Table Of ContentQuantification of
Biophysical Parameters
in Medical Imaging
Ingolf Sack
Tobias Schaeff ter
Editors
123
Quantification of Biophysical Parameters
in Medical Imaging
Ingolf Sack • Tobias Schaeffter
Editors
Quantification of
Biophysical Parameters
in Medical Imaging
Editors
Ingolf Sack Tobias Schaeffter
Department of Radiology Medical Physics
Humboldt University and Metrological Information Technology
of Berlin Charité University Hospital Physikalisch-Technische Bundesanstalt
Berlin Berlin
Germany Germany
ISBN 978-3-319-65923-7 ISBN 978-3-319-65924-4 (eBook)
https://doi.org/10.1007/978-3-319-65924-4
Library of Congress Control Number: 2017964376
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Acknowledgements
The authors of this book wish to thank the many colleagues and collaborators from
whom they received continuous support, inspiration, scientific advice, and positive
criticism. The endeavor of writing a book like this would not have been possible
without prospering communication and collaboration across the established borders
of disciplines and institutions. Many researchers involved in writing chapters of this
book have ties to both clinical research and basic science and dedicate themselves
to obtaining a higher precision, improved accuracy, and better understanding of
clinical imaging markers. This spirit has evolved in the new research training group
BIOQIC, Biophysical Quantitative Imaging towards Clinical Diagnosis, which is
funded by the German Research Foundation (DFG GRK2260). The authors are
grateful to the DFG and their reviewers for having been granted this unique oppor-
tunity to establish such an interdisciplinary training program in the important fields
of biophysics, medical imaging technologies, and clinical radiology as covered by
this book.
v
Contents
1 Introduction: Medical Imaging for the Quantitative Measurement
of Biophysical Parameters . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
Ingolf Sack and Tobias Schaeffter
Part I Biological and Physical Fundamentals
2 The Fundamentals of Transport in Living Tissues
Quantified by Medical Imaging Technologies . . . . . . . . . . . . . . . . . . . . . . 9
Sebastian Hirsch, Tobias Schaeffter, and Ingolf Sack
3 Mathematical Modeling of Blood Flow
in the Cardiovascular System . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45
Alfonso Caiazzo and Irene E. Vignon-Clementel
4 A Biphasic Poroelasticity Model for Soft Tissue . . . . . . . . . . . . . . . . . . . 71
Sebastian Hirsch
5 Physical Properties of Single Cells and Collective Behavior . . . . . . . . . . 89
Hans Kubitschke, Erik W. Morawetz, Josef A. Käs,
and Jörg Schnauß
6 The Extracellular Matrix as a Target for Biophysical
and Molecular Magnetic Resonance Imaging . . . . . . . . . . . . . . . . . . . 123
Angela Ariza de Schellenberger, Judith Bergs, Ingolf Sack, and
Matthias Taupitz
Part II Medical Imaging Technologies
7 Mathematical Methods in Medical Image Processing . . . . . . . . . . . . . . 153
Gitta Kutyniok, Jackie Ma, and Maximilian März
8 Acceleration Strategies for Data Sampling in MRI . . . . . . . . . . . . . . . . 167
Christoph Kolbitsch and Tobias Schaeffter
9 4D Flow MRI . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 187
Sebastian Schmitter and Susanne Schnell
vii
viii Contents
10 CEST MRI . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 213
Martin Kunth and Leif Schröder
11 Innovative PET and SPECT Tracers . . . . . . . . . . . . . . . . . . . . . . . . . . . 255
Ulrich Abram
12 Methods and Approaches in Ultrasound Elastography . . . . . . . . . . . . 281
Heiko Tzschätzsch
13 Photoacoustic Imaging: Principles and Applications . . . . . . . . . . . . . . 303
Jan Laufer
14 Fundamentals of X-Ray Computed Tomography: Acquisition
and Reconstruction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 325
Marc Dewey and Marc Kachelrieß
Part III Applications
15 Quantification of Myocardial Effective Transverse Relaxation
Time with Magnetic Resonance at 7.0 Tesla for a Better
Understanding of Myocardial (Patho)physiology . . . . . . . . . . . . . . . . . 343
Till Huelnhagen, Teresa Serradas-Duarte, Fabian Hezel,
Katharina Paul, and Thoralf Niendorf
16 Extracellular Matrix-Specific Molecular MR Imaging Probes
for the Assessment of Aortic Aneurysms . . . . . . . . . . . . . . . . . . . . . . . . 373
Julia Brangsch, Carolin Reimann, and Marcus R. Makowski
17 Diffusion-Based MRI: Imaging Basics and Clinical
Applications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 383
Michael Scheel
18 Quantification of Functional Heterogeneities in Tumors
by PET Imaging . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 395
Winfried Brenner, Florian Wedel, and Janet F. Eary
19 Tumor Characterization by Ultrasound Elastography
and Contrast-Enhanced Ultrasound . . . . . . . . . . . . . . . . . . . . . . . . . . . 411
Thomas Fischer, Anke Thomas, and Dirk-André Clevert
20 Sensitivity of Tissue Shear Stiffness to Pressure
and Perfusion in Health and Disease . . . . . . . . . . . . . . . . . . . . . . . . . . . . 429
Jing Guo, Florian Dittmann, and Jürgen Braun
21 Radionuclide Imaging of Cerebral Blood Flow . . . . . . . . . . . . . . . . . . . 451
Ralph Buchert
22 Cardiac Perfusion MRI . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 471
Amedeo Chiribiri
23 Myocardial Perfusion Assessment by 3D and 4D
Computed Tomography . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 487
Marc Dewey and Marc Kachelrieß
Introduction: Medical Imaging 1
for the Quantitative Measurement
of Biophysical Parameters
Ingolf Sack and Tobias Schaeffter
Abstract
Medical imaging is the backbone of modern clinical diagnosis with the vast
majority of images obtained by high-end tomographic modalities such as com-
puted tomography (CT), magnetic resonance imaging (MRI), 3D ultrasound
(US), and emission tomography (PET, SPECT). However, the current radiologi-
cal practice is based on visual inspection of images and most diseases are rated
in qualitative terms, which results in limited comparability and accuracy of
image-based diagnostic decisions. Therefore, there is a need for quantitative
imaging technologies for the assessment of biophysical tissue parameters. This
book focuses on imaging approaches to obtain quantitative information of fluid
transport, soft tissue mechanics, and tissue structures; and gives examples on
clinical applications that benefit from quantitative imaging.
Medical imaging is the backbone of modern clinical diagnosis. More than 5 billion
imaging investigations are performed worldwide each year [1, 2]. The vast majority
of radiological imaging procedures are based on high-end tomographic modalities
such as computed tomography (CT), magnetic resonance imaging (MRI), 3D ultra-
sound (US), and emission tomography (PET, SPECT) [3]. Medical imaging is a
prospering market with estimated sales of $31.9 billion in 2019 alone in the United
I. Sack (*)
Department of Radiology, Charité - Universitätsmedizin Berlin, Berlin, Germany
e-mail: [email protected]
T. Schaeffter
Medical Physics and Metrological Information Technology,
Physikalisch-Technische Bundesanstalt, Berlin, Germany
e-mail: [email protected]
© Springer International Publishing AG 2018 1
I. Sack, T. Schaeffter (eds.), Quantification of Biophysical Parameters in Medical
Imaging, https://doi.org/10.1007/978-3-319-65924-4_1
2 I. Sack and T. Schaeffter
States.1 The growing demand for high-end imaging systems reflects advancing tech-
nologies, aging demographic trends, evolving epidemiological patterns, and chang-
ing patient care strategies. Notwithstanding this success, medical imaging has not
yet fulfilled all expectations about its reproducibility, consistency, and accuracy.
1.1 Most Clinical Imaging Examinations Are Still
Nonquantitative
Medical imaging modalities today can acquire morphological information with
high spatial resolution in a relatively short time. This technical progress has led to
the current radiological practice to make decisions based on visual inspection of
images and to rate diseases in qualitative terms like “enlarged,” “small,” or
“enhanced.” Such qualitative information often suffers from the limited comparabil-
ity among readers, modalities, platforms, and scan protocols.
Virtually none of the currently used diagnostic information is related to the
biophysical properties of the affected tissue. The lack of established quantitative
and biophysics-based medical imaging markers has wide implications for clinical
practice. Today, radiologists need many years of training to master the interpreta-
tion of subtle morphometric variations. Although principally feasible using current
imaging methods, quantitative staging of many diseases such as tumors, hepatic
fibrosis, neurodegeneration, and cardiovascular pressure usually requires invasive
interventional methods and/or histological examination for final validation.
For example, in cardiovascular disease diagnosis and therapeutic decisions are
based on vascular anatomical features, such as the diameter of stenotic vessels,
rather than on the physiological relevance of the stenotic vessels for the heart or
brain. The COURAGE trial [4] showed no significant outcome difference in coro-
nary catheterization in comparison with standard medical therapy alone when
vascular anatomical features were used. However, it was shown in a sub-study
that certain patients can benefit from such expensive intervention, when the blood
flow is measured in tissue (called perfusion) as a biophysical property [5]. Also in
cancer new imaging parameters are required for early diagnosis and teatment of
tumors. There is a strong trend to expand the current metrics, which have been
based solely on size, like the World Health Organization (WHO) criteria and the
Response Evaluation Criteria in Solid Tumors (RECIST) [6, 7].
Both applications also require medical imaging to asess therapy effects non-inva-
sively. However, such serial imaging is often hampered by a low reproducibility due
to technical variations and inter-observer variability. The same applies to multi-
center studies, where different scanners and interobserver variability add to the
issues. For these reasons, the demand for quantitative imaging technologies has
been identified as the major new challenge for the advancement of medical imaging
over the next decade [8].
1 www.freedoniagroup.com.
1 Introduction: Medical Imaging for the Quantitative Measurement of Biophysical 3
1.2 Toward Quantitative Medical Imaging
The mission to improve the value and practicality of quantitative imaging biomarkers
has emerged in worldwide alliances such as QIBA (Quantitative Imaging Biomarkers
Alliance2) by RSNA, the Quantitative Imaging Network (QIN3) of the National
Cancer Institute in the United States, or EIBALL (European Imaging Biomarkers
Alliance) by ESR [9], which are all committed to making medical imaging a more
quantitative science. In parallel, graduate training programs emerge which specifi-
cally focus on quantitative medical imaging. One activity is BIOQIC—Biophysical
Quantitative Imaging towards Clinical diagnosis [10]—a graduate school centered in
Berlin, Germany, and funded by the German Research Foundation (DFG), which
addresses the investigation and clinical application of new quantitative imaging
approaches. Most of the authors of this book are involved in BIOQIC activities—from
defining clinical needs to current research and teaching activities.
1.3 About the Book
This book has the mission of teaching medical imaging sciences to interdisciplinary
scientists and PhD students with emphasis on quantitative biophysical tissue param-
eters. These parameters include transport-related parameters, such as flow velocity;
tracer kinetics; diffusion; perfusion; and mechanical properties, like stiffness, elastic-
ity, and viscoelasticity, and structure-related properties like cell density, heterogene-
ity, and anisotropy. Many of these properties also influence each other, e.g., the
anisotropy of tissue microstructure strongly affects the amount and direction of dif-
fusion. This book emphasizes imaging research in modality- and system-i ndependent
biophysical properties. This biophysics-based view on quantitative medical imaging
is complementary to the worldwide research effort in quantitative parameter map-
ping of modality-related parameters such as relaxation times in MRI or CT attenua-
tion coefficients. Figure 1.1 illustrates the primary parameter classes addressed by
biophysical quantitative imaging, that is, fluid transport, soft tissue mechanics, and
tissue structures. The close interrelation of soft tissue structures including vascular
components and cellular and extracellular networks at multiple scales naturally
yields an overlap in the prescribed parameter categories. For example, the cross-
linking of extracellular matrix influences the macroscopic mechanical response of
the tissue. Another example is the microstructures such as microvessels and intersti-
tial spaces, which significantly control the fluid transport properties in the tissue.
Chapter 2 outlines the theoretical foundations for the parameter categories as illus-
trated in Fig. 1.1. The interrelation between fluid transport, structures, and mechani-
cal properties in biological tissues is of high relevance for basic research and clinical
applications in imaging sciences.
2 http://www.rsna.org/QIBA/.
3 https://imaging.cancer.gov/informatics/qin.htm.
