophages at the shoulder of a plaque. OCT has the ability to identify tissue-based macrophages, particularly following application of an oscillating magnetic field27. The iron oxide nanoparticles can be placed in an oscillating motion by the magnetic field, moving the tissue that surrounds them on a nanometer scale which can be detected by OCT. The magnetic force acting on the nanoparticles can be increased by applying a sinusoidal current to a solenoid containing a conical ferrite core that focuses the magnetic field strength (Bmax = 0.14 Tesla). Since the magnetic force is proportional to the square of the magnetic field strength, the frequency response of the tissue is twice that of the stimulus frequency. In our studies, differential phase OCT (DP-OCT) and conventional OCT have been performed in the livers of apoE knockout mice that have been injected with iron oxide particles. DP-OCT can detect the optical path length change in tissue in response to the applied magnetic field due to the iron oxide nanoparticles (Figure 15.10). Tissue motion in the range of 50–200 nm has been detected. M-mode OCT scans have also demonstrated nanoparticle movement under similar oscillating magnetic field excitation (Figure 15.11). In the control apoE knockout mouse that did not receive iron oxide injections, optical path length change and oscillation of M-mode scans was not observed. The results of these experiments indicate that OCT has a potential of identifying tissue based macrophages by using iron oxide particles excited by a focused oscillating magnetic field. Injections of iron oxide in patients is considered safe26 and is currently approved by the Food and Drug Administration (FDA) for patient use. The iron oxide particles are biodegradable. As they degrade, the iron enters the plasma iron pool and is subsequently incorporated into red cell production and other natural uses of iron. Eventually, it is secreted from the body as the body iron stores turn over.
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Figure 15.5 Stable plaque. A thick fibrous cap (maximum 108 µm by histology) is imaged by OCT (a) and corresponding histology with H&E staining (b). The OCT image starts in the plaque, and excludes part of the media and the entire adventitia
METHODS TO REDUCE LIGHT ATTENUATION DUE TO BLOOD The blood flow between the OCT optical fiber and artery can obscure light penetration into the vessel wall. One solution is the use of saline flushes. Saline injections into the mouse heart during OCT imaging were used to reduce the amount of light scattering in experimental models. However, the amount of
saline injections required to significantly reduce light attenuation for a long period of time to perform OCT imaging along the entire vessel wall may not be technically feasible in humans, since myocardial ischemia eventually occurs in the distal myocardium. One solution is to increase imaging acquisition speed and resolution with the use of spectral domain rather than time domain OCT28, which may provide renewed enthusiasm for the use of saline flushes.
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Figure 15.6 Calcification of plaque. (a) The upper panel is the Von Kossa stain demonstrating the presence of calcification of the atherosclerotic plaque by the speckled black staining. The lower panel is the corresponding OCT image showing only a portion of the plaque which contains the speckled calcium. Since the index of refraction of calcium hydroxyapatite is high at 1.65 and that of cholesterol is close to 1.33, due to its high water content, the calcium appears as bright speckles in the OCT image. (b) Areas of linear calcification are shown by OCT (above) and histology with Von Kossa stain (below). The bright regions in the OCT image are due to calcification
0°
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Figure 15.7 Demonstration that the fibrous cap of the rodent chow-fed apoE knockout mouse is birefringent. Bottom right is the H&E stain, and the Sirius red-stained polarization images with incident light at 0, 45, and 90° are shown. The black arrows demonstrate the fibrous cap with varying amounts of birefringence evident by the changing concentration of white in the fibrous cap. The maximum birefringence is at a 45° incident polarization light
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Figure 15.8 (a) Described in the text. (b) Demonstration that polarization-sensitive OCT (PS-OCT) can detect differences in birefringence in the fibrous cap of an intact ex vivo innominate artery. The OCT image is shown, and polarization state transformations for two regions corresponding to the center and shoulder of the fibrous cap are plotted on the Poincaré sphere. The center of the fibrous cap has shorter arcs and less organized collagen, while the shoulder has longer arcs and more organized collagen. A gradient in the collagen properties is evident, consistent with a vulnerable fibrous cap
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Figure 15.9 Prussian Blue staining of murine apoE knockout aortic atherosclerotic plaque. Superoxide iron nanoparticles can be seen at the shoulder of the plaque where macrophages predominate
Nanoparticles and Control
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Another approach to eliminate light scattering is to make the contents of blood more homogeneous. Since the refractive index of plasma and red blood cells is different, penetration of light through blood is poor. If the refractive index of plasma could be brought closer to that of the red blood cell membrane, then light penetration could theoretically be increased. This concept, called refractive index matching, has been tested by using agents such as dextran and intravenous contrast, with some success29. An in vitro experiment which pumped blood through transparent tubing did show significant improvements (69 ± 12% for dextran, 45 ± 4% for intravenous contrast) in OCT light penetration with the use of these agents. One limitation of this approach is that it does not take into account the light attenuation that occurs
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Magnet On
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Figure 15.11
M-mode OCT scan demonstrating nanoparticle movement under oscillating magnetic field excitation
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Figure 15.12 Optical properties of several dilutions of murine blood with Oxyglobin®. The reduced scattering coefficient (µs′) is dependent on the number of red blood cells per unit volume. The absorption coefficient (µa) depends on the concentration of hemoglobin. Hct, hematocrit. (With permission from reference 34)
on the surface of the red cell membrane, which is different from the hemoglobin that resides in the cytoplasm30–32. In fact, light attenuation in whole blood containing red blood cells is 7–20 times greater than in a solution containing the same concentration of hemoglobin33. The other drawback is that the injection of large amounts of dextran or intravenous contrast required to raise the refractive index of plasma may not be feasible in humans. An alternative approach to minimize scattering is to lower the number of red blood cells by replacing them with a hemoglobin-based blood substitute34. These agents are artificial oxygen carriers that are optically transparent at near-infrared wavelengths and will thereby make the refractive index of blood more uniform. They are considered safe for humans and are starting to be used in clinical medicine,
although they are not yet FDA approved. The use of these agents to reduce light scattering has been successfully demonstrated in our experiments where mouse blood was replaced by Oxyglobin® (Biopure, Cambridge, MA), an artificial blood substitute approved for use in veterinary medicine. The optical properties of the blood substitute are demonstrated in Figure 15.12, which shows the results of scattering and absorption measurements using whole murine blood diluted with Oxyglobin. Optical properties were determined at an OCT source wavelength of λ0 = 1310 nm. The scattering properties of whole murine blood decreased from 1.801 ± 0.245 to 0.253 ± 0.176 l/mm (p < 0.05) when the hematocrit was reduced from physiological levels to < 10%. In contrast, the absorption properties of whole murine blood were similar over the same range, decreasing from 0.344 ± 0.127 to 0.129 ± 0.118 (NS). These results demonstrate that dilution of whole blood with Oxyglobin improves light penetration primarily by reducing light scattering. The utility of this approach was demonstrated in vivo for the assessment of heart function in the mouse. In Figure 15.13 OCT images of the right ventricular wall are shown at baseline in the presence of whole blood and after reduction of the hematocrit with Oxyglobin. The epicardial boundary was easily delineated in both baseline and low-hematocrit images. However, the endocardial boundary was visible in only the low-hematocrit images. Therefore, OCT imaging of a beating heart can be improved by the use of blood substitutes which reduce light scattering due to the myocardial blood34. An advantage of this technique is that replacement of red blood cells with an artificial oxygen carrier buys time for the OCT operator to study the vessel wall, as opposed to other techniques, such as saline injection or vessel occlusion, which produce ischemia.
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Our studies in the apoE knockout model of atherosclerosis show that OCT has the ability to measure the fibrous cap thickness and the size of the lipid core beneath the cap, and to determine whether gradients in collagen deposition exist with the fibrous cap. Finally, nanoscale metals are used to identify the presence of tissue-based macrophages, all key features of vulnerable plaque. The unique capability of OCT to characterize plaque features which imply vulnerability holds promise for identification of vulnerable plaque in patients, as this technology is moved into the cardiac catheterization laboratory.
ACKNOWLEDGMENTS This study was supported by the National Institutes of Health Grant HL-59472 (TEM), the Janey Briscoe Center for Excellence in Cardiovascular Research and the SCAI/GE Fellowship Grant (MC).
REFERENCES 1. Falk E, Shah PK, Fuster V. Coronary plaque disruption. Circulation 1995; 92: 657–71 2. Davies MJ, Thomas A. Thrombosis and acute coronary-artery lesions in sudden cardiac ischemic death. N Engl J Med 1984; 310: 1137–40 3. Davies MJ, Thomas AC. Plaque fissuring – the cause of acute myocardial infarction, sudden ischaemic death, and crescendo angina. Br Heart J 1985; 53: 363–73 4. Davies MJ, Richardson PD, Woolf N, et al. Risk of thrombosis in human atherosclerotic plaques: role of extracellular lipid, macrophage, and smooth muscle cell content. Br Heart J 1993; 69: 377–81 5. Mann JM, Davies MJ. Vulnerable plaque. Relation of characteristics to degree of stenosis in human coronary arteries. Circulation 1996; 5: 928–37 6. Virmani R, Kolodgie FD, Burke AP, et al. Lessons from sudden coronary death; a comprehensive morphological classification scheme for atherosclerotic lesions. Arterioscler Thromb Vasc Biol 2000; 20: 1262–75
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Figure 15.13 OCT images of the murine right ventricle at baseline and after reduction to < 10% hematocrit (Hct) with Oxyglobin®. Scale bar is 0.40 mm. Horizontal axis is time in seconds. (With permission from reference 34)
7. Libby P. Molecular bases of the acute coronary syndromes. Circulation 1995; 91: 2844–50 8. Burleigh MC, Briggs AD, Lendon CL, et al. Collagen types 1 and 3, collagen content, GAGs, and mechanical strength of human atherosclerotic plaque caps: span wise variations. Atherosclerosis 1992; 96: 71–81 9. Frias JC, Williams KJ, Fisher EA, et al. Recombinant HDL-like nanoparticles: a specific contrast agent for MRI of atherosclerotic plaques. J Am Chem Soc 2004; 126: 16316–7 10. Huang D, Swanson EA, Lin CP, et al. Optical coherence tomography. Science 1991; 254: 1178–81 11. Tearney GJ, Brezinski ME, Bouma BE, et al. In vivo endoscopic optical biopsy with optical coherence tomography. Science 1997; 276: 2037–9 12. Brezinski ME, Tearney GJ, Bouma BE, et al. Optical coherence tomography for optical biopsy. Circulation 1996; 93: 1206–13 13. Fujimoto JG, Boppart SA, Tearney GJ, et al. High resolution in vivo intra-arterial imaging with optical coherence tomography. Heart 1999; 82: 128–33 14. Jang IK, Bouma BE, Kang DH, et al. Visualization of coronary atherosclerotic plaques in patients using optical coherence tomography: comparison with intravascular ultrasound. J Am Coll Cardiol 2002; 39: 604–9 15. Yaushita H, Bouma BE, Houser SL, et al. Characterization of human atherosclerosis by optical coherence tomography. Circulation 2002; 106: 1640–5 16. Tearney GJ, Yabushita H, Houser SL, et al. Quantification of macrophage contents in atherosclerotic plaques by optical coherence tomography. Circulation 2003; 107: 113–19 17. Plump AS, Smith JD, Hayek T, et al. Severe hypercholesterolemia and atherosclerosis in apolipoprotein E-deficient mice created by homologous recombination in embryonic stem cells. Cell 1992; 71: 343–53 18. Zhang SH, Reddick RL, Piedrahita JA, et al. Spontaneous hypercholesterolemia and arterial lesions in mice lacking apolipoprotein E. Science 1992; 258: 468–71 19. Fazio S, Lee YL, Ji ZS, et al. Type III hyperlipoproteinemic phenotype in transgenic mice expressing dysfunctional apolipoprotein E. J Clin Invest 1993; 92: 1497–503
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20. Nakashima Y, Plump AS, Raines EW, et al. ApoE-deficient mice develop lesions of all phases of atherosclerosis throughout the arterial tree. Arterioscler Thromb 1994; 14: 133–40 21. Johnson JL, Jackson CL. Atherosclerotic plaque rupture in the apolipoprotein E knockout mouse. Atherosclerosis 2001: 154: 399–406 22. Cilingiroglu M, Oh JH, Sugunan B, et al. Detection of vulnerable plaque in a murine model of atherosclerosis with optical coherence tomography. Cathet Cardiovasc Interv 2006; 915–23 23. Granada JF, Kaluza GL, Raizner AE, et al. Vulnerable plaque paradigm: prediction of future clinical events based on a morphological definition. Cathet Cardiovasc Interv 2004; 62: 364–74 24. Lendon CL, Davies MJ, Born GV, et al. Atherosclerotic plaque caps are locally weakened when macrophage density is increased. Atherosclerosis 1991; 87: 87–90 25. Shah PK. Mechanisms of plaque vulnerability and rupture. J Am Coll Cardiol 2003; 41(4 Suppl S): 15S–22S 26. Bulte JW, Kraitchman DL. Iron oxide MR contrast agents for molecular and cellular imaging. NMR Biomed, 2004; 17: 484–99 27. Oldenburg AL, Gunther JR, Boppart SA. Imaging magnetically labeled cells with magnetomotive optical coherence tomography. Opt Lett 2005; 30: 747–9
28. de Boer JF, Cense B, Park BH. Improved signalto-noise ratio in spectral-domain compared with time-domain optical coherence tomography. Opt Lett 2003; 28: 2067–9 29. Brezinski M, Saunders K, Jesser C, et al. Index matching to improve optical coherence tomography imaging through blood. Circulation 2001; 103: 1999–2003 30. Twersky V. Absorption and multiple scattering by biological suspensions. J Opt Soc Am 1970; 60: 1084–93 31. Roggan A, Friebel M, Dörschel K. Optical properties of circulating human blood in the wavelength range 400–2500 nm. J Biomed Opt 1999; 41: 36–46 32. Steinke JM, Sheppard AP. Role of light scattering in whole blood oximetry. IEEE Trans Biomed Eng 1986; 33: 294–301 33. Kramer K, Elam JO, Saxton GA, et al. Influence of oxygen saturation, erythrocyte concentration and optical depth upon the red and near-infrared light transmittance of whole blood. Am J Physiol 1951; 165: 229–46 34. Villard JW, Feldman MD, Kim J, et al. Use of a blood substitute to determine instantaneous murine right ventricular thickening with optical coherence tomography. Circulation 2002; 105: 1843–9
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CHAPTER 16 Long-term effects of endovascular radiation after balloon angioplasty: assessment by OCT and histology Heleen MM van Beusekom, Evelyn Regar, Ilona Peters, Wim J van der Giessen
INTRODUCTION
MATERIALS AND METHODS
Irradiation has the ability to induce cell cycle arrest and, thus, has antiproliferative as well as antiangiogenic effects. The combination of irradiation and percutaneous coronary intervention (PCI) was thought to be an effective method to reduce the incidence of restenosis. It is now known, however, that irradiation itself can induce restenosis, especially in the case of the so-called ‘geographic-miss’ where the injured artery is not completely irradiated. Intracoronary radiation has also been associated with late stent thrombosis. With the advent and success of drug-eluting stents, the niche for brachytherapy has subsequently diminished. Still, a large number of patients have been treated with coronary brachytherapy to date1–5. Information regarding the long-term effects of such treatment is largely unknown. We therefore studied the effect of vascular irradiation on the iliac arteries of Yucatan microswine following balloon angioplasty at 2 years following brachytherapy. The current model, balloon injury followed 3 days later by brachytherapy, allows the initiation of vascular wound healing and neointima formation prior to inhibition by brachytherapy. Histology is considered the gold standard to assess vascular morphology, but new imaging modalities such as optical coherence tomography (OCT) have been developed that allow accurate and sensitive (high resolution) in vivo imaging. It may eventually allow serial in vivo imaging over time. The current chapter describes the long-term effects of brachytherapy at 2 years’ follow-up, as studied by OCT imaging and histology.
Animal preparation Experiments were performed in six non-atherosclerotic adult female Yucatan micropigs (20–30 kg). The protocol was approved by the Committee on Experimental Animals of Erasmus Medical Center Rotterdam. The day before the procedure, antiplatelet prophylaxis was started with 150 mg clopidogrel (Plavix, SanofiSynthelabo) and 300 mg carbosalatum calcium, (Ascal, Viatris) orally. After an overnight fast, the animals were sedated with 20 mg/kg ketamine hydrochloride (Dopharama). Anesthesia was induced with 11 mg/kg thiopental (Pentothal, Abbot). After endotracheal intubation, the pigs were connected to a ventilator that administered a mixture of oxygen and nitrous oxide (1:2, vol/vol). Anesthesia was maintained with 0.5–2 vol% isoflurane (Forene, Abbott Laboratories). Intramuscular antibiotic prophylaxis was administered with 200 mg procaine–benzylpenicillin and 250 mg streptomycin (Eurovet). Under sterile conditions, a 9 F introduction sheath was placed in the left carotid artery, and 5000 IU heparin was administered. An 8 F guiding catheter was advanced via the descending aorta into the iliac or renal artery under fluoroscopic guidance.
Percutaneous coronary intervention and angiography Angiography was performed with Iomeprol (Iomeron 370, Bracco-Byk) as a contrast agent. Balloon angioplasty was performed in the renal arteries and the internal iliac arteries, as previously described6. Briefly, quantitative angiography (QCA: CAAS II, Pie
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Medical) was performed and a coronary angioplasty balloon was advanced through the guide catheter to the predefined delivery site. Balloons (length 20 mm) were inflated to achieve a balloon/artery ratio of 1.2:1 based on online QCA. After the final quantitative angiogram, the animals were allowed to recover from anesthesia and returned to the animal care facilities for postoperative recovery. Throughout the follow-up period, 300 mg aminosalicylic acid (Ascal) and 75 mg clopidogrel was given daily.
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Balloon angioplasty followed by brachytherapy 3 days later was performed in 32 arteries. The arteries were irradiated using a 90-yttrium/strontium source (BetaCath™, Novoste) with a source length of 30 mm. The source was carefully positioned in such a way that the previously dilated segment was completely covered and received full-dose radiation. Dose prescription was 20 Gy (at 2 mm from the center of the source axis) resulting in a dwell time of 3–4 minutes. For further histology and OCT analysis, the following artery segments were defined:
3
Figure 16.1 Angiography (right) and longitudinal reconstruction of an OCT pullback (left) with several cross-sectional images showing the irradiated and dilated narrowed segment, which widens out towards the distal untreated segment. The OCT images clearly show that narrowing is mainly the result of constrictive remodeling and minimal neointimal thickening. Dotted areas indicate arterial segments 1 to 3
were processed for paraffin embedding. Sections were collected of the non-injured artery and at three levels of the injured arteries. Hematoxylin–eosin was used as a routine stain, and resorcin–fuchsin as an elastin stain.
Follow-up study After the follow-up period of 2 years, the animals returned for angiographic restudy and OCT imaging. Thereafter euthanasia was performed by administering an overdose of pentobarbital intravenously and the vessels were fixed in situ using approximately 500 ml 4% buffered formaldehyde.
RESULTS Angiography and brachytherapy
OCT imaging was performed using a 0.019-inch imaging wire (LightLab Imaging, Boston, MA) with a 1300-nm superluminescent diode light source and an output power of 5.0–8.0 mW. The imaging wire had an axial resolution of 15 µm and a lateral resolution of 25 µm. The OCT imaging was performed by continuous motorized pullback (1 mm/s) of the imaging probe during proximal, low-pressure balloon occlusion and saline flush (approximately 1 ml/s).
The balloon/artery ratio, used to induce a vascular healing response prior to brachytherapy, was 1.2 ± 0.13. Follow-up angiography at 24 months following treatment (Figure 16.1) showed a clear narrowing in some of the treated segments, while others were less affected. Quantitative coronary angiography indicated a late lumen loss of 0.21 ± 1.29 mm in the irradiated and balloon-injured segment (segment 1). There was no correlation with balloon size as measured by QCA. The irradiated control segment without balloon dilatation showed a net lumen gain of only 0.15 ± 0.7 mm over the 2-year period (segment 2), while the distal non-dilated and non-irradiated segment (segment 3) showed an increase of 0.73 ± 0.65 mm (p = 0.005, T-test).
Histology
OCT imaging
The arteries, including 1–2 cm both proximally and distally adjacent to the dilated and radiated site,
OCT mean pullback length was 40 ± 2 mm. The images of the untreated segments showed the two-layered
OCT imaging
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Figure 16.2 Overview and details (a–f) of OCT images and histology (g–i) of an internal iliac artery following percutaneous coronary intervention and brachytherapy at 24 months’ follow-up from segments 1, 2 and 3. OCT (a) and histology (g) of segment 1 shows a strongly OCT-reflective and collagen-rich intima and media, but not adventitia. Similarly, segment 2 (b, h) shows a strongly reflective and collagen-rich intima and especially dense collagen-rich adventitia but not media. This segment is narrowed as compared to segments 1 and 3. Segment 3 (c, i) shows the normal appearance of a healthy artery with only reflective internal and external elastic laminae (arrows). I, intima; M, media; A, adventitia
appearance of a normal healthy artery with a clear demarcation between a moderately reflective media and a highly reflective adventitia (Figures 16.1 and 16.2). The treated area showed several changes in vascular architecture: (1) the appearance of a threelayered artery with the intima becoming more pronounced and the media and adventitial layers becoming more reflective, indicating collagen deposition (Figure 16.2a,b); (2) the appearance of eccentric neointimal tissue (Figure 16.2b,e); (3) the disappearance of a clear demarcation between media and adventitia, indicating disappearance or fibrosis of the media (Figure 16.2b,e); (4) inward remodeling (Figure 16.2b,e). Imaging of a non-irradiated side branch in the close proximity of the treated artery, revealed fibrosis as a side-effect of radiation dose fall-off (Figure 16.3).
Histology The uninjured distal segment showed the normal two-layered appearance of the artery (Figure 16.2i). Irradiation induced fibrosis of the media and especially of the adventitia (Figure 16.2h). The injured and irradiated segments showed the appearance of a slight neointima but most apparent was fibrosis of all other tissue layers, media and adventitia, similar to the irradiated segment (Figure 16.2g,h). Even a noninjured side branch that was not directly irradiated, but received a low dose as a result of dose fall-off from the treated artery, showed extensive fibrosis (Figure 16.3). A chance finding was a histological section of a side branch that was later located in the OCT pullback, showing the accuracy of imaging (Figure 16.4).
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2.0 1.5
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Figure 16.4 OCT image and histology of an artery at the site of a small side branch (*). The strong correlation in size and shape illustrates the high resolution with which OCT images the artery
DISCUSSION The long-term effect of brachytherapy on vascular architecture has not been studied in detail. We used OCT imaging and histology to assess the long-term changes induced by vascular injury followed by brachytherapy. Angiography showed that vascular segments treated with irradiation only, showed almost no difference in vascular dimensions over the 2-year period, despite a growth of the animal of 18 ± 1.5 kg, indicating that brachytherapy blocked adaptive vascular growth. The injured and irradiated segment showed a slight decrease in vascular diameter over time, indicating that the impact of balloon injury was minimal and irradiation had abbrogated any correlation between prior balloon injury and late loss. OCT showed that intimal thickening as a result of balloon injury was minimal, and the main cause for lumen loss was inward vessel remodeling. It also showed extensive fibrosis in all tissue layers as indicated by a more reflective image and loss of
definition between the tissue layers (no two- or three-layered appearance). The small dimension of the imaging catheter allowed investigation of a small, non-irradiated side branch in vivo. OCT identified fibrosis in an untreated side branch, subjected to dose fall-off irradiation only. Histological analysis confirmed the irradiationinduced fibrosis of media and adventitia. It also showed that the adventitia increased in thickness and density. Injured segments indeed showed minimal neointima formation but extensive fibrosis that extended to all tissue layers: intima, media and adventitia.
CONCLUSION Intravascular irradiation at 20 Gy induces fibrosis in all tissue layers and abbrogates adaptive vessel growth. OCT imaging correlated well with histological assessment, in terms of both vessel size and tissue composition.
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ACKNOWLEDGMENT This work was supported by a grant from the Netherlands Heart Foundation.
REFERENCES 1. Garcia-Barros M, Paris F, Cordon-Cardo C, et al. Tumor response to radiotherapy regulated by endothelial cell apoptosis. Science 2003; 300: 1155–9 2. Urban P, Serruys P, Baumgart D, et al. A multicentre European registry of intraluminal coronary beta brachytherapy. Eur Heart J 2003; 24: 604–12 3. Sabate M, Costa MA, Kozuma K, et al. Geographic miss: a cause of treatment failure in radio-oncology
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applied to intracoronary radiation therapy. Circulation 2000; 101: 2467–71 4. Costa MA, Sabate M, van der Giessen WJ, et al. Late coronary occlusion after intracoronary brachytherapy. Circulation 1999; 100: 789–92 5. Degertekin M, Serruys PW, Foley DP, et al. Persistent inhibition of neointimal hyperplasia after sirolimuseluting stent implantation: long-term (up to 2 years) clinical, angiographic, and intravascular ultrasound follow-up. Circulation 2002; 106: 1610–13 6. van Der Giessen WJ, Regar E, Harteveld MS, et al. ‘Edge Effect’ of (32)p radioactive stents is caused by the combination of chronic stent injury and radioactive dose falloff. Circulation 2001; 104: 2236–41
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CHAPTER 17 Acute OCT findings after stenting Jean-François Surmely, Yoshihiro Takeda, Tatsuya Ito, Takahiko Suzuki
INTRODUCTION
healing process of the stent-induced vessel injury. To date, only few data have been published about OCT evaluation of coronary stenting9–11. We here discuss our experience and provide insights into the potential benefit and advantage of OCT over IVUS.
Percutaneous coronary intervention (PCI) and in particular stent implantation has evolved into the most common treatment of coronary artery disease1,2. Mechanisms involved in the acute lumen gain postPCI include plaque compression and redistribution, vessel stretching and fracture (dissection) of the intimal plaque. PCI-induced vessel injury, as well as stent deployment characteristics, have been shown to correlate with the occurrence of acute complication, in-stent thrombosis and in-stent restenosis (ISR). Procedural outcome has been dramatically improved by development in stent design, by better antiplatelet therapy and, more recently, by the advent of drugeluting stents3. Intravascular ultrasound (IVUS) imaging has played an important role in understanding failure and optimizing outcome in stent treatment. In particular, it has clarified the problems of incomplete expansion, incomplete apposition and asymmetric expansion which have led to the concept of high-pressure stent deployment4. The introduction of drug-eluting stents and the observation of new entities such as late stent malapposition have shifted the interest towards even more detailed analysis of the interaction between stent and vessel wall. IVUS is limited by a relatively low resolution, in the range of 150 µm. Additionally, the high echogenicity of stent struts makes it difficult to evaluate adjacent structures such as small dissections and tissue prolapse. Optical coherence tomography (OCT) is a lightbased imaging modality with a high resolution (10 µm). Catheter-based intravascular OCT imaging has been shown to obtain qualitative information about arterial anatomy and plaque composition in vitro and in vivo with a high accuracy5-8, as discussed in detail in other chapters of this book. The high resolution of OCT allows us to assess the acute results of coronary stenting as well as the
ACUTE RESULT POST-STENTING Adequate stent deployment is of primary importance in order to achieve a favorable long-term patency and clinical outcome. Angiographic guidance does not allow an adequate evaluation and, when using intravascular ultrasound, it was shown that, despite a successful angiographic result, a large number of stents were not adequately deployed12. In the deployment of stents, issues of greatest concern as seen by IVUS are incomplete apposition of the stent struts against the vessel wall, incomplete stent expansion, as well as vessel injury post-stenting.
Stent apposition Incomplete apposition is defined as a clear separation between at least one stent strut and the vessel wall. This may cause local flow turbulence and increase the risk for subacute thrombosis. Furthermore, this might delay the healing process, leaving thrombogenic stent struts uncovered by neointimal formation. The Predictors and Outcomes of Stent Thrombosis (POST) study retrospectively analyzed the angiogram and the IVUS of 55 patients who suffered stent thrombosis after high-pressure deployment (> 14 atm). Analysis showed that 90% of patients had suboptimal IVUS results, such as incomplete stent apposition (47%), incomplete stent expansion (52%), edge tears or dissection (26%) and evidence of thrombus (24%), whereas only 25% of the angiograms showed detectable problems13. Those data were compared to data from the STRUT (Stent Treatment Region 153
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incomplete apposition was 230 ± 114 µm (± SD), the maximum was 360 µm, the minimum was 120 µm15 (Figure 17.2). The better resolution of OCT over IVUS is illustrated in Figure 17.3. Incomplete apposition can be seen over a wide range of magnitude (Figure 17.4). It is possible that the fate of an incomplete apposition depends on its magnitude. Followup studies addressing this issue are needed.
35 Number of stents
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12 8
7 3
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0 Tissue prolapse OCT
Incomplete apposition
Irregular struts
Stent expansion
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Figure 17.1 Comparison of OCT and IVUS findings post-stenting, obtained in 39 patients with 42 stents. (From reference 9)
assessed by Ultrasound Tomography) study, the AVID (Angiography Versus Intravascular ultrasound Directed stent placement) study and the CRUISE (Can Routine Ultrasound Influence Stent Expansion)14 study. The per cent of stent expansion was similar in those studies, but the incidence of incomplete apposition, edge tears/dissection and intra-stent thrombus was significantly higher in the POST study. In a study by Bouma et al., OCT and IVUS findings post-stenting were analyzed. Evaluation of 42 stents in 39 patients showed the following results: incomplete apposition observed in seven (17%) stents by OCT, three (7%) of them with IVUS; tissue prolapse in 29 (69%) stents by OCT, 12 (29%) of them with IVUS; and dissection in eight (19%) cases by OCT, two (5%) of them with IVUS9 (Figure 17.1). In our center, we compared OCT with IVUS immediately after high-pressure stent implantation in 18 patients. Incomplete apposition was observed in five (28%) stents by OCT, and in one (6%) by IVUS; tissue prolapse was observed in 14 (78%) cases by OCT, and in eight (44%) by IVUS. The mean magnitude of
a
18 16 14 12 10 8 6 4 2 0
Stent expansion post-deployment can show different patterns: adequate expansion, incomplete or underexpansion, asymmetric expansion or overexpansion. Incomplete expansion occurs when a portion of the stent is fully pressed into the vessel wall but inadequately expanded compared with the distal and proximal reference dimensions. Incomplete expansion occurs most frequently in areas of the vessel where dense fibrocalcific or calcified plaque is present. A number of studies have shown that stent expansion as measured by IVUS is a powerful predictor of angiographic or clinical restenosis following stenting16,17. In the Multicenter Ultrasound Guidance of Stents in Coronaries (MUSIC) trial, strict adherence to IVUS optimization criteria led to a very low target vessel revascularization rate of 9%18. In MUSIC, the following IVUS criteria of optimal stent expansion were used: (1) complete apposition of the stent over its entire length against the vessel wall; (2a) in-stent minimal lumen area of ≥ 90% of the average reference lumen area or ≥ 100% of the smallest reference area (in case the in-stent luminal area was < 9.0 mm2); (2b) in-stent minimal lumen area of ≥ 80% of the average reference lumen area or ≥ 90% of the smallest reference area (in case the instent luminal area was > 9.0 mm2); (3) symmetric stent expansion with minimal/maximal lumen diameter of ≥ 0.7. Several other studies14,19,20 using those criteria have confirmed their safety and that some of them could show reduction in target vessel revascularization14,20.
b
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Figure 17.2 (a) The frequency of stent incomplete apposition and tissue prolapse per lesion-based assessment in 18 patients (18 stents). Comparison of OCT and IVUS findings post-stenting. (b) Total number of prolapse sites. (From reference 15)
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Figure 17.3 After stent deployment, incomplete apposition (arrows) is difficult to identify with IVUS, but is clearly seen on the corresponding OCT image. (a) Angiographic view post-stent implantation (BX velocity 3.5 × 33 mm) in the middle left anterior descending artery. Yellow shows the location of the stent, and the red bar shows the location of the corresponding IVUS (b) and OCT (c) images
a
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Figure 17.4 OCT visualization of different degrees of apposition: full (a), mild incomplete (b) and pronounced incomplete (c). In (c), a dissection extending into the media can be seen (arrow)
Stent expansion can also be evaluated by OCT. In the study by Bouma et al.9, stent asymmetry was evaluated by looking at irregular stent strut distribution, and was defined as a variation in interstrut separation greater than 200%. OCT and IVUS showed similar performance in the detection of stent asymmetry (Figure 17.5). In a porcine model, a proportional response between the severity of acute vessel injury and the extend neointimal thickness at follow-up was observed. This has led to concern about the use of high-pressure stent deployment, which is likely to result in greater vascular trauma, and may in turn elicit more aggressive neointimal hyperplasia. Two randomized trials, however, have failed to demonstrate any significant differences in clinical or angiographic outcomes based on stent deployment pressure21,22. In those studies, IVUS was not used for guiding stent deployment. Overexpansion with marked overstretching of the vessel wall can be visualized by OCT, but no follow-up data are yet available (Figure 17.6).
Identification of vessel injury post-stenting Vessel injury is an unavoidable event occurring during PCI. Specifically related to stent implantation is the occurrence of tissue prolapse and edge dissection. Prolapse is defined as a protrusion of tissue between stent struts extending inside a circular or connecting adjacent strut. Those vessel injuries have been shown to be associated with the occurrence of subacute and late stent thrombosis. In a series of 7484 patients treated with stenting, 27 (0.4%) had angiographically documented subacute closure < 1 week after PCI. Causes identified by retrospective IVUS analysis included dissection (17%), thrombus (4%) and tissue protrusion within the stent struts leading to lumen compromise (4%), as well as inadequate post-procedure lumen dimensions23. In an autopsy study of 13 cases of late stent thrombosis, the pathological mechanisms included disruption of adjacent vulnerable plaques and extensive plaque prolapse24.
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1mm Figure 17.5 Example of incomplete stent expansion with a minimal/maximal ratio of 3 months) tend to have inward remodeling (vessel area at the lesion less than vessel area of the distal
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reference) of the vessel, so that the occluded segment becomes shrunk after occlusion over time22.
POST-INTERVENTIONAL IVUS ASSESSMENT OF LESION COVERAGE AND STENT EXPANSION The use of DES for CTO dramatically improved both clinical and angiographic mid-term results, mainly by reduction of neointimal hyperplasia in the treated segment. However, for long-term results, there are still some problems remaining, including stent thrombosis and restenosis13–16. There have been no randomized studies comparing the efficacy of routine IVUS usage with DES to improve the clinical outcome of PCI. Dedicated IVUS studies, however, have shown that IVUS provides essential information to optimize DES implantation, minimizes residual stenosis, avoids gaps between stents and edge dissection, and detects the presence of stent malapposition. CTO lesions typically show accumulated calcification and/or fibrocalcific plaque, which complicates optimal stent expansion and lesion coverage. Stent underexpansion and residual reference segment stenosis can also be risk factors of subacute thrombosis and restenosis5,6,23, but they are often overlooked with angiographic guidance alone24. Furthermore, although many interventional cardiologists rely on manufacturers’ compliance charts to select stent size and optimize stent expansion according to inflation pressures, in human coronary arteries, minimal stent diameter measured by IVUS is significantly smaller than the diameter predicted by in vitro compliance charts25. In order to identify late stent malapposition, IVUS is also helpful in comparison with angiographic guidance alone. For such lesions, high-pressure dilatation using an appropriately sized non-compliant balloon might be deemed appropriate and IVUS will show the appropriate balloon size. In the bare-metal stent era, IVUS-guided stent overexpansion (final in-stent lumen greater than reference lumen cross-sectional area) was accompanied by a higher periprocedural CK-MB release, but a lower target lesion revascularization and a trend towards lower mortality at 1 year. Increased periprocedural CK-MB release appeared as a trade-off for optimal stent implantation and lower clinical restenosis26. On the other hand, the SIRIUS substudy identified very conservative thresholds of minimal stent area assessed by IVUS, to predict an adequate follow-up minimal lumen area: 5 mm2 for SES and 6.5 mm2 for bare-metal stent27. These values suggest that we might no longer need aggressive stenting with maximal stent expansion. On the other hand, another contemporary study in other complex lesions such as bifurcation lesions suggests that
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suboptimal stent expansion may be related to a high restenosis rate of side branches treated with the complex double stenting technique28.
OTHER INTRAVASCULAR IMAGING MODALITIES IN CURRENT USE Optical coherence tomography Our initial experience showed that OCT (LightLab Imaging, Westford, MA) could detect calcification or intimal dissections more clearly than IVUS in a CTO lesion after balloon dilatation with a 1.5-mm balloon (Figure 19.7), but clinical experience of OCT for CTO–PCI is still very limited. OCT has potential advantages as real-time guidance for CTO–PCI compared with IVUS. First, the maximum diameter of the OCT image wire is 0.019 inch connecting to 15-mm length of the 0.014 inch tip (Figure 19.8), much thinner than the IVUS catheter, and it may be able to avoid making large unfavorable dissections into a false lumen when it is advanced for simultaneous cross- sectional image acquisition. A new delivery system for the OCT image wire without the need for balloon dilatation is mandatory to make the most of this advantage. Second, OCT has an 8–10 times higher image resolution compared with IVUS, and will enable identification of sheer tissues in the lumen or small dissections made by wire manipulation. Third, it can show the plaque behind the calcification, which is impossible for IVUS owing to acoustic shadow. On the other hand, OCT has some limitations. It is mandatory to clear the artery of blood during image acquisition, and necessitates continuous flushing. This approach carries a risk of extending the dissection by flushing continuously in the false lumen. The limited penetration of 1.5–2.0 mm and scan range of approximately 5.0 mm is not adequate for tracing the wire movement. OCT is still being developed, and has some physical limitations such as a distal wire tip that is too long (15 mm), a non-radiopaque body, a radiolucent lens and non-uniform rotational distortion. Furthermore, we noted rotation of the image wire itself during guidewire manipulation. All of these complicate accurate image assessment. Current conditions and limitations of this technique will be discussed in detail in other chapters of this book.
Optical coherence reflectometry OCR is not a true imaging device, but an application of reflectometry. The Safe Cross-FA guidewire (Intraluminal Therapeutics, Carlsbad, CA) combines three functions: (1) steerability of a conventional 0.014-inch intermediate-stiffness guidewire; (2) OCR to warn the operator when the wire tip is located within 1 mm of
the vessel wall (Figure 19.9); and (3) delivery of radiofrequency energy pulses to the wire tip to facilitate passage through an occluded segment. The first prospective, non-randomized, multicenter trial included 116 patients with old chronic coronary occlusion (median duration of occlusion > 22 months). It achieved device success in 54.3%. Based on these data, the device has been approved in Europe29.
NOVEL APPROACHES UNDER ACTIVE INVESTIGATION Pioneer catheter The concept of the Pioneer catheter is to allow for IVUS-guided subintimal lumen re-entry. This 6 F, 130 cm long catheter has a tapered tip that integrates a nitinol curved needle with a phased-array intravascular transducer (Volcano Therapeutics, San Diego, CA), which allows for two-dimensional imaging of the target vessel as well as color imaging (Chromaflow) of the lumen. The Pioneer catheter is passed into the false lumen over a 0.014-inch support wire, and using IVUS and color flow the true lumen is identified and the curved needle advanced. When the needle has entered the true lumen, a second wire is passed through it, the Pioneer catheter is removed and angioplasty is performed to dilate the channel connecting the false lumen with the true lumen. A stent is finally deployed to secure the result. At present only total occlusions in peripheral arteries have been investigated with a technical success ranging between 62 and 92%30–33.
Forward-looking ultrasound One of the most long-awaited intravascular imaging modalities, especially for tortuous CTOs or those that are angiographically difficult to visualize, is a forward-looking system to display the true lumen ahead of the occlusion. Proof of principle was demonstrated more than a decade ago with a forward-looking mechanically rotated ultrasound catheter, which created a conical imaging volume 5–10 mm ahead of the catheter tip34,35. In the past few years, interest has shifted to forward-viewing electronic systems36, and considerable progress has been made with a 20-MHz solid-state annular array using capacitive micromachined ultrasonic transducer (CMUT) technology that may have sufficient power, depth of penetration and resolution when mounted at the tip of a catheter to be clinically useful33,37.
CONCLUSIONS CTO–PCI requires greater operator skills and procedural time, increases radiation exposure to both
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a
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B
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b
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Figure 19.7 Comparison between IVUS and OCT of a chronic total occlusion. (A) Simultaneous bilateral contrast injection (anterograde contrast injection via the guiding catheter and simultaneous contrast injection into the contralateral artery which provides retrograde filling of the occluded artery via collaterals) shows complete occlusion of the midsegment of the left circumflex artery. (B) After successful wire advancement and multiple dilatations with a balloon of 1.5 mm diameter, OCT and IVUS cross-sectional images of the occluded segment were obtained (a and b). (a-1) 40MHz IVUS shows a huge concentric accumulation of fibrocalcific plaque with superficial calcification as a signal-rich band extending from 4 to 7 o’clock (two white arrows), please note that the lumen is almost occluded with the IVUS catheter. (a2) OCT is advanced almost at the same position as (a-1), but the thin OCT imaging wire does not occlude the small lumen. Calcification is identified as a well-delineated signal-poor region (two white arrows). Another guidewire used as a double wire can be seen (a-2, arrow head), causing a 70° artifact. (b-1) A second IVUS image shows that the external elastic membrane diameter is 3.2mm which is larger than that expected with angio-guidance, with a concentric fibrocalcific plaque. (b2) OCT clearly shows an intimal dissection (white arrow) made by balloon dilatation. The identical intimal dissection is identified with IVUS retrospectively (b-1, white arrow). In OCT images the external elastic membrane cannot be detected because of its limited penetration.
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A Occlusion balloon and center marker
Occlusion balloon catheter tip Marker
5 mm 10 mm
B
15 mm
6–7 mm Lens to RO tip before pullback
Imaging lens
15 mm RO imagewire spring tip
Figure 19.8 (A) OCT image wire (LightLab Imaging, Boston, MA). The wire maximum diameter is 0.019 inch tapering to 0.014 inch at the 15-mm distal end, much thinner than any IVUS catheter. (B) The whole OCT system is composed of two devices: one is the image wire, the other is the 4.0 F over-the-wire Helios occlusion balloon catheter. A compliant balloon inflatable at 0.5–0.75 atm (arrow) is positioned proximal to the lesion to be assessed, with continuous flush started from the distal end at a flush rate of 0.5–1.0 ml/s
Figure 19.9 The intraluminal system. The system is based on optical coherence reflectometry. A simple red/green display warns the operator when the wire tip is located within 1mm of the vessel wall
patients and operators, and consumes greater resources than standard PCI of non-occluded vessels. Although much evidence has already shown the efficacy of recanalizing CTOs, its success rate has not yet been comparable to that of other non-CTO PCI. The emergence of the DES has suggested that this would drastically improve the long-term result of CTO–PCI, but it cannot improve immediate success for crossing the occlusion. We believe intravascular imaging techniques such as ultrasound or OCT can improve our success rate of CTO–PCI, and help to improve the mid- and long-term results, mainly by decreasing the incidence of suboptimal stenting. These techniques could also
help us to learn many things such as lesion characteristics or causes of procedural failure.
REFERENCES 1. Saito S, Tanaka S, Hiroe Y, et al. Angioplasty for chronic total occlusion by using tapered-tip guidewires. Cathet Cardiovasc Interv 2003; 59: 305–11 2. Noguchi T, Miyazaki S, Morii I, et al. Percutaneous transluminal coronary angioplasty of chronic total occlusions. Determinants of primary success and long-term clinical outcome. Cathet Cardiovasc Interv 2000; 49: 258–64
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3. Mintz GS, Nissen SE, Anderson WD, et al. American College of Cardiology Clinical Expert Consensus Document on Standards for Acquisition, Measurement and Reporting of Intravascular Ultrasound Studies (IVUS). A report of the American College of Cardiology Task Force on Clinical Expert Consensus Documents. J Am Coll Cardiol 2001; 37: 1478–92 4. Di Mario C, Gorge G, Peters R, et al. Clinical application and image interpretation in intracoronary ultrasound. Study Group on Intracoronary Imaging of the Working Group of Coronary Circulation and of the Subgroup on Intravascular Ultrasound of the Working Group of Echocardiography of the European Society of Cardiology. Eur Heart J 1998; 19: 207–29 5. Fujii K, Carlier SG, Mintz GS, et al. Stent underexpansion and residual reference segment stenosis are related to stent thrombosis after sirolimus-eluting stent implantation: an intravascular ultrasound study. J Am Coll Cardiol 2005; 45: 995–8 6. Takebayashi H, Kobayashi Y, Mintz GS, et al. Intravascular ultrasound assessment of lesions with target vessel failure after sirolimus-eluting stent implantation. Am J Cardiol 2005; 95: 498–502 7. Kinoshita I, Katoh O, Nariyama J, et al. Coronary angioplasty of chronic total occlusions with bridging collateral vessels: immediate and follow-up outcome from a large single-center experience. J Am Coll Cardiol 1995; 26: 409–15 8. Kimura BJ, Tsimikas S, Bhargava V, et al. Subintimal wire position during angioplasty of a chronic total coronary occlusion: detection and subsequent procedural guidance by intravascular ultrasound. Cathet Cardiovasc Diagn 1995; 35: 262–5 9. Sirnes PA, Golf S, Myreng Y, et al. Sustained benefit of stenting chronic coronary occlusion: long-term clinical follow-up of the Stenting in Chronic Coronary Occlusion (SICCO) study. Am Coll Cardiol 1998; 32: 305–10 10. Buller CE, Dzavik V, Carere RG, et al. Primary stenting versus balloon angioplasty in occluded coronary arteries: the Total Occlusion Study of Canada (TOSCA). Circulation 1999; 100: 236–42 11. Asakura Y, Furukawa Y, Ishikawa S, et al. Successful predilation of a resistant, heavily calcified lesion with cutting balloon for coronary stenting: a case report. Cathet Cardiovasc Diagn 1998; 44: 420–2 12. Gruberg L, Mehran R, Dangas G, et al. Effect of plaque debulking and stenting on short- and long-term outcomes after revascularization of chronic total occlusions. J Am Coll Cardiol 2000; 35: 151–6 13. Ge L, Iakovou I, Cosgrave J, et al. Immediate and midterm outcomes of sirolimus-eluting stent implantation for chronic total occlusions. Eur Heart J 2005; 26: 1056–62 14. Nakamura S, Muthusamy TS, Bae JH, et al. Impact of sirolimus-eluting stent on the outcome of patients with chronic total occlusions. Am J Cardiol 2005; 95: 161–6 15. Werner GS, Krack A, Schwarz G, et al. Prevention of lesion recurrence in chronic total coronary occlusions by paclitaxel-eluting stents. J Am Coll Cardiol 2004; 44: 2301–6 16. Hoye A, Tanabe K, Lemos PA, et al. Significant reduction in restenosis after the use of sirolimus-eluting
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stents in the treatment of chronic total occlusions. J Am Coll Cardiol 2004; 43: 1954–8 Suttorp MJ. Presented at Transcatheter Cardiovascular Therapeutics, October 2005. Website at: http:// www.tctmd.com/csportal/appmanager/tctmd/descoe ?_nfpb=true&_pageLabel=DESCenterContent&hdCon =1332282&srcId=3&destId=4) Lemos PA, Saia F, Ligthart JM, et al. Coronary restenosis after sirolimus-eluting stent implantation: morphological description and mechanistic analysis from a consecutive series of cases. Circulation 2003; 108: 257–60 Hoye A, Ong ATL, Aoki J, et al. Drug-eluting stent implantation for chronic total occlusions: comparison between the Sirolimus- and Paclitaxel-eluting stent. EuroIntervention 2005; 2: 193–7 Moreno R, Fernandez C, Hernandez R, et al. Drugeluting stent thrombosis: results from a pooled analysis including 10 randomized studies. J Am Coll Cardiol 2005; 45: 954–9 Iakovou I, Schmidt T, Bonizzoni E, et al. Incidence, predictors, and outcome of thrombosis after successful implantation of drug-eluting stents. J Am Med Assoc 2005; 293: 2126–30 Nishida T, Di Mario C, Briguori C, et al. Characterization of total occlusions with intracoronary ultrasound: the importance of the duration of occlusion. J Invasive Cardiol 2001; 13: 1–8 Fujii K, Mintz GS, Kobayashi Y, et al. Contribution of stent underexpansion to recurrence after sirolimuseluting stent implantation for in-stent restenosis. Circulation 2004; 109: 1085–8 Fitzgerald PJ, Oshima A, Hayase M, et al. Final results of the Can Routine Ultrasound Influence Stent Expansion (CRUISE) study. Circulation 2000; 102: 523–30 de Ribamar Costa J Jr, Mintz GS, Carlier SG, et al. Intravascular ultrasonic assessment of stent diameters derived from manufacturer’s compliance charts. Am J Cardiol 2005; 96: 74–8 Iakovou I, Mintz GS, Dangas G, et al. Increased CKMB release is a ‘trade-off’ for optimal stent implantation: an intravascular ultrasound study. J Am Coll Cardiol 2003; 42: 1900–5 Sonoda S, Morino Y, Ako J, et al. SIRIUS investigators. Impact of final stent dimensions on long-term results following sirolimus-eluting stent implantation: serial intravascular ultrasound analysis from the sirius trial. J Am Coll Cardiol 2004; 43: 1959–63 Costa RA, Mintz GS, Carlier SG, et al. Bifurcation coronary lesions treated with the ‘crush’ technique: an intravascular ultrasound analysis. J Am Coll Cardiol 2005; 46: 599–605 Baim DS, Braden G, Heuser R, et al. Guided Radio Frequency Energy Ablation of Total Occlusions Registry Study. Utility of the Safe-Cross-guided radiofrequency total occlusion crossing system in chronic coronary total occlusions (results from the Guided Radio Frequency Energy Ablation of Total Occlusions Registry Study). Am J Cardiol 2004; 94: 853–8 Shaw MB, DeNunzio M, Hinwood D, et al. The results of subintimal angioplasty in a district general hospital. Eur J Vasc Endovasc Surg 2002; 24: 524–7
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31. Treiman GS, Whiting JH, Treiman RL, et al. Treatment of limb-threatening ischemia with percutaneous intentional extraluminal recanalization: a preliminary evaluation. J Vasc Surg 2003; 38: 29–35 32. Yilmaz S, Sindel T, Yegin A, Luleci E. Subintimal angioplasty of long superficial femoral artery occlusions. J Vasc Interv Radiol 2003; 14: 997–1010 33. Stone GW, Colombo A, Teirstein PS, et al. Percutaneous recanalization of chronically occluded coronary arteries: procedural techniques, devices, and results. Cathet Cardiovasc Interv 2005; 66: 217–36 34. Gatzoulis L, Watson RJ, Jordan LB, et al. Threedimensional forward-viewing intravascular ultrasound
imaging of human arteries in vitro. Ultrasound Med Biol 2001; 27: 969–82 35. Back MR, Kopchok GE, White RA, et al. Forwardlooking intravascular ultrasonography: in vitro imaging of normal and atherosclerotic human arteries. Am Surg 1994; 60: 738–43 36. Wang Y, Stephens DN, O’Donnell M. Optimizing the beam pattern of a forward-viewing ring-annular ultrasound array for intravascular imaging. IEEE Trans Ultrason Ferroelectr Freq Control 2002; 49: 1652–64 37. Demirci U, Ergun AS, Oralkan O, et al. Forwardviewing CMUT arrays for medical imaging. IEEE Trans Ultrason Ferroelectr Freq Control 2004; 51: 887–95
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CHAPTER 20 OCT-guided wiring technique for chronic total coronary occlusion Yoshihiro Takeda, Osamu Katoh
Chronic total coronary occlusions (CTOs) are common, and found in about one-third of patients with significant coronary artery disease who undergo angiography. In the analysis of a selected patient cohort from the Bypass Angioplasty Revascularization Investigation (BARI) study1, CTOs represented approximately 30% of lesions even after excluding many patients with a CTO who had been ineligible for randomization to the revascularization procedure of the original BARI study. Among patients undergoing coronary angiography during a 1-year period, Kahn2 also documented one or more CTOs in approximately one-third of cases during a 1-year period. Previous studies have demonstrated the importance of revascularization of CTOs, with improvement in symptomatic angina, exercise capacity and left ventricular function3–5. In addition, successful recanalization reduces the subsequent need for bypass surgery and, more importantly, patients with a successful recanalization of a CTO have an increased survival rate compared to patients with a failed CTO procedure6,7. In a consecutive series of 2007 patients undergoing a planned percutaneous coronary intervention (PCI) of CTOs between 1980 and 1999, technical success was achieved in 74.4% and procedural success in 69.9% of cases6. Long-term survival was significantly greater in patients with a successful CTO recanalization than in patients with a failed CTO revascularization attempt (10-year survival 73.5% with CTO success versus 65.1% with CTO failure; p = 0.001). By multivariate analysis, failure to successfully recanalize the CTO was an independent predictor of reduced survival. Similarly, in the prospective TOAST-GISE study, successful PCI of CTOs (390 lesions attempted in 369 patients) was associated with a reduced 12-month incidence of cardiac death or myocardial infarction (1.1% versus 7.2%; p = 0.005), and a reduced need for bypass surgery (2.5% versus 15.7%; p < 0.0001)7. PCI of CTOs has been well accepted, accounting for approximately 10–15% of all angioplasties. In
addition, recent studies have demonstrated that drugeluting stents decrease restenosis and reocclusion rates in patients with CTO8–10. These studies suggest that CTO may be a preferred indication for drug-eluting stents. However, the PCI of CTOs is characterized by a limited primary success rate that is mainly caused by the inability to cross the lesion with a guidewire11. In order to increase the recanalization success rate of CTOs, different strategies and specific devices for CTOs have been developed. In the following paragraphs, we review briefly some basic knowledge about CTOs, as well as describing some of the available strategies, before focusing on the optical coherence tomography (OCT)-guided wire technique.
PATHOLOGICAL FEATURES OF CHRONIC TOTAL CORONARY OCCLUSION The low success rates observed, especially in older CTOs, are probably related to changes in histology that occur with increased occlusion duration. Whereas the intimal plaque in younger CTOs ( 20 bar
Figure 21.5 Example of an experimental pressure-controlled angioplasty under OCT guidance in real time. (a) The balloon is not inflated and the four-time folding of the balloon inside the lumen of a heavily stenotic artery segment is clearly visible. (b-e) Continuous manometer-controlled increase of the inflation pressure leads to an increase in luminal diameter and a partial fracture of the fibrocalcific ring (calc, fib). (f) Further increase of the pressure causes the rupture of the vessel wall. The OCT probe is located within the balloon
first-choice revascularization procedure in patients with limb ischemia26. The primary technical success rate is very high in these stenoses (> 90%) even in long and complex lesions25,27. However, the restenosis rate in peripheral arteries is higher than in coronaries, ranging up to 40% after only 6 months and to 78% by 1 year27,28. Endoluminal procedures lead to localized vessel trauma caused by small vessel-wall ruptures which can initiate the development of restenosis. From coronary interventions we learned that less vessel trauma leads to better long-term results29. Under intravascular real-time guidance with OCT, the pressure within the angioplasty balloon could gradually be increased until the stenosis were successfully treated or the calcified plaque had ruptured (Figure 21.4). A further increase in balloon pressure only increases the risk for vessel trauma. The presence of intimal flaps has also been associated with an increased restenosis rate30. By means of angiography only large dissections of the vessel wall can be assessed. The use of IVUS was a step forward to a more precise lesion analysis31–33. However, there still remain a substantial number of small intimal tears and dissections not visible with IVUS. OCT can visualize these small tears and dissections with its unique spatial resolution of about 15 µm. OCT can
provide more detailed information in lesion morphology pre- and post-intervention. An example of the visualization of minimal intimal flaps is given in Figure 21.5. The detection of these lesions might have an impact on therapy stratification, such as complementary long-term low-pressure dilatation.
CUTTING BALLOON ANGIOPLASTY Cutting balloon angioplasty was first used in coronary interventions34, but has also proven to be useful in peripheral arteries35,36. The cutting balloon consists of a PTA balloon with three to five microsurgical blades (0.1 to 0.4 mm in thickness) mounted longitudinally on its surface. During inflation of the balloon the microsurgical blades erect so that much of the inflation pressure is concentrated on the small and sharp surface of the blades. This enables the device to overcome more resistant stenoses. Moreover, the blades are radially aligned, which leads to a more organized (rather than a random) rupture of the plaque. OCT can successfully be used to monitor the interventional procedure from inside the cutting balloon, to control the exact positioning of the blades (Figure 21.6) as well as to assess the results of the
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0 40% of the plaque area). Next to the straightforward quantitative analysis of, for example, lipid core and cap dimensions and backscatter amplitude, quantitative data analysis of the OCT signal allows measurement of light attenuation by the local tissue components, which can facilitate spatial discrimination between plaque constituents on a quantitative basis. Localized optical properties such as the attenuation coefficient µt (mm−1) can be determined by analyzing the OCT signal as a function of depth. Measurement of this intrinsic tissue parameter is important because this information can be used for further discrimination of tissue constituents: the contrast in OCT images is primarily caused by differences in refractive index n of different tissue constituents, but, unfortunately, contrast is limited because for most tissues n ~ 1.4. The differentiation between different types of atherosclerotic plaque is therefore mostly based on qualitative differences in gray levels and structural appearance. In this chapter we discuss the mathematical model used in our experiments followed by validation
THEORY The OCT signal is influenced by the optical properties of the tissue (absorption and scattering) as well as by the optical components of the OCT imager. The attenuation coefficient can be measured from the OCT signal by fitting a model relation to this signal from a region of interest in an OCT image. Currently, three models are available. Widely used3–5 are descriptions based on the so-called single scattering model, which assumes that only light that has been backscattered once contributes to the OCT signal. A model taking into account multiple scattering, based on the extended Huygens–Fresnel (EHF) formalism, was introduced by Thrane et al.6 and has recently been used to extract optical properties of atherosclerotic lesions7 and human skin8. Turchin et al. recently introduced a different approach based on the small angle approximation of the radiative transfer equation9. An important question in choosing a model for the OCT signal is: can multiple scattering effects be ignored? Since imaging depths generally do not exceed ~ 1 mm, this may well be justified for weakly scattering media. We compared the single scattering model to the EHF multiple scattering model using calibrated scattering samples with µt ranging from 2 mm−1 to 6 mm−1 10. It was concluded that, for low NA set-ups (such as the clinically used, catheter-based systems), the single scattering model is valid for 231
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about six scattering depths, i.e. for imaging depths of 1 mm with µt < 6 mm−1 which is in accordance with approximately four mean free paths (mfp = µtd) in the findings by Bizheva, Wax and Pan and their colleagues11–13. In Chapter 1, calculations based on the EHF model are presented which support these findings. To illustrate, Figure 25.1 shows the average of 500 A-scans obtained from an excised piece of highly scattering calcified tissue (black line) and fits using the single scattering model (red line) as well as the EHF model (blue line). Both models resulted in comparable values of µt, similar well fitting statistics, and both fit agreed well with the data (based on visual inspection). Because the single scattering model provides for simpler algorithms, that model was used in our experiments. Focusing optics in the sample arm suppress the detection of light scattered from outside the focal volume, similar to confocal microscopy. In clinically used probes and catheters, the optical components of the sample arm are fixed. Therefore, for quantitative extraction of µt, the confocal properties of the OCT
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system have to be taken into account, i.e. the change of the OCT signal with increasing distance between the probed location in the tissue and location of the focus. We have recently derived a general expression for the confocal axial point spread function (PSF) for single mode fiber (SMF)-based OCT systems11.The major advantage of this PSF is that it is described by one parameter only, the Rayleigh length Z0 (half the depth of focus), which can easily be determined experimentally. The geometry under consideration is shown in Figure 25.214. The OCT signal as function of depth i(d) is then found by combining this PSF with the single scattering model–Equation 1: 1 i(d) ∝ exp(−2µt d) d − X0 2 =1 2Z0
The square root appears because the OCT signal is proportional to the electric field returning from the sample, rather then intensity. The first term under the square root describes the PSF, where X0 is the position of the focus, Z0 is the Rayleigh length (and 2Z0 is the depth of focus). The Rayleigh length Z0 of a Gaussian beam is given by Z0 = πnω2/λ0 with ω the beam waist at the focus and λ0 the center wavelength of the light source; n is the refractive index of the medium. The second term under the square root describes attenuation of the light in the tissue according to the single scattering model, where µt is the attenuation coefficient. The factor 2 in the exponential accounts for round trip attenuation through the sample. The derivation of the point spread function can be found in the appendix of reference 11. The PSF (in the true sense) can be found by considering the response of a small reflector somewhere in the probe beam. The surface rendering was calculated using ωr = 4 µm as the probe beam waist; λ = 800 nm; zr = 65 µm. The reflector size is 100 nm Both the contributions of scattering and the PSF can be easily understood. Suppose scattering is very low, i.e. µt ∼ 0. In that case, the OCT signal would consist
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PHANTOM MEASUREMENTS To verify the ability of the model presented by equation 1, we extracted the attenuation coefficient from several calibrated samples with several fixed (but known) positions of the focus in the sample10. Figure 25.4, left panel, shows the fitted µt versus the location of the focus zcf with respect to the sample boundary. For all samples, µt is underestimated when the focus is located near the sample boundary, and overestimated when the focus is located inside the sample, an effect that increases with increasing µt. The accuracy of the µt measurements regardless of the focus position is calculated by averaging the fixed focus µt over zcf; the result is shown in the right panel of Figure 25.3. For all samples, the standard deviation in µt was < 0.8 mm−1. The correspondence with the dynamic focusing (DF) µts, where there is no influence of the confocal gating, is excellent. The systematic underestimation of µt when the focus is located near the sample boundary can partly be caused by the influence of the sample boundary itself. Provided single scattering applies, our model is also able to determine the attenuation coefficient in a layered medium14. We verified this on a phantom consisting of a plastic scattering layer with attenuation coefficient of 10 mm−1 underneath 0.5 mm intralipid
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only of the point spread function, as the exponential term would (nearly) equal unity. In reality, of course, this is not very often the case. On the other hand, in a special case of OCT imaging called ‘focus tracking’ or ‘dynamic focusing’ the imaging lens is continuously adjusted to have the imaging position ‘sharp’ in focus. In terms of equation 1, d = X0 in that case, and only the exponential term remains. Figure 25.3 shows that special case (the blue line is the fit) along with the measurement on a sample which was very weakly
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Figure 25.5 Average OCT A-scan data obtained from the intralipid/plastic phantom (thin black line) with the fitted signal, using equation 1 (see text), of the plastic lower layer (thick red line). The intralipid (upper layer) varies in concentration (a, water; b, 0.2%; c, 0.5%; d, 1%; e, 2%). In (f) the measured µt is shown as a function of the µt of the intralipid. The focus was placed at 0.61 mm in depth. (Adapted from reference 14)
solutions with different concentrations (0–2%) and thus different attenuation coefficients (0–6 mm−1). We tested the capability of determining the attenuation of the deeper layer as a function of the attenuation coefficient of the upper intralipid layer. OCT imaging of the phantom visualized the two layers as two different regions of back-scattered OCT light. In the average A-scans of the region of interest (ROI), the OCT signal of the second layer fitted very well to the model (equation 1) for the whole range of µt of the upper layer of intralipid (Figure 25.5 A–E). Furthermore, the determined µt for the deeper second layer corresponded very well with the expected value of 10 mm−1, as determined in a separate measurement (Figure 25.5F).
PLAQUE IMAGING In the experiments on ex vivo arterial samples described below14,16,17, we used both a high-resolution OCT system at 800nm and a system operating around the clinically more relevant wavelength of 1300 nm. The high-resolution system was based on a Ti : sapphire laser, with an axial resolution of 3.5 µm and lateral resolution of approximately 6 µm. The depth of focus of the sample arm optics was ~100 µm in air. The 1300-nm SLD-based system had 15 µm axial and 20 µm lateral resolution, with a depth of focus of approximately 250 µm. The signal processing of both time-domain systems consisted of demodulation at the appropriate frequency, low-pass filtering, and
storage of both amplitude and phase of the signal for post-processing. Images were over-sampled by a factor of 2–5 in both axial and lateral dimensions. In all experiments, the position of the focus inside the sample was known, and kept fixed. Using ink markers and anatomical landmarks, the images that were obtained using OCT were matched to histology. Using the histology, different ROIs were identified in the OCT images, e.g. diffuse intimal thickening in conjunction with developing lipid-rich areas, and calcifications (an example of images obtained with the high-resolution OCT system at 800 nm is shown in Figure 25.6). Based on differences in gray values in the OCT images, different features of the atherosclerotic lesion are clearly distinguishable. Diffuse intimal thickening shows as the uppermost reflective layer, bound by a high reflective internal elastic lamina. The media is present as a uniform mediocer backscattering layer underneath the internal elastic lamina, and is encompassed by the external elastic lamina, which appears as a strong backscattering layer. Lipid-rich areas and calcifications observed in the histology could be matched to regions with a lower backscatter signal in the OCT images (Figure 25.6B,C). In the 800-nm OCT images, calcifications could be differentiated from the lipid-rich region by their demarcation, which was sharp for calcification and less defined in case of lipidrich regions. This is similar to the findings of Jang et al.2 for 1300-nm imaging. Using the fitting algorithm (equation 1), it was possible to fit µt for all ROIs. In Figure 25.7A, the average A-scan (thin line) of the ROI marked in Figure 25.6A
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Figure 25.6 OCT images at 800 nm (A-C) with corresponding histology (D-F) of different lesions. (A and D) early intimal thickening (i), with media (m) and the underlying external elastic lamina (eel) clearly visible. (B and E) Calcified lesions (c) in the intima and media. (C and F) Lipid-rich region (l) in the intima. Bars indicate 0.5 mm. The rectangles indicate the region from which the data depicted in Figure 25.7 are taken. (From reference 16)
is plotted. The resulting fits for the intimal and medial layers (Figure 25.7A, red line) show a very good correlation (R2 = 0.99) with the plotted A-scan (Figure 25.7A, gray line). For calcified and lipid-rich tissue (Figure 25.7B and C, respectively), similar results with high correlation between fitted and measured signals were obtained (0.7 < R2 < 0.98). The data of graph Figure 25.7B are derived from the calcified tissue sample shown in Figure 25.6B, and the data of graph Figure 25.7C were derived from the lipid-rich tissue sample shown in Figure 25.6C. The 95% confidence interval of the fitted µt was approximately 0.1 mm−1 in our data set. The attenuation coefficients for the different plaque components were determined at 800 nm and 1300 nm, and are shown in Figure 25.8. At 800 nm, the attenuation coefficient was found to be highest in the thrombus (11.2 ± 2.3 mm−1, not shown in the figure), whereas lipid-rich regions have the lowest attenuation of OCT light (3.2 ± 1.1 mm−1). The diffuse intimal tissue shows an intermediate attenuation (5.5 ± 1.2 mm−1). The attenuation coefficient of calcified tissue was 11.3 ± 4.9 mm−1 and for medial tissue media 9.9 ± 1.8 mm−1. The attenuation coefficients of both diffuse intimal tissue and lipid-rich tissue were found to differ significantly from other plaque components (p < 0.01). Compared to the 800-nm light
of the high-resolution OCT system, the attenuation coefficients at 1300 nm were lower but followed the trend. For the 1300-nm OCT light, the lipid-rich regions had the lowest attenuation of OCT light (2.3 ± 0.5 mm−1). The diffuse intimal and the medial tissue showed intermediate attenuations (3.2 ± 1.2 mm−1 and 6.7 ± 1.1 mm−1, respectively). The attenuation coefficient of calcified tissue was largest (26 ± 3.2 mm−1) and differed significantly from diffuse intimal tissue and lipid-rich tissue (p < 0.01). Our measurements of the µt measured for thrombus are quite high (11.2 ± 2.3 mm−1, not shown in Figure 25.8). Macroscopically, this thrombus was assessed as red thrombus, consisting of mainly red blood cells (RBCs) and fibrin. RBCs are known to be highly scattering, which explains the high value of µt. The more advanced white thrombus (platelet-rich), or advanced intramural thrombi, will contain fewer RBCs, which probably will result in a lower attenuation coefficient. Thrombotic events are induced by plaque rupture and/or erosion, leading to exposure of thrombogenic species to the blood. In a recent study, it was found that, in at least 50% of patients with acute ST-elevation myocardial infarction, the thrombi were days or weeks old. Moreover, it has been shown that the accumulation of erythrocyte membranes within atherosclerotic plaques, via the deposition of free cholesterol,
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increased attenuation for calcified ROI for 1300 nm OCT compared to 800 nm OCT light remains unclear. Further studies are needed to clarify whether the low backscattering within the calcification combination with the large index of refraction mismatch between the calcified tissue and its surrounding may lead to an overestimation of the attenuation coefficient as determined by our model.
Temperature effects Since the temperature dependence of optical properties is widely known, we compared measurements of refractive index n and attenuation coefficient µt of individual, isolated vascular wall layers and plaque components at room and body temperatures. A decrease of n and µt was observed in all samples, with the most profound effect on samples with high lipid content, as shown in Table 1.1 in Chapter 118. Clearly, the sample temperature is of influence on the quantitative measurements within OCT images. For extrapolation of ex vivo experimental results, especially for structures with high lipid content, this effect should be taken into account. Also note the very large (glass-like) refractive index of calcification. The large difference from other plaque constituents explains the sharp demarcations of calcified nodes in the OCT images.
Comparison to other models
1 0.0 0.2 0.4 0.6 0.8 1.0 1.2 Depth (mm)
Figure 25.7 Average OCT A-scan data at 800 nm (thin gray line) of the regions depicted by the white 25.6 rectangles in the OCT figures A-C, respectively, and the fitted signal using equation 1 (see text) (thick red line) with the calculated attenuation coefficient µt (± 95% CI) depicted for several regions of interest. (Adapted from reference 14)
macrophage infiltration and enlargement of the necrotic core, can increase the risk of plaque destabilization. To clarify the role of these processes on plaque (de-)stabilization, characterization of thrombus (age) can be important. From Figure 25.8, the differences between attenuation coefficients for the arterial wall and plaque constituents followed the same trend as for the high-resolution (800 nm) OCT system, except for the calcified lesions. The data clearly demonstrate the lower attenuation for the 1300-nm OCT light, which explains the deeper imaging with this system. This finding is in agreement with the work of Schmitt et al., who demonstrated that the attenuation coefficients at 1300 nm were approximately 20% lower than at 800 nm for rat aortic tissue3. The reason for the
In 1994, Schmitt et al. were among the first to apply the single scattering model for measuring the optical properties of rat aorta by OCT3. They calculated the attenuation coefficient for the medial layer from specular reflections of the glass–tissue interfaces of the sample holder with their 830-nm OCT set-up to be 14.9 ± 2.3 mm−1, which is higher than our results of 9.9 ± 1.9 mm−1. We attribute this difference to the high content of strong backscattering elastin in aortic segments compared to our, less elastic, carotid samples, and differences in set-up. Schmitt et al. recognized the effect of multiple scattering on the signal attenuation for OCT and proposed a more comprehensive model, based on the EHF formalism that included the degradation of the mutual coherence of the sample beam. This approach was further explored by Thrane et al 6. Recently, Levitz et al. used the EHF model to determine µs of 14 aorta segments at 1300 nm OCT light7. The trends between the measured µs of the different tissue types should be and are similar to our results, except for calcified lesions. However, their findings, described in ranges rather than average values, gave larger values than ours. Approximately 95% of their normal arterial samples had µs between 15 and 39mm−1, while µs was lower than 15 mm−1 in about 60% of lipid-rich and fibrocalcific plaques. Furthermore, fibrous lesions demonstrated considerable variation in
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Figure 25.8 Attenuation coefficient of plaque components as measured at 800 nm (at gray bars) and 1300 nm (dark gray bars) resolution OCT. The numbers in the bars represent the number of samples. (Adapted from reference 16)
µs. These absolute differences cannot be attributed to the difference in wavelength used, since attenuation coefficients at 1300 nm are approximately 20% lower than at 800 nm3. Alternatively, an explanation could be found in the effect of the temperature of their samples during imaging. In our measurements, care was taken to keep the samples at 37°C because especially the µt of fatty tissue depends on the temperature.
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The position of the focal plane within the tissue is of influence on the amount of detected light, and subsequently on the OCT signal, i.e. it affects the appearance of a certain feature. In Figure 25.9, OCT images of the same sample were taken with the focus position at the lumen–intima boundary (Figure 25.9A), and a second image (Figure 25.9B) with the focus shifted approximately 0.1 mm downwards compared to Figure 25.9A. Note the difference in backscattered signal of the calcification, marked ‘c’, and its surroundings. Whereas the position of the focus in the tissue sample clearly influences the backscattered signal in ROI, the measurement of the attenuation coefficient should not be affected, owing to the correction within the denominator of the fit function (equation 1). Indeed, the attenuation of the light in this ROI did not differ and was measured as 10.4 ± 0.7 mm−1 and 11.1 ± 0.6 mm−1, respectively. This observation was typical for all other lesion types, imaged with different focus positions. These results correspond well with the phantom measurements presented in Figure 25.3.
IMPLICATIONS Using quantitative measurements of local attenuation coefficients, even in this limited data set, the
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Figure 25.9 The effect of focus position on gray values is depicted in the two OCT images of a calcification (c) in the intimal layer (i). The arrows indicate the position of the focus in each image. The vascular lumen is marked (L). The rectangles indicate the ROI with the calculated µt
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Figure 25.10 Examples of OCT images, previously shown in Figures 25.2A and B, combined with a false color overlay of the attenuation coefficient µt. To accentuate calcified (a) lesions, areas with µt larger than 12 mm−1 are plotted, to accentuate lipid lesions (b), areas with µt smaller than 4 mm−1 are plotted. Bars indicate 0.5 mm; color bars depict values of µt. (Adapted from reference 14)
clinically interesting lipid-rich regions were distinguishable from calcifications, which is not possible based on the mean OCT signal (the gray level) alone. In the future, this quantitative analysis of intrinsic and thus characteristic (optical) properties of the different tissue types could increase sensitivity and specificity of OCT in plaque detection and differentiation and, thus, its diagnostic potential. Figure 25.10 demonstrates the value of this additional information: using a color overlay, the µt data of selected ROIs are added to the morphological OCT image, facilitating the identification of calcification (Figure 25.10A) or lipid-rich regions (Figure 25.10B) at a glance. These results demonstrate that additional information from the raw OCT data can be obtained, enabling plaque constituents to be differentiated on the basis of their differences in µt. This additional information depends on the µt of the tissue and not on the experimental conditions. The ability to detect and monitor the vulnerable plaque is keenly sought to define its natural history and support the studies to evaluate progression and regression. Currently, OCT is the only intravascular imaging technique capable of imaging the morphology of the vascular wall with a resolution that allows visualization of small anatomical structures. Its clinical applicability is currently under investigation, and already has shown great potential18. Additional information is available in the backscattered signal, which can also be used to obtain quantitative information of imaged tissue structures. Using a false color overlay indicating regions with a low or high attenuation coefficient (Figure 25.10) on the original image may help to identify plaque types more rapidly. Although the selection of the ROI for these overlays was done manually, preliminary analysis indicates that generating automated color overlay by means of a sliding window
will result in roughly the same images as presented here. Further research on the optimization of the algorithm to present the tissue properties has to be performed. For example, the calcification appears less thick in the attenuation coefficient overlays, compared to the OCT and the histological images. This is probably due to the inherent loss of signal with depth. The resulting poor signal-to-noise ratio in deeper regions of the image will result in underestimation of the local attenuation coefficient. A similar explanation can be given for the indication of lipid in the lower right corner in Figure 25.10B. In these studies, carotid arteries with relatively small and beginning lesions were used rather than vulnerable lesions in coronary arteries. Still, the combination of gray-scale OCT images with tissue-specific attenuation coefficients was feasible. Quantitative analysis can enhance the diagnostic capabilities of OCT. Therefore, the use of carotid arteries with incipient atherosclerotic lesions and the lack of genuine atheroma should not be considered to be a problem. Furthermore, the detected differences in µt of the incipient fat deposits compared to the other tissue components indicate a high sensitivity for lipid detection, by means of our algorithm. Consequently, in situ analysis of µt of the different plaque and arterial wall constituents can be used as a virtual histology tool for OCT images. In conclusion, simple quantitative analysis of the OCT signals allows in situ determination of the intrinsic optical attenuation coefficient of atherosclerotic tissue components within ROIs. Combining morphological imaging by OCT with the observed differences in optical attenuation coefficients of the various regions may enhance differentiation between various plaque types within the vessel wall. This may contribute to better detection and management of the vulnerable plaque.
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REFERENCES 1. Brezinski ME, Tearney GJ, Bouma BE, et al. Optical coherence tomography for optical biopsy – properties and demonstration of vascular pathology. Circulation 1996; 93: 1206–13 2. Jang IK, Bouma BE, Kang DH, et al. Visualization of coronary atherosclerotic plaques in paients using optical coherence tomography: comparison with intravascular ultrasound. J Am Coll Cardiol 2002; 39: 604–9 3. Schmitt JM, Knüttel A, Yadlowsky M, Eckhaus MA. Optical-coherence tomography of a dense tissue: statistics of attenuation and backscattering. Phys Med Biol 1994; 39: 1705–20 4. Esenaliev RO, Larin KV, Larina IV, Motamedi M. Noninvasive monitoring of glucose concentration with optical coherence tomography. Opt Lett 2001; 26: 992–4 5. Kholodnykh AI, Petrova IY, Larin KV, et al. Precision of measurement of tissue optical properties with optical coherence tomography. Appl Opt 2003; 42: 3027–37 6. Thrane L, Yura HT, Andersen PE. Analysis of optical coherence tomography systems based on the extended HuygensFresnel principle. J Opt Soc Am 2000; 17: 484–90 7. Levitz D, Thrane L, Frosz MH, et al. Determination of optical scattering properties of highly-scattering media in optical coherence tomography images. Opt Express 2004; 12: 249–59 8. Knuettel A, Bonev S, Knaak W. New method for evaluation of in vivo scattering and refractive index properties obtained with optical coherence tomography. J Biomed Opt 2004; 9: 232–73 9. Turchin IV, Sergeeva EA, Dolin LS, et al. Novel algorithm of processing optical coherence tomography
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images for differentiation of biological tissue pathologies. J Biomed Opt 2005; 10: 064024 Faber DJ, van der Meer FJ, Aalders MCG. Quantitative measurement of attenuation coefficients of weakly scattering media using optical coherence tomography. Opt Express 2004; 12: 4353–65 Bizheva KK, Siegel AM, Boas DA. Path-lengthresolved dynamic light scattering in highly scattering random media: the transition to diffusing wave spectroscopy. Phys Rev E 1998; 58: 7664–7 Pan Y, Birngruber R, Engelhardt R. Contrast limits of coherence-gated imaging in scattered media. Appl Opt 1997; 36: 2979–83 Wax A, Yang C, Dasari RR, Feld MS. Path-lengthresolved dynamic light scattering: modeling the transition from single to diffusive scattering. Appl Opt 2001; 40: 4222–7 Van der Meer FJ, Faber DJ, Baraznji Sassoon DM, et al. Localized measurement of optical attenuation coefficients of atherosclerotic plaque constituents by quantitative optical coherence tomography. IEEE Trans Med Im 2005; 24: 1369–76 Van Leeuwen TG, Faber DJ, Aalders MC. Measurement of the axial point spread function in scattering media using single-mode fiber-based optical coherence tomography. IEEE J Sel Top Quant 2003; 9: 227–33 Van der Meer FJ, Faber DJ, Perrée J, et al. Quantitative optical coherence tomography of arterial wall components. Lasers Med Sci 2005; 20: 41 Van der Meer FJ, Faber DJ, Cilisiz I, et al. Temperature dependent optical properties of individual vascular wall components, measured by optical coherence tomography. J Biomed Opt 2006; in press Yabushita H, Bouma BE, Houser SL, et al. Characterization of human atherosclerosis by optical coherence tomography. Circulation 2002; 106: 1640–5
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CHAPTER 26 Polarization-sensitive OCT: detection of vulnerable atherosclerotic plaque Seemantini K Nadkarni, Mark C Pierce, Brett E Bouma, Guillermo J Tearney, Johannes F de Boer
INTRODUCTION
Collagen in atherosclerotic plaques
Myocardial infarction annually affects over 20 million people worldwide and is one of the primary causes of death in industrialized nations. The rupture of an unstable coronary atherosclerotic plaque is often the mechanistic precursor to acute myocardial infarction1. When a plaque ruptures, the contents of the thrombogenic lipid core enter the circulating bloodstream causing thrombosis which occludes the coronary artery, resulting in myocardial ischemia and infarction. The composition of atherosclerotic plaques is an important determinant in the progression of thrombus-mediated acute coronary syndromes. Necrotic core fibroatheromas (NCFA) comprise the majority of plaques implicated at the site of a coronary thrombus. An NCFA consists of a fibrous cap, overlying a necrotic core, which shields the highly thrombogenic core from contact with the coronary blood circulation (Figure 26.1)2,3. The onset of occlusive coronary thrombosis is initiated by the fracture of the fibrous cap, followed by the release of thrombogenic contents of the plaque into the circulation.
Collagen is a key constituent of the extracellular matrix of the vascular wall and determines the mechanical stability of the NCFA. The collagen molecule consists of three procollagen chains which are assembled in a triple helical structure. There are at least 19 different types of collagen in the vessel wall of which type I and type III collagen fibers are predominant and are responsible for imparting tensile strength and elasticity4. In atherosclerotic plaques, smooth muscle cells (SMCs) from the media migrate to the site of the intimal lesion and are responsible for synthesizing new collagen, causing a net increase in collagen content5. The proteolysis of collagen by metalloproteinases released by activated macrophages, accompanied by the apoptosis of intimal SMCs which impedes collagen synthesis, are key mechanisms leading to plaque instability5,6. Mediated by endothelial production of nitric oxide, TGF-β, and plasmin, this dynamic imbalance between collagen synthesis and degradation causes a net reduction in collagen content, which may predispose a plaque to
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Figure 26.1
Histology image showing a necrotic core fibroatheroma with (a) thin fibrous cap, and (b) thick fibrous cap 241
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Phase retardation δ = 2π(ns−nf)L λ
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Figure 26.2 When polarized light travels through a birefringent tissue such as collagen, electric-field components resolved along directions parallel and perpendicular to the fiber orientation travel at different velocities, incurring a relative phase retardation or delay, δ
rupture7. A recent study has suggested that increased collagenase expression yields thinner collagen fibers with disorganized fiber organization, which may be associated with decreased mechanical stability8. Plaque stabilization with lipid-lowering therapy reverses this process in both the cap and the lipid pool, by restoration of collagen production and reversal of collagen degradation9,10. These mechanisms explain the significant reduction in acute coronary events achieved with statin therapy, despite minimal angiographic reduction in luminal stenosis.
from tissue is measured and displayed as a gray-scale image, providing cross-sectional information about tissue microstructure17. Imaging catheters for OCT have been developed and recent clinical studies have demonstrated the high accuracy of OCT for the characterization of plaque microstructure, identification of thin-cap fibroatheromas (TCFA) and quantification of macrophage content in vivo18,19. In addition to the backscattered amplitude measured by OCT, an additional source of tissue contrast is inherent in the polarization state of light.
Imaging techniques to detect vulnerable coronary plaques
Polarization and birefringence
A variety of catheter-based techniques have been investigated for the detection of unstable plaque. Recent advances in intravascular ultrasound (IVUS), including the use of high-frequency transducers11, radiofrequency signal processing12 and integrated backscatter IVUS13, have improved the capability of IVUS for the assessment of plaque components. Studies have also demonstrated the capability of intravascular MRI in plaque characterization14. However, the limited resolution of both IVUS and intravascular MRI inhibits the evaluation of microstructural features such as fibrous caps. Recent advances in optical diagnostic techniques have demonstrated the use of light for plaque characterization. Infrared and Raman spectroscopy evaluate the spectrum of light reflected from the vessel wall to provide spectroscopic information about lipid, collagen and cholesterol crystals15,16. These methods provide composite information of bulk plaque constituents by averaging the detected signal measured over the illuminated volume. Due to the large amount of optical scattering by atherosclerotic plaque tissue, high-resolution depth-resolved information is difficult to obtain with these techniques. OCT is a high-resolution (~ 10 µm) imaging method, in which the amplitude of light reflected
Light is an electromagnetic wave, and its polarization state is defined as the direction of oscillation of the electric field. Tissues containing proteins with an ordered structure and orientation, such as organized collagen fibers, exhibit birefringence, which is a material property that can change the polarization state of light. When light of a given polarization state travels through birefringent tissue such as collagen, light polarized along directions parallel and perpendicular to the fiber orientation travel at different velocities, incurring a relative phase retardation or delay (d), which produces a change in the polarization state of light (Figure 26.2). For organized tissue structures, the accumulated phase retardation is dependent on the difference in the effective refractive index (∆n) for light polarized along and perpendicular to the fiber axis, and the distance, L, traveled through the birefringent tissue: δ=
2π nL , where λ is the wavelength of light (1) λ
Therefore, the phase retardation, δ, accumulates as light travels deeper through tissue, and the rate at which δ accumulates with depth is proportional to the magnitude of birefringence.
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Figure 26.3 (a) Transformation of Stokes vectors for a single pair of incident polarization states, corresponding to doublepass phase retardation accumulated from the sample surface to a depth of 235 µm in muscle tissue. (b) Backscattered intensity and phase retardation maps, 5 mm × 1.2 mm. In the phase retardation map, black = 0°, white = 180°. (c) Mean phase retardation as a function of depth across the full width of the sample, with a linear fit to the data and calculated slope
POLARIZATION-SENSITIVE OCT Background and development of polarization-sensitive OCT As we discussed earlier, OCT provides crosssectional images of tissue microstructure, by measuring the intensity of light reflected from different depths17. Polarization-sensitive OCT (PS-OCT) measures not only the intensity, but also the depthresolved polarization state of backscattered light, providing measurements of tissue birefringence in addition to structural information provided by conventional OCT20. Cross-sectional birefringence imaging in biological tissue using PS-OCT was first reported in 1997 by de Boer et al., who demonstrated a loss of birefringence in a collagen-rich tendon sample in response to laser heating21. This study was followed by PS-OCT measurements in other birefringent tissues22,23. These early PS-OCT systems, constructed with bulk optic components, illuminated the sample with circularly polarized light and measured the intensity of interference signals formed between light returning from sample and reference arms, in orthogonal polarization states. Sample birefringence as a function of depth was related to the ratio of interference signals at each detector. These implementations of PS-OCT were limited by their inability to fully determine the polarization state of backscattered light, and advanced systems were subsequently introduced with analyses based either on the use of Stokes vectors to describe the polarization state of light24, or on Mueller matrix formalisms25 to describe the polarization-related properties of the sample. The Stokes vector-based approach has been more widely demonstrated in clinical studies, and will be described here.
Birefringence measurements using PS-OCT The Stokes parameters (I, Q, U, V) describe the polarization state of light in terms of the amplitude and phase of orthogonal electric field components. I is the intensity of light, while Q, U and V describe the amounts of linear, 45° and circularly polarized light, respectively. These three parameters form a Stokes vector [Q, U, V], which can be plotted in a three-dimensional co-ordinate system called the Poincaré sphere (Figure 26.3A). All possible polarization states of light are represented by points on the surface of the sphere, with a change in polarization state producing a transformation of the Stokes vector. By determining the polarization state of light backscattered from all depths within a sample and analyzing transformations of the corresponding Stokes vectors with respect to the tissue surface, we can calculate the local sample birefringence. Figure 26.3 shows the transformations of a pair of incident Stokes vectors as a function of depth, for a single location on a sample of muscle tissue. The angle through which each vector rotates, relative to the surface polarization state, represents the double-pass phase retardation, since detected light has traveled both in and out of the sample.
Fiberoptic based PS-OCT As OCT was extended to clinical studies, robust, portable systems were required and bulk optic elements were replaced by fiberoptic components, to improve stability and reduce susceptibility to misalignment. The random birefringence of optical fibers introduced several design problems for PS-OCT, requiring the use of multiple incident polarization
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P.Mod
Source PC
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Figure 26.4 Schematic diagram of a fiber-based PS-OCT system. PC, polarization controller; Pol, polarizing cube; P. Mod, polarization modulator; OC, optical circulator; 90/10, fiber coupler; RSOD, rapid-scanning optical delay line; HP, scanning handpiece; FPB, fiberoptic polarizing beamsplitter. Reprinted from reference 27 with permission from the Optical Society of America
states and generalized analysis algorithms, to ensure correct determination of sample birefringence26. A schematic diagram of a fiber-based PS-OCT system is shown in Figure 26.4. The design of the fiber-based PS-OCT system and the mathematical methods to generate the PS-OCT images have been previously described in detail27,28. Briefly, the fiberoptic PS-OCT system provides measurements of tissue birefringence that are independent of the polarization state of light incident on tissue, a fundamental requirement for intracoronary imaging. For alternate A-lines, light from the source is modulated between two incident polarization states orthogonal in the Poincaré sphere. Sample retardation is determined from the weighted mean of rotation angles experienced by each Stokes vector, relative to the vector describing the polarization state at the tissue surface28. By scanning the incident beam across the sample and calculating retardation angles as described, the (double-pass) phase retardation at each point in depth introduced by the sample is obtained and displayed as a gray-scale image with black corresponding to δ = 0° (at the tissue surface shown in red) and white to δ = 180° (Figure 26.3b). This information can be presented graphically, by determining the mean phase retardation angle as a function of depth, for all A-line pairs in the image (Figure 26.3c). A linear fit to these data yields a value for phase retardation per unit depth [°/µm], which is proportional to sample birefringence, as indicated in equation (1).
PS-OCT IN CARDIOLOGY: CORONARY ARTERY DISEASE Assessment of plaque collagen with PS-OCT In this section we present examples of ex vivo PS-OCT images of human atherosclerotic plaques obtained at autopsy. Figure 26.5 shows illustrative PS-OCT images and the corresponding histopathological sections of two fibrous plaques. Plaque collagen content within a region of interest (ROI) was determined using digitized circularly polarized light microscopy images of Picrosirius Red (PSR)-stained sections. The PSR dye enhances the birefringence of collagen when viewed under circularly polarized light and it is known that the polarization colors of PSR-stained sections vary with structure and diameter of collagen fibers29. Thicker, well-organized collagen fibers appear orangered, whereas thinner more disorganized collagen fibers appear yellow-green when viewed under circularly polarized light. The fibrous plaque in Figure 26.5a-d shows an abundance of collagen fibers constituting 90% of the ROI, predominantly consisting of thicker collagen fibers (orange-red) seen in the polarized light microscopy image of the PSR-stained slide (Figure 26.5d). In the PS-OCT image (Figure 26.5c), the presence of these highly birefringent collagen fibers effects a sharp transition in phase retardation from black at the tissue surface to white. The fibroatheroma in Figure 26. 5e-h shows evidence of depleted collagen constituting
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Figure 26.5 (a),(e) OCT images of fibrous plaques. (b), (f) Trichrome-stained histology images. (c) PS-OCT image of fibrous plaque showing high birefringence as seen by the rapid transition of the image from black to white, corresponding to 0–180° phase retardation. (d) Picrosirius Red (PSR)-stained histology section showing orange-red fibers (thicker fibers) under polarized light microscopy. (g) PS-OCT image of fibrous plaque showing black region corresponding to low birefringence below the luminal surface. (h) Corresponding PSR-stained histology section showing yellow-green (thinner fibers) under polarized light microscopy. Bars = 500 µm
20% of the ROI, with small amounts of thin collagen fibers (yellow-green) (Figure 26.5h). Low birefringence in this plaque is seen by PS-OCT as a black region corresponding to low phase retardation below the luminal surface of the plaque (Figure 26.5g). In Figure 26.6, phase retardation values averaged over the width of the central ROI in the two PS-OCT images shown in Figure 26.5c and g are plotted as a function of depth. The phase retardation angle remains low at the surface and increases at different rates with depth. Least squares fits over a depth of 200 µm for each plot indicate that, for the highly birefringent fibrous plaque constituting 90% collagen (displayed in Figure 26.5a-d), phase retardation increases at a rate of 0.43°/µm, compared to a significantly lower rate of 0.16°/µm in the plaque constituting 20% collagen (displayed in Figure 26.5e-h). Figure 26.7 shows illustrative PS-OCT images of a necrotic core fibroatheroma with the corresponding histology sections. The OCT image in Figure 26.7a shows a signal-poor lipid pool with poorly delineated borders beneath a signal-rich band corresponding to the fibrous cap. Low birefringence of the fibrous cap is seen in the PS-OCT image (Figure 26.7c) as a black region corresponding to low phase retardation values,
and beneath the fibrous cap, the necrotic core appears ‘noisy’. When light enters the core, due to the large variance in scattering structures in the necrotic debris, the polarization state of light may become randomized after multiple scattering events20. Deep within the lipid pool, the signal is greatly attenuated and PS-OCT measurements of birefringence cannot be obtained. The sharp transition in birefringence observed between the fibrous cap and necrotic core in PS-OCT images may improve NCFA cap measurement accuracy. Figure 26.7e-h shows an example of a fibrocalcific plaque. Calcific nodules do not contain enough signal for PS-OCT measurements and therefore the birefringence of calcifications could not be established from PS- OCT images. However, calcific nodules can be easily detected with high sensitivity in conventional OCT images as sharply delineated regions within the plaque, having signal-poor interiors30. Cholesterol crystals, which appear as linear signal-rich regions, may offer another source of birefringence in PS-OCT images (Figure 26.8). In OCT images, cholesterol crystals can be easily distinguished from other plaque components by their linear and highly reflecting appearance30.
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thick collagen fibers, the detection of high birefringence in PS-OCT images may imply increased plaque stability. Conversely, low PS-OCT birefringence may indicate compromised plaque stability due to low collagen content and fewer thick collagen fibers. These findings suggest that plaque stability may be determined in part by measuring birefringence from PS-OCT images. The value of this measurement may be emphasized by noting that birefringence in PSOCT images is measured within a cross-sectional image, allowing evaluation of microanatomical structures, such as the fibrous cap.
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Figure 26.6 Phase retardation, δ, angles averaged over the width of the central ROI (500 µm × 200 µm) in the two PS-OCT images shown in Figure 26.5c and 5g are plotted as a function of depth. Least squares fits over a depth of 200 µm for each plot show that PS-OCT birefringence measured as the slope as the phase retardation plot is higher (φ = 0.43°/µm) for the plaque constituting 90% collagen (displayed in Figure 26.5a-d), compared with a lower birefringence (φ = 0.16°/µm)for the plaque with depleted collagen (displayed in Figure 26.5e-h)
PS-OCT imaging via an intracoronary catheter Similar to conventional intracoronary OCT, PSOCT images may be obtained through catheters in patients18,19. However, the scanning of the catheter during imaging induces stress birefringence in the optical fiber, resulting in a dynamically changing polarization state of light at the sample. In PS-OCT, any birefringence introduced by sample arm scanning must be isolated from the sample birefringence that we wish to measure. This can be effectively achieved by analysis of depth-resolved polarization states with respect to the instantaneous
These results indicate that PS-OCT birefringence may provide a significant index of plaque stability. Since increased birefringence is related to abundant
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Figure 26.7 (a) OCT image of a necrotic core fibroatheroma (NCFA). (b), (f) Trichrome-stained histology images. (c) PS-OCT image of NCFA showing fibrous cap with low birefringence. (d) Picrosirius Red (PSR)-stained histology section of depleted collagen in fibrous cap and necrotic core. (e) OCT image of fibrocalcific plaque showing calcification with well-defined border. (g) PS-OCT image of fibrocalcific plaque. (h) Corresponding PSR-stained histology section. Bars = 500 µm
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Figure 26.8 (a) OCT image of a necrotic core fibroatheroma (NCFA). (b) Corresponding PS-OCT image showing birefringence within the fibrous cap overlying a region of the lipid pool. Cholesterol crystals, shown by the arrows, appear birefringent below the fibrous cap
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Figure 26.9 (a) (OCT) image. (b) PS-OCT image of human coronary obtained using intracoronary imaging catheter. (c) Trichrome-stained section. Reprinted from reference 31 with permission from the Optical Society of America
(not averaged) surface polarization states, for each A-line31. A fiberoptic cathether with an outer diameter of 1.0 mm (3 F) was incorporated with the PS-OCT system. Images were acquired as the catheter was rotated through 360° about its axis. Figure 26.9 shows an illustrative PS-OCT image of a human coronary obtained coaxially with the rotary-scanning probe. The main layers of the artery wall are indicated in the intensity image (Figure 26.9a), as intima (i), media (m) and adventitia (a). In the PS-OCT image (Figure 26.9b), the transition in phase retardation angles with increasing depth demonstrates the birefringence of the arterial wall. The development of intravascular PS-OCT potentially enables the measurement of collagen birefringence in vivo, providing a powerful index of plaque stability in patients.
SUMMARY PS-OCT is unique in that it provides images of birefringence, which are co-registered with high-resolution, cross-sectional images of plaque morphology obtained by conventional OCT. Beyond the measurement of cap thickness, PS-OCT provides additional information about the composition of plaques and NCFA fibrous caps, where low birefringence probably indicates increased instability. Given the potential significance of the additional information provided by PS-OCT, and its promise for intracoronary application, we anticipate that this technology will be useful for improving our understanding of the mechanisms of plaque progression and rupture and for the detection of high-risk plaques prior to the occurrence of an acute coronary event.
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REFERENCES 1. Falk E, Shah PK, Fuster V. Coronary plaque disruption. Circulation 1995; 92: 657–71 2. Virmani R, Kolodgie FD, Burke AP, et al. Lessons from sudden coronary death: a comprehensive morphological classification scheme for atherosclerotic lesions. Arterioscler Thromb Vasc Biol 2000; 20: 1262–75 3. Fernandez-Ortiz A, Badimon JJ, Falk E, et al. Characterization of the relative thrombogenicity of atherosclerotic plaque components: implications for consequences of plaque rupture. J Am Coll Cardiol 1994; 23: 1562–9 4. Plenz GA, Deng MC, Robenek H, Volker W. Vascular collagens: spotlight on the role of type VIII collagen in atherogenesis. Atherosclerosis 2003; 166: 1–11 5. Newby AC, Zaltsman AB. Fibrous cap formation or destruction – the critical importance of vascular smooth muscle cell proliferation, migration and matrix formation. Cardiovasc Res 1999; 41: 345–60 6. Rekhter MD, Hicks GW, Brammer DW, et al. Hypercholesterolemia causes mechanical weakening of rabbit atheroma: local collagen loss as a prerequisite of plaque rupture. Circ Res 2000; 86: 101–8 7. Slager CJ, Wentzel JJ, Gijsen FJ, et al. The role of shear stress in the destabilization of vulnerable plaques and related therapeutic implications. Nat Clin Pract Cardiovasc Med 2005; 2: 456–64 8. Deguchi JO, Aikawa E, Libby P, et al. Matrix metalloproteinase-13/collagenase-3 deletion promotes collagen accumulation and organization in mouse atherosclerotic plaques. Circulation 2005; 112: 2708–15 9. Aikawa M, Rabkin E, Voglic SJ, et al. Lipid lowering promotes accumulation of mature smooth muscle cells expressing smooth muscle myosin heavy chain isoforms in rabbit atheroma. Circ Res 1998; 83: 1015–26 10. Libby P, Aikawa M. Mechanisms of plaque stabilization with statins. Am J Cardiol 2003; 91: 4B–8B 11. Prati F, Arbustini E, Labellarte A, et al. Correlation between high frequency intravascular ultrasound and histomorphology in human coronary arteries. Heart 2001; 85: 567–70 12. Komiyama N, Berry GJ, Kolz ML, et al. Tissue characterization of atherosclerotic plaques by intravascular ultrasound radiofrequency signal analysis: an in vitro study of human coronary arteries. Am Heart J 2000; 140: 565–74 13. Kawasaki M, Takatsu H, Noda T, et al. In vivo quantitative tissue characterization of human coronary arterial plaques by use of integrated backscatter intravascular ultrasound and comparison with angioscopic findings. Circulation 2002; 105: 2487–92 14. Rogers WJ, Prichard JW, Hu YL, et al. Characterization of signal properties in atherosclerotic plaque components by intravascular MRI. Arterioscler Thromb Vasc Biol 2000; 20: 1824–30 15. Moreno PR, Lodder RA, Purushothaman KR, et al. Detection of lipid pool, thin fibrous cap, and inflammatory cells in human aortic atherosclerotic plaques by near-infrared spectroscopy. Circulation 2002; 105: 923–7
16. Romer TJ, Brennan JF 3rd, Schut TC, et al. Raman spectroscopy for quantifying cholesterol in intact coronary artery wall. Atherosclerosis 1998; 141: 117–24 17. Huang D, Swanson EA, Lin CP, et al. Optical coherence tomography. Science 1991; 254: 1178–81 18. Jang IK, Tearney GJ, MacNeill B, et al. In vivo characterization of coronary atherosclerotic plaque by use of optical coherence tomography. Circulation 2005; 111: 1551–5 19. MacNeill BD, Jang IK, Bouma BE, et al. Focal and multi-focal plaque macrophage distributions in patients with acute and stable presentations of coronary artery disease. J Am Coll Cardiol 2004; 44: 972–9 20. de Boer JF, Milner TE. Review of polarization sensitive optical coherence tomography and Stokes vector determination. J Biomed Opt 2002; 7: 359–71 21. de Boer JF, Milner TE, Nelson JS. Two-dimensional birefringence imaging in biological tissue by polarization-sensitive optical coherence tomography. Opt Lett 1997; 22: 934–6 22. Hitzenberger CK, Gotzinger E, Sticker M, et al. Measurement and imaging of birefringence and optic axis orientation by phase resolved polarization sensitive optical coherence tomography. Opt Express 2001; 9: 780–90 23. Götzinger E, Pircher M, Sticker M, et al. Measurement and imaging of birefringent properties of the human cornea with phase-resolved, polarization-sensitive optical coherence tomography. J Biomed Opt 2004; 9: 94–102 24. de Boer JF, Milner TE, Nelson JS. Determination of the depth-resolved Stokes parameters of light backscattered from turbid media by use of polarization-sensitive optical coherence tomography. Opt Lett 1999; 24: 300–2 25. Yao G, Wang LV. Two-dimensional depth-resolved Mueller matrix characterization of biological tissue by optical coherence tomography. Opt Lett 1999; 24: 537–9 26. Saxer CE, de Boer JF, Park BH, et al. High-speed fiberbased polarization-sensitive optical coherence tomography of in vivo human skin. Opt Lett 2000; 25: 1355–7 27. Pierce MC, Park HB, Cense B, de Boer JF. Simultaneous intensity, birefringence, and flow measurements with high-speed fiber-based optical coherence tomography. Opt Lett 2002; 27: 1534–6 28. Park BH, Saxer C, Srinivas SM, et al. In vivo burn depth determination by high-speed fiber-based polarization sensitive optical coherence tomography. J Biomed Opt 2001; 6: 474–9 29. Junqueira LC, Cossermelli W, Brentani R. Differential staining of collagens type I, II and III by Sirius Red and polarization microscopy. Arch Histol Jpn 1978; 41: 267–74 30. Yabushita H, Bouma BE, Houser SL, et al. Characterization of human atherosclerosis by optical coherence tomography. Circulation 2002; 106: 1640–5 31. Pierce MC, Shishkov M, Park BH, et al. Effects of sample arm motion in endoscopic polarization-sensitive optical coherence tomography. Opt Express 2005; 13: 5739–49
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CHAPTER 27 OCT elastography: a possibility to detect vulnerable plaque? Gijs van Soest, Frits Mastik, Patrick W Serruys, Evelyn Regar, Anton FW van der Steen
INTRODUCTION: VULNERABLE PLAQUE DETECTION BY ELASTOGRAPHY
fibrous and fatty plaque components are different8,9, and so the observed strain pattern will depend on plaque structure and composition. Elastography is particularly attractive for identification of plaque vulnerability because the high-strain spots correlate directly with the proneness of the cap to rupture. Therefore, methods that are capable of measuring the radial strain provide information on plaque composition that can be rele osed a method to measure the strain in tissue using ultrasound. The tissue was strained by applying an external force on it. Different strain values were found in tissues with different mechanical properties. This elasticity imaging method offers the potential for intravascular purposes to detect a vulnerable plaque by identification of different plaque components and detection of high-strain regions. The principle of intravascular elastography is illustrated in Figure 27.1. A cross-sectional image of an artery is acquired at a low intraluminal pressure, and a second at a higher intraluminal pressure (in the current practice of IVUS elastography, the pressure differential is approximately 2.5 mmHg). The deformation or strain s is calculated from s = (d2 – d1)/∆z, where d1 and d2 are the displacements at distances z and z + ∆z. The elastogram (image of the radial strain) is plotted as a complementary image to the gray-scale image. The different strategies to perform intravascular elastography (i.e. assess the local deformation of the tissue) are in the way of detecting the strain and the source of deforming the vascular tissue11.
Unstable atherosclerotic lesions may rupture and cause a thrombotic reaction. If this occurs in a coronary artery it may lead to acute myocardial infarction and death1,2. A technique to unequivocally determine coronary plaque vulnerability is still lacking at present, but intravascular elastography is a strong contender3. The extension of OCT to in vivo intracoronary elastography would augment the technique’s highresolution imaging capability with valuable information on the arterial wall’s mechanical properties. This chapter aims to identify the potential of OCT elastography for the identification of vulnerable atherosclerotic lesions. It is organized as follows: we first review the principles of elastography and present the current state-of-the-art in intravascular ultrasound (IVUS) elastography. We then discuss the existing literature on OCT elastography, and summarize what has been achieved to date. We end with a discussion of the promises and limitations of OCT elastography compared to IVUS elastography. The elasticity of the arterial wall depends on its composition: fibrous, fatty and calcified tissues differ in their compliance to an applied force. A vulnerable lesion (or thin-cap fibroatheroma; TCFA) is characterized by an eccentric plaque with a large lipid pool shielded from the lumen by a thin fibrous cap4–6. Inflammation of the cap by macrophages further increases the vulnerability of these lesions7. The cap covering the lipid pool will rupture if it is unable to withstand the stress applied on it by the systemic blood pressure variation. A high intraluminal pressure results in increased circumferential stress, which will result in an increased radial deformation (strain) of the tissue due to the incompressibility of the material. As said, the mechanical properties of
IVUS ELASTOGRAPHY A radial strain is derived from the analysis of differences between the images obtained across the pressure differential. An example of an IVUS elastogram of a 249
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Figure 27.1 The principle of elastography depicted in a schematic diagram of an arterial section with a soft plaque: the displacement (yellow arrows) of the tissue is tracked from one image at low pressure (black contours) to one at high pressure (red contours). The displacements are drawn radially outward from the catheter (blue circle). Strain, the quantity of interest in elastography, is found from the differential displacement, i.e. the differences between the inner and outer arrows (more layers are possible). In this image, high strain is visualized near the shoulders of the plaque (S) by a large difference in displacement of the inner and outer layers. In real soft plaques, high strain is observed in these locations; see also Figure 27.2
technique works only for large strains and has not been developed into a clinical application. Elastography experience showed18 that most clinically relevant strain information is in the first few hundred micrometers of arterial wall tissue. The strain at the intraluminal boundary is calculated as described above, color coded and depicted on the lumen boundary. An example is shown in Figure 27.3; the technique to analyze the tissue deformation of the innermost layer of the vessel is called palpography19–21. Palpograms can be acquired while pulling back the catheter, allowing 3D examination of the whole artery, and is currently in clinical use. All the work discussed above has been done using a phased-array catheter which is not rotating. Saijo and co-workers22 have performed IVUS elastography using a rotating, single-element catheter. They were able to identify hard and soft plaques, but the analysis was partly hampered by artifacts due to non-uniform rotation of the catheter. Based on IVUS elastograms, Baldewsing et al.23 have iteratively calculated the local elastic, or Young’s, modulus of the tissue, using a finite element model (FEM) of an artery slice. The Young’s modulus E is the ratio between the applied stress and the deformation of a material, and can be used to discern fibrous/calcified tissue (large E) and lipid (small E). This technique was dubbed ‘modulography’.
OCT ELASTOGRAPHY: PRINCIPLE human coronary in vivo3,12 is shown in Figure 27.2. Typically in intravascular elastography, the strain induced in vascular tissue by the pressure differential is in the order of 1%. This means that a block of tissue with an initial size of 100 µm will be deformed to 99 µm, requiring submicrometer estimation of the displacement field. These small deformations can be produced with pressure differentials resulting from the systemic blood pressure variation and pose no danger of damaging the tissue. According to Varghese and Ophir13, the elastography technique that has the highest signal-to-noise ratio for small strains is a cross-correlation of the so-called radio-frequency (RF) signals, the recorded echoes along one or a few A-lines. The cross-correlation function is a measure of the similarity of two signals, and so can be used to estimate a shift between the two: the peak of the cross-correlation function is found at the position representing the displacement of the tissue. A less sensitive but more robust alternative for cross-correlation of RF data is spectral strain imaging14,15, exploiting the relation between the tissue strain and the frequency of the scattered sound wave. Another option is speckle-tracking in a B-mode image using the envelope of the echo signal16,17, but this
Compared to IVUS, the imaging resolution of OCT is a factor of 10 better; experimental systems achieve even more improvement. Particularly the capability to resolve the thin cap (< 65 µm) of a TCFA is a powerful asset. OCT elastography augments the highquality imagery with an assessment of the local mechanical properties of the vascular wall, making it possible to identify focal high-strain spots in a grayscale OCT image showing the morphology of the vascular tissue. Both these measurements can be done with the same catheter, as with IVUS elastography, and preferably in one catheter pullback, as with palpography. The basic principles of OCT and IVUS elastography are identical: a base image and an image of deformed tissue are compared, and from the local displacement vectors, the tissue strain is calculated. There are a few practical discrepancies, however, both advantageous and disadvantageous, that we outline below. An advantage of OCT elastography over IVUS elastography is the potential to directly measure the strain in two or possibly even three dimensions. Due to the well-defined beam and the resulting good transverse resolution of OCT, tissue motion can in principle be tracked in all directions in the sampled volume. Measurement of the full strain tensor allows
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Figure 27.2 Intravascular echogram and elastogram of a coronary artery obtained in vivo in a patient. The echogram suggests a calcified region between the 12 and 3 o’clock positions. The elastogram reveals low strain values in this region, corroborating this finding. High strain values were found at the shoulders of this eccentric plaque. From reference 12
b
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Figure 27.3 Palpography in vivo. Angiogram (a) of the right coronary artery shows an intermediate plaque. (b) 3D palpogram of vessel segment between white lines in angiogram. (c) Cross section of 2D palpogram plus echogram as indicated in 3D palpogram. A high-strain plaque on shoulders of eccentric plaque (yellow) can be clearly identified. From reference 20
calculation of the so-called ‘principal strain’24. Radial strain measurements are sensitive to the catheter position in the artery, while the principal strain measures the strain magnitude independent of catheter position. Some differences between IVUS and OCT give rise to difficulties that need to be overcome in the development of a reliable elastography method. We discuss three of them here.
Radiofrequency signals versus envelope As outlined above, IVUS elastography is best performed using the RF signals of the recorded echo. The physical advantage of the RF signal is the presence of rapid sign changes, to which the cross-correlation function is very sensitive. In OCT, such a signal is usually not present. The rapidly oscillating ‘carrier wave’ is usually filtered out electronically before the signal is
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Figure 27.4 (a) Image of the axial displacements inside a pork meat sample, calculated by cross-correlation analysis. The black areas outlined with dotted lines indicate where the signal-to-noise ratio was too low for calculation of the displacements. (b) Strains estimated for the areas marked ‘1’ and ‘2’ in the displacement image on the left. From reference 27
digitized, and only the ‘envelope’ is used in constructing the image. Even if the full carrier wave is recorded, as is the case in some research OCT systems, there is usually no relation between the phase of the wave between subsequent images, so the exact location of the zero-crossings is random and does not contain any information on the tissue displacement. The optimal methods for IVUS elastography rely on the use of RF data, and have to be adapted for use with OCT.
Speckle stability An OCT image consists of speckle, which is a random interference phenomenon producing a fine mottled pattern of bright and dark spots. The speckle arises because the phase of the scattered wave field is random. The superb resolution of OCT is achieved by using a light source with a short coherence length, approximately 10 µm or less. As discussed in the early chapters of this book, constructive interference between the reference and sample beams of the interferometer occurs only within the coherence length of the light, and so, by scanning the path length in the reference arm, a coherence gate can be scanned through the tissue. Unfortunately, as a result of the same physical phenomenon, the speckle changes if either the source or the scattering medium moves by a distance larger than the coherence length25,26. Since tissue and probe stability better than 10 µm is very difficult to achieve in vivo, algorithms for OCT elastography will have to allow for speckle decorrelation.
Catheter rotation OCT catheters are always of the rotating type. It is not possible with the current glass fiber technology to make the optical analog of an IVUS multi-element catheter and achieve sufficient angular sampling for
imaging, i.e. scan at least 200 lines per frame, since a separate fiber would be needed for every observation angle. Using fewer angular lines would impair image quality, which is the main asset of OCT. Catheter rotation does have some drawbacks, however, for elastographic applications. First, there will be non-uniform rotation artifacts that are much larger than those seen in IVUS elastography. Second, the rotating fiber may move so much that the speckle stability discussed above is compromised. In summary, OCT elastography holds promise, but comes with its own challenges. The technology and algorithms for realizing the potential of the concept will have to be robust, and can certainly not be copied directly from its IVUS counterpart.
OCT ELASTOGRAPHY: CURRENT STATUS Since the pioneering work of Schmitt27, a small number of papers have been published on OCT elastography26,28,29. All experiments to date have been performed in a benchtop geometry, in which a probe (a fiber tip or a microscope objective) is scanned across a planar tissue sample or phantom. Pressure is applied by either pushing down a glass plate on top of the sample, or by stretching the sample longitudinally. Schmitt27 and Rogowska et al.28 adopted a technique that relies on the tracking of features: they incorporated small particles in a gel phantom that could be tracked from the base image to the displaced image by means of pixel selection and crosscorrelation. In tissue, Schmitt27 could discern the difference in stiffness between fat and muscle in a pork meat sample (Figure 27.4). In their experiment on in vitro aortic tissue, Rogowska et al.28 Rogowska et al.28 were the first to derive strain maps of arterial tissue in vitro; an example is shown in Figure 27.5. In a recent follow-up study29, they quantified the
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Figure 27.5 Displacement image (a) and axial strain map (b) from aortic tissue in vitro; strain scale in per cent. The correlation window was optimized by comparing measurements on a gel phantom with calculated displacements. From reference 28
elastic modulus of the tissue, using a calibrated phantom as a reference. Large-scale variations in elasticity could be discerned in the strain maps, but in places where the images were feature-poor, the maps were noisy: in the absence of markers to track, the cross-correlation between two speckle fields is calculated. Speckles are sensitive to small disturbances, as discussed above, potentially resulting in loss of correlation, which would lead to errors in the displacement estimates. Chan et al.26 presented a method to reduce the noise of the displacement estimates by imposing additional constraints: tissue incompressibility and smoothness of the displacement fields, implemented in an FEM of the tissue. Using an in vitro aortic tissue sample, they calculated a strain field from base and displaced images. The strain field is smoother than that computed without the use of tissue biomechanics (Figure 27.6). Khalil et al.30 extended this framework with a simulation of a system to estimate tissue elasticity. The relative weight of the smoothness constraint in the calculation strongly determines the strain estimate and so has to be chosen carefully. The only in vivo OCT elastography experiment was performed on the skin of a finger27. The deformation was estimated on a line-by-line basis. This technique allowed the calculation of lateral and
axial displacement fields, and qualitative interpretation of those in terms of strain. Two other manuscripts are of interest for the development of OCT elastography. Nadkarni et al.31 were able to distinguish the elasticities of different atherosclerotic plaque components by studying the decorrelation rate of laser speckle in light scattered off the tissue. A stiffer material will exhibit less motion and hence slower decorrelation. This method sacrifices imaging capability, however, for elasticity measurement. Finally, Chau and co-workers32 have calculated stress and strain fields of plaques from OCT morphology and literature values for the elastic moduli. In view of cardiovascular applications, what is obviously lacking from the experimental data at present is OCT elastography in a vessel geometry, using a rotating probe. The circular continuity of the displacement field poses its own requirements to the algorithm. In addition, the non-uniformity of the rotation causes a specific type of artifact, which needs to be corrected for elastography.
SUMMARY Published research has shown that tissue displacement can be qualitatively estimated with OCT, which is a
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Figure 27.6 OCT elastography using tissue incompressibility and requiring a smooth displacement field. (a) Estimated lateral velocity and (b) strain fields for an aortic specimen undergoing lateral stretching (right-to-left in the imaging plane). From reference 26
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12.
conditio sine qua non for an eventual elastographic application. Strain fields, required for assessing plaque vulnerability, modulography, or tissue identification, are not sufficiently reliable at present for quantitative analysis. The full potential of OCT elastography has yet to be demonstrated, and much work still has to be done before a clinical application such as palpography can be developed on the basis of OCT. On the other hand, the present literature has not shown the impossibility of OCT elastography, and given the potential possibilities of the technique, continuing research will try to realize a reliable measurement of tissue elasticity by means of OCT.
13.
14.
15.
16.
ACKNOWLEDGMENTS
17.
This work was financially supported by LightLab Imaging and Volcano.
18.
REFERENCES 19. 1. Davies MJ, Thomas AC. Plaque fissuring – the cause of acute myocardial infarction, sudden ischaemic
death, and crescendo angina. Br Heart J 1985; 53: 363–73 Richardson PD, Davies MJ, Born GV. Influence of plaque configuration and stress distribution on fissuring of coronary atherosclerotic plaques. Lancet 1989; 2: 941–4 De Korte CL, Carlier SG, Mastik F, et al. Morphological and mechanical information of coronary arteries obtained with intravascular elastography. Feasibility study in vivo. Eur Heart J 2002; 23: 405–13 Schaar JA, Muller JE, Falk E, et al. Terminology for high-risk and vulnerable coronary artery plaques. Eur Heart J 2004; 25: 1077–82 Kolodgie FD, Burke AP, Farb A, et al. The thin-cap fibroatheroma: a type of vulnerable plaque – the major precursor lesion to acute coronary syndromes. Curr Opin Cardiol 2001; 16: 285–92 Falk E, Shah PK, Fuster V. Coronary plaque disruption. Circulation 1995; 92: 657–71 Moreno PR, Falk E, Palacios IF, et al. Macrophage infiltration in acute coronary syndromes. Implications for plaque rupture. Circulation 1994; 90: 775–8 Loree HM, Tobias BJ, Gibson LJ, et al. Mechanicalproperties of model atherosclerotic lesion lipid pools. Arterioscler Thromb 1994; 14: 230–4 Lee RT, Richardson SG, Loree HM, et al. Prediction of mechanical properties of human atherosclerotic tissue by high-frequency intravascular ultrasound imaging – an in vitro study. Arterioscler Thromb 1992; 12: 1–5 Ophir J, Cespedes I, Ponnekanti H, et al. Elastography – a quantitative method for imaging the elasticity of biological tissues. Ultrason Imaging 1991; 13: 111–34 Van der Steen AFW, De Korte CL, Cespedes EI. Intravascular ultrasound elastography. Ultraschall Med 1998; 19: 196–201 Van der Steen AFW, De Korte CL, Schaar JA, et al. 3D intravascular ultrasound palpography for vulnerable plaque detection. In: ISBI. Arlington, VA: IEEE, 2004: 49–52 Varghese T, Ophir J. Characterization of elastographic noise using the envelope of echo signals. Ultrasound Med Biol 1998; 24: 543–55 Talhami HE, Wilson LS, Neale ML. Spectral tissue strain: a new technique for imaging tissue strain using intravascular ultrasound. Ultrasound Med Biol 1994; 20: 759–72 Konofagou EE, Varghese T, Ophir J, et al. Power spectral strain estimators in elastography. Ultrasound Med Biol 1999; 25: 1115–29 Bohs LN, Trahey GE. A novel method for angle independent ultrasonic-imaging of blood-flow and tissue motion. IEEE Trans Biomed Eng 1991; 38: 280–6 Shapo BM, Crowe JR, Erkamp R, et al. Strain imaging of coronary arteries with intraluminal ultrasound: experiments on an inhomogeneous phantom. Ultrason Imaging 1996; 18: 173–91 Cespedes EI, De Korte CL, Van der Steen AFW. Intraluminal ultrasonic palpation: assessment of local and cross-sectional tissue stiffness. Ultrasound Med Biol 2000; 26: 385 Schaar JA, De Korte CL, Mastik F, et al. Intravascular palpography for high-risk vulnerable plaque assessment. Herz 2003; 28: 488–95
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20. Schaar JA, Regar E, Mastik F, et al. Incidence of highstrain patterns in human coronary arteries – assessment with three-dimensional intravascular palpography and correlation with clinical presentation. Circulation 2004; 109: 2716–9 21. Schaar JA, De Korte CL, Mastik F, et al. Three-dimensional palpography of human coronary arteries – ex vivo validation and in-patient evaluation. Herz 2005; 30: 125–33 22. Saijo Y, Tanaka S, Owada N, et al. Tissue velocity imaging of coronary artery by rotating-type intravascular ultrasound. Ultrasonics 2004; 42: 753–7 23. Baldewsing RA, Schaar JA, Mastik F, et al. Assessment of vulnerable plaque composition by matching the deformation of a parametric plaque model to measured plaque deformation. IEEE Trans Med Imaging 2005; 24: 514–28 24. Baldewsing RA, Mastik F, Schaar JA, et al. Robustness of reconstructing the Young’s modulus distribution of vulnerable atherosclerotic plaques using a parametric plaque model. Ultrasound Med Biol 2005; 31: 1631–45 25. Goodman JW. Statistical properties of laser speckle patterns. In: Dainty JC, ed. Laser Speckle and Related Phenomena, 2nd edn. Berlin: Springer-Verlag, 1984: 9–76
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26. Chan RC, Chau AH, Karl WC, et al. OCT-based arterial elastography: robust estimation exploiting tissue biomechanics. Opt Express 2004; 12: 4558–72 27. Schmitt JM. OCT elastography: imaging microscopic deformation and strain of tissue. Opt Express 1998; 3: 199–211 28. Rogowska J, Patel NA, Fujimoto JG, et al. Optical coherence tomographic elastography technique for measuring deformation and strain of atherosclerotic tissues. Heart 2004; 90: 556–62 29. Rogowska J, Nirlep Patel N, Plummer S, Brezinski ME. Quantitative optical coherence tomographic elastography: method for assessing arterial mechanical properties. BR J Radiol 2006, doi: 10.1259/bjr/ 22522280 30. Khalil AS, Chan RC, Chau AH, et al. Tissue elasticity estimation with optical coherence elastography: toward mechanical characterization of in vivo soft tissue. Ann Biomed Eng 2005; 33: 1631–9 31. Nadkarni SK, Bouma BE, Helg T, et al. Characterization of atherosclerotic plaques by laser speckle imaging. Circulation 2005; 112: 885–92 32. Chau AH, Chan RC, Shishkov M, et al. Mechanical analysis of atherosclerotic plaques based on optical coherence tomography. Ann Biomed Eng 2004; 32: 1494–503
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CHAPTER 28 Limiting ischemia by fast Fourier-domain imaging Joseph M Schmitt, Robert Huber, James G Fujimoto
The need to interrupt blood flow to the heart adds complexity and risk to the intracoronary OCT imaging procedure. Recent advances in OCT imaging technology promise to enable interventional cardiologists to obtain high-resolution images of long segments of the major coronary arteries, in spite of the time constraints imposed by balloon occlusion or flushing. This capability is made possible by a new detection method for OCT imaging, called Fourier-domain OCT (FD-OCT), that enables acquisition of OCT images 15–50 times faster than that of currently available time-domain OCT (TD-OCT) scanners1–8. This chapter provides an overview of on-going work aimed at developing FD-OCT for intravascular imaging. The material presented here is the result of a collaborative effort between scientists and engineers at LightLab Imaging, Inc., and the Massachusetts Institute of Technology (MIT).
interest. Shortly after completion of the pullback, a longitudinal (L)-mode display showing a longitudinal section through the segment would appear. The interventionist would then review the stored image sequence at slow speed and, if desired, assess lesion characteristics frame by frame. For example, suppose that the proximal segment of the left coronary artery of a patient shows evidence of diffuse disease by angiography and that at least 3 cm of the left anterior descending artery (LAD) must be imaged to assess the characteristics of lesions within the target segment. To carry out the rapid-scan OCT procedure, the interventionist would first place the imaging catheter distal to the target segment. A bolus of saline would be injected through the guide catheter to initiate image acquisition and automatic pullback of the tip of the optical fiber inside the OCT catheter. Injection of a total of 16–20 ml of saline over a 3-s period would enable a 3–4 cm length of the vessel to be scanned at a pullback speed of 15 mm/s. During the pullback, capture and storage of images would occur at an acquisition rate of 100 frames/s, thereby providing a three-dimensional spiral image with an effective interval between adjacent frames equal to approximately 100 µm. Since the optical thickness of an OCT image slice is typically 25–40 µm (determined by the transverse spot size of the focusing lens), nearly complete coverage of the longitudinal dimension of the vessel segment would be achieved. Typically, for convenient review of the images, the video sequence of OCT images would be played back at a fraction of the rate at which the images were captured, using the L-mode display as a navigational guide. This example illustrates the foremost potential advantage of the rapid-scan OCT imaging method: its ability to expand the clinical utility of the saline flush for blood clearance. As an alternative to the balloon occlusion, flushing simplifies the OCT imaging procedure by easing constraints on the delivery and positioning of the imaging catheter. In particular, if
THE RAPID SCAN CONCEPT Central to the application of FD-OCT to coronary imaging is the concept of image capture at high speed and image display on an elongated time scale. Interventional cardiologists are already familiar with this mode of imaging in their daily practice. In the standard cineangiography procedure, X-ray images are captured in quick succession during rapid injection of contrast medium. The resultant angiogram is then reviewed as each frame plays back slowly on the display monitor. Often, to detect subtle features, the cardiologist must view the angiogram multiple times. A similar imaging protocol is envisioned for rapid-scan Fourier-domain OCT imaging. During injection of a bolus of saline or brief inflation of an occlusion balloon to clear blood from the target vessel, a sequence of images would be captured in rapid succession. Simultaneously, the fiberoptic imaging core of the OCT catheter would be pulled back rapidly through the vessel segment of 257
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flushing through the guide catheter for blood clearance can be made safe and efficient, the crossing profile of the imaging catheter could be reduced substantially by eliminating the need for inflation and flush lumina in the imaging catheter. Studies with coronary angiography9 and OCT10 indicate that saline injected through the guide catheter displaces blood most effectively when infused rapidly in a bolus, with peak flow rates of 4–5 ml/s. In OCT imaging procedures in which blood is cleared by a 10–20-ml bolus of flush solution through the guide catheter without balloon occlusion, the maximum image duration is typically 2–5 s, a period long enough to permit only a few snapshots of adjacent cross sections of a vessel to be acquired11. Flushing for long periods increases the risk of myocardial ischemia, with the additional risk of fluid overload or alteration of electrolyte balance in susceptible patients. Although lower flush rates can be effective when saline is infused through ports located closer to the imaging aperture, OCT imaging catheters that incorporate selective flush ports are larger and more complex. Regardless of the flushing method, increased image acquisition speed translates into reduced infusion times and total flush volumes. Even in those cases in which balloon occlusion is the preferred method for blood clearing, the rapidscan OCT imaging method has substantial clinical advantages. The instructions for use of the LightLab HeliosTM occlusion balloon catheter, used in conjunction with the LightLab ImageWireTM for OCT imaging, recommend a maximum inflation time of 35 s. At a pullback speed of 1 mm/s, continuous visualization is limited to vessel segments shorter than 3.5 cm. Rapid-scan OCT would give the interventionist the opportunity to scan the entire length of artery distal to the occlusion balloon in a single scan without increasing the duration of occlusion.
FD-OCT PRINCIPLES Standard OCT uses time-domain detection, where echoes of light at different time delays are detected sequentially. In time-domain detection, the position of the mirror in the reference arm of the interferometer is scanned mechanically to generate different optical delays. This conventional method of OCT detection has two distinct disadvantages. First, the need for scanning the optical delay mechanically imposes constraints on the maximum axial scan rate of the OCT system. The second problem arising from the conventional time-domain detection techniques is that they detect light echoes sequentially as the optical delay is scanned. Because the measurement time for each echo delay is very short, the maximum achievable sensitivity is limited. Since there is a tradeoff of imaging speed with sensitivity, the maximum imaging speed is
also limited. Typically, time-domain systems operate at less than 5000–10 000 axial scans per second. A promising way to overcome these problems is FD-OCT detection1,2, in which echoes of light from all time delays are detected simultaneously. In FD-OCT, axial scan information is obtained by Fourier transforming or extracting the frequency content of a spectral analysis of the interferometric backscattering signal from all sample depths simultaneously. This provides the same information as in OCT with timedomain detection, but without the requirement of moving the mirror in the reference arm. The spectral information can be extracted either by the use of a spectrometer instead of a single photodiode (spectral OCT) or by a rapid wavelength-swept laser (sweptsource OCT). In swept-source FD-OCT, also known as optical Fourier domain imaging (OFDI), a high-speed, frequency-swept narrowband light source is used to perform the spectral analysis of the backscattered signal1–3,5–8. Acquisition rates of more than 100 000 axial lines or A-scans per second are possible6,7,8,12. The concept of swept-source FD-OCT is illustrated in Figure 28.1a. Analogous to OCT with time-domain detection, light is split into a sample and a reference arm and the back-reflected or backscattered fields from both arms are overlaid on a photodetector. However, in contrast to OCT with time-domain detection, the input light in FD-OCT is a sequence of narrowband optical- frequency sweeps in the form of signals with continuously changing wavelength. The reference reflector is not moved, but remains stationary, thus defining the reference arm length z = 0. The sketch in Figure 28.1b illustrates the situation for a single reflection in the sample arm. The difference in the arm lengths of sample and reference arm causes the back-reflected or backscattered optical fields to reach the detector at slightly different times. The sum of two delayed swept waveforms results in a chirped waveform with a modulated amplitude – a beat signal (Figure 28.1b). The frequency of the envelope of this beat signal corresponds to the instantaneous difference in frequency and, therefore, is proportional to the delay or the path-length difference of the sample and reference arm lengths. The photodetector records intensity (power) rather than the field of the optical waveform, and it is too slow to resolve the individual fringes at optical frequencies. Consequently, the output of the photodetector gives the envelope of the intensity profile of the beat signal. An electronic signal is generated with a frequency that is proportional to the path length difference or, equivalently, the depth in the sample. For the case in which the sample is a biological tissue, the back-reflected or backscattered signal from the sample arm consists of many signals from different depths, which is equivalent to many overlaid waveforms with different delays and different amplitudes. Consequently, the electronic signal from the
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overlayed optical field (sum) on detector (E1 + E2)
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Figure 28.1 (a) Basic concept of swept-source FD-OCT. Analogous to OCT with time-domain detection, light is split into a sample and a reference arm, and the back-reflected or backscattered fields from both arms are overlayed on a photodetector. The interference signals recorded by the photodetector are proportional to differences in the optical frequencies generated by reflections from the sample and fixed reference arm. (b) Interference signals generated by a single reflection in the sample arm
detector is composed of a sum of different frequencies with different amplitudes. This electronic signal is recorded and processed with a computer. A mathematical algorithm, Fourier transformation, is used to decompose the recorded waveform into its different frequency components, where each frequency component corresponds to a certain sample depth. An axial scan is then constructed by mapping the intensity of the different frequency components, provided by the Fourier transformation, to the brightness of a pixel in the corresponding sample depth. Analogous to OCT with time-domain detection, transverse scanning of the beam on the sample is then used to acquire a twodimensional or three-dimensional image from a series of such axial scans. Figure 28.2 compares the basic building blocks of TD-OCT and FD-OCT systems. At the heart of both systems is an optical interferometer, which is represented here by a 50:50 fiberoptic coupler. Light from a broadband source travels through a single-mode
fiber and splits into two beams that propagate through separate fibers. One fiber directs light to the tissue and the other directs light to a reference mirror. The reflected light from the reference and sample arms of the interferometer travels back through the same fibers to the coupler, where it is recombined. The fraction of the recombined light that interferes on the surface of the photodetector generates an interference signal, which is then digitized, processed by a computer, and displayed as an image on the monitor. Although the basic skeleton of the interferometers employed in the TD-OCT system and the FD-OCT system is the same, this is where the similarity ends. In the time-domain system, the light source emits a broad band of wavelengths simultaneously and the profile of reflections from different depths in the tissue is reconstructed by recording the amplitudes of the interference signals generated as the reference mirror oscillates back and forth. Interference occurs when light reflects from an embedded object located
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Tissue
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Main component blocks of (a) time-domain and (b) swept-source FD-OCT systems
at the same optical distance away from the light source as the reference mirror. The coherence length of the light source, which is inversely proportional to the optical bandwidth of the light source, determines the depth resolution. The range over which the mirror moves determines the scan depth. In contrast, in FD-OCT, the reference mirror does not move and the light source is a laser that sweeps its output rapidly over a broad band of wavelengths. The positions of objects embedded at the different optical distances relative to the fixed mirror are encoded in the frequencies of the interference signals generated by reflections from the objects. Fourier transformation of the interference signals stored during a single sweep reconstructs the amplitude profile of the reflections, which in analogous to
a single A-line in an ultrasound scan. The spectral purity of the laser and the wavelength range over which it sweeps determine the scan depth and axial resolution, respectively, of the FD-OCT system; lasers with narrow linewidths and wide sweep ranges enable the acquisition of OCT images with high resolution over a wide range of depths. Because the wavelength of the laser can be scanned faster than a mirror can be translated mechanically, very rapid acquisition of A-lines can be achieved in FD-OCT systems. Line rates exceeding 100 kHz have been demonstrated6,7,8,12, compared to a maximum of 4 kHz in commercially available OCT systems. At a line rate of 100 kHz, images consisting of 500 lines/frame can be acquired at rates above 200 frames/s. However, such high acquisition rates are of little value unless
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Figure 28.3
High-speed rotary coupler and pullback mechanism
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signal strength sufficient for formation of high-quality images, can be maintained. Here, too, FD-OCT has an advantage over TD-OCT. As a result of the noise-rejection properties of Fourier-domain signal acquisition, the detection sensitivity of FD-OCT can, in principle, exceed that of time-domain OCT by 2–3 orders of magnitude at comparable A-line scan rates4,5.
LIGHTLAB FOURIER-DOMAIN OCT PROTOTYPE Although the compelling features of fast Fourierdomain OCT make this new technology attractive for intravascular imaging, numerous technical challenges must be met before FD-OCT systems become available for widespread clinical use. This section describes a prototype intravascular FD-OCT imaging system that is being developed to address these challenges. Since this work is still in an early phase, the material presented here provides only a snapshot of the evolution of a rapidly moving field of investigation. Figure 28.3 is a simplified block diagram of the prototype FD-OCT intravascular imaging system. Its architecture corresponds roughly to that of the basic FD-OCT system shown in Figure 28.2b, with several important differences. A scanning laser, with an output that sweeps over a range of wavelengths between 1260 and 1360 nm, serves as the light source. The sample beam passes through a fiberoptic rotary joint that couples the rotating imaging catheter to a stationary optical fiber connected to the interferometer inside the imaging engine. Instead of employing separate optical fibers as the sample and reference arms, as in the conventional time-domain and basic FD-OCT systems (Figure 28.2), the reference mirror is integrated into the tip of the fiberoptic core of the imaging catheter in the sample
arm. The mirror reflects a small fraction of the light from the source back into the interferometer. Both the light scattered back from the sample and the reference light travel through the same fiber and are directed to the photodetector by the optical circulator. This shared-fiber configuration improves the optical efficiency of the interferometer and eliminates polarization artifacts induced by bends and twists of the sample-arm fiber. The basic FD-OCT system shown in Figure 28.2b has lower optical efficiency and is susceptible to polarization fading that results from any mismatch between the polarization states of light returning from the sample and reference arms. An alternative arrangement, based on combining signals from two orthogonal polarization channels, enables polarization-insensitive detection without requiring integration of the reference mirror into the tip of the imaging catheter, but it adds considerable cost and complexity to the interferometer and data acquisition system13. The processing and control electronics perform several important tasks: (1) amplification and filtering of the interference signals; (2) triggering of data acquisition; (3) synchronization of the acquired data with the wavelength sweep of the laser; and (4) control of the pullback and rotation of the imaging catheter. The high-speed analog-to-digital converter, which operates at peak acquisition speeds of 210 million samples/s, digitizes the interference signals and stores them in the computer’s random-access memory. To reconstruct an OCT axial scan A-line, the computer calculates the Fourier transform of 1024 stored samples of the interference signal generated during a single wavelength sweep of the laser. The processing and control electronics ensure that the samples are separated by equal optical frequency intervals. Prior to display, the A-lines are framed and formatted for display. The components of the FD-OCT system that pose the most difficult technical challenges are the scanning
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Table 28.1
Performance of FD-OCT prototype compared with the LightLab OCT M2 system
Line scan rate Maximum frame rate Scan diameter (in saline) Signal-to-noise ratio Axial resolution (in tissue)
FD-OCT prototype
LightLab M2 system
45 kHz 80 frames/s (at 562 lines/frame) 7.2 mm 98–102 dB 11–17 µm
3.1 kHz 15.6 frames/s (at 200 lines/frame) 6.8 mm 100 dB 15 µm
laser, data acquisition system and imaging catheter. The prototype system employs a ring laser that incorporates a piezoelectric Fabry–Perot tunable filter in a novel delayed-feedback configuration that permits the generation of high optical intensities with narrow spectral widths (< 0.05 nm) at rapid scan rates (up to 50 nm/µs)6–8. As currently configured, the ring laser generates an average power of 8–10 mW and scans over a range of approximately 100 nm in 8 µs at a scan repetition rate of 45 kHz. The spectral line width of the laser (~ 0.08 nm) enables a scan depth greater than 5 mm in air (3.6 mm in tissue). The data acquisition system is based on a custom analog-to-digital converter board that is designed to acquire and store the OCT signals continuously at 130 MB/s. The display and control software is configured in a manner similar to that of the LightLab M2 OCT system. It operates in one of three different modes: preview, fast acquisition, or review. In the preview mode, which is designed to simulate operation of a conventional OCT or IVUS scanner, the catheter rotates at 20 rotations/s, and images are displayed and stored synchronously at 20 frames/s. Pullback speeds are limited to 1 mm/s. In the fast acquisition mode, the catheter accelerates to its maximum rotation speed (currently 80 rotations/s) and the pullback mechanism is set for pullback at its maximum speed (currently 15 mm/s). During pullback, images are stored in memory at full speed (80 frames/s), but are displayed at a preset fraction (typically 1/5) of the full scan rate. In the review mode, the image sequence can be reviewed as a video stream at a user-defined playback rate or as individual frames corresponding to the position of a cursor on the L-mode display. Two prototype versions of the imaging catheter have been developed for high-speed imaging. The first version (version A) is a modified LightLab ImageWire, which is composed of a fiberoptic imaging core that rotates inside a 0.019-inch transparent polymer sheath14. The sheath is filled with a mixture of fluids with a viscosity chosen to minimize non-uniform rotational distortion (NURD) within the range of 20–100 rotations/s. For applications in which a saline flush alone is used to clear blood from the target artery, the high-speed ImageWire can be delivered through the central lumen of an over-the-wire angiographic flush
catheter. Alternatively, it can be delivered through the LightLab Helios occlusion-balloon catheter, as in the OCT imaging procedure currently used with the LightLab OCT imaging system. The second version (version B) of the high-speed imaging probe, modeled after a mechanically rotating IVUS probe, consists of a fiberoptic imaging core threaded through a hollow torque wire (0.45 mm- diameter) that rotates within a 2.7 F transparent plastic sheath. The torque wire is designed for operation at higher rotational speeds than a conventional IVUS catheter. The microlens assembly at the tip of the optical fiber extends slightly beyond the distal end of the torque wire. A short channel at the distal tip of the sheath provides a lumen for rapid-exchange delivery over a 0.014-inch guidewire. Unlike the high-speed ImageWire, the catheter is not sealed, and the space between the torque wire and sheath is filled with saline prior to use. In addition to providing lubrication, the saline also reduces reflections at the lens and sheath interfaces inside the catheter.
EX VIVO BENCH TESTS A series of bench tests was carried out to measure the performance of the prototype FD-OCT system. Table 28.1 compares the results of these tests with the specifications of the current LightLab (time-domain) OCT system. The FD-OCT system achieved comparable signal-to-noise ratio over the entire scan range, in spite of its 15 times faster line rate. The ranges for signal-to-noise ratio and axial resolution of the FD-OCT system in Table 28.1 correspond to measurements made at the inner and outer edges of the scan diameter; both specifications degrade as scan depth increases. Pullback scans of formalin-fixed pig arteries were acquired using the prototype FD-OCT system to evaluate image quality. A modified ImageWire (version A, described in the previous section) was inserted into the lumen of a segment of the left anterior descending artery filled with physiological saline. With the rotation rate set at 80/s at a pullback speed of 10 mm/s, images were acquired continuously for 5 s over a 5-cm length of the artery. For comparison, the same segment of the artery was scanned with a standard
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Figure 28.4 Comparison of still-frame images of the same cross section of a pig artery acquired with (a) the prototype swept-source FD-OCT system (80 frames/s, 562 lines/frame, 10 mm/s pullback speed) and (b) a conventional time-domain OCT scanner (15.6 frames/s, 200 lines/frame, 1 mm/s pullback speed)
ImageWire connected to the LightLab M2 OCT imaging engine. At a catheter rotation rate of 15.6 rotations/s at a pullback speed of 1 mm/s, approximately 50 s were required to scan the same 5-cm segment of the artery. Figure 28.4 shows an example of images extracted from video sequences acquired with the LightLab M2 OCT imaging engine and the prototype swept-source FD-OCT system. In spite of the high frame rate and pullback speed at which the FD-OCT image was acquired, its quality compares favorably with that of the image acquired at the standard frame rate. The improvement in image resolution that results from the higher line density of the FD-OCT image is apparent; as a result of the smaller lateral speckle size, the external elastic membrane appears sharper and the texture of the tissue reflections is finer. However, the FD-OCT image suffers from ring-down artifacts close to the catheter. These artifacts are a consequence of the susceptibility of the FD-OCT system to parasitic autocorrelation signals in the sample or reference arm13. Also, because neither the internal reflector nor the polarization-diversity detection techniques were implemented in the prototype, subtle polarization artifacts that correlate with rotation of the fiber appear as dark regions in the FD-OCT image. Both of these deficiencies will be addressed in future prototypes. An additional bench experiment was performed to evaluate the ability of FD-OCT to detect damage to the endothelium of radial arteries harvested for coronary bypass surgery. In this experiment, a segment of radial artery harvested from a cadaver using standard surgical techniques was fixed in formalin shortly after excision. Immersed in saline, the artery was scanned with the prototype FD-OCT system over a 5-cm length using the torque-wire imaging probe (version B, described in the previous section). At a pullback rate of 10 mm/s, the time required to acquire the OCT imaging sequence was 5 s. The three-dimensional reconstruction of the
artery in Figure 28.5, which includes longitudinal and transverse cross sections, illustrates how the fine sampling density (4500 lines/mm) of spiral FD-OCT image sequences enables the visualization of endothelial damage at high resolution within long arterial segments. This capability may be valuable for assessment of the integrity of bypass grafts prior to implantation, while encouraging the development of less traumatic harvesting techniques14.
IN VIVO FEASIBILITY STUDIES The performance of the prototype FD-OCT system was evaluated in two sets of experiments in an anesthetized pig model. The objective of the first set of experiments was to obtain pullback images with a high-speed ImageWire delivered through the LightLab Helios occlusion-balloon catheter. The objective of the second set of experiments was to obtain flushonly pullback images with version B of the OCT probe, modified for rapid-exchange delivery. In both sets of experiments, a 7 F guide catheter was first inserted through the femoral artery into the left coronary ostium, with the tip of the catheter oriented toward the origin of the LAD. The tip of a 0.014-inch guidewire was then inserted through the guide catheter into a distal position within the LAD. For the first set of experiments (balloon occlusion), the Helios occlusion-balloon catheter was advanced over the guidewire until the tip of the catheter rested in the mid-LAD, about 5 cm distal to the origin of the circumflex artery. The guidewire was then removed and exchanged with the high-speed ImageWire. With the ImageWire held in place, the Helios catheter was pulled back to expose the imaging window of the ImageWire. A similar catheterization procedure was followed for the second set of experiments, except that the rapid-exchange OCT probe was advanced
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1 mm
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Figure 28.5 Three-dimensional reconstruction of a 5-cm segment of an excised radial artery from a cadaver. The longthreadlike appendage visible in the longitudinal cross section is a portion of the endothelium that was peeled from the vessel wall during harvesting
over the guidewire, which remained in the artery during the imaging. The prototype FD-OCT system performed well in both the balloon occlusion and guide flush experiments. Figure 28.6 shows a few examples of crosssectional FD-OCT images extracted from a sequence of pullback images acquired in vivo from the LAD of a pig during a 10-s balloon occlusion. With the pullback rate set at 5 mm/s, a 5-cm segment of the artery was scanned in 10 s. Although the total occlusion time was only about 1/3 of the maximum occlusion time recommended for imaging with the time-domain M2 OCT system (10 vs. 30 s), a longer segment of the artery was covered (50 mm vs. 30 mm) than current clinical protocols allow. Although there is no fundamental physical limitation to pulling the optical fiber back inside the high-speed ImageWire at a faster rate during balloon occlusion, the stability of the acquired images was found to be unacceptable at pullback speeds above 5 mm/s. The viscous drag of the silicone fluid in the high-speed ImageWire imposed an upper limit on the rotational speed at which orientational stability of images could be maintained during pullback. Conventional wirewound torque cables cannot be used to overcome this problem, because their diameters are too large to fit inside the small- diameter lumen of the over-the-wire occlusion-balloon catheter. Images of comparatively high quality were obtained in the second set of experiments in which boluses of lactated Ringer’s solution were injected through the guide catheter to clear blood from the imaging field. As expected, available imaging time was short: injection of a 20-ml bolus of saline solution was found to clear blood from the field of view for only 2–4 s, depending
on the location of the imaging probe and the proximity of side branches. Therefore, fast rotation and pullback rates, combined with tight synchronization of the movement of the pullback mechanism, were essential for imaging segments of an artery longer than a few centimeters. Figure 28.7 shows an example of a pullback image of a stent that was recorded during manual injection of a bolus of lactated Ringer’s solution through the guide catheter into the LAD. In this imaging sequence, a 45-mm length of artery was scanned at a pullback speed of 10 mm/s as 30 ml of saline was injected over a period of approximately 5 s. With the rapid-exchange catheter used in these experiments, pullback speeds as high as 15 mm/s could be achieved without significant loss of cross-sectional image quality or orientational stability. The results of these preliminary experiments suggest that the rapid-scan, flush-only mode of intravascular imaging with the FDOCT system may provide a clinically viable alternative to OCT imaging with balloon occlusion. An important remaining question is whether effective blood clearing can be achieved consistently using guide flush alone in patients with differing coronary anatomies and lesion severities. Even if guide flushing proves to be impractical in certain cases, the faster image acquisition of FDOCT would still be valuable for reducing ischemia during balloon occlusion or local (directed) flushing.
SUMMARY AND OUTLOOK Although still in a relatively early stage of development, FD-OCT imaging has been shown to be a powerful enabling technology for intracoronary
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Figure 28.6 FD-OCT images extracted from a sequence of pullback images acquired in vivo from a pig artery during a 10-s balloon occlusion. A 50-mm segment of the left-anterior artery was imaged at 80 frames/s, with a pullback speed of 5 mm/s. The cross sections were recorded from (a) distal, (b) middle, and (c) proximal segments of the artery
GW
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Figure 31.3 Shear rate determination from the velocity profile in the thoracic aorta as obtained by phase contrast MRI. (a) Velocity profile measured at one moment during the cardiac cycle after smoothing is applied. (b) Visualization of determination of velocity gradient (dv/dr). (c) Shear rate at each pixel in the lumen. (d) Determination of shear rate at the vessel wall, with the maximum shear rate in regions of approximately 22.5° at the outer 10% of the radius. (e) Shear rate at 16 locations along the circumference. From reference 37
described in subsurface vessels of laboratory animals: for example, in blood vessels in the skin of a rat41,42, the rat cerebral microvessels43, the ear skin of a rodent44 and rat femoral artery45. The earliest utilization of color Doppler OCT in human vessels concerned blood flow measurements in vessels in the skin, which allowed more advanced diagnosis and therapeutic control of cutaneous disorders7,46,47. Furthermore, OCT allows the assessment of the retinal blood flow dynamics and seems a promising technique for diagnosis of diabetic retinopathy and glaucoma48. Although the technique is still in development, including needle-based Doppler OCT49 and real-time flow assessment45,50,51, its application for coronary flow measurements has not yet been shown. However, the use of OCT for research on the flow-related changes in atherosclerotic plaque is promising, because of its demonstrated capacity to differentiate plaque constituents ex vivo and in vivo5–7,52,53.
Computational fluid dynamics Another way to obtain shear stress is by computational fluid dynamics. Computational fluid dynamics is the general term of all the numerical techniques to calculate the velocity of fluid elements at each location in a certain geometry. In order to calculate these velocities of fluid elements at each location, the incompressible Navier Stokes equations need to be solved. The
Navier Stokes equations are three-dimensional (3D) non-linear differential equations and describe the movement of fluid elements based on conservation of energy and mass54. Since these equations are nonlinear and are applied to complex geometries, numerical techniques need to be used to solve them. Nowadays, several numerical techniques are applied and in development. From these calculations the velocity profiles, the pressure differences and the shear stress are obtained. The great advantage of these numerical techniques, compared to all the ultrasound-based techniques, is that the velocity is determined in an uncompromised directional way, in contrast to the ultrasound Doppler techniques, which provide information only in the direction of the ultrasound beam. Therefore, secondary velocity fields, perpendicular to the flow transition field, cannot be studied with ultrasound-based techniques. Application of computational fluid dynamics to an artery requires a 3D description of the studied artery. For large arteries non-invasive imaging modalities are emerging that allow segmentation of the 3D lumen information. MRI55–61, ultrasound62–64 and computed tomography (CT)65 are often used for that purpose in the carotid arteries, femoral arteries and aorta aneurysms. Although much effort has been applied to obtaining 3D reconstructions of coronary arteries in vivo, based on either angiography66–68, IVUS69–71 or MRI72, the resolution or the accuracy of the 3D
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Figure 31.4 Validation of one-dimensional velocity measurement and shear rate determination in a circular tube. (a) By short-time fourier transform (STFT) of part of the A-scan, the frequency shift of the OCT light was determined (centroid). Knowing the angle between light and flowing direction of the intralipid, the velocity profile was determined (b). The intensity of the signal (color bar, dB) as a function of the velocity (x-axis, in mm/s) shows the expected parabolic flow profile as a function of the depth (y-axis, in mm). The theoretically expected profile for a 4.0 ml/min laminar flow in a 0.97 mm diameter tube is depicted in red. The correlation of the measured flow with the theoretically expected flow was high (c). Similarly, after 30 µm window smoothing, the calculated shear rate as a function of depth is obtained (d). Note the good correlation with the theoretically expected (V shaped) shear rate profile. Adapted from reference 40
curvature of the artery of those methods has been limited. For reaching a better resolution and accuracy we developed a new 3D reconstruction method based on a combination of angiography and intravascular ultrasound (ANGUS)73. The IVUS technique provided high cross-sectional resolution, while the angiography was used for 3D reconstruction of the 3D curvature of the artery74. Currently, more groups in Europe75–77 and the USA78–80 apply this or a related technique for 3D reconstruction of human coronary arteries based on fusion of angiography and IVUS. In Figure 31.6 ANGUS reconstructions from three different coronary arteries are shown. The combination of ANGUS with computational fluid dynamics offers the unique possibility to acquire a detailed measurement of the local wall geometry and to combine this with local shear stress values (Figure 31.7). Table 31.1 summarizes the described techniques in their application to determine local velocity profiles and the local wall geometry. Although all the described techniques are to a certain extent able to measure flow or velocity profiles and/or geometry accurately, for simultaneous assessment of shear stress and wall information in the coronary arteries the only technique available is the combination of
3D reconstruction techniques and computational fluid dynamics. The new combination of methods lacks the possibility of measuring the flow itself, but is still valuable, as it resolves the local differences in shear stress values in an artery. In case absolute shear stress values are required, these should be obtained during the same catheterization procedure performing additional flow measurements. Current developments in modalities of coronary lumen imaging, including multislice computed tomography81,82, may extend the possibilities towards non-invasive assessment of the coronary tree. However, no possibilities of flow assessment by multislice computed tomography in human coronary arteries have yet been described.
ROLE OF SHEAR STRESS IN VASCULAR BIOLOGY The shear stress imposed on the endothelium by the movement of blood deforms the endothelial cells by a very small amount. These small deformations are picked up by the mechanosensors, which trigger a variety of signaling systems responsible for the
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Figure 31.5 Cross-sectional OCT images of capillary tube perfused with 1% intralipid solution, flowing at 4.0 ml/min. Left images contain color-coded equivelocity bands of the symmetric (top) and asymmetric (bottom) flow profile due to bends in the capillary. Right images show the shear rate distribution in s-1 (gray scale) derived from the velocity distribution in the corresponding left images. Note the low and high shear rate regions at the 2 and 9 o’clock positions, respectively, in the bottom right panel. Adapted from reference 40
Table 31.1
Determination of shear rate and wall thickness in coronary arteries
Ultrasound (external) Ultrasound (TEE) IVUS Doppler MRI OCT CFD + true 3D reconstruction technique
Flow
Velocity profiles
Wall thickness
Applicable in coronary arteries
+ + + + + + −
+ − ± − − ± +
+ ± + − ± + +
− ± + + ± ± +
IVUS, intravascular ultrasound; MRI, magnetic resonance imaging; CFD, computational fluid dynamics; TEE, transesophageal echocardiography; +, the ability to measure either flow, velocity profiles, or wall thickness in coronary arteries; ±, in principle possible; −, not possible
functional reaction. However, the exact location and molecular identity of the mechanosensors is still under debate. In a review paper, Ali and Schumacker described the different mechanisms of mechanosensing that have been proposed with respect to signaling systems that are activated in response to stress and strain in the endothelium83. It has been well established that the vessel dilates under the influence of an
acute increment in flow, thereby controlling the shear stress in an artery (flow-dependent vasodilatation84). Furchgott and Zawadzki showed that products released by the endothelium play a crucial role in maintaining shear stress at a certain level85. A variety of vasoactive substances are produced by the endothelium under the influence of shear stress86. The most described and potent factor is nitric oxide (NO), which
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LAD
LCX
RCA
Figure 31.6 3D reconstructions of lumen and wall of coronary arteries based on fusion of angiography (left and right panels) and IVUS (ANGUS74): left anterior descending coronary artery (top), a left circumflex coronary artery (middle) and a right coronary artery (bottom). Viewing directions on the 3D ANGUS reconstruction correspond as closely as possible to the X-ray biplane projection views. From reference 156
Figure 31.7 Shear stress distribution on 3D reconstruction of lumen and wall of human coronary artery based on ANGUS technique. The cross sections show the local IVUS information
is produced in a shear stress-dependent way. The endothelium also produces, as a response to alterations of shear stress, prostacyclin and endothelin-186,87. There is a delicate interplay between NO, endothelin-1 and prostacyclin production86,87.
After sustained periods of shear stress alterations, the endothelial cells accommodate to the new environment through the activation of several genes, including the early response genes (c-myc, c-fos and c-jun), the NOS III gene, TGF β-1, ICAM-1, VCAM-1
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and PDGF-B genes. It has been shown that the regulation of some of these genes is dependent on shear responsive elements88. More sustained stimulation with shear stress remodels the organization of F-actin microfilaments and aligns the endothelial cells to the streamlines of the flow88. Furthermore, integrins in the cell membrane tend to cluster after chronic shear stress increments, which amplifies the shear stress signal, making the endothelial cells more susceptible to shear stress89.
ROLE OF SHEAR STRESS IN PLAQUE LOCALIZATION Atherosclerotic plaques are not uniformly distributed in the arterial system8,90. Well-known predilection sites are located at the inner curve of coronary arterial segments, the bulb of the corotid artery and near side-branches of the aorta and coronary arteries12,90–95. These observations imply that, beside the systemic risk factors, additional localizing factors must be involved in the atherosclerotic process. The localizations of atherosclerotic plaques are shown to be related to local hemodynamics and in particular to the local shear stress of the blood at the vessel wall10,12,91,96–98. Even in young children unaffected by atherosclerosis, intimal thickening occurs due to smooth-muscle-cell (SMC) migration and proliferation at predilection sites for later atherosclerosis99,100. Likewise, plaque progression in human coronary arteries was shown to be localized at low shear stress regions in contrast to high shear stress regions11. However, at the moment that the plaque encroaches into the lumen, the often-observed inverse relationship between wall thickness and shear stress is lost101. The action of endothelial cells (ECs) in the presence of a low average shear stress largely explains the local arterial susceptibility to atherosclerosis. Low shear stress enhances the oxidation of lipids and their accumulation in the intima. At those locations increased oxidative stress at the wall is present because of the enhanced expression and activity of angiotensin converting enzyme102. Angiotensin converting enzyme disables bradykinin, which otherwise stimulates the enzyme endothelial nitric oxide synthase103 to produce the potent antiproliferative, antithrombotic and antiatherogenic agent NO104. In addition, expression of endothelial nitric oxide synthase and its production of NO are reduced, since both are stimulated proportionally to shear stress105. Low shear stress also diminishes the levels of many antioxidant proteins, including heme oxygenase-1, since normal shear stress activates the antioxidant-responsive element106. In addition, low shear stress enhances expression of E-selectin and vascular cell adhesion molecules by ECs104, which stimulate recruitment of monocytes and
leukocytes, an inflammatory process that promotes atherosclerosis107. By contrast, normal or enhanced shear stress protects ECs against inflammatory activation106,108, stimulates EC proliferation104, prevents EC apoptosis109 and increases EC expression and activity of antioxidant enzymes, including superoxide dismutase110 and endothelial nitric oxide synthase111.
ROLE OF SHEAR STRESS IN GENERATION OF VULNERABLE PLAQUE Thin-cap fibroatheromas112, suspected to be vulnerable atherosclerotic plaques because of risk of rupture, consist of a lipid-rich core covered by a thin fibrous cap locally infiltrated by large numbers of inflammatory cells (macrophages and T cells). Large eccentric plaques showing only minor hemodynamic obstruction and localized at low shear stress regions occur frequently and seem prone to rupture113. Glagov et al.114 observed that, during plaque build up, human coronary arteries became enlarged and thereby preserved normal lumen dimensions until relative plaque area occupied roughly 40% of the area encompassed by the external elastic lamina. Since plaques were usually eccentric, it was proposed that the plaque-free wall controls lumen dimensions during vascular remodeling by responding to the rise in shear stress if the plaque intrudes into the lumen114. This view was supported by the observation of Zarins et al. that lumen diameter regulation by shear stress was preserved in atherosclerotic animals115. Glagov et al.114 also suggested an alternative explanation, namely that lumen preservation could be related to plaque retraction and wall expansion at the site of the plaque. This process is mediated by atrophy of the underlying media and enzymatic destruction of plaque matrix and, indeed, can also induce significant outward remodeling116. At present the relative contribution of both mechanisms to compensatory remodeling has not been determined. Studies addressing the role of the plaque-free wall sector over time have not been performed, and unambiguous methods to distinguish between enlargement of the internal elastic lamina induced by the plaque or by the plaque-free wall do not exist. Postmortem studies in coronary arteries are inconclusive and show weak positive117 or negative118 relations between external elastic lamina remodeling and plaque-free wall. Findings from intracoronary ultrasound studies showed that outward remodeling was greatest for eccentric rather than concentric plaques119,120, suggesting that the remaining plaquefree wall sector, not the circumferential extension of plaque, stimulates outward remodeling. Indeed, to clarify the control of lumen dimensions in atherosclerosis, the most plausible explanation remains the effect of shear stress acting on the residual ‘healthy’ plaque-free wall115.
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As a consequence of lumen preservation, the natural shear stress distribution will remain for a longer period of time including the unfavorable eccentric low shear stress conditions. This finding might explain the frequent observation of American Heart Association type IV large focal and eccentric lipid-laden atherosclerotic plaques in the presence of a preserved lumen.
ROLE OF SHEAR STRESS IN DESTABILIZATION OF THE VULNERABLE PLAQUE At a certain moment during the atherosclerotic process, the previously described lumen preservation stops and the plaque starts to encroach into the lumen, causing lumen narrowing. Lumen narrowing is thought to be attributed to intraplaque hemorrhage and/or fissuring and healing101. As a consequence, the local shear stress acting locally at the endothelium will increase at the upstream side of the plaque. Figure 31.8 shows the shear stress distribution over a plaque when lumen narrowing is present. The question arises as to the consequences of the increase in shear stress for the underlying plaque composition and possibly for plaque vulnerability121. Because plaque ruptures or ulcers are frequently observed at the upstream side of the plaque122–124 it has been suggested that shear stress mechanically induces rupture of the cap125. This seems highly unlikely, since, even in the presence of 75% stenosis, the shear stress remains several orders of magnitude lower than, for example, the tensile stress induced by the blood pressure pulse. However, shear stress induces major biological effects in ECs that can affect the crucial balance between cap-enforcing matrix synthesis by synthetic SMCs and matrix breakdown by metalloproteinases produced by macrophages126,127.
Shoulders At the plaque shoulders rupture is frequently observed113 and has been explained to result from long-term repetitive cyclic tensile stress causing cap fatigue113. No proof exists for this mechanism, and stress that remains below the limits of tissue strength (fracture stress) might actually stimulate mechanisms to counteract the stress. Cyclic strain determines the orientation and phenotype of SMCs, stimulates their growth and the production of extracellular matrix128. Tensile stress is at its maximum at the cap shoulder because of local plaque morphology, and might partly explain fissuring and rupture at the shoulders129. Focal weak points, however, showing accumulation of foam cells, could explain tears that were not at the point of maximum stress129. A sudden blood-pressure surge probably acts as the trigger for rupture of weakened upstream regions.
297
Midcap upstream of stenoses At the midcap upstream side of the stenosis, shear stress is maximally enhanced compared to the situation without lumen narrowing (Figure 31.8). There is a large body of evidence that ECs excellently adapt to modifications in their biochemical and biomechanical environment by changing their phenotype104,130–136, such that high shear stress induces an antiproliferative, anti-inflammatory, antithrombotic action of the endothelium. The increase in shear stress upstream of a plaque, because of lumen narrowing, could induce a similar action in ECs covering the plaque. Indeed, Tricot et al. showed that ECs were less apoptotic at the upstream side of the plaque compared to the downstream side of the plaque137, which implies a higher functionality of the endothelium at the upstream side of the plaque. Even in an inflammatory or oxidative environment, ECs were shown to be sensitive to changes in shear stress, which counteracted endothelial apoptosis induced by either oxidative stress, oxidized low-density lipoprotein (oxLDL) or tumor necrosis factor (TNF)α138. Increased shear stress has been shown to induce regression of graft hyperplasia in baboons139. Likewise, the increased shear stress at the midcap of the plaque might induce regression of the fibrous cap. Fibrous tissue regression is attributed to cell apoptosis140 and matrix degradation.
Tissue regression due to SMC apoptosis It was suggested that the high shear stress-induced tissue regression due to SMC apoptosis is mediated by NO production. Experiments blocking NO, however, did not prove its necessity in the process of tissue regression140. Nonetheless, the upregulation of the endothelial nitric oxide synthase during those experiments still strongly suggests the contribution of NO140. The role of NO in inducing apoptosis of SMCs is complex. In an inflammatory environment SMCs change from contractile into synthetic phenotypes; they may sustain their synthetic state by expression of inducible nitric oxide synthase and its high-output delivery of NO141. When high shear stress stimulates ECs to counteract inflammation, however, such as by upregulation and activation of transforming growth factor β, this could result in inhibition of the expression and activity of inducible nitric oxide synthase142. Therefore, SMCs that stop producing NO themselves may respond to the relatively high amount of NO produced by the endothelium being stimulated by high shear stress. NO can upregulate surface Fas, a receptor for apoptosis, on SMCs and the corresponding death factor, surface Fas-ligand, on macrophages143. The Fas/Fas-L interaction induces cell–cell proximitydependent SMC apoptosis143. Special attention deserves to be paid to the protection of NO produced by the endothelium against oxidation by high shear
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Low shear stress
high
Cap
Blood flow High shear stress
Figure 31.8
Lipid core
Macrophages
low
Shear stress distribution in color scale over a lumen-intruding vulnerable plaque
stress co-stimulated production of intracellular superoxide dismutase134 and by production of extracellular superoxide dismutase by SMCs stimulated by NO144. Superoxide dismutase efficiently scavenges the superoxide anion that might diffuse from the lipid core145. If this action did not take place, the anion would react with NO to produce peroxynitrite, which has recently been shown to activate gelatinases (matrix metalloproteinase (MMP)-2 and -9), which is involved in matrix regression146.
Tissue regression due to matrix regression In the previously mentioned hyperplasia experiments in baboons139, shear stress-induced regression of the extracellular proteoglycan matrix was explained by the high shear stress-stimulated endothelial production of serine proteinases, including urokinase and plasmin147. Plasmin production in the ECs on top of a fibrous cap of a vulnerable plaque could, in addition, activate proMMPs (MMPs 1, 3, 9, 10 and 13), which are secreted by the abundantly present macrophages148, stimulating the breakdown of the fibrous cap. Figure 31.9 summarizes the previously described molecular mechanisms involved in cap weakening and thinning (lower left panel).
CLINICAL OBSERVATIONS Currently, in human coronary arteries, the relationship between shear stress and plaque composition is under investigation. A new IVUS-derived technique, dubbed virtual histology, is used. This technique allows visualization of the plaque components based on the ultrasound RF signals149,150. The first study in the left coronary artery bifurcation showed that, at
acknowledged low shear stress locations, i.e. opposite to the flow divider, the size of the plaque is larger, the lipid core larger and plaque more eccentric than at the main branch151. These data support the previously described mechanisms that, at the low shear stress locations, generated plaques with a more vulnerable plaque phenotype. We recently studied, in coronary segments of 12 patients, the relationship between shear stress and fibrous cap strength. Cap weakness was determined by the local strain as results from variations in blood pressure. Strain can be assessed by the ultrasoundbased technique palpography152. High strain of the fibrous cap was linearly related to high shear stress153. Furthermore, from another study it became clear that the number of high strain spots observed in a patient was correlated with both clinical presentation and levels of C-reactive protein154. These results corroborate the hypothesis that high shear stress is involved in cap weakening and therefore could be responsible for cardiovascular events.
CONCLUSION Low and oscillatory shear stress explains the focal susceptibility of the arterial system to atherosclerosis. By contrast, normal to high shear stress is atheroprotective. At a smaller scale, in areas surrounding plaques that intrude into the lumen, differences in shear stress can lead to focal differences in plaque composition and to spatial variations in the susceptibility to plaque rupture, atherosclerosis progression and thrombus formation. High shear stress acting on the endothelium has a regressive effect on underlying intimal tissue and, therefore, can disturb the balance that determines the stability of caps of vulnerable
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Figure 31.9 Proposed shear stress-related mechanisms that influence the composition of a vulnerable plaque; ECs, endothelial cells; EC-SOD, extracellular superoxide dismutase; HDL, high-density lipoprotein; LDL, low-density lipoprotein; MMPs, matrix metalloproteinases; NO, nitric oxide; O2−, superoxide; ONOO−, peroxynitrite; Ox-LDL, oxidized low-density lipoprotein; pro-MMP, pro-matrix metalloproteinase; SMC, smooth-muscle cell; SS, shear stress; TGF-β, transforming growth factor-β. Adapted from reference 121
plaques, which is the precursor of cardiovascular events. To study the large impact of shear stress on the vasculature in human (coronary) arteries in vivo, computational fluid dynamics applied to patient-specific geometries derived from (non)-invasive image modalities seems the most promising approach. Furthermore, adding OCT, which has surfaced as one of the most promising imaging methodologies in assessment of local high-resolution wall characteristics, to the current 3D reconstruction techniques opens new avenues for the study of the influence of hemodynamics on plaque vulnerability.
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88. Resnick N, Yahav H, Khachigian LM, et al. Endothelial gene regulation by laminar shear stress. Adv Exp Med Biol 1997; 430: 155–64 89. Takahashi M, Ishida T, Traub O, et al. Mechanotransduction in endothelial cells: temporal signaling events in response to shear stress. J Vasc Res 1997; 34: 212–19 90. Friedman MH, Bargeron CB, Deters OJ, et al. Correlation between wall shear and intimal thickness at a coronary artery branch. Atherosclerosis 1987; 68: 27–33 91. Ku DN, Giddens DP, Zarins CK, Glagov S. Pulsatile flow and atherosclerosis in the human carotid bifurcation. Positive correlation between plaque location and low oscillating shear stress. Arteriosclerosis 1985; 5: 293–302 92. Masawa N, Glagov S, Zarins CK. Quantitative morphologic study of intimal thickening at the human carotid bifurcation: I. Axial and circumferential distribution of maximum intimal thickening in asymptomatic, uncomplicated plaques. Atherosclerosis 1994; 107: 137–46 93. Moore J Jr, Xu C, Glagov S, et al. Fluid wall shear stress measurements in a model of the human abdominal aorta: oscillatory behavior and relationship to atherosclerosis. Atherosclerosis 1994; 110: 225–40 94. Sabbah HN, Khaja F, Brymer JF, et al. Blood velocity in the right coronary artery: relation to the distribution of atherosclerotic lesions. Am J Cardiol 1984; 53: 1008–12 95. Kornet L, Lambregts J, Hoeks AP, Reneman RS. Differences in near-wall shear rate in the carotid artery within subjects are associated with different intima–media thicknesses. Arterioscler Thromb Vasc Biol 1998; 18: 1877–84 96. Asakura T, Karino T. Flow patterns and spatial distribution of atherosclerotic lesions in human coronary arteries. Circ Res 1990; 66: 1045–66 97. Zarins CK, Giddens DP, Bharadvaj BK, et al. Carotid bifurcation atherosclerosis. Quantitative correlation of plaque localization with flow velocity profiles and wall shear stress. Circ Res 1983; 53: 502–14 98. Friedman MH, Deters OJ, Bargeron CB, et al. Sheardependent thickening of the human arterial intima. Atherosclerosis 1986; 60: 161–71 99. Velican C, Velican D. Coronary arteries in children up to the age of ten years II. Intimal thickening and its role in atherosclerotic involvement. Med Interne 1976; 14: 17–24 100. Stary HC, Blankenhorn DH, Chandler AB, et al. A definition of the intima of human arteries and of its atherosclerosis-prone regions. A report from the Committee on Vascular Lesions of the Council on Arteriosclerosis, American Heart Association. Circulation 1992; 85: 391–405 101. Wentzel JJ, Janssen E, Vos J, et al. Extension of increased atherosclerotic wall thickness into high shear stress regions is associated with loss of compensatory remodeling. Circulation 2003; 108: 17–23 102. Rieder MJ, Carmona R, Krieger JE, et al. Suppression of angiotensin-converting enzyme expression and activity by shear stress. Circ Res 1997; 80: 312–19
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103. Hecker M, Dambacher T, Busse R. Role of endothelium-derived bradykinin in the control of vascular tone. J Cardiovasc Pharmacol 1992; 20(Suppl 9): S55–61 104. Malek AM, Alper SL, Izumo S. Hemodynamic shear stress and its role in atherosclerosis. J Am Med Assoc 1999; 282: 2035–42 105. Ziegler T, Silacci P, Harrison VJ, Hayoz D. Nitric oxide synthase expression in endothelial cells exposed to mechanical forces. Hypertension 1998; 32: 351–5 106. Chen XL, Varner SE, Rao AS, et al. Laminar flow induction of antioxidant response element-mediated genes in endothelial cells. A novel anti-inflammatory mechanism. J Biol Chem 2003; 278: 703–11 107. Libby P, Ridker PM, Maseri A. Inflammation and atherosclerosis. Circulation 2002; 105: 1135–43 108. Tedgui A, Mallat Z. Anti-inflammatory mechanisms in the vascular wall. Circ Res 2001; 88: 877–87 109. Dimmeler S, Haendeler J, Rippmann V, et al. Shear stress inhibits apoptosis of human endothelial cells. FEBS Lett 1996; 399: 71–4 110. Inoue N, Ramasamy S, Fukai T, et al. Shear stress modulates expression of Cu/Zn superoxide dismutase in human aortic endothelial cells. Circ Res 1996; 79: 32–7 111. Malek AM, Izumo S. Control of endothelial cell gene expression by flow. J Biomech 1995; 28: 1515–28 112. Schaar JA, Muller JE, Falk E, et al. Terminology for high-risk and vulnerable coronary artery plaques. Report of a meeting on the vulnerable plaque, June 17 and 18, 2003, Santorini, Greece. Eur Heart J 2004; 25: 1077–82 113. Falk E, Shah PK, Fuster V. Coronary plaque disruption. Circulation 1995; 92: 657–71 114. Glagov S, Weisenberg E, Zarins CK, et al. Compensatory enlargement of human atherosclerotic coronary arteries. N Engl J Med 1987; 316: 1371–5 115. Zarins CK, Zatina MA, Giddens DP, et al. Shear stress regulation of artery lumen diameter in experimental atherogenesis. J Vasc Surg 1987; 5: 413–20 116. Bentzon JF, Pasterkamp G, Falk E. Expansive remodeling is a response of the plaque-related vessel wall in aortic roots of ApoE-deficient mice: an experiment of nature. Arterioscler Thromb Vasc Biol 2003; 23: 257–62 117. Varnava AM, Mills PG, Davies MJ. Relationship between coronary artery remodeling and plaque vulnerability. Circulation 2002; 105: 939–43 118. Clarijs JA, Pasterkamp G, Schoneveld AH, et al. Compensatory enlargement in coronary and femoral arteries is related to neither the extent of plaque-free vessel wall nor lesion eccentricity. A postmortem study. Arterioscler Thromb Vasc Biol 1997; 17: 2617–21 119. Ito K, Higashikata T, Hanatani A, et al. Effect of disease eccentricity on compensatory remodeling of coronary arteries: evidence from intravascular ultrasound before interventions. Int J Cardiol 2002; 86: 99–105 120. von Birgelen C, Mintz GS, de Vrey EA, et al. Atherosclerotic coronary lesions with inadequate compensatory enlargement have smaller plaque and vessel volumes: observations with three dimensional
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146. Castier Y, Brandes RP, Leseche G, et al. p47phoxDependent NADPH oxidase regulates flow-induced vascular remodeling. Circ Res 2005; 97: 533–40 147. Kenagy RD, Fischer JW, Davies MG, et al. Increased plasmin and serine proteinase activity during flowinduced intimal atrophy in baboon PTFE grafts. Arterioscler Thromb Vasc Biol 2002; 22: 400–4 148. Lijnen HR. Plasmin and matrix metalloproteinases in vascular remodeling. Thromb Haemost 2001; 86: 324–33 149. Nair A, Calvetti D, Vince DG. Regularized autoregressive analysis of intravascular ultrasound backscatter: improvement in spatial accuracy of tissue maps. IEEE Trans Ultrason Ferroelectr Freq Control 2004; 51: 420–31 150. Nair A, Kuban BD, Tuzcu EM, et al. Coronary plaque classification with intravascular ultrasound radiofrequency data analysis. Circulation 2002; 106: 2200–6 151. Rodriguez-Granillo GA, García-García HM, Wentzel J, et al. Plaque composition and its relationship with acknowledged shear stress patterns in coronary arteries. J Am Coll Cardiol 2006; 47: 884–5 152. Schaar JA, de Korte CL, Mastik F, et al. Intravascular palpography for high-risk vulnerable plaque assessment. Herz 2003; 28: 488–95 153. Gijsen FJH, Wentzel JJ, Thury A, et al. Shear stress and 3D intravascular ultrasound palpography in human coronary arteries. Proceedings of IEEE International Ultrasonics Symposium, Rotterdam, 2005 154. Schaar JA, Regar E, Mastik F, et al. Incidence of high-strain patterns in human coronary arteries: assessment with three-dimensional intravascular palpography and correlation with clinical presentation. Circulation 2004; 109: 2716–19 155. Kornet L, Hoeks APG, Lambregts J, Reneman RS. Mean wall shear stress in the femoral arterial bifurcation is low and independent of age at rest. J Vasc Res 2000; 37: 112–22 156. Slager CJ, Wentzel JJ, Schuurbiers JCH, et al. Coronary 3-D angiography, 3D ultrasound and their fusion. In: Saijo Y, van der Steen AFW, eds. Vascular Ultrasound. Tokyo: Springer, 2003: 121–47
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CHAPTER 32 Principles of Doppler OCT Victor XD Yang, I Alex Vitkin
INTRODUCTION
whereby a moving sound source seems more highly pitched when approaching an observer, and of lower pitch when receding from one. This apparent shift in frequency also occurs when the source is stationary and the observer is moving, emphasizing the importance of the relative velocity between the two. Further, the effect can be generalized beyond sound to all types of wave phenomena, including electromagnetic radiation, as was done several years later in France by Armand Fizeau. The Doppler effect has been used extensively in many branches of science and engineering – for example in astronomy to quantify the approach and recession speeds of various interstellar and extragalactic objects (culminating in Hubble’s formulation of the expanding universe). Closer to home, the most familiar use of the Doppler effect is in automobile radars that detect speeding motorists on local roads. In the biomedical arena, the past ~40 years have witnessed the utilization of the Doppler effect for non-invasive assessment of tissue blood flow. The most familiar examples of these biomedical Doppler techniques are laser Doppler flowmetry (LDF) and Doppler ultrasound imaging. LDF, the traditional optical technique employed for microcirculation assessment in the clinical setting, has the advantage of being compatible with endoscopic (and intravascular) tissue access through to the use of fiberoptics19. This method employs monochromatic laser light, most commonly a frequency stabilized HeNe laser at 633 nm, to illuminate the tissue of interest. When photons are scattered back from moving red blood cells (RBCs), the frequency of the light is shifted due to the Doppler effect. By analyzing the spectral content change of the scattered light, this technique can detect flow velocities from 0.08 to over 1 mm/s20, which is relevant to the speed of slow-moving RBCs in the capillary bed. However, multiple scattering of photons in tissue makes it extremely difficult to determine the scattering angle of each photon-to-RBC interaction, and thus
Over the past decade, several new contrast mechanisms that supplement OCT microstructural imaging capability have been investigated. Of these, one of the most significant and physiologically relevant is the ability to detect tissue blood flow. This functional extension of OCT has become variously known as optical Doppler tomography, color Doppler OCT, or Doppler OCT (D-OCT); we will use the last term for simplicity. Despite the terminology, it is interesting to note that the classical Doppler effect (frequency shift of the reflected wave caused by the motion of the object) may not play a primary role in some methods of signal detection, as elaborated below. Since its first demonstrations in the mid-1990s1–3, several research groups have worked intensely on using D-OCT to detect blood flow in tissues. Various methods of D-OCT flow detection have been explored, different experimental systems have been implemented and initial in vivo results in a variety of tissues have been obtained4–12. Most of these developments have utilized the time-domain OCT methods; very recently, both spectral and frequency domain OCT approaches have been adopted for D-OCT imaging as well13–18. This chapter briefly reviews the underlying physics of D-OCT, discusses Doppler image display modes, describes experimental implementation methods and highlights selected clinical demonstrations. As a case study example, a particular phasesensitive time domain D-OCT system that has enabled microvascular blood flow detection in endoscopic and interstitial settings is presented in more detail. We conclude with a discussion of the future of this promising approach for simultaneous high-resolution real-time non-invasive imaging of tissue structure and function. Initially investigated by the Austrian physicist Christian Johann Doppler in the mid-1800s, the effect named after its discoverer is a phenomenon
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Absolute Doppler frequency (Hz)
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Figure 32.1 Schematic plot showing Doppler frequency induced by different flow velocities when using Doppler ultrasound at 5 and 50 MHz, in comparison to Doppler OCT at 1.3 µm (assuming the refractive index of tissue is 1.4, and a frequency independent detection noise floor of ~3 Hz)
LDF cannot provide the absolute measurement of RBC velocity (see Figure 32.2 and equation 2). Another problematic aspect of LDF is its inherent lack of depth resolution. Due to light scattering at this wavelength, the effective interrogation volume of LDF is about 1 mm3, with a penetration depth on the order of hundreds of micrometers21. The LDF signal is the integral within this volume and the technique has no depth discrimination. Nevertheless, LDF (especially combined with optical beam scanning) remains an excellent tool for detecting relative blood flow changes in superficial tissues, such as the skin22, retina23, and gastrointestinal tract24. Doppler ultrasound (US), using frequencies ranging from approximately 2 to 15 MHz, is the most widely used clinical imaging modality for accessing the vasculature, typically targeting larger vessels such as the carotid. Various methods of assessing blood flow have been developed25, as briefly described below, and various modes of data display are employed, typically involving an overlay of a selected blood flow Doppler map over the structural reflectivity (brightness or B-mode) image. The Doppler US displays are dynamic in nature, employing fast update (often real-time or video rates) visualizations, not static images common to other medical imaging modalities. This ability to provide the clinician with real-time visualization and guidance is particularly important for Doppler imaging, because tissue blood flow can be pulsatile, intermittent, rapidly changing and otherwise variable in time. The difference between dynamic imaging as seen in the clinical setting, and static image displays as reported in publications (and, for example, Chapter 14) must
be borne in mind – Doppler US and Doppler OCT, as described below, are often more impressive and more valuable in their real-life environment. In addition to its broad clinical acceptance, active research in Doppler US continues, particularly in extending its velocity sensitivity and spatial resolution to enable imaging of slower flows in smaller blood vessels. Advances in power Doppler26, microbubble contrast agent27 and broadband signal processing28 are particularly noteworthy in this regard. In practice, it remains difficult to use Doppler US at clinical (~10 MHz) frequencies to detect blood flow slower than a few centimeters per second. By increasing the US frequency to reduce the interrogation wavelength, this situation can be improved (Figure 32.1). High-frequency ultrasound (HFUS) in the 40–100 MHz range, or ultrasound biomicroscopy (UBM), has been used to image tumor models in animal studies29. Doppler UBM systems can image the microcirculation and detect blood flow velocity on the order of a few millimeters per second in vessels as small as 20 µm in diameter30. In animal studies, it has been used for quantitative monitoring of the effect of antivascular therapy based on integrated Doppler power measurement within the imaged tumor volume31. The main practical difficulty in using this technology in vivo has been the lack of high-frequency multielement transducer arrays. As a result, single-element transducer systems have been used to make two-dimensional images in both B- and Doppler modes. Although realtime frame rates can be achieved in B-mode, the transducer lateral scanning speed must be sufficiently slow to avoid motion artifacts when operating in Doppler mode. Consequently, the frame rate in Doppler mode
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cannot reach realtime*, which limits its use. A related difficulty in using UBM in endoscopic/intravascular applications is that catheter-based US probes typically use a single transducer, and thus suffer from the same problem.
TISSUE BLOOD FLOW AND THE DOPPLER EFFECT
307
x
z
V
θ
The equation describing the Doppler effect states that the Doppler shift of waves scattered from a moving target is proportional to the frequency of the interrogating wave (and inversely proportional to its wavelength): fD =
2Vz 2Vz 2Vz f0 = f0 ≈ c − Vz c λ0
(1)
where fD is the Doppler shift, Vz is the target velocity in the direction of the wave propagation which travels at c (c Vz) with a frequency of f0, and the wavelength is λ0. From Figure 32.1, it is clear that, by using shorter waves such as infrared light at 1.3 µm (effective wavelength in tissue is approximately 0.9 µm due to tissue refraction index, which is assumed to be 1.4) flow velocities as slow as 2 µm/s can be measured . In addition, shorter wavelength also allows the light beam to be focused tighter than ultrasound, increasing spatial resolution and reducing the sampling volume to possibly smaller than a single capillary, which may permit detailed imaging of the microvasculature. Extending the Doppler equation (1) to the case of arbitrary interrogation geometry represented by the Doppler angle θ (Figure 32.2), the Doppler frequency fD of the blood flow, is proportional to the velocity: fD =
2ntV cos θ 2ntV cos θ f0 = c λn
(2)
where V is the blood flow velocity (= Vz/cosθ), f0 is the center frequency of the broadband light, and nt is the refractive index of tissue. When investigating the same flow velocity, using a 1.3 µm OCT system can elicit a Doppler frequency over two orders of magnitude higher than using 5MHz US. By using the shorter wavelength in the optical regimen, the Doppler signal from slow flow is raised above the noise floor and detected. The smaller fractional bandwidth of the OCT system also favors the identification of the Doppler frequency with enhanced accuracy25. A simple example will illustrate the relevant numbers inherent in Doppler imaging of tissues.
Figure 32.2 Coordinate system for blood flow (red) through the optical beam (blue)
Physiological blood flow velocities range from 10−6 to 10−2 m/s in the microcirculatory circuits (including capillaries, arterioles and venules) and can reach > 1 m/s in the major vessels32. Assuming a perfusionlevel blood flow velocity of 2 mm/s, tissue refractive index of 1.4, OCT center wavelength of 1300 nm and a Doppler angle of 60°, the OCT Doppler shift fD is approximately 2.2 kHz (equation (2) and Figure 32.1). The same flow detected with a HFUS system (50 MHz, λ ~23 µm) would yield a much lower ° US Doppler frequency shift of only ~130 Hz, owing to the longer US wavelength and the Doppler frequency’s inverse dependence on it. This much smaller blood-flow-related frequency shift is harder to detect, and points to D-OCT’s potential advantage in terms of minimum velocity resolution and sensitivity to slower (perfusion-level) blood flows. However, even with the higher fD inherent in the optical approach, such frequency shifts represent tiny portions of the frequency bandwidth content of either OCT or pulsed-wave US systems, and considerable ingenuity (and often indirect methods) are required for accurate flow determination.
OCT METHODS TO DETECT PHYSIOLOGICAL DOPPLER SIGNALS There are several approaches for detecting tissue blood flow using OCT, as described in the literature
*Among other parameters, the spatial resolution, lateral scanning speed, and minimum detectable velocity are related in Doppler UBM. For a typical 40 MHz UBM scanning an 8 mm wide image, and if the desired minimum detectable velocity resolution of 1 mm/s will yield a frame rate will be approximately 0.5 s−1.
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and reviewed in several book chapters8,9; the major ones will be briefly highlighted below. Arguably the most direct is to detect the true Doppler shift by analyzing the time-domain OCT data in Fourier (frequency) space, in order to distinguish the flowinduced modulation components superposed on the interferometric signal due to the (constant) reference arm velocity. The mean velocity of flowing scatterers (e.g. RBCs in blood vessels) as a function of depth is estimated from the computed centroid frequency of the local interferometric reflectance, by using overlapped short time Fourier transforms (ST-FT). Although computationally intensive and thus difficult to apply to in vivo dynamics, this allows an estimation of Doppler shifts along each scan line, which may be transformed to mean blood velocities if the Doppler angles are known. Considerations related to optimal selection of the time window for the ST-FT processing, the amount of overlap between adjacent windows, and trade-offs between improved velocity resolution and reduced imaging speeds in ST-FT methods have been extensively described and reviewed in the literature2,8,9,33. Another approach for OCT Doppler flow imaging is to detect the local (depth-resolved) phase change by comparing sequential or adjacent depth scans3,34–36. For example, one can determine the phase by performing the Hilbert transform on the OCT signal, and then obtain the Doppler shift by dividing the phase difference between two adjacent scans by the time between line acquisitions (Ta = 1/[A-scan rate] = 1/fa). The potential advantages of such phase-based methods over the ST-FT approaches are that the velocity sensitivity and spatial resolution are not in direct opposition, and the computational requirements are lighter. Phase-based Doppler OCT imaging can also yield increased axial scanning speed, higher frame rates and reduced speckle noise3,34–37. Besides Hilbert-transform approaches, other methods of phase resolved Doppler OCT imaging are possible – for example, the Kasai flow estimator as described in greater detail below. Before proceeding to a particular illustrative example of phase resolved Doppler OCT imaging, it should be noted that, besides the ST-FT and phase methods, several other means of OCT flow detection are possible. These include Doppler broadening and shift approaches that permit the estimation of the total velocity vector V without knowing the Doppler angle38–40 and speckle analysis of structural (B-mode) OCT images, allowing velocity estimation without invoking the Doppler effect41. Also, signal processing techniques have been developed recently that lead to flow information extraction in spectral and frequency-swept OCT systems13–18,42. Thus, a variety of approaches exist to enable a researcher to detect blood flow with OCT, each with its own advantages and drawbacks. We now describe an example of time domain D-OCT flow imaging in some detail, in order
to demonstrate the central concepts and outline the biomedical potential of Doppler OCT.
IMPLEMENTATION: EXAMPLE OF PHASE-RESOLVED TIME DOMAIN DOPPLER OCT SYSTEM BASED ON KASAI VELOCITY ESTIMATOR Doppler US is an established field with many validated techniques for clinical imaging, and Doppler OCT, while differing from it in many respects, also shares considerable similarities. It is therefore useful to discuss Doppler OCT with reference to many mature concepts common in the US literature, and to explore the utility and benefit of their adoption into the OCT domain. A widely used velocity estimation method in Doppler ultrasound for real-time flow imaging was based on calculating the phase change between successive echoes from moving blood, as shown in 1982 by Namekawa et al.43 Known as the Kasai estimator44, this autocorrelation technique stemmed from radar research performed in the early 1950s, and was first described in the US literature in the 1970s45. Subsequently, this technique was developed into the first commercial color-flow Doppler US imaging system and gained wide clinical and commercial acceptance25. The basic setup required to perform time-domain D-OCT employing the Kasai technique is depicted in Figure 32.3, showing the in-phase and quadrature demodulator module after the basic OCT system. The above implementation employs hardware demodulation to extract the in-phase (I) and quadrature (Q) components of the complex D-OCT signal, and then computes phase via a two-dimensional software-based Kasai algorithm (from which the Doppler shift, and then perhaps velocity, can be obtained if the Doppler angle(s) are known). Further, removal of background bulk tissue Doppler signal is incorporated into the system. The resulting frame rates, signal-to-noise ratio, velocity sensitivity and dynamic range can enable a variety of in vivo Doppler applications46, as summarized below. It is important to realize that there is no unique and optimum way to represent Doppler blood flow information. Therefore, multiple Doppler modes have been developed in clinical Doppler US systems, each with its own set of advantages and disadvantages; the most common ones include color Doppler, sonogram (also known as Doppler spectrum or Doppler waveform) and power Doppler modes. Analogous D-OCT modes of display can also be calculated from the digital I and Q signals furnished by the above setup. While I and Q are primarily used for phase estimation, as below, the structural images are obtained simultaneously by displaying in gray-scale the logarithm of [I 2 + Q2]1/2 at each pixel location.
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a PC
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Figure 32.3 (a) Schematic diagram of the time-domain D-OCT system based on the Kasai velocity estimator method. LS, light source; PC, polarization controller; OC, optical circulator; 3dB, 50–50 fiber coupler; PM, phase modulator; RSOD, rapid scanning optical delay line; BPD, balanced photo-detector; PMD, phase modulator driver; I&Q, in-phase and quadrature demodulator; SD-1 and -2, scanner drivers; COMP, computer. (b) The hardware and software signal conditioning chain, particularly the I&Q demodulator. TIA, trans-impedance amplifier; HPF and LPF, high- and low-pass filters; DGC, depth-gain-compensation amplifier; DF, depth feedback signal; SIN and COS, 0° and 90° shifted carrier frequency, synchronized to the PMD; ADC, analog-to-digital converter; BGC, digital bias and gain compensation46
Color Doppler mode The total mean velocity V at any pixel can be evaluated by the Kasai autocorrelation equation: v =
λ 0 fD , and 2nt cos(θ)
⎫ −1 M N 1 ⎪
(Im,n+1 Qm,n − Qm,n+1 Im,n ) ⎪ ⎬ fa fa Y M (N − 1) m = 1 n=1 = fD = arctan arctan −1 M N ⎪ 2π ⎪ 2π X 1 ⎪ ⎭ ⎩ (Qm,n+1 Qm,n + Im,n+1 Im,n ) ⎪ M (N − 1) m = 1 n=1 ⎧ ⎪ ⎪ ⎨
where fD is the Doppler frequency shift, nt is the tissue index of refraction (~ 1.4), θ is the Doppler angle, m and n denote the indices in the depth and lateral directions, respectively, and Y, X will be used for estimating the variance of velocity estimate (see below). The phase estimation is performed within an M × N window to improve accuracy through averaging. The size of the window and the degrees of twodimensional overlap are user-selectable and can be optimized based on desired frame rates, velocity estimation accuracy and spatial resolutions. The resultant Doppler image information is usually displayed as a color map overlaid on the structural image, enabling simultaneous visualization of both tissue structure and functions as well as providing anatomical landmarks for flow information. As
(3)
the Doppler angles are often unknown, especially in a complex three-dimensional geometry characteristic of tissue microcirculation, the velocity vectors are not calculated and the mean Doppler frequency shifts fD at each pixel are displayed. Color Doppler is perhaps the most direct way to display blood flow information; one potential disadvantage of this mode is noise due to phase aliasing effects that occur at higher velocity flows (Vz > ~4 mm/s for the reported system with fa ~8 kHz).
Doppler spectrum mode Another clinically important display mode for Doppler ultrasound is the so-called sonogram or spectral (waveform) display, essentially a joint
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time–frequency analysis of the flow signal using a transform method such as short-time Fast Fourier Transform (ST-FFT). The spectral display is usually calculated at a particular location within a blood vessel, often identified by color Doppler imaging, to illustrate the velocity (or Doppler frequency) distribution as a function of time. The Doppler spectrum of that location at time nTa (Ta = 1/fa is the period for one axial scan) is calculated by FFT over a window length NFFT: S(f ˆ D )nTa ,M = 1 M
M
FFT {W [S(t)n−N
FFT
,n,m ]}
(4)
m=1
where S(t)n–NFFT,n,m is the complex OCT signal sequence from time (n–NFFT)Ta to n Ta, M is the depth window length (called range gate length in US literature), and W is a window function. This display mode is especially useful for examining time-varying flow patterns such as pulsatile flow. Associated with the spectral display, an audio output of the Doppler frequency distribution is often presented to the physician in Doppler US. The possibility of audible output stems from the fortuitous fact that, for typical physiological flow velocities, the resulting Doppler frequency shifts are around the low kilohertz range (see Figure 32.1), and can thus be directly heard by the human ear. Audio presentation can also be accomplished in the D-OCT system10,46.
Power Doppler mode Another approach to flow mapping is power Doppler, in which the area under the Doppler spectrum (excluding the DC component due to stationary tissue, known as tissue clutter) is calculated: N M 1 2 2 ) PD = (I + Qm,n MN m=1 n=1 m,n
is performed, relatively little additional computation is required to obtain the velocity variance (or standard deviation) information. If S* is the complex conjugate of the OCT signal S, then the normalized velocity variance is: M N −1 ⎞ ∗ MN S S m,n m,n+1 ⎟ 2 ⎜ σν m = 1 n=1 ⎟ =⎜ ⎠ ⎝1 − −1 M N fa2 ∗ Sm,n Sm,n M (N − 1) ⎛
m = 1 n=1
= 1−
√
X 2
+ S 2
Y 2
(6)
This can be evaluated efficiently, since <X>, and <S2> have all been calculated for the color Doppler and structural display modes. Velocity variance mapping eliminates aliasing and can greatly increase the velocity measurement range. Since variance generally increases from the laminar to the turbulent regime, this mode can detect areas of significant turbulence, such as flow near obstructions and bifurcations in vessels; it can also help distinguish true blood flow from bulk tissue motion (e.g. heart wall motion, which typically moves together and thus exhibits low velocity variance). As in the power Doppler imaging, however, blood flow orientation information is lost. However, by combining information from both the color Doppler and the variance modes, one can also display ‘directional velocity variance’ data, similar to the directional power Doppler mode in clinical US.
Velocity histogram method for motion artifact rejection
(5)
where I′ and Q′ are high pass filtered from I and Q using a digital filter to remove signal from bulk tissue. The resultant integrated power Doppler signal is related to the volume of moving blood within the imaging volume and results in the loss of blood velocity and directionality information46,47. Although popular in US, this mode turns out to be rather computationally intensive in D-OCT because of the complex nature of noise due to bulk tissue motion (see Velocity histogram method, below). It must therefore be computed during post-processing and does not appear well suited for D-OCT real-time imaging.
Velocity variance mode The velocity variance (or standard deviation) mode is not typically used as a stand-alone display in Doppler US systems, but has gained some popularity in D-OCT10,11,38,46,48. If the color-Doppler calculation
A major difficulty in Doppler imaging of tissue blood flow is the signal contamination by bulk tissue motion that also produces a (blood-unrelated) Doppler signal. Thus, direct application of any of the Doppler modes described above may give erroneous results due to other sources of motion, which would require signal processing to remove6,34. An alternative approach to isolate the blood Doppler signal is based on velocity histogram analysis49. The basic approach is to derive the distribution of mean Doppler frequency shifts along each depth scan line, using the color Doppler imaging approach. The most prominent peak with a narrow velocity distribution is then probably due to bulk tissue motion, and can be eliminated from further analysis. Note that this method can remove motion artifacts both faster and slower than the blood flow velocity. Figure 32.4 demonstrates the effective use of this technique for in vivo Doppler imaging. Details of implementation, performance and assumptions of this important component of tissue Doppler OCT imaging can be found in the original literature46,49.
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Figure 32.4 Cross-sectional in vivo D-OCT image of the human finger (dorsal skin surface above the nail root). Image size: 0.575 mm × 3 mm. (a) Structural image; (b) color Doppler image without motion artifact rejection; (c) a typical velocity histogram, showing the main peak with block tissue motion and the side band due to blood flow (*); (d) the corresponding color Doppler image with motion artifact rejection49
60
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It is thus evident that different methods of flow detection, and different modes of flow analysis/ display, are available for D-OCT. The time-domain Kasai-based D-OCT system described above can acquire, process and display data in real time at > 16 frames per second with 500 lines per image. At such frame rates, the histogram method for velocity noise filtering is applied in real time, and bidirectional color Doppler as well as velocity variance images are also displayed in real time. The methodology has enabled catheter-based D-OCT implementation for Doppler blood flow detection in endoscopic and interstitial settings, as illustrated below. The Kasai estimator can also be implemented in digital hardware to further improve the computation speed. It is likely that the continuing advances in spectral- and frequencydomain OCT will yield clinical systems with faster speeds and higher signal-to-noise ratio for Doppler imaging in these challenging clinical settings.
160 µm
DOPPLER OCT BIOIMAGING WITH THE ABOVE IMPLEMENTATION The following illustrative examples of various flow display modes in a variety of in vivo systems demonstrate the technological and clinical potential of Doppler imaging with OCT, using the time domain Kasai-based implementation described above. Figure 32.5 shows D-OCT imaging results for measuring steady flow in a rectangular flow phantom46. The scattering suspension of 0.25% Intralipid is flowing out of the page. The rings due to phase aliasing wrap-around
0
Figure 32.5 (a) Structural, (b) color Doppler, and (c) normalized power Doppler OCT images of a glass channel flow phantom of 0.25% Intralipid. Note the aliasing effect in (b) due to phase wrap-around, and the loss of flow directionality in (c)46
are clearly seen in the color Doppler image. The color codes for Doppler shifts are indicated on the scale bar, and are assigned to be blue for flow towards the observer and red for flow away from the observer in
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a
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−4 0 +4 Doppler Shift (kHz)
0.0 0.5 1.0 Velocity Variance (a.u.)
Figure 32.6 DOCT image of a Xenopus tadpole, imaging the left and right branches of the truncus arteriosis (L and R), acquired at 8 fps. (a) Structural (B-mode) image of the aortic branches cross-section. Notice a smaller vessel (V). Bar = 500 µm. (b) 2 × zoom of the yellow rectangular region in (a), showing the micro-structure of L. The break in the yellow line indicates the location from which Doppler spectrum information is collected and encoded into spectral (audio) format (see Figure 32.7), demonstrating the velocity distribution within L. Bar = 250 µm. (c) Color-Doppler image, showing the corresponding velocity map in the cross-section. The Doppler angle (α) is estimated to be ~ 63° for R. The small blood vessel (V) is much better visualized in the color-Doppler mode, allowing estimation of its diameter to be less than 70 µm. The peak Doppler shift is ~ 9 kHz considering aliasing effects. (d) Velocity variance image, showing the increased variance of the blood flow within L and R. Each individual image was recorded at 450 × 508 pixels10
accordance with the Doppler US convention. A region of flow disturbance, represented by an additional set of smaller aliasing rings, is also seen in the bottom right corner. The normalized power Doppler display removes this aliasing artifact, although the directional flow information is lost. Since the power Doppler mode does not measure flow velocity but indicates absence or presence of flow, the entire phantom cross section appears uniformly bright. This is in contrast to velocity variance image, which also loses directional flow information, but does appear brighter in the center corresponding to regions of higher velocity flow (further details in reference 46). Thus, in comparing the power Doppler with the velocity variance D-OCT modes, the latter is seen to be less computationally intensive while also regaining some of the velocity gradient information. The cardiac dynamics of the Xenopus laevis tadpole as visualized by D-OCT, using a hand-held probe, are illustrated in Figure 32.646. This important developmental biology model can be used to study the phenotypic expression of genetic abnormalities, normal embryonic development, structure–function relationships and longitudinal disease progression; it can also
serve as a useful in vivo test bed for D-OCT system refinement and characterization. Shown in the figure are left and right branches of the truncus arteriosis leading to the three chambers of the tadpole heart. This view permits cross-sectional visualization of blood flow in the left branch, as well as determination of the Doppler angle in the right branch for absolute flow velocity calculations. The higher signal intensities in the velocity variance image corresponding to the locations of the vessels are clearly distinguishable from the adjacent lower-variance signals corresponding to heart wall motion. This, in combination with other D-OCT display modes and the histogram velocity filtering technique, allows one to detect the blood flow Doppler signal in the presence of other confounding tissue motions. The dynamic nature of the beating heart is better appreciated by viewing the associated videos, as contained in the original publication that supports multimedia formats10. The Doppler spectrum display corresponding to the left branch of the truncus arteriosis location is shown in Figure 32.7. The time-varying spectral distribution waveform clearly demonstrates the need for (and the ability of) fast D-OCT imaging, enabling
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systole
50
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25
diastole −2
Spectral intensity (dB)
Doppler shift spectrum (kHz)
+4
0 −4 0
0.5
1.0 Time (s)
1.5
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Figure 32.7 Doppler spectral display of the D-OCT system. The full Doppler spectrum represents the velocity distribution of the blood flowing within the analysis window, which is the gap in the yellow line shown in Figure 32.6(b). Notice the rapid onset of systole and the aliasing caused by the peak velocity (arrows), as well as the relatively longer time for diastole (heart relaxation)10
the temporal capture of both systole and diastole events in the rapidly beating tadpole heart. Again, the reader is referred to the original multimedia publication for more complete video and audio representations of the cardiac dynamics10. One of the major limitations of OCT is its shallow depth of imaging, limited to 1–3 mm in most tissues because of the extensive tissue scattering. While this depth is probably adequate for a variety of applications, such as studies of skin, epithelial linings of body orifices (e.g. gastrointestinal tract) and intravascular structures, deeper tissues and organs are difficult to access by OCT. One method to partially overcome this limitation is to image at the tip of a fine needle that can be inserted deep into tissue to the desired location50,51. This methodology can be engineered into a sub-millimeter-diameter imaging probe capable of imaging bidirectional blood flow and microstructure several centimeters deep into the tissue. Figure 32.8 demonstrates proof-of-principle Doppler imaging capability of this approach when applied to Doppler OCT52. The small size of the probe, its visualization and guidance by conventional imaging modalities (US, X-rays), and the high-quality structural and functional blood flow information afforded by the interstitial approach will be likely to expand the range of anatomical sites and clinical/research scenarios accessible to D-OCT. To conclude this section of illustrative examples of D-OCT imaging in vivo, Figures 32.9 and 32.10 display the results of microvascular blood flow detection in the superficial layers of the human gastrointestinal tract, performed during routine esophageal endoscopy in the clinic12,46. It should be noted that endoscopic D-OCT represents a significant technological challenge. The small size of the blood vessels, the
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slow blood velocities, and the relative motion between the distal imaging tip and the gastrointestinal tissue (caused by the mechanics of the imager, the motion of the endoscope, or the physiology of the patient) demand a D-OCT system with high-velocity sensitivity and a robust noise-suppression algorithm, all while remaining compatible with the temporal and spatial constraints of a clinical endoscopy suite. If successful, however, the addition of Doppler blood flow information to previously reported high-resolution microstructural gastrointestinal OCT imaging may prove valuable for vascular diagnosis, early disease detection, disease prognosis and real-time assessment of local therapies. The results in normal esophagus (Figure 32.9) and in a pre-neoplastic condition known as Barrett’s esophagus (Figure 32.10) demonstrate the clinical feasibility of blood flow detection in human gastrointestinal tissues during routine endoscopy. It was found that, in addition to differing microstructures, normal and diseased gastrointestinal tissues demonstrate different mucosal/submucosal microcirculation patterns. The scientific and clinical utility of this D-OCT finding is currently being evaluated.
SUMMARY Accurate assessment of blood flow in tissues is important in biomedicine. It has been said that ‘where blood does not flow, life does not go’, and that assertion underscores the importance of hemodynamics that can benefit from non-invasive high-resolution imaging of the blood flow, especially at the microvascular level. This importance is further highlighted in oncological applications with the recent emphasis on tumor vasculature, angiogenic processes and developments of antiangiogenic therapies in combating the cancer burden. While a variety of medical imaging modalities have been developed for blood flow imaging in the body, including ultrasound, MRI and CT, none can directly image microvascular blood flow in intact tissues. D-OCT, with its ability to simultaneously furnish structural and functional images with micrometer-scale spatial resolution and sub-mm/s blood flow sensitivity up to a depth of ~2 mm in most mammalian tissues, can thus fill a useful niche in medical imaging for blood flow detection. D-OCT’s particular strengths – its high imaging speed, non/minimally invasive nature, affordability, robustness, portability, wide velocity dynamic range, and exceptionally high spatial and velocity resolution – can be advantageously exploited in various clinical scenarios. In addition to selected areas highlighted in this monogram, detailed knowledge of in vivo blood flow is important for studies of ocular (retinal) hemodynamics, burn depth estimation, assessment of viability of transplanted tissue viability, laser treatment planning of port wine stains,
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a
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A
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e
f
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21 36 Reflectivity (dB)
−4 0 +4 Doppler shift (kHz)
0.0
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Figure 32.8 In vivo demonstration of interstitial D-OCT. (a) The needle probe inserted in the thigh of an anesthetized rat. (b) Fluoroscopy showing the needle position (arrow) with relation to the femur (F) and knee joint (KJ). Another needle tip (*) marked the surface of the skin, showing that the needle was ~15 mm into the tissue. (Scale bar = 1mm). (c) Interstitial-D-OCT image of an artery (A)–vein (V) pair, probably the femoral profunda of the rat. The flow velocity in the artery was higher, as shown by the aliasing effect. The venous flow could be obliterated by compressing the vessel (see d). (e) Smaller blood vessels (arrows: diameter ~ 100 µm) were detected in the rat gluteal muscle. (f) In rat abdomen, both small and large blood vessels were detected. Due to the large velocity differences in these vessels, the larger vessels were shown with velocity variance color scale, while the smaller vessels were shown with Doppler shift color scale (green window). Imaging depth was reduced beneath large blood vessels, probably due to blood attenuation of the signal. Interstitial D-OCT image dimension: 2.5 mm × 1.5 mm
treatment response monitoring and optimization for a variety of therapies (PDT, chemotherapy, radiation therapy), determination of flow abnormalities associated with occlusive vascular disease, embryological and developmental biology studies of angiogenesis (in both wild-type and transgenic models), assessment of bleeding risks of superficial vasculature and longitudinal studies of microvascular development in neoplastic transformations. Further improvements in Doppler OCT methods, including, dynamic focusing, coherence gate focus tracking with a microelectromechanical mirror (MEMS)53,54, expanding use of spectral- and frequencydomain D-OCT approaches13–18,55, development of flow and structural (molecular) contrast agents56,57 and
quantification of blood flow metrics as surrogate markers for a particular indication may further enhance the adoption of this promising technology into the biomedical imaging armamentarium.
ACKNOWLEDGMENTS We sincerely thank our many friends and colleagues in the field of biomedical optics, both in Canada and abroad, particularly those in OCT research. The valuable contributions of collaborating scientists, clinicians, engineers, machinists, nurses, clinical fellows and the many students and post-doctoral fellows are gratefully acknowledged. Our OCT research is
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ep lp mm sm mp
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Figure 32.9 Human endoscopic D-OCT images of normal esophagus with the five layers indicated: epithelium (ep), lamina propria (lp), muscularis mucosa (mm), submucosa (sm) and muscularis propria (mp). (a and b) Color Doppler images showing vessels of microcirculation in the muscularis mucosa, submucosa and muscularis propria. The color rings in the larger vessels were caused by aliasing, due to the flow velocity exceeding the maximum detection range (± 4 mm/s). (c and d) Velocity-variance images of the microcirculation in the lamina propria, muscularis mucosa, submucosa and muscularis propria. Note that few blood vessels were observed in the epithelium. The probe was in contact with the epithelial surface and the compression flattened the epithelium in (a) and (c)12
a
b
ps
ps
c
d
*
0.5 mm
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21 Reflectivity (dB)
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Figure 32.10 Velocity-variance endoscopic D-OCT images of Barrett’s esophagus (BE) from two different patients, and histological comparison. (a and c) Sub-squamous BE with mucosal glands underneath the clearly delineated epithelium–lamina propria interface (arrows). Note the microvasculature surrounding the glands indicated by the velocity variance signal in (a) and by vascular staining using CD34 stain (dark brown) in (c), which was not observed in normal esophagus. The probe surface (ps) was in contact with the squamous epithelium. (b and d) BE with superficial glandular structure (arrows) and microvasculature close to the surface. Note the lack of distinct normal layers. Diffuse patterns of blood vessels are seen, consistent with the CD34 staining12
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supported by the Natural Sciences and Engineering Research Council of Canada, Canadian Institutes of Health Research, Ontario Research and Development Challenge Fund, Photonics Research Ontario, Premier’s Research Excellence Award, and donations from the Gordon Lang and Samuel B. McLaughlin Foundations.
15.
16.
REFERENCES 17. 1. Wang XJ, Milner TE, Nelson JS. Characterization of fluid flow velocity by optical Doppler tomography. Opt Lett 1995; 20: 1337–9 2. Izatt JA, Kulkarni MD, Yazdanfar S, et al. In vivo bidirectional color Doppler flow imaging of picoliter blood volumes using optical coherence tomography. Opt Lett 1997; 22: 1439–41 3. Chen Z, Milner TE, Digant D, Nelson SJ. Optical Doppler tomographic imaging of fluid flow velocity in highly scattering media. Opt Lett 1997; 22: 64–6 4. Yazdanfar S, Rollins AM, Izatt JA. Imaging and velocimetry of the human retinal circulation with color Doppler optical coherence tomography. Opt Lett 2000; 25: 1448–50 5. Yazdanfar S, Rollins AM, Izatt JA. In vivo imaging of human retinal flow dynamics by color Doppler optical coherence tomography. Arch Ophthalmol 2003; 121: 235–9 6. Zhao Y, Brecke KM, Ren H, et al. Three-dimensional reconstruction of in vivo blood vessels in human skin using phase-resolved optical Doppler tomography. IEEE J Sel Top Quantum Elec 2001; 7: 931–5 7. Westphal V, Yazdanfar S, Rollins AM, Izatt JA. Realtime, high velocity-resolution color Doppler optical coherence tomography. Opt Lett 2002; 27: 34–6 8. Milner TE, Yasdanfar S, Rollins AM, et al. Doppler optical coherence tomography In: Bouma BE, Tearney GJ, eds. Handbook of Optical Coherence Tomography. New York: Marcel Dekker, 2002: 203–36 9. Rollins AM, Yazdanfar S, Radhakrishnan S, et al. Real-time imaging of microstructure and blood flows using optical coherence tomography. In: Tuchin V, ed. Handbook of Biomedical Optics. Bellingham: SPIE Press, 2003: 939–86 10. Yang VXD, Gordon ML, Qi B, et al. High speed, wide velocity dynamic range Doppler optical coherence tomography (part II): imaging in vivo cardiac dynamics of Xenopus laevis. Opt Express 2003; 11: 1650–8 11. Yang VXD, Gordon ML, Tang S, et al. High speed, wide velocity dynamic range Doppler optical coherence tomography (part III): in vivo endoscopic imaging of blood flow in the rat and human gastrointestinal tract. Opt Express 2003; 11: 2416–24 12. Yang VXD, Tang S, Gordon ML, et al. Endoscopic Doppler optical coherence tomography in the human GI tract: initial experience. Gastrointest Endosc 2005; 61: 879–90 13. White BR, Pierce MC, Nassif N, et al. In vivo dynamic human retinal blood flow imaging using ultra-highspeed spectral domain optical Doppler tomography. Opt Express 2003; 11: 3490–7 14. Leitgeb RA, Schmetterer L, Drexler W, et al. Real-time assessment of retinal blood flow with ultra fast
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32. Fung YC. Biomechanics of Microcirculation, 2nd edn. New York: Prentice Hall, 1997 33. Kulkarni MD, van Leeuwen TG, Yazdanfar S, Izatt JA. Velocity estimation accuracy and frame rate limitations in color Doppler optical coherence tomography. Opt Lett 1998; 23: 1057–9 34. Chen Z, Zhao Y, Srinivas SM, et al. Optical Doppler tomography. IEEE J Sel Top Quantum Electron 1999; 5: 1134–41 35. Zhao Y, Chen Z, Saxer C, et al. Phase-resolved optical coherence tomography and optical Doppler tomography for imaging blood flow in human skin with fast scanning speed and high velocity sensitivity. Opt Lett 2000; 25: 1358–60 36. Pierce MC, Park BH, Cense B, de Boer JF. Simultaneous intensity, birefringence, and flow measurements with high speed fiber-based optical coherence tomography. Opt Lett 2002; 27: 1534–6 37. Chen Z, Milner TE, Srinivas S, et al. Noninvasive imaging of in vivo blood flow velocity using optical Doppler tomography. Opt Lett 1997; 22: 1119–21 38. Proskurin SG, He Y, Wang RK. Determination of flow velocity vector based on Doppler shift and spectrum broadening with optical coherence tomography. Opt Lett 2003; 28: 1227–9 39. Wu L. Simultaneous measurement of flow velocity and Doppler angle by the use of Doppler optical coherence tomography. Opt Laser Eng 2004; 42: 303–13 40. Piao D, Quing Z. Direct bidirectional angle-insensitive imaging of the flow signal intensity in Doppler optical coherence tomography. Appl Opt 2005; 44: 348–57 41. Barton JK, Stromski S. Flow measurement without phase information in optical coherence tomography images. Opt Express 2005; 13: 5234–9 42. Leitgeb RA, Schmetterer L, Hitzenberger CK, et al. Real-time measurement of in vitro flow by Fourierdomain color Doppler optical coherence tomography. Opt Lett 2004; 29: 171–3 43. Namekawa K, Kasai C, Tsukamoto M, Koyano A. Realtime blood flow imaging system utilizing auto-correlation techniques In: Lerski RA, Morley P, eds. Proceedings of Ultrasound’ 82. New York: Pergamon Press, 1982: 203–8 44. Kasai C, Namekawa K, Koyano A, Omoto R. Real-time two-dimensional blood flow imaging using an autocorrelation technique. IEEE Trans Son Ultrason 1985; 32: 458–63 45. Grandchamp PA. A novel pulsed directional Doppler velocimeter: the phase detection profilometer. In:
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CHAPTER 33 OCT blood flow imaging Stephen J Matcher, Julian Moger
INTRODUCTION
THE DOPPLER PRINCIPLE
Since first being described in 19911, optical coherence tomography (OCT) has undergone intense development. Hardware improvements have increased the image acquisition rate from one frame per minute to video rates2,3 and axial resolution from 20–30 µm to less than 1 µm4,5. The attractiveness of the technique lies not only in the relative cheapness of the necessary hardware but also in the rich variety of contrast mechanisms, a feature that it shares with other forms of optical imaging and which sets it somewhat apart from ultrasound and X-ray computed tomography (CT). Whilst fundamentally incapable of imaging incoherent radiation arising from fluorescence, the technique can still image tissue function as well as structure. Conventional OCT images map tissue refractive index boundaries and are thus well suited to delineating boundaries between different tissue types. Polarization-OCT6–9 is sensitive to the birefringence of tissues, which in turn yields biochemical information concerning the density and structural alignment of anisotropic proteins such as collagen10,11. Second-harmonic OCT12 likewise reveals information on collagen organization. Spectroscopic OCT13 uses measurements of the tissue absorption at multiple wavelengths to image the distribution of tissue chromophores such as water, hemoglobin and indocyanine green14. Molecular contrast OCT15 is a recently reported extension to OCT in which exogenous dyes are introduced into the tissue and whose presence is detected indirectly, for example using a pump-probe technique in which their absorption undergoes reversible bleaching by a pump laser. A contrast mechanism of particular interest to the cardiovascular/microvascular physiologist, however, is Doppler OCT (D-OCT), which yields images of blood flow velocity with a depthresolution sufficient to yield an effective sampling volume in the picoliter range16.
The Doppler effect is named after the Austrian scientist Christian Doppler, who first noticed the effect in relation to the apparent change in pitch of sound waves that were emitted first from a stationary source and then from the same source but now in motion relative to the listener. His early experiments apparently involved placing musicians armed with brass instruments onto a moving railway bogey and asking them to play a fixed note whilst approaching and then passing a platform on which listeners were stationed. The effect is also readily audible to the casual listener who listens to the change of pitch of an ambulance siren or similar; the pitch audibly drops when the vehicle passes by and begins to recede from the listener. Essentially the same effect occurs with light waves, namely that the optical frequency increases (i.e. the wavelength falls) when a source of light approaches an observer and conversely the optical frequency falls (i.e. wavelength increases) when it recedes. Although some modifications to the basic theory of optical Doppler shifts were made by Einstein in his Special Theory of Relativity, the corrections are not of practical importance when applied to everyday situations, and so Doppler’s original formula remains valid; namely, that if light of frequency f0 and wavelength λ is reflected (i.e. backscattered) from a moving object whose speed, along the line of sight is V, then the frequency f of the backscattered light will be changed to f = f0 + fDoppler = f0 +
2V λ
(1)
where V is positive if the object is approaching the observer and negative if it is receding. The optical carrier frequency f0 is of the order 1016 Hz. Typical values of 2V/λ in a clinical setting are about 104 Hz and so the fractional change in frequency is very
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Figure 33.1 (a) OCT image, processed using a fixed demodulation frequency, of a 300 µm diameter glass conduit containing heparinated whole blood flowing with an average velocity of 7.5 mm s−1, immersed in a scattering medium (Intralipid 20%). (b) Doppler OCT image of the same capillary processed using the ‘short-time-Fourier-transform’ (STFT) scheme
small. Laser Doppler techniques, including D-OCT, thus rely on the generation of a beat-frequency 2V/λ, by mixing the Doppler-shifted signal with a reference signal of frequency f0. In conventional LDF, this reference signal is often derived from backscattering from static cells within the tissue itself; in D-OCT the signal is generated in the reference arm of the interferometer. In both cases an optical detector then produces an output signal that oscillates at the Doppler frequency 2V/λ. In order to obtain the velocity of blood cells within tissue from the Doppler shift imparted on a laser beam, one must take into account the angle of incidence of the laser to the direction of flow and the refractive index of the tissue surrounding the cells. The Doppler shifted component of equation 1 now becomes fDoppler =
2nt V cos θ λ
(2)
where θ is the angle of incidence between the incident laser beam and the direction of blood flow and nt is the refractive index of the media surrounding the blood cells.
DOPPLER OCT Doppler OCT (D-OCT) uses a Michelson interferometer illuminated with a source of short coherence length: essentially the same hardware as is used for OCT16,17. The essential difference lies in the data post-processing that is used. OCT demodulates the detected interference signal at a fixed demodulation frequency. That is, if the modulation frequency is fixed at 100 kHz then the photodiode signal is band pass filtered around 100 kHz and the resulting demodulated signal amplitude is recorded as a function of depth. In D-OCT, objects in the sample arm are moving so that the optical wave in the sample
arm is Doppler shifted. It is straightforward to show that this Doppler shift manifests itself as a change in the apparent modulation frequency of the photodiode signal. If an object in the sample arm at depth z moves towards the observer so as to produce a Doppler shift of, say, 8 kHz then the apparent fringe modulation frequency will increase to 108 kHz for those fringes corresponding to the depth z. Conventional OCT processing is then not appropriate, because if these fringes are demodulated at 100 kHz then the mismatch will produce a very low apparent signal. Flowing fluid then appears as a black area devoid of any signal (Figure 33.1). Various schemes are possible to rectify this situation; the most powerful but also the slowest is to simply record, using an analog-to-digital converter and PC, the interference fringes versus depth and then apply suitable post- processing. The simplest scheme is to divide up the fringe pattern into successive short ‘windows’ and then use Fourier techniques to measure the apparent fringe modulation frequency in each one16. By comparing this with the original modulation frequency, the Doppler shift and hence line-of-sight velocity can be calculated. For the example of scattering fluid (blood) flowing in a glass capillary, the difference between fixed demodulation (OCT) and this ‘short-time-Fourier-transform’ (STFT) processing scheme is clear (Figure 33.1). The D-OCT image color-codes Doppler shift and hence flow velocity. The flow generated was at Reynolds number 2000 and hence shows a parabolic flow profile (Figure 33.2). To further illustrate the capabilities of D-OCT, let us consider a more interesting flow pattern: that produced by a highly non-Newtonian fluid such as blood. Since blood is a suspension of deformable particles suspended in plasma, it displays a rich variety of rheological phenomena at both low and high flow rates, including rouleaux formation, plug formation, flow profile blunting and bilateral cell migration18–20. Our group have demonstrated that D-OCT can image
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these phenomena with high resolution in capillaries. It is known, for example, that the parabolic flow profile predicted by Poiseuille’s equation is only an approximation when the fluid is a suspension of particles immersed in an ideal Newtonian liquid. The precise flow profile that is established is a complicated function of the particle size, shape, concentration and deformability as well as the fluid shear rate, and no exact theory exists to predict its form. However, a semi-empirical formula can be used which is of the form20: V (r) = Vmax
K r r + b , −1 ≤ ≤ 1, a ≥ 0 1 − a R R
Parameter K indicates how far the flow distribution deviates from ideal Poiseuille behavior. It is found that, at low shear rate, suspensions of blood cells in plasma generally display ‘plug-like’ flow, i.e. K > 2 and the profile is ‘flatter’ over its central section, especially when the cells are rigidified. Figure 33.3 illustrates this effect and also demonstrates the achievable measurement accuracy using D-OCT. In this experiment red blood cells, either normal or fixed using formaldehyde, were forced through a 150 µm diameter tube at flow speeds of (a) 0.56 mm s−1; (b) 5.6 mm s−1; (c) 11.1 mm s−1; and (d) 19.5 mm s−1. The solid line is a model fit to the data points and shows the parameter K decreasing from 3.0 at 0.56 mm s−1 to 2.0 at 11.1 mm s−1 and above. In a later section (Doppler amplitude OCT) we will show how the concentration distribution of red cells varies with shear rate and how this can also be detected using the D-OCT technique.
D-OCT aims to measure flow velocities in a clinical setting. Key figures of merit for a D-OCT device are thus the range of velocities that can be measured (i.e. the minimum and maximum detectable velocity) and the acquisition speed at which measurements can be obtained. A high acquisition speed is clearly desirable in order to minimize sample motion artifacts and to image dynamic phenomena. Acquisition speed is often quoted as the A-scan acquisition speed i.e. the rate at which depth-resolved flow profiles can be measured at a fixed point on the sample. However, a fundamental trade-off exists between the A-scan acquistion speed and the velocity sensitivity (i.e. the minimum detectable flow velocity) when the STFT method is used to measure the Doppler shift21. Whilst a detailed understanding of this effect requires the use of Fourier theory, for our purposes it is sufficient to note the key result of interest: in order to measure the frequency of a sinusoidal signal of frequency f using Fourier methods, one must observe the signal long enough for at least one complete period of the oscillation to occur, i.e. for a time 1/f. If one observes the signal for a shorter time, then its signal becomes contaminated by those of all frequencies from zero up to f. Such frequencies generally contain large amounts of noise and small changes in frequency of the genuine signal become masked. In conventional time-domain D-OCT, the signal is observed for an effective time equal to the A-scan integration time divided by the number of axial pixels in the image. If one acquires, for example, velocity profiles with 100 axial pixels at a rate of 100 per second (yielding a 2D 100 × 100 image acquisition rate of 1 Hz), then this analysis implies a minimum detectable Doppler shift of 10 kHz. Combining this with equation 1, assuming a probe angle of 45°, a tissue refractive index of 1.4 and a source wavelength of 1.3 µm implies a minimum detectable velocity of 6.5 mm s−1. Whilst adequate for measuring high flow rates in arterioles, the slower velocities in venules and capillaries renders this inadequate for many applications (e.g. the flow velocity in capillaries is below 1 mm s−1). Worse still, the velocity sensitivity degrades proportionally with the image acquisition speed: for ‘real-time’ imaging (defined here as 10 images per second) the velocity sensitivity degrades to 65 mm s−1 which is too large to be of practical interest when studying the circulation. A solution to this problem is phase-resolved DOCT22,23. Phase-resolved D-OCT is possible because OCT is an interferometric technique and is thus sensitive to sub-wavelength displacement of a target object via the change in the phase of the interference signal. Interference theory tells us that, if an object is displaced along the line of sight by a distance λ/2,
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then the interference signal undergoes a phase-shift of 360° and further the phase-shift is directly proportional to the displacement. Consider now a particle, e.g. a red blood cell moving with velocity 25 µm per second along the line of sight. If this motion is tracked using D-OCT with an A-scan rate of 100 Hz then the cell will have moved a distance of 0.25 µm between two successive A-scans. By the above relation this represents, given λ/2 = 0.65 µm for a 1.3 µm source, a phase-shift of 360° × (0.25/0.65), i.e. 139°. It follows that by measuring this phase shift and knowing the time interval between the successive A-scans, the line-of-sight velocity can be inferred. Phase-resolved D-OCT thus involves measuring this phase-shift between two (or more) successive Ascans recorded at the same or nearly the same lateral position. The line-of-sight velocity is then calculated using the general formula: V =
λ [ϕ(n−1) − ϕ(n) ] 4πnt
(3)
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Figure 33.3 Low and high shear-rate flow profiles exhibited by a 60% volume fraction of rigidified red blood cells flowing in a 150 µm diameter glass capillary, measured using STFT-based D-OCT. Note the increasingly flattened flow profile at low shear rates
rather to the minimum detectable phase-shift. Phasenoise in interferometers arises from various sources, including fundamental photon shot noise as well as mechanical instabilities in the sample and reference arms, jitter in the axial coherence scanner and timing electronics. However, phase-shifts of a degree or so are readily measurable, giving phase-resolved DOCT a much higher velocity sensitivity than STFTbased D-OCT, particularly as the A-scan frequency increases. In fact, phase-resolved D-OCT requires high A-scan rates. Recalling that phase can only be measured modulo 360°, we see that interferometric methods of measuring displacement are restricted to displacements in the range zero to λ/2. Furthermore, if bidirectional motion can occur, then the phase-shift produced by a moving red blood cell must not fall outside the range − 180 to 180° because, for example, 190° of phase advance cannot be distinguished from −170° of phase-lag. If the A-scan rate is fa then these requirements impose an upper limit on the permissible velocity, given by Vmax =
λfa 4nt
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Velocities above this value will be measured incorrectly, i.e. their value will only be returned modulo Vmax.
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Figure 33.4 shows a schematic diagram of a typical fiber-based phase-resolved D-OCT system. The scheme implements a novel method for detecting the phase of the detected fringes, which is to use the intrinsic phase-shifts generated by a 3 × 3 fused fiber coupler to implement real-time quadrature detection24. Figure 33.5 compares parabolic profiles measured using conventional STFT-based D-OCT and phaseresolved D-OCT. The plot on the left shows a typical flow profile as measured using a conventional STFTbased D-OCT system and that on the right with a phase-resolved system. The integration time for the left-hand plot was 200 s and, to obtain a useable velocity resolution, the profile acquisition rate had to be limited to 5 per second. The long integration time leads to a smooth, low-noise flow profile. The righthand plot shows the results from a phase-resolved system. The effective profile acquisition rate is much higher (4000 per second for the system used here) and the effective integration time used here is 0.025 s, as the profile represents an average of 100 individual flow profiles collected at the same point. The noise is consequently much higher but the velocity precision is considerably greater than if STFT processing had been used, in which case the minimum detectable velocity would have been about 2.5 mm s−1.
Figure 33.5 (Left) D-OCT profile of steady Poiseuille flow in a 150 µm tube, with 200-second effective integration time. (Right) Phase-resolved D-OCT image of steady fluid flow in a 250 µm diameter glass tube. Effective integration time 0.025 seconds
Phase-resolved D-OCT can be combined with a powerful variant of OCT, spectral OCT. In this technique the need to scan the reference arm mirror mechanically is obviated by performing the detection in the spectral domain using a spectrometer and linear CCD detector. These latter components have undergone rapid development over the past decade for use in document scanners, etc., and enable effective profile acquisition rates of up to 60 000 per second without any moving mechanical components. Leitgeb et al.25 have applied this technique to measuring the dynamics of blood flow in retinal blood vessels in vivo, demonstrating parabolic flow profiles at both the systolic and diastolic phase of the cardiac cycle (Figure 33.6).
DOPPLER AMPLITUDE OCT Doppler amplitude OCT (also known as power Doppler in the ultrasound community) provides an additional contrast mechanism for visualizing flows: sensitivity to the concentration of moving scatterers as a function of position. This can be done by calculating the relative area enclosed under the Doppler peak in an STFT. This signal is proportional to the total backscattering coefficient of the medium at a
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particular velocity (or range of velocities) and can be thought of as OCT but with a spatially varying and adaptive demodulation frequency. Moger et al.19 have described the data processing steps involved in producing a Doppler amplitude image and have applied the technique to measuring changes in the concentration distribution of red blood cells in glass capillary tubing. Figure 33.7 shows some illustrative results. On the left is a velocity profile through a 150 µm diameter capillary containing whole blood flowing with a mean velocity of 20 mm s−1. On the right is a plot of the area under the Doppler-shifted peak in an STFT window, corrected for attenuation, as a function of axial depth through the capillary. Consider that the concentration of red cells varies with depth according to some function C(z). The concentration of red cells will affect the intensity of back-scattered radiation collected by the D-OCT system by (a) reducing the intensity of incident light reaching a given depth z by scattering photons out of the incident beam; and (b) giving rise to backscattering of the incident beam at the depth z. By making suitable simplifying assumptions about the nature of light scattering in blood, these effects can be condensed into the following equation:
z
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I (z) = σB C(z)exp −2
(5)
0
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780
Figure 33.6 Extracted flow profiles at systolic (blue line) and diastolic (red line) part of the heart cycle. Shown is the longitudinal velocity component. (Taken from reference 25)
surface and z, that the back-scattering efficiency at this depth depends on the concentration of red cells at the depth z and that the light undergoes an equal attenuation on propagating from z back to the surface as it does from the surface down to z. Also included is an assumption that light attenuation follows the well-known Beer–Lambert law:
I () = I0 exp(−σF C)
(6)
where I0 is the intensity of incident light and I is the intensity of unscattered light transmitted after traveling a distance through a material containing a concentration C of scattering particles. The quantity σF is an intrinsic property of an individual scattering particle and so is taken to be a constant in the above equation. To convert the measured backscattered light intensity into a concentration distribution for the red cells we introduce a parametric model to describe this distribution. Since the glass tube is azimuthally symmetric, we assume that the concentration depends only on the radius r from the centre of the tube and hence only on the magnitude of the axial distance from the center of the tube, when considering A-scans taken directly through the center of the tube. Denoting this distance as z, it is then convenient to describe C(z) as a polynomial in z2, i.e. C(z) = C0 (1 + a1 z 2 + a2 z 4 + a3 z 6 + . . . )
(7)
In practice we have generally found six terms to be sufficient. This equation can readily be used to generate a
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polynomial model for the backscattered intensity which can then be least-squares fitted to the measurements, yielding estimates for the coefficients a 1,2…5 (Figure 33.8, left column). C(z) can then be constructed from these coefficients (Figure 33.8, right column). Note how, at low shears, the concentration distribution is uniform over the tube cross section (excluding the edges, where boundary effects such as the Fahreus– Lundquist effect, as well as measurement artifacts, conspire to reduced the signal). At higher shear-rates, and especially when the red cell deformability is reduced by, for example, fixation in formalin, an interesting phenomenon known as ‘bilateral migration’ or ‘tubular pinch’ becomes visible. The concentration distribution shows a depletion of cells near the center and the edge, with the cells concentrated in an annulus of radius 2R/3, where R is the tube radius. This phenomenon is associated with the flow of rigid bodies suspended in a viscous liquid, but can also be seen with normal, deformable red cells at high shear rates, when the cells become so compressed by viscous forces that they eventually reach their limit of compressibility and begin to behave as rigid bodies. It is worth mentioning that the systematic decrease of red cell concentration near the vessel wall has a significant physiological effect in vivo: it produces the ‘plasma-skimming’ effect whereby the hematocrit in a branching capillary is markedly lower than that in the feeding vessel.
EFFECTS OF TISSUE SCATTERING Whilst of academic, and possibly some diagnostic value, the measurement of flow dynamics in glass conduits or excised vessels represents only a small fraction of the D-OCT application area. To be of direct clinical value, D-OCT must be performed in real biological tissues. It is a well-known feature of most body tissues (excepting the aqueous humor, CSF and synovial fluid) that they are turbid26. Turbidity renders tissues opaque to optical radiation, not because
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Figure 33.7 Illustrative results of velocity profiles and particle densities measured with Doppler amplitude OCT
the light is absorbed but rather because it is scattered. The distinction is an important one; absorption results in a complete loss of light energy (i.e. photons) and therefore simply reduces the intensity of image features as one probes to greater depths into tissue. Scattering, on the other hand, results in a randomization of the trajectory of the light energy; it therefore not only degrades the intensity of image features but produces a blurring effect. It is thus important to quantify the effects of scattering on D-OCT images. There are two important cases to consider: (1) the effects of multiple scattering by moving red blood cells within a vessel27; and (2) the effects of multiple scattering by fixed scatterers located above the flowing red-cell column. Effect (1) will lead to a detected photon experiencing multiple Doppler shifts, producing an apparent flow velocity that is incorrect. Effect (2) will lead to an apparent path length increase for the detected photon and may also distort the angle-of-incidence between the photon and the flow velocity direction, thus leading to an erroneous velocity measurement. In order to quantify effect (2), Moger et al. have performed a series of experiments in which a glass tube containing flowing red cells was immersed in Intralipid solution28. Intralipid is an emulsion of lipid (soy bean oil) vesicles in water and is commonly used for intravenous feeding. It is also widely used by the biomedical optics community as a ‘tissue phantom’, i.e. a material whose optical scattering properties are closely matched to those of biological tissues such as skin29. By establishing a flow profile that is known to be parabolic and then immersing the tube to various depths in the Intralipid, the effects of multiple light scattering in the medium outside the tube can be experimentally assessed. Figure 33.9 summarizes the results. The tube can readily be detected as deep as 800 µm using the color-coded Doppler image, but it is much harder to see the tube using the structural OCT image. The flow profiles are all quasi-parabolic, illustrating that multiple scattering does not degrade the measurement accuracy
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Figure 33.8 DA-OCT profiles of erythrocyte suspension flowing in a 150 µm glass capillary at various shear rates: (a) 0.5 mm s−1 (b) 6 mm s−1 (c) 12 mm s−1 (d) 20 mm s−1. Left-hand column: the measured DA-OCT profile (dots) representing the column-integral of erythrocyte density versus depth together with a 5th-order polynomial fit (solid line). Right-hand column: the recovered concentration profile model (solid line), obtained by differentiating the polynomial model from the left-column. Dotted symbols in the right-hand figures are the data points attenuation-corrected using the model concentration profile. Note the ‘tubular pinch’ effect at high shear rates, with bilateral migration of erthrocytes away from the centre and wall of the capillary. Taken from reference 19.
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Figure 33.9 (Left) OCT and (center) D-OCT images of a 300 µm diameter capillary containing whole blood flowing with a mean velocity of 7.5 mm s−1 immersed (a) 50, (b) 150, (c) 300 and (d) 800 µm below the surface of a tissue phantom. (Right) Velocity profiles across the capillary at each depth
severely. However, a rigorous statistical analysis of the goodness-of-fit of a parabolic profile to these data suggests that systematic distortion of the profiles occurs at depths greater than about 150 µm, with the profiles appearing to become blunted. This has implications if D-OCT were to be used to study subtle rheological properties in vivo, but the effect is not likely to be deleterious in less critical applications such as measuring volume flow rates accurately.
CONCLUSIONS Doppler OCT is a powerful technique for the non- or minimally invasive characterization of blood flow, especially in the microcirculatory compartment of the circulation, where vessel diameters are typically less than 200 µm. The technique is conceptually similar to Doppler ultrasound but complementary to it. Doppler ultrasound remains the method of choice to study flow in the larger vessels, because the depth penetration of the technique allows the measurement of flow profiles across the largest blood vessels such as the aorta. D-OCT is best suited to the study of arterioles, venules and capillaries lying within 1 mm of the nearest accessible surface. This limitation nevertheless
offers tremendous scope for measuring flow in the upper dermal plexus of the skin, the retina, the exposed visual cortex, etc. Indeed, any situation in which conventional LDF has been used but in which the results are difficult to interpret because of depthvarying heterogeneities in vessel distribution, D-OCT has the potential to offer valuable new data. The clinical uptake of D-OCT will doubtless accelerate when mainstream medical device manufacturers begin to offer machines with this capability. This has been the experience with OCT, where the commercial availability of an ophthalmic retinal scanner from Carl Zeiss and others has resulted in an installed clinical base of OCT machines of several thousand since 1998.
REFERENCES 1. Huang D, Swanson EA, Lin CP, et al. Optical Coherence Tomography. Science 1991; 254: 1178–81 2. Rollins AM, Kulkarni MD, Yazadnfar S, et al. In vivo video rate optical coherence tomography. Opt Express 1998; 3: 219–29 3. Zvyagin AV, FitzGerald JB, Silva KKMBD, et al. Real-time detection technique for Doppler optical coherence tomography. Opt Lett 2000; 25: 1645–7
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4. Nishizawa N, Chen Y, Hsiung P, et al. Real-time, ultrahigh-resolution, optical coherence tomography with an all-fiber, femtosecond fiber laser continuum at 1.5 mu m. Opt Lett 2004; 29: 2846–8 5. Drexler W, Morgner U, Kartner FX, et al. In vivo ultrahigh-resolution optical coherence tomography. Opt Lett 1999; 24: 1221–3 6. de Boer JF, Milner TE. Review of polarization sensitive optical coherence tomography and Stokes vector determination. J Biomed Opt 2002; 7: 359–71 7. Stifter D, Burgholzer P, Hoglinger O, et al. Polarisation-sensitive optical coherence tomography for material characterisation and strain-field mapping. Appl Phys Materials Sci Processing 2003; 76: 947–51 8. Gotzinger E, Pircher M, Sticker M, et al. Measurement and imaging of birefringent properties of the human cornea with phase-resolved, polarization-sensitive optical coherence tomography. J Biomed Opt 2004; 9: 94–102 9. Pierce MC, Strasswimmer J, Hyle Perk B, et al. Birefringence measurements in human skin using polarization-sensitive optical coherence tomography. J Biomed Opt 2004; 9: 287–91 10. Matcher SJ, Winlove C.P, Gangnus SV. The collagen structure of bovine intervertebral disc studied using polarization-sensitive optical coherence tomography. Phys Med Biol 2004; 49: 1295–306 11. Ugryumova N, Attenburrow DP, Winlove CP, et al. The collagen structure of equine articular cartilage, characterized using polarization-sensitive optical coherence tomography. J Phys D-Appl Phys 2005; 38: 2612–19 12. Applegate BE, Yang C, Rollins AM, Izatt JA. Polarization-resolved second-harmonic-generation optical coherence tomography in collagen. Opt Lett 2004; 29: 2252–4 13. Morgner U, Drexler W, Kartner FX, et al. Spectroscopic optical coherence tomography. Opt Lett 2000; 25: 111–13 14. Yang CH, McGuckin LEL, Simon JD, et al. Spectral triangulation molecular contrast optical coherence tomography with indocyanine green as the contrast agent. Opt Lett 2004; 29: 2016–18 15. Rao KD, Choma MA, Yazdanfar S, et al. Molecular contrast in optical coherence tomography by use of a pump-probe technique. Opt Lett 2003; 28: 340–2 16. Izatt JA, Kulkami MD, Yazdanfar S, et al. In vivo bidirectional colour Doppler flow imaging of picoliter
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blood volumes using optical coherence tomography. Opt Lett 1997; 22: 1439–41 Chen ZP, Zhao YH, Srinivas SM, et al. Doppler optical coherence tomography. IEEE J Sel Top Quant 1999; 5: 1134–42 Caro CG, Pedley TJ, Schroter RC, Seed WA, et al. The Mechanics of the Microcirculation. Oxford: Oxford Medical Publications, 1978 Moger J, Matcher SJ, Winlove CP, Shore A. Measuring red blood cell flow dynamics in a glass capillary using Doppler OCT and Doppler amplitude OCT. J Biomed Opt 2004; 9: 982–94 Tangelder GJ, Slaaf DW, Muijtjens AM, et al. Velocity profiles of blood platelets and red blood cells flowing in arterioles of the rabbit mesentery. Circ Res 1986; 59: 505–14 Kulkarni MD, van Leeuwen TG, Yazdanfar S, et al. Velocity-estimation accuracy and frame-rate limitations in colour Doppler optical coherence tomography. Opt Lett 1998; 23: 1057–9 Zhao YH, Chen ZP, Saxer C, et al. Phase-resolved optical coherence tomography and optical Doppler tomography for imaging blood flow in human skin with fast scanning speed and high velocity sensitivity. Opt Lett 2000; 25: 114–16 Sarunic MV, Choma MA, Yang CH, Ding ZH, Zhao YH, Ren HW, et al. Real-time phase-resolved optical coherence tomography and optical Doppler tomography. Opt Express 2002; 10: 236–45 Sarunic MV, Chorma MA, Yang CH, et al. Instantaneous complex conjugate resolved spectral domain and swept-source OCT using 3 × 3 fiber couplers. Opt Express 2005; 13: 957–67 Leitgeb RA, Schmetterer L, Drexler W, et al. Real-time assessment of retinal blood flow with ultrafast acquisition by color Doppler Fourier domain optical coherence tomography. Opt Express 2003; 11: 3116–21 Cheong WF, Prahl SA, Welch AJ. A review of the optical-properties of biological tissues. IEEE J Quant Electronics 1990; 26: 2166–85 Borovoi A, Naats E, Oppel U. Scattering of light by a red blood cell. J Biomed Opt 1998; 364–72 Moger J, Matcher SJ, Winlove CP, et al. The effect of multiple scattering on velocity profiles measured using Doppler OCT. J Phys D-Appl Phys 2005; 38: 2597–605 van Staveren HJ, Moes CJM, van Marle J, et al. Lightscattering in Intralipid-10-percent in the wavelength range of 400–1100 nm. Appl Opt 1991; 30: 4507–14
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CHAPTER 34 Summary and outlook Ton G van Leeuwen, Evelyn Regar, Patrick W Serruys
As known from the vital cell imaging field, highresolution imaging can provide the necessary morphologic and functional information to diagnose diseases. The transfer of high-resolution imaging techniques from the laboratory into the (bio)medical environment is complicated by fundamental and practical constraints. When successful, high-resolution imaging techniques for in vivo imaging of organs by noninvasive or minimally invasive methods have enormous potential applications for pre-clinical and clinical research imaging and for monitoring of vascular diseases and their treatment. OCT, already introduced in the beginning of the previous decade, has become such a prospective imaging tool for the understanding and treatment of cardiovascular diseases. In this book all the important features of OCT have been discussed: (1) (2)
(3)
cardiovascular diseases: for example, stent deployment in general1,2 and follow-up to study, for example, the effect of drug- eluting stents and assessment of the healing of aneurysms3. The functional information that currently can be obtained by OCT ranges from the determination of the lipid4,5, collagen6 and macrophage content7 of the lesions to their mechanical properties via elastography.
FUTURE DIRECTIONS Vascular applications of OCT The atherosclerotic process is complicated and still not totally understood. Plaque rupture, erosion and progression are sometimes competing processes in the same lesion. Furthermore, the plaque rupture is often not a single event and the interaction with and causes of plaque stabilization and destabilization are currently being extensively investigated (for example, see reference 8, and references therein). In at least 50% of patients with acute ST-elevation myocardial infarction, the thrombi were days or weeks old9. Consequently, the acute thrombotic event was preceded by one or more successive thrombotic events. Recently, it has also been demonstrated that the accumulation of erythrocyte membranes within atherosclerotic plaques, via the deposition of free cholesterol, macrophage infiltration and enlargement of the necrotic core, can increase the risk of plaque destabilization10. Therefore, to clarify the role of these processes on plaque (de-)stabilization, further research is needed, which is dependent on the competences of the state-of-the-art imaging techniques such as OCT. We foresee that, in the near future, functional information such as lipid and macrophage content, the contribution of collagen to the plaque and its mechanical properties via elastography will be investigated and potentially introduced into the clinical
OCT is an optical technique, which allows realtime, intravascular imaging via flexible catheters. OCT has an unprecedented, almost histologylike resolution, which allows the clinician to detect early minor lesions in the arterial wall, to assess advanced lesions in great structural detail and to visualize features of plaque vulnerability. As OCT imaging does not alter lesion properties and can be repeated over time, it will help the understanding of the pathogenesis and dynamic of cardiovascular disease in vivo and monitoring of the arterial response after intervention. As well as the morphological imaging capabilities, OCT can also provide local so-called functional information. In this book, new developments are presented which are based on its ability to determine, via specific optical properties of the investigated tissue and advanced signal analysis, functionally related parameters of the tissue.
The first two characteristics of OCT have provided new insights into the progress and treatment of 329
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environment. Further development of these techniques and extensive calibration studies will eventually lead to quantitative measurement of plaque composition and will help to optimize the understanding of the process of plaque (de-)stabilization. Ultimately, a robust automatic-running computer program that facilitates the enhanced online visualization of the important functional parameters of cardiovascular lesions has to be built, and the quantitative signal analysis has to be incorporated into an OCT catheter-based system.
tissue and may be less critically hampered by the scattering of blood. OCT imaging has the potential to elucidate structural and functional aspects of the pathogenesis and the dynamics of cardiovascular events. These unique capabilities might help the development of preventive strategies for the leading cause of morbidity and mortality in Western countries. We hope this book will stimulate interest in this fascinating optical imaging technology and encourage further research.
REFERENCES Technical developments Extension of the functional imaging to molecular imaging is a new and intriguing concept. This molecular imaging can either be based on the intrinsic contrast, e.g. in combination with second-harmonic-generation or polarization-resolved spectroscopy11 or be based on specially adapted pump–probe spectroscopy to optical coherent tomography (PPOCT)12. Molecular contrast can also be enhanced by combining Raman spectroscopy with OCT, or even better using coherent antiStokes Raman scattering (CARS)13. A more straightforward method to increase contrast is to add a contrast agent to the sample. For OCT, the optical contrast agents that are currently studied are encapsulating microspheres, which are based not on fluorescence but on scattering nanoparticles within the shell or core. Since the contrast enhancement is based on a local increase in refractive index mismatches, no photobleaching will occur14. Attaching gold nanoparticles to probe molecules with high affinity for specific cellular biomarkers could allow specific molecular imaging and could serve as a diagnostic tool15. The development of spectrographic OCT detection schemes has created a new view on novel methods for high-speed imaging with good signal-to-noise ratios16. These spectroscopic detection schemes have made the need for a high-speed moving reference mirror superfluous. However, the spectral resolution of the spectrograph determines the maximal imaging range of the Fourier-domain (FD)-OCT system, which should be several millimeters to bridge the space between catheter and vascular wall for intravascular OCT. Furthermore, infrared (1300 nm) CCD cameras have limited efficiency. A better alternative for FDOCT is using fast sweeping sources, because only one detector optimized for the near-infrared wavelength region can be used and the instantaneous bandwidth of the light source can be narrow enough to image several millimeters deep17. Finally, new light sources with higher sweep rates (and thus higher A-scan rates) and potentially with wavelengths that are attenuated less in tissue will be developed. These light sources may facilitate deeper imaging in the
1. Jang IK, Tearney G, Bouma B. Visualization of tissue prolapse between coronary stent struts by optical coherence tomography: comparison with intravascular ultrasound. Circulation 2001; 104: 2754 2. Bouma BE, Tearney GJ, Yabushita H et al. Evaluation of intracoronary stenting by intravascular optical coherence tomography. Heart 2003; 89: 317–20 3. Thorell WE, Chow MM, Prayson RA, et al. Optical coherence tomography: a new method to assess aneurysm healing. J Neurosurg 2005; 102: 348–54 4. van der Meer FJ, Faber DJ, Baraznji Sassoon DM, et al. Localized measurement of optical attenuation coefficients of atherosclerotic plaque constituents by quantitative optical coherence tomography. IEEE Trans Med Imaging 2005; 24: 1369–76 5. van der Meer FJ, Faber DJ, Perree J, et al. Quantitative optical coherence tomography of arterial wall components. Lasers Med Sci 2005; 20: 45–51 6. de Boer JF, Srinivas SM, Malekafzali A, et al. Imaging thermally damaged tissue by polarization sensitive optical coherence tomography. Opt Express 1998; 3: 212–18 7. Tearney GJ, Yabushita H, Houser SL, et al. Quantification of macrophage content in atherosclerotic plaques by optical coherence tomography. Circulation 2003; 107: 113–19 8. Virmani R, Kolodgie FD, Burke AP, et al. Atherosclerotic plaque progression and vulnerability to rupture: angiogenesis as a source of intraplaque hemorrhage. Arterioscler Thromb Vasc Biol 2005; 25: 2054–61 9. Rittersma SZ, van der Wal AC, Koch KT, et al. Plaque instability frequently occurs days or weeks before occlusive coronary thrombosis: a pathological thrombectomy study in primary percutaneous coronary intervention. Circulation 2005; 111: 1160–5 10. Kolodgie FD, Gold HK, Burke AP, et al. Intraplaque hemorrhage and progression of coronary atheroma. N Engl J Med 2003; 349: 2316–25 11. Applegate BE, Yang C, Rollins AM, et al. Polarizationresolved second-harmonic-generation optical coherence tomography in collagen. Opt Lett 2004; 29: 2252–4 12. Rao KD, Choma MA, Yazdanfar S, et al. Molecular contrast in optical coherence tomography by use of a pump-probe technique. Opt Lett 2003; 28: 340–2 13. Bredfeldt JS, Vinegoni C, Marks DL et al. Molecularly sensitive optical coherence tomography. Opt Lett 2005; 30: 495–7
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14. Lee TM, Oldenburg AL, Sitafalwalla S, et al. Engineered microsphere contrast agents for optical coherence tomography. Opt Lett 2003; 28: 1546–8 15. Sokolov K, Follen M, Aaron J, et al. Real-time vital optical imaging of precancer using anti-epidermal growth factor receptor antibodies conjugated to gold nanoparticles. Cancer Res 2003; 63: 1999–2004
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16. Nassif N, Cense B, Park BH, et al. In vivo human retinal imaging by ultrahigh-speed spectral domain optical coherence tomography. Opt Lett 2004; 29: 480–2 17. Yun SH, Tearney GJ, de Boer JF, et al. Pulsed-source and swept-source spectral-domain optical coherence tomography with reduced motion artifacts. Opt Express 2004; 12: 5614–24
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Page numbers in italics refer to illustrations or tables. absorption 3, 45 absorption coefficient 3, 4 skin 11 water 116, 117 ABT-578-eluting stent 163, 164 in-stent restenosis 165, 166 acute coronary syndromes 72, 75, 79 CT imaging 88, 90 plaque characterization 126–9 culprit and remote plaque morphology 126, 127 macrophage concentration and distribution 126–9, 128–9 acute myocardial infarction (AMI) 72, 73–5 culprit lesion 84 see also acute coronary syndromes aneurysm see cerebral aneurysm angina 72 culprit lesion 83, 84 plaque characterization 126, 127, 128 angiography 116–17 with IVUS (ANGUS technique) 293, 295 angioplasty cutting balloon 196–7, 196, 198 percutaneous transluminal angioplasty 195–6, 196 subintimal angioplasty 198–9 angioscopy 117 plaque characterization 80–5, 81–5 thin-cap fibroatheroma 79, 80 ANGUS technique 293, 295 apoE knockout murine model 133–45 fibrous cap assessment 136, 136–8 collagen detection 138–9, 142 macrophage accumulation 139, 143–4
plaque assessment calcium identification 136–8, 141 lipid core 136, 137–9 protocols 134–6 ex vivo studies 135 histology 135–6 in vivo studies 134–5, 134 OCT image analysis 135–6 OCT instrumentation 135 reduction of light attenuation due to blood 140–4, 144 atheroma necrotic core fibroatheroma (NCFA) 241, 241, 246–7 thick fibrous cap atheroma 84, 85, 140 see also atherosclerotic plaque; thin-cap fibroatheroma atherosclerosis 115, 116, 289 see also apoE knockout murine model atherosclerotic plaque 21, 57, 71–6, 73–6 attenuation coefficients 235, 236, 236, 237 characterization 67, 67–9, 79–85, 126–9 angioscopy versus OCT 79, 80–5, 82 future directions 329–30 IVUS-VH (virtual histology) versus OCT 95–9, 97–100 morphology 122, 123, 126 multislice CT versus OCT 87–91, 90–2 OCT feasibility studies 121, 124–5 spectroscopic OCT analysis 234–8, 235, 237–8 see also calcification; fibrous cap; vulnerable plaque collagen content 241–2 assessment 138–9, 142, 244–6, 245–6 333
in vitro studies 71–2 plaque geometry 54, 54 prolapse following stent implantation 155–6, 156–7, 198 quantitative assessment 75–6 rupture 72 see also apoE knockout murine model; thin-cap fibroatheroma (TCFA); vulnerable plaque shear stress and 296 stabilization 111–12, 112 thrombosed/unstable plaque 109 see also fibrous cap; peripheral arterial disease (PAD); vulnerable plaque atherothrombosis 109 see also thrombus attenuation coefficient 231, 233–4, 233 plaque components 235, 236–8, 236, 237 thrombus 235 whole blood 7 balloon, OCT imaging through low-pressure Metricath™ balloon 57, 58 proximal balloon occlusion and continuous flush delivery 62–3, 63 PTCA balloon 55–7, 56, 57 Beer’s law 45 bilateral migration 325 biolimus-eluting stent 164 birefringence 138–9, 141–2, 242, 242 measurements 243, 243, 246 see also polarization-sensitive OCT (PS-OCT) blood 7 attenuation coefficient 7, 9 reduction of light attenuation 140–4, 144
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dilution effects on optical properties 36–7, 36, 144, 144 intracoronary imaging and 54 blood flow 305, 313–14 Doppler effect and 307, 307 OCT detection methods 307–8 see also Doppler OCT (D-OCT) blood vessels injury during stenting 156, 156–8 measurement of dimensions 192–4, 194 tortuosity 53 see also coronary artery; peripheral arterial disease (PAD) ‘blow-out’ effect 161, 162 bovine serum albumin (BSA) microspheres 269 brachytherapy vascular effects 147–50 histology 149, 149, 150 OCT imaging 148–9, 149, 150 bypass surgery, peripheral arterial disease 199, 200 calcification CT detection 89, 90 mouse model 136–8, 141 spectroscopic OCT analysis 237, 237–8 calcified nodule 110 cardiac development see embryonic cardiac development cell targeting by microspheres 272, 273 cerebral aneurysm 203, 204 healing after treatment 203–6, 205–6 chick embryonic cardiac development 209–10, 211–13, 212–15 chronic total occlusion (CTO) 171, 183 IVUS assessment peri-interventional 176–7 post-interventional 177–8 IVUS guidance of guidewire advancement 171–6, 184–5, 185–7 identification of origin of occlusion 171–3, 172–3 novel approaches 178 forward-looking ultrasound 178 Pioneer catheter 178 OCT applications 178, 179–80 OCT-guided wiring technique 185–7, 188–9 indications 187 limitations 190
optical coherence reflectometry 178, 180 parallel wire technique 184, 184 IVUS guidance 174–6, 174–6 pathological features 183–4 coherence gate 47 coherent anti-Stokes Raman scattering (CARS) 285–6, 287 collagen in atherosclerotic plaque 241–2 birefringence 138–9, 141–2, 242, 242 detection 138–9, 142 with polarization-sensitive OCT 138–9, 244–6, 245–6 computational fluid dynamics 292–3, 295 computed tomography (CT) 87–8, 117 coronary artery stenosis detection 89 electron-beam CT (EBCT) 88, 89 multislice CT (MSCT) 88, 89–91, 105, 106 plaque characterization 87–91, 90–2 calcification detection 89, 90 scanners 87, 88 confocal laser scanning microscopy (CLSM) 210–11 contrast enhancement 7, 267–8 see also microsphere contrast agents; molecular contrast OCT coronary artery 19, 20, 27, 44 calcification CT detection 89, 90 mouse model 136–8, 141 intimal thickening 54, 65 assessment 65–6, 66 following stent implantation 157–8, 159 occlusion see chronic total occlusion (CTO) remodeling 99 size 54 wall thickness 294 stenosis 89, 127 stents see stenting tortuosity 53 see also atherosclerotic plaque; optical coherence tomography (OCT) cutting balloon angioplasty 196–7, 197, 198 Cypher stent 162–3, 163, 166 DICOM image format 104 digital subtraction angiography (DSA) 194 directed flush catheter 59–62, 59–61 with image wire 60–2, 61–2
dissection, following stent implantation 155–6, 157 Doppler effect 305, 319–20 tissue blood flow and 307, 307 Doppler OCT (D-OCT) 305–14, 320–7, 320 bioimaging examples 311–13, 311–15 detection of physiological Doppler signals 307–8 Doppler amplitude OCT 323–5, 325, 326 phase-resolved D-OCT 321–3, 323 phase-resolved time domain system example 308–11, 309 color Doppler mode 309 Doppler spectrum mode 309–10 power Doppler mode 310 velocity histogram analysis 310, 311 velocity variance mode 310 tissue scattering effects 325–7, 327 Doppler ultrasound 306–7, 306 shear stress determination 290 drug-eluting stents 161–9 appearances at follow-up 42, 161–4, 162–5 hypodense areas 164, 165 chronic total occlusion 177–8 in-stent restenosis 165–6, 166, 167–9, 168 OCT role in follow-up 167–9 efficacy and performance assessment 167 failure analysis 167–9 quantitative and qualitative analysis 169 tissue prolapse 157 see also stenting dynamic focusing 233, 233 elastography 118, 249–54, 250 IVUS elastography 249–50, 251–2, 251 OCT elastography 250–4 catheter rotation 252 current status 252–3, 252–4 radiofrequency signals versus envelope 251–2 speckle stability 252 vulnerable plaque detection 249 electromagnetic spectrum 44–5, 45 electron-beam CT (EBCT) 88, 89 see also computed tomography (CT) embryonic cardiac development imaging technologies 210–11 model systems 209–10 OCT imaging 211–15 chick embryo 211–13, 212–15 mouse embryo 213–15, 213
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endoscopic OCT imaging 24–5, 25, 26 endothelial cells (ECs) 293–6 esophagus 26, 313, 315 everolimus-eluting stent 163, 164 fetal cardiac development see embryonic cardiac development fibroatheroma 125 see also atherosclerotic plaque; necrotic core fibroatheroma; thin-cap fibroatheroma fibrous cap 68–9, 69, 74, 127, 137 birefringence 138–9, 141–2 characterization 127, 128 collagen detection 138–9, 142, 244–6, 245–6 mouse model 136, 136 thickness assessment 122, 123, 136, 136–8 thick fibrous cap 84, 85, 140 see also macrophage accumulation in fibrous cap; thin-cap fibroatheroma (TCFA) flush delivery catheter 59–62, 59–61 with image wire 60–2, 61, 62 with proximal balloon occlusion 62–3, 63 focus tracking 233 forward-looking ultrasound 178 Fourier-domain detection 31–2, 31 Fourier-domain OCT (FD-OCT) 257 LightLab™ prototype 261–2, 261 ex vivo bench tests 262–3, 262, 263, 264 in vivo feasibility studies 263–4, 265 outlook 264–5 principles 258–61, 259–61 rapid scan concept 257–8 frequency domain OCT (FD-OCT) parallel FD-OCT techniques 223–5, 228–9 full field time-encoded FD-OCT using detector array 225–8, 228 using line illumination 224–5, 224, 225 single point scanning FD-OCT 222–3 versus time domain techniques 222, 223 see also optical coherence tomography (OCT) gastrointestinal tract 313, 314 Goodtec diagnostic catheter 60–1, 62 ground state recovery pump-probe OCT (gsrPP-OCT) 283–5, 284
heart development see embryonic cardiac development Helios™ balloon occlusion catheter 39, 62–3, 63, 180 hematoma, intramural 175, 175 Henyey–Greenstein phase function 5 high-frequency ultrasound (HFUS) 306 Hilbert transform 308 imaging depth 6–8, 10, 7, 22 in-stent restenosis 63, 157, 165–6, 166, 167–9, 168 index of refraction 12, 12 integrin receptors, microsphere contrast agents 272, 273 interferometric microscope 227 interferometry 23, 23, 29–32 Fourier-domain detection 31–2, 31 low-coherence interferometry 29–30, 30 non-linear interferometric vibrational imaging (NIVI) 285–6, 287 intima–media thickness (IMT) 65 assessment 65–6, 66 intimal thickening 54, 65, 98 following stent implantation 157–8, 159 intracoronary OCT see optical coherence tomography (OCT) intracoronary ultrasound (ICUS) see intravascular ultrasound (IVUS) intravascular OCT imaging 24–5, 26–7, 35–41, 44 catheter design 47–8, 48 clinical experience 41 design specifications 37–8 general considerations 35–7 LightLab™ system 38–41, 40 see also optical coherence tomography (OCT) intravascular ultrasound (IVUS) 41, 44, 103, 118, 242 catheter design 47–8, 48 combined with MSCT 105, 106, 107, 108 combined with OCT 72–5, 73, 75–6, 106 rationale 104–5 single platform for analysis 105–7, 106 elastography 249–50, 251–2, 251 forward-looking ultrasound 178 gray-scale imaging 96, 96 image acquisition 103–4 image storage and processing 104
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intimal thickening evaluation 65–6, 66 shear stress determination 290–1, 291 intravascular Doppler 290 with angiography (ANGUS technique) 293, 295 see also specific conditions; ultrasound intravascular ultrasound-virtual histology (IVUS-VH) 95, 96, 97 analysis 97 data acquisition 96–7 plaque characterization 97–9 remodeling detection 99 thin fibrous cap detection 98 with OCT 99, 99–100 technical aspects 95, 96 iron oxide, macrophage detection 139, 143–4 irradiation vascular effects 147–50 histology 149, 149, 150 OCT imaging 148–9, 149, 150 ischemia limitation 257 rapid scan concept 257–8 see also Fourier-domain OCT (FD-OCT) myocardial susceptibility 54–5 Kasai velocity estimator method 308–11, 309 Lambert–Beer’s law 3 laser Doppler flowmetry (LDF) 305–6, 320 light definition 43 imaging applications 46–7 physics of 43–5 polarization 43, 47, 242 sources 119 tissue interaction 45–6 LightLab™ Fourier-domain OCT prototype 261–2, 261 ex vivo bench tests 262–3, 262, 263, 264 in vivo feasibility studies 263–4, 265 OCT imaging system 35, 38–41, 40 ImageWire 38, 40–1 imaging engine 38–9 imaging software 39 probe interface unit 40 macrophage accumulation in fibrous cap 68, 71–2, 128, 129 assessment 122–4, 125, 126–9 ex vivo validation 124 mouse model 139, 143–4 quantitative analysis 124
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magnetic resonance imaging (MRI) 117, 242 embryonic cardiac studies 210 shear stress determination 291, 292 Massachusetts General Hospital feasibility studies 121, 124–5 OCT use in percutaneous coronary intervention 125–6 OCT platform 121 plaque characterization 122–4, 126–9 fibrous cap thickness 122, 123 macrophage accumulation 122–4, 125, 126–9 morphology 122, 123, 126 Metricath™ balloon system 57, 58 microsphere contrast agents 267–77 in vivo applications 274–5, 275–7 optical characterization 273–4, 274 preparation chemistry 268–70, 268–70 size distribution 268, 269–70, 269 surface functionalization for cell targeting 272 integrin receptors 272, 273 surface modification 270–2, 270, 271 bioconjugation with covalent surface modification 271 nanoparticle modification of protein-shell surfaces 272 molecular contrast OCT (MC-OCT) 281–7, 282 depth resolution ambiguities of contrast agents 286 development of 281 non-linear interferometric vibrational imaging (NIVI) 285–6, 287 pump-probe OCT 283–5, 284 ground state recovery PP-OCT (gsrPP-OCT) 283–5, 284 second harmonic OCT 285, 286 spectroscopic OCT 282–3, 283 motion artifacts during heart cycle 54, 55 velocity histogram analysis 310, 311 mouse models embryonic cardiac development 210, 213–15, 213 see also apoE knockout murine model multislice CT (MSCT) 88, 89 combined image analysis 105, 106, 107, 108
plaque characterization 88–91, 90–2 see also computed tomography (CT) myocardial ischemia, susceptibility to 54–5 Navier Stokes equations 292 necrotic core fibroatheroma (NCFA) 241, 241 collagen assessment 245, 246–7 see also atherosclerotic plaque; vulnerable plaque nitric oxide (NO) 294–5, 296 shear stress-induced smooth muscle cell apoptosis and 297–8 non-linear interferometric vibrational imaging (NIVI) 285–6, 287 occlusion see chronic total occlusion (CTO) optical coherence reflectometry (OCR) 178, 180 optical coherence tomography (OCT) 19–21, 118–19, 203, 329 ‘blow-out’ effect 161, 162 catheter and endoscopic imaging 24–5, 25, 26–7 see also intravascular OCT imaging combined use with IVUS 72–5, 73, 75–6, 106 rationale 104–5 single platform for analysis 105–7, 107 consequences of tissue optics 6–7 determination of tissue optical properties 10–11 diagnosis of different tissue types 191–2, 192 Doppler see Doppler OCT elastography 250–4 current status 252–3, 252–4 principle 250–2 embryonic cardiac studies 211 Fourier-domain see Fourier-domain OCT frequency domain techniques see frequency domain OCT future directions 329–10 technical developments 330 vascular applications 329–30 image acquisition 23–4, 104, 104, 105 image quality 27–8, 29 image resolution 27 image storage and processing 104
interferometry 23, 23, 29–32 Fourier-domain detection 31–2, 31 low-coherence interferometry 29–30, 30 intracoronary application catheter design 55–63 catheter design, balloon 55–7, 56, 57 catheter design, continuous flush delivery 57–62, 59–62 catheter design, proximal balloon occlusion and continuous distal flush delivery 62–3, 63 see also intravascular OCT imaging intracoronary challenges 53–5 access 53 blood 54 coronary size 54 ischemia susceptibility 54–5 motion during heart cycle 54, 55 plaque geometry 54, 54 polarization-sensitive OCT 246–7, 247 safety 53 vessel tortuosity 53 polarization-sensitive see polarization-sensitive OCT shear stress determination 291–2, 293, 294 spectroscopic see spectroscopic OCT theory 19–21, 231–3 time domain techniques see time domain OCT ultrahigh resolution 221 versus other imaging technologies 22–3, 22, 65–6, 66 see also specific conditions optical Fourier domain imaging (OFDI) see Fourier-domain OCT (FD-OCT) optical properties of tissues see tissue optics paclitaxel-eluting stent 164, 177 palpography 250, 251 parallel OCT techniques see frequency domain OCT (FD-OCT); time domain OCT (TD-OCT) parallel wire technique 184, 184 IVUS guidance 174–6, 174–6 percutaneous coronary intervention OCT use 125–6 PTCA balloon 55–7, 56, 57 see also specific conditions
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percutaneous transluminal angioplasty 195–6, 196 peripheral arterial disease (PAD) 191 atherosclerotic plaque types 193 OCT-guided interventional procedures 194–5 bypass control 199, 200 cutting balloon angioplasty 196–7, 197, 198 percutaneous transluminal angioplasty 195–6, 195, 196 stent placement 197–9, 198, 199 subintimal angioplasty 198–9 peripheral artery 192 phase function 8–9 5, 5 phase-resolved Doppler OCT see Doppler OCT photoacoustic spectroscopy 10 Pioneer catheter 178 plaque see atherosclerotic plaque point spread function (PSF) 232–3, 232 polarization 43, 47, 242 polarization-sensitive OCT (PS-OCT) 47, 243–8 birefringence measurements 243, 243, 246 development of 243 fiberoptic based PS-OCT 243–4, 244 plaque collagen assessment 138–9, 244–6, 245–6 via intracoronary catheter 246–7, 248 polyethylene glycol (PEG) 271 power Doppler 310, 323–5 prolapse, following stent implantation 155–6, 156–7, 198 pulsed photothermal radiometry 9, 10 pump-probe OCT (PP-OCT) 283–5, 284 ground state recovery PP-OCT (gsrPP-OCT) 283–5, 284 radiotherapy see irradiation vascular effects rapid scan concept 257–8 Rayleigh phase function 5 refractive index 44 remodeling 99 resolution 22, 22, 27, 28 retina 19–21, 20–1, 28 scattering 3–9, 45–6, 46, 231–2 anisotropy 3, 5 effects in Doppler OCT 325–7, 327
multiple scattering 5–7, 6, 46 phase function 4–5, 5 single scattering model 231–2, 232, 236–7 scattering coefficient 4 skin 11 second harmonic OCT 285, 286 shear stress clinical observations 298 definition 289 determination 289–93, 294 computational fluid dynamics 292–3, 295 external ultrasound 290, 290 intravascular Doppler ultrasound 290 IVUS 290–1, 291 MRI 291, 292 OCT 291–2, 293, 294 mechanosensors 294 role in plaque localization 296 role in vascular biology 293–6 vulnerable plaque destabilization and 297–8, 298 matrix regression 298, 299 midcap upstream of stenoses 297–8 shoulders 297 smooth muscle cell apoptosis 297–8 vulnerable plaque generation and 296–7 short time fast Fourier transform (ST-FFT) 310 short time Fourier transform (ST-FT) 308, 320, 320–2 see also Doppler OCT shot noise limit 37 signal-to-noise ratio (SNR) 37 sirolimus-eluting stent 177 skin absorption coefficient 11 scattering coefficient 11 smooth muscle cells (SMCs) 115, 241 shear stress-induced apoptosis 297–8 sound definition 43 imaging applications 46–7 physics of 43–5 tissue interaction 45–6, 46 spectroscopic OCT (S-OCT) 47, 282–3, 283 phantom measurements 233–4, 234 plaque imaging 234–8, 235–7 implications 237–8 temperature effects 236 theory 231–3, 232–3 ST-segment elevation AMI 72, 73, 74 plaque characterization 126–9, 127
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stenting 153 acute results 153–8 stent apposition 153–4, 154–5 stent expansion 154–5, 156 vessel injury 156, 156–8 assessment 41, 41, 61, 68, 69, 91 intermediate follow-up 157–8, 158–9 OCT versus IVUS findings 107, 154 chronic total occlusion 177–8 in-stent restenosis 63, 157 drug-eluting stents 165–6, 166, 167–9, 168 malapposition 61, 153–4, 154–5 follow-up 159 peripheral arterial disease 195, 197–9, 198, 199 biodegradable stents 197–8 see also drug-eluting stents Stokes vectors 243, 243 subarachnoid hemorrhage 203 subintimal angioplasty 198–9 superficial plaque erosion 110 Taxus stent 162, 162, 167, 167 in-stent restenosis 165, 166 thrombosis 169, 169 thermography 118 thick fibrous cap 84, 85, 140 necrotic core fibroatheroma 241, 241 see also atherosclerotic plaque thin-cap fibroatheroma (TCFA) 79, 80, 84, 95, 110, 296 acute coronary syndromes and 126 angioscopy 79, 81–3, 83 IVUS 98 necrotic core fibroatheroma 241, 241 OCT 80, 104–5 thickness assessment 123 versus angioscopy 81–3, 83 see also atherosclerotic plaque; vulnerable plaque thrombosis 109, 115, 116, 235–6 following stent implantation 169, 169 thrombus 72, 73–5, 116 angioscopy versus OCT 79, 80, 81, 84 attenuation coefficient 235 CT imaging 88 in unstable angina 83 spectroscopic OCT analysis 235–6 time domain OCT (TD-OCT) 221 parallel 2D TD-OCT 221–2
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versus frequency domain techniques 222, 223 see also optical coherence tomography (OCT) time-domain detection 29–30, 30 tissue blood flow see blood flow tissue optics 3–12, 13–15 absorption 3 biological variation 9 consequences for OCT 6–7 determination of tissue optical properties 8–12 index of refraction 12 using OCT 10–11 imaging depth 7, 7 light transport in tissue 7–8 phase function 4–5, 5 scattering 3–4, 231–2 anisotropy 4–5 single versus multiple scattering 5–7, 6, 231–2 truncus arteriosus, Xenopus 312–13, 312, 313 tubular pinch 325 ultrahigh resolution OCT (UHR-OCT) 221 ultrasonic spectrum 45
ultrasound 22–3, 46–7, 117–18, 118 embryonic cardiac studies 210 high-frequency ultrasound (HFUS) 306 shear stress determination 290, 290 tissue interaction 45–6, 46 see also Doppler ultrasound; intravascular ultrasound (IVUS) ultrasound biomicroscopy (UBM) 306–7 unstable plaque see vulnerable plaque velocity histogram analysis 310, 311 velocity variance 310 vessels see blood vessels video microscopy 210 virtual histology 47 vulnerable patient 109 vulnerable plaque 110–12, 115–16, 241 assessment 72, 88–9, 126–9 elastography 249 imaging techniques 110, 116–19, 242 OCT 68–9, 71, 97, 122–9 plaque morphology 122, 123, 126
polarization-sensitive OCT 138–9, 244–7, 246–7 see also apoE knockout murine model definition 109, 115 diagnosis 96, 110 fibrous cap thickness 122, 123, 136, 136–8 macrophage accumulation 122–4, 125, 126–9, 128–9 mouse model 139, 143–4 markers 116 pathology 110 shear stress and 296–8, 298 destabilization 297–8, 299 vulnerable plaque generation 296–7 therapy 110–12 future approaches 112 local/regional therapy 111 systemic therapy 111–12, 112 see also atherosclerotic plaque; thin-cap fibroatheroma (TCFA) wavelength 3 Wiener–Khintchin theorem 222 X-rays 116 Xenopus model 312–13, 312, 313
