Tissue Elasticity Imaging: Volume 2: Clinical Applications
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About this ebook
Tissue Elasticity Imaging: Volume Two: Clinical Applications offers an extensive treatment of the fundamentals and applications of this groundbreaking diagnostic modality. Techniques and results are presented for the assessment of breast, prostate, heart, liver and thyroid tissues. For each application, details are provided on how to perform each technique, along with methods of interpretation, diagnostic criteria, quality assurance, challenges and case studies. This book is an essential resource for all researchers and practitioners (including scientists, radiologists, urologists, fellows and residents) interested in any elasticity imaging modality.
As many diseases, including cancers, alter tissue mechanical properties, it is not always possible for conventional methods to detect changes, but with elasticity images that are produced by slow tissue deformation or low-frequency vibration, these changes can be displayed.
- Offers the first comprehensive reference on elasticity imaging
- Discusses the fundamentals of technology and their limitations and solutions, along with advanced methods and future directions
- Addresses the technologies and applications useful to both researchers and clinical practitioners
- Includes an online reference section regularly updated with advances in technology and applications
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Tissue Elasticity Imaging - S. Kaisar Alam
Tissue Elasticity Imaging
Volume 2: Clinical Applications
Editors
S. Kaisar Alam
The Center for Computational Biomedicine Imaging and Modeling (CBIM), Rutgers University, Piscataway, NJ, United States
Brian S. Garra
Division of Imaging, Diagnostics, and Software Reliability Office of Science and Engineering Laboratories, Center for Devices and Radiological Health, FDA, Silver Spring, MD, United States
Table of Contents
Cover image
Title page
Copyright
Contributors
About the editors
Foreword
Preface
Acknowledgments
Chapter 1. Clinical elasticity estimation and imaging: applications and standards
1. Introduction
2. Elastography: different methods with different capabilities
3. Clinical applications of strain and shear wave elastography
4. Elastography applications: translation to clinical use
5. Future directions
Chapter 2. Breast elastography
1. Introduction/background
2. Principles/techniques
3. Diseases and applications
4. Opportunities
5. Artifacts and limitations
6. Summary/conclusions
Chapter 3. Clinical applications of elastographic methods to improve prostate cancer evaluation
1. Introduction
2. Static deformation by compression
3. Dynamic deformation exerted by external mechanical vibrators
4. Excitation by acoustic radiation force
5. Current status and future trends
6. Conclusions
Chapter 4. Cardiovascular elastography
1. Cardiac imaging
2. Vascular imaging
Chapter 5. Ultrasound-based liver elastography
1. Introduction to chronic liver disease: etiology, screening, and diagnosis
2. Transient elastography
3. Point shear wave elastography
4. Two-dimensional shear wave elastography
5. Comparative studies
6. Strain elastography
Chapter 6. Thermal therapy monitoring using elastography
1. Introduction
2. Principles and techniques
3. Elastographic methods for thermal therapy monitoring
4. Diseases and applications
5. Future opportunities
6. Conclusion
Chapter 7. Thyroid elastography
1. Thyroid pathology
2. Strain elastography
3. Shear wave elastography
4. Artifacts in thyroid elastography
5. Conclusion
Chapter 8. Elastography applications in pregnancy
1. Introduction
2. The cervix
3. The placenta
4. Conclusions
Chapter 9. Musculoskeletal elastography
1. Introduction
2. Compression (strain) elastography
3. Shear wave elastography
4. Transient elastography
5. Applications of sonoelastography in the musculoskeletal system
6. Muscles
7. Nerves
8. Plantar fascia
9. Tumor and tumorlike masses
10. Future perspectives
11. Limitations and conditions of good practice
12. Conclusion
Index
Copyright
Elsevier
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Copyright © 2020 Elsevier Inc. All rights reserved.
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This book and the individual contributions contained in it are protected under copyright by the Publisher (other than as may be noted herein).
Notices
Knowledge and best practice in this field are constantly changing. As new research and experience broaden our understanding, changes in research methods, professional practices, or medical treatment may become necessary.
Practitioners and researchers must always rely on their own experience and knowledge in evaluating and using any information, methods, compounds, or experiments described herein. In using such information or methods they should be mindful of their own safety and the safety of others, including parties for whom they have a professional responsibility.
To the fullest extent of the law, neither the Publisher nor the authors, contributors, or editors, assume any liability for any injury and/or damage to persons or property as a matter of products liability, negligence or otherwise, or from any use or operation of any methods, products, instructions, or ideas contained in the material herein.
Library of Congress Cataloging-in-Publication Data
A catalog record for this book is available from the Library of Congress
British Library Cataloguing-in-Publication Data
A catalogue record for this book is available from the British Library
ISBN: 978-0-12-809662-8
For information on all Elsevier publications visit our website at https://www.elsevier.com/books-and-journals
Publisher: Susan Dennis
Acquisition Editor: Anita Koch
Editorial Project Manager: Lindsay Lawrence
Production Project Manager: Paul Prasad Chandramohan
Cover Designer: Matthew Limbert
Typeset by TNQ Technologies
Contributors
M. Abd Ellah
Department of Radiology, Medical University of Innsbruck, Innsbruck, Tyrol, Austria
Department of Diagnostic Radiology, South Egypt Cancer Institute, Assiut University, Assiut, Egypt
Radiology/Neuroradiology Department Rehabilitationskliniken Ulm, Germany
Richard G. Barr, Radiology, Northeast Ohio Medical University, Radiology Consultants Inc., Youngstown, OH, United States
Fanny L. Casado, Instituto de Ciencias Ómicas y Biotecnología Aplicada, Pontificia Universidad Católica del Perú, Lima, Perú
Benjamin Castaneda, Laboratorio de Imágenes Médicas, Pontificia Universidad Católica del Perú, Lima, Peru
Manjiri Dighe, Department of Radiology, Abdominal imaging section, University of Washington, Seattle, WA, United States
Helen Feltovich
Maternal-Fetal Medicine, Intermountain Healthcare, Provo, UT, United States
Quantitative Ultrasound Laboratory, Department of Medical Physics, University of Wisconsin, Madison, WI, United States
Brian S. Garra, Division of Imaging, Diagnostics and Software Reliability, Office of Science and Engineering Laboratories, CDRH, FDA, Silver Spring, Maryland, MD, United States
Eduardo Gonzalez
Laboratorio de Imágenes Médicas, Pontificia Universidad Católica del Perú, Lima, Peru
Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, United State
Kullervo Hynynen, Physical Sciences Platform, Sunnybrook Research Institute; Department of Medical Biophysics and Institute of Biomaterials and Biomedical Engineering, University of Toronto, Toronto, ON, Canada
A.S. Klauser, Department of Radiology, Medical University of Innsbruck, Innsbruck, Tyrol, Austria
Elisa Konofagou, Columbia University, New York, NY, United States
Roxana Şirli, Department of Gastroenterology and Hepatology, Victor Babeş University of Medicine and Pharmacy, Timişoara, Romania
Ioan Sporea, Department of Gastroenterology and Hepatology, Victor Babeş University of Medicine and Pharmacy, Timişoara, Romania
M. Taljanovic, Department of Medical Imaging, University of Arizona, College of Medicine, Banner- University Medical Center, Tucson, AZ, United States
About the editors
S. Kaisar Alam, Ph.D.
President and Chief Engineer, Imagine Consulting LLC, Dayton, NJ, United States
Visiting Research Faculty, Center for Computational Biomedicine Imaging and Modeling (CBIM), Rutgers University, Piscataway, NJ, United States
Adjunct Faculty, Electrical & Computer Engineering, The College of New Jersey (TCNJ), Ewing, NJ, United States
Dr. S. Kaisar Alam received his B.Tech (Honors) from IIT, Kharagpur, India. Following a 3-year stint as a Lecturer at RUET, Bangladesh, he came to the University of Rochester, Rochester, New York, for graduate studies and received his M.S. and Ph.D. degrees in electrical engineering in 1991 and 1996, respectively. After spending 3 years (1995–1998) as a postdoctoral fellow at the University of Texas Health Science Center, Houston, Dr. Alam was a Principal Investigator at Riverside Research, New York, from 1998 to 2013, working on a variety of research topics in biomedical imaging. He was the Chief Research Officer at Improlabs Pte Ltd, an upcoming tech startup in Singapore until 2017. Then he founded his own consulting company for biomedical image analysis, signal processing, and medical imaging. He has also been involved in training and mentoring high school students. He has been a visiting research professor at CBIM, Rutgers University, Piscataway, New Jersey (since 2013), a visiting professor at IUT, Gazipur, Bangladesh (2010 and 2012), and an adjunct faculty at The College of New Jersey (TCNJ), Ewing, New Jersey (since 2017).
Dr. Alam has been active in research for more than 30 years. His research interests include diagnostic and therapeutic applications of ultrasound and optics, and signal/image processing with applications to medical imaging. The areas of his most active research include elasticity imaging and quantitative ultrasound; he is among a few researchers with experience in both quasistatic and dynamic elasticity imaging. Dr. Alam has written over 40 papers in international journals and holds several patents. He is a coauthor of the textbook Computational Health Informatics (to be published late 2019 or early 2020 by CRC Press). He is a Fellow of AIUM, a Senior Member of IEEE, and a Member of Sigma Xi, AAPM, ASA, and SPIE. Dr. Alam has served in the AIUM Technical Standards Committee and the Ultrasound Coordinating Committee of the RSNA Quantitative Imaging Biomarker Alliance (QIBA). He is an Associate Editor of Ultrasonics (Elsevier) and Ultrasonic Imaging (Sage). Dr. Alam was a recipient of the prestigious Fulbright Scholar Award in 2011–2012.
Brian S. Garra, M.D.
Division of Imaging, Diagnostics, and Software Reliability, Office of Science and Engineering Laboratories, Center for Devices and Radiological Health, FDA, Silver Spring, MD, United States
Dr. Brian S. Garra completed his residency training at the University of Utah and spent 3 years as an Army radiologist in Germany before returning to Washington DC and the National Institutes of Health in the mid 1980s. After 4 years at the NIH, he joined the faculty of Georgetown University as Director of Ultrasound. In 1998, he left Georgetown to become Professor & Vice Chairman of Radiology at the University of Vermont/Fletcher Allen Healthcare. In 2009, Dr. Garra returned to the Washington DC area as Chief of Imaging Systems & Research in Radiology at the Washington DC Veterans Affairs Medical Center. In April 2010, he also joined the FDA as an Associate Director in the Division of Imaging and Applied Mathematics/OSEL. In 2018, he left the VA and currently splits his time between the FDA and private practice radiology in Florida.
Dr. Garra's clinical activities include spinal MRI and general ultrasound. His research interests include PACS, digital signal processing, and quantitative ultrasound including Doppler, ultrasound elastography, and photoacoustic tomography. He was chair of the FDA radiological Devices Panel from 1999 to 2002 and has been involved in the approval of several new technologies including high resolution breast ultrasound, the first digital mammographic system, the first computer-aided detection system for mammography, and the first computer-aided nodule detection system for chest radiographs as well as the ultrasound contrast agent albunex. He also led the team that developed the AIUM breast ultrasound accreditation program, and helped develop the ARDMS registry in breast ultrasound. He is currently also Vice Chairman of the Ultrasound Coordinating Committee of the RSNA Quantitative Imaging Biomarker Alliance (QIBA) and is the Principal Author of the forthcoming QIBA Ultrasound Shear Wave Speed Profile which will provide a standard approach to acquisition of shear wave speed data for research, clinical application, and regulatory testing.
Foreword
Given the heavy relatively successful use of manual palpation over the past few thousand years, the ultrasound community, and medicine in general, was very excited to understand and realize the possibility of measuring and imaging the stiffness of tissues. This included tissues too deep for manual palpation. Improving the spatial and quantitative fidelity of elasticity images was addressed aggressively. Also pursued were many extensions related to elastic properties, such as the anisotropy of elasticity, the complex elastic modulus (viscous and elastic components), and elasticity as a function of time under compression.
This two-volume book Tissue Elasticity Imaging extensively covers the principles, implementation, and applications of all these approaches to image the biomechanical properties of tissues. The achieved and future biomedical applications of these many capabilities are also well explained, as are important optical and magnetic resonance imaging techniques that followed, and that sometimes leaped ahead of the many ultrasound developments.
These rapid advances are brought to life for the reader of these books by physicians and other imaging scientists and engineers who made leading advances in each of the covered areas. I initially wished to list key lead authors with a summary of their contributions, but that would essentially be repeating most of the table of contents. The editors of these books, Drs. Brian Garra and S. Kaisar Alam, excelled in recruiting the many luminaries to author the various chapters, defining the topics, and editing the work for readability by the target audience of imaging scientists, engineers, entrepreneurs, clinicians, and operators of the systems. The work should serve as a definitive reference for those teaching and those writing shorter explanations for various groups. This is a much-needed work in the field. Luckily, it will not be the last, as advances are and will continue to be made.
Paul L. Carson, Ph.D.
University of Michigan
Ann Arbor, Michigan
United States
July 14, 2019
Preface
Volume 2 of Tissue Elasticity Imaging reviews the state of clinical elastographic applications as of late 2018. It begins with an introductory chapter that contains a brief explanation of elastography basics, discusses attempts to standardize quantitative elastography for applications such as detection and imaging of liver fibrosis, and discusses some of the reasons why elastography is not used more widely (at least in the United States) 20 years after the technique was first introduced. Potential avenues for future work in elastography are also discussed. The later chapters discuss various specific applications and their potential for widespread clinical use from the perspective of leaders in clinical elastography who author those chapters.
As clinical elastography in all of its forms is progressing rapidly and evolving as new clinical applications are explored, some of the specifics in each chapter may quickly become somewhat outdated. The reader can however use the authors of the chapters and the references given in each chapter as resources to obtain guidance on the latest in each area of clinical elastography. The reader may contact the chapter and reference authors by email or other means and may search via google or other search engines on the type of elastography and the author name(s) to obtain new information on each clinical elastographic application and information on new applications coming online.
I hope that clinicians new to elastography will find this volume useful as a way to get introduced to the use of elastography in your area of clinical interest and expertise. If the brief explanations of the basic science and methods by which elastograms are created are not sufficient, I suggest that the reader refer to the more detailed discussions in Volume 1. The readers can also consult the companion website for this book at https://www.elsevier.com/books-and-journals/book-companion/978-0-12-809662-8.
For those clinicians already using elastography, I hope you will find in this volume some tips on how to refine your elastographic techniques and insight into new ways to use elastography that you may not have already considered. For basic scientists interested in elastography, this volume will introduce to you the wide variety of clinical applications and problems with using the current technology for those applications. It should help you to explore ways in which elastography technology can be improved to provide easier to use elastographic systems that provide higher quality imaging and quantification for clinical use.
I look forward to participate with you all in helping elastography realize its vast potential for improving the health of patients. The future will be exciting!
Brian S. Garra
May 31, 2019
Chapter reviewers:
Volume 2: Clinical applications
Arrigo Fruscalzo
Caroline Malecke
David Cosgrove (now deceased)
Eleni E Drakonaki
Giovanna Ferraioli
Jonathan Langdon
Manjiri D. Dighe
Matthew Urban
Mohammad Mehrmohammadi
Ogonna K. Nwawka
Qian Li
Remi Souchon
Richard Barr
Richard Lopata
Siddhartha Sikdar
Acknowledgments
Editing this important reference book was much harder and at the same time, much more fulfilling than I could have ever imagined. First and foremost, I want to thank the Almighty. He gave me the power to pursue my dreams and this book. I could never have done this without my faith in Him. This book happened because He wished it to be.
I am ever grateful to my deceased parents who always encouraged me to pursue my dreams. Thank you my dear wife, daughter, and son for your constant patience and support, especially during difficult times. My younger brother and sister have been my source of strength since they were born. Their spouses and children have been a source of inspiration and joy for me. I have a large number of uncles, aunts, cousins, nephews, and nieces, who have always supported me. I am lucky to have all of you as my family.
I also want to thank many individuals whom I regard as mentors and friends. They include my childhood mentor Dr. Kazi Khairul Islam, my doctoral advisor Dr. Kevin J. Parker, my postdoc supervisor late Dr. Jonathan Ophir, my former supervisors Dr. Ernie Feleppa and late Dr. Fred Lizzi, and my coeditor Dr. Brian Garra. (Brian also provided the artwork used to design the cover.).
I am also indebted to many family members, friends, and colleagues, and it would be impossible to thank them all individually. I am lucky to have been your family, friend, and colleague. Thank you all!
Last but not the least, thanks to everyone in the Elsevier team. Special thanks to our Acquisition Editor (Dr. Anita Koch), Editorial Project Managers (Lindsay Lawrence, Jennifer Horigan, and Amy Clark), Project Manager (Paul Prasad Chandramohan), Cover Designer (Matthew Limbert), and many other individuals who worked behind the scenes to make this book a reality.
S. Kaisar Alam
Dayton, New Jersey, USA
October 1, 2019
Chapter 1
Clinical elasticity estimation and imaging: applications and standards
Brian S. Garra Division of Imaging, Diagnostics and Software Reliability, Office of Science and Engineering Laboratories, CDRH, FDA, Silver Spring, Maryland, MD, United States
Abstract
The three major elastographic types can be classified either by the type of mechanical tissue deformation used to estimate stiffness or by the parameter that is computed/imaged. Each method has advantages and disadvantages relating to image quality, frame rate, and stiffness quantification. Shear wave elastography is the dominant method because of its ability to quantify tissue stiffness as shear wave speed or elastic (Young's) modulus.
Quality of the results depends on careful acquisition. Many problems result from poor user training. Standardization efforts by the European Federation of Societies for Ultrasound in Medicine and Biology and the Society of Radiologists in Ultrasound have resulted in more consistent results and reporting. The Radiological Society of North America (RSNA) Quantitative Imaging Biomarkers fAlliance (QIBA) profile will yield a validated international standard for liver fibrosis grading from shear wave speed. Strain-based elastic modulus estimates, tissue heterogeneity, and anisotropy correction plus tissue viscosity estimation and correction will help elastography to realize its vast potential.
Keywords
Elastography; Poisson's ratio; QIBA; Shear wave; Strain; Strain rate; Transient elastography; Young's modulus
1. Introduction
Since the initial development of elastographic imaging in the early 1990s [1], the method has been tested on a wide variety of disorders including cancers and noncancerous diseases both focal and diffuse. Although sonoelasticity imaging [2] was developed slightly earlier and was maturing at the same time [3], elastography in the form of strain elastography of the breast was the first method to be adopted clinically because it requires no external vibration source, it can produce high-quality images when performed correctly, and it is able to accurately classify breast tumors as benign or malignant [4–7]. With the subsequent development of shear wave elastography (SWE), evaluation of breast lesions using SWE was generally successful [8], although reports of significant numbers of false-negative cases have more recently appeared [9,10]. As SWE is capable of providing quantitative estimates of stiffness, the technique is being applied successfully to the evaluation of liver fibrosis [11–13], demonstrating its usefulness in the evaluation of diffuse liver disease.
As is often the case with new technologies, enthusiastic initial reports on the breast gave way to more sobering studies with uneven results, and currently breast elastography use is considerable, but not as widespread as it could be. Elastographic evaluation of most other disorders remains in the experimental stage. An exception is in liver evaluation where SWE use for the estimation of liver fibrosis has quickly assumed a dominant role in evaluating diffuse liver disease. It has become one of the most important tools for management of patients with chronic hepatitis [14], replacing liver biopsy in many if not most centers for evaluation and management of chronic hepatitis C.
Although elasticity imaging and quantification in all its forms has produced variable clinical results so far, the technology is maturing, is evolving, and shows great promise for future success in several areas of clinical practice. The term elastography has become the most common name for all elasticity imaging and quantification methods and so will be used throughout the remainder of this chapter.
2. Elastography: different methods with different capabilities
All elastography is based on tissue response to the applied pressure or pressure waves. However, different methods of creating elastograms exist, which produce elastograms displaying tissue stiffness differently. Elastographic methods are currently classified in two main ways (Fig. 1.1). The first scheme classifies the major methods into three types of mechanical deformation applied to tissue.
The first method, termed static
or quasi-static
elastography, is performed by compressing tissue or other material slowly and measuring the movement of the material in response to the compression at different distances from the compressor. Each compression therefore produces a pre
dataset and a post
dataset that are compared to one another to measure tissue movement as a function of depth.
In the second method, termed dynamic
elastography, a vibration is applied to the tissue (amounting to multiple small compressions at some frequency). The resulting waves of tissue displacement are displayed or their speed of travel through the tissue is measured and reported or imaged. Sonoelasticity imaging is one form of dynamic elastography, but the far more widely used method is SWE that uses either a mechanical vibrator (as in magnetic resonance elastography [15]) or an acoustic radiation force impulse (ARFI) to stimulate the production of shear waves in tissues [16].
Figure 1.1 Elastography classification.
In the third method, termed transient
elastography [17], successive rapid compressions are applied and after each compression, the resulting wave of tissue displacement (shear wave) is observed using a-mode ultrasound and the speed of the wave is measured.
The second classification scheme is based on the parameter that is imaged or computed as a surrogate for the Young's (elastic) modulus. In the quasi-static method, tissue displacement is measured by comparison of the pre- and postradiofrequency ultrasound data, and from the displacement data, an image of the strain values is created. For this reason the method is known as strain elastography.
In the most common dynamic elastography method, the speed of travel of a shear wave is measured and an image of the shear wave speed (SWS) values is created. This method is therefore termed SWE.
In another dynamic approach well suited to rapidly moving tissues, Doppler ultrasound or other methods are used to track the change in tissue velocity over short-distance ranges giving the strain rate (the change in strain over short-distance intervals). This method is therefore termed strain rate imaging [18].
The second classification scheme is currently the most popular in medical imaging because the parameter being imaged is explicitly described.
The transient elastography approach previously mentioned does not produce an image at all, only a single numerical value for tissue stiffness. Strain rate imaging is primarily performed in the heart where Doppler ultrasound is already widely used.
With respect to strain elastography and SWE, there are advantages and disadvantages to each method.
For strain elastography the advantages are that it is easier to implement at a lower cost on current ultrasound systems because it involves only different processing of received radiofrequency signals without changes to hardware. In addition, the signal processing is relatively simple and not processor-intensive. It is a relatively mature technique having been initially implemented over 25 years ago and produces high-quality images when performed properly.
Disadvantages of the method include the following:
1. The amount of tissue displacement and strain depend on the amount of pressure applied (the stress
in Hooke's law [19] that states that stress is proportional to the strain
or F=kx, where F is the stress in units of pressure, k is the proportionality constant, and x=strain). Strain is the change in length or thickness of a material in response to the force. For freehand real-time elastography the force applied cannot be reliably reproduced from one image to another. The strain values therefore cannot be used to estimate absolute tissue stiffness because the strain depends on the tissue stiffness and the applied pressure (which is unknown). For this reason, strain images are not mapped to a physical property of tissue but are mapped relative to the median value of the specific image they belong to, much like MRI images. These images of relative
strain are excellent for the detection and characterization of focal lesions but cannot be used to estimate overall stiffness or diffuse stiffening of tissue filling an entire image.
2. High-quality strain elastograms require meticulous attention to the acquisition technique and even a minor variation in the compression technique can result in very poor elastograms with many artifacts and much noise (Fig. 1.2). The good thing about strain elastography is that images can be generated in real time, making large numbers of images available for review. However, of the 20–30 images generated during a single compression, only about 3–4 may be of high enough quality to be interpretable.
3. Manufacturers typically compute and display a quality index for each image to help inform the user of the images that should not be used for diagnosis due to quality issues (Fig. 1.3). But many times a given lesion may appear quite different on each of the several good
images, making it hard for a reader to determine which findings are real and which are not.
Figure 1.2 Poor-quality strain elastogram in which most of the image is dominated by noise. A noisy section is shown