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Файл:Ординатура / Хирургия / Библиотека им академика М.И. Перельмана / Книга_3592_Библиотеки_им_академика_М_И_Перельмана
.pdf
Vascular and Intravascular Imaging Trends, Analysis, and Challenges, Volume 1
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5.5.5 Flow diverter length change and future research 5-16
References 5-17
Section III Vessel and stent segmentation
6 Graph-based cross-sectional intravascular image segmentation
6.1 Introduction 6-1
6.2 Pre-processing 6-3
6.3 Feature extraction 6-3
6.3.1 Steerable filter 6-4
6.3.2 The log-Gabor filter 6-4
6.3.3 Local phase 6-5
6.3.4 Circulation density features 6-5
6.4 Single- and double-interface segmentation 6-7
6.4.1 Graph construction 6-7
6.4.2 Cost function 6-9
6.4.3 Compute the minimum closed set 6-10
6.4.4 Post-processing 6-11
6.5 Results: IVUS 6-11
6.5.1 Single-interface segmentation 6-12
6.5.2 Double-interface segmentation 6-13
6.6 Results: OCT 6-14
6.7 Conclusion 6-22
References 6-22
6-1
7 Blind inpainting and outlier detection using logarithmic
transformation and total variation
7.1 Introduction 7-1
7.1.1 Related work 7-3
7.1.2 Contributions and organization 7-4
7.2 Blind inpainting 7-4
7.2.1 Blind inpainting for additive noise 7-5
7.2.2 Blind inpainting for Rayleigh multiplicative noise 7-7
7.3 Experimental results 7-9
7.3.1 Blind inpainting 7-9
7.3.2 Outlier maps for lumen segmentation 7-11
7-1
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7.4 Conclusions and future work 7-15
References 7-16
8 Differential imaging for the detection of extra-luminal blood
8-1
perfusion due to the vasa vasorum
8.1 Introduction 8-2
8.1.1 The vasa vasorum 8-2
8.1.2 Intravascular ultrasound 8-3
8.2 Methods 8-5
8.2.1 Data acquisition protocol 8-5
8.2.2 Computer-aided detection of perfusion 8-5
8.3 Results 8-13
8.3.1 Human cases 8-15
8.3.2 Animal cases 8-16
8.4 Discussion 8-19
8.5 Conclusion 8-21
References 8-21
9 Assessment of atherosclerosis in large arteries from PET images 9-1
9.1 Introduction 9-1
9.2 The formation of atherosclerosis 9-2
9.3 Management of atherosclerosis 9-4
9.4 Detection of atherosclerosis 9-6
9.4.1 Biomarkers 9-6
9.4.2 Imaging 9-6
9.5 Imaging of atherosclerosis with PET/CT 9-9
9.5.1 Fast quantitative assessment 9-10
9.5.2 Kinetic modeling 9-12
9.5.3 Multiple approaches in atherosclerosis quantitation with PET 9-14
9.6 Discussion 9-16
9.7 Conclusions 9-16
References 9-16
10 3D–2D registration of vascular structures 10-1
10.1 Clinical interventions and 3D–2D registration 10-1
10.2 Mathematical definition of 3D–2D registration 10-3
10.3 Classification of 3D–2D registration 10-4
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10.3.1 Image modality 10-4
10.3.2 Spatial transformation 10-5
10.3.3 Dimensional correspondence 10-6
10.3.4 Number of views 10-8
10.3.5 Registration basis 10-8
10.4 Review of registration bases 10-8
10.4.1 Calibration-based methods 10-9
10.4.2 Extrinsic methods 10-10
10.4.3 Intensity-based methods 10-10
10.4.4 Feature-based methods 10-11
10.4.5 Gradient-based methods 10-13
10.5 Review of transformation estimation approaches 10-13
10.5.1 Iterative methods 10-13
10.5.2 Stratified methods 10-16
10.5.3 Regression-based methods 10-17
10.6 Validation procedures 10-17
10.6.1 Gold standard creation 10-18
10.6.2 Registration error 10-20
10.6.3 Performance evaluation 10-21
10.7 Validation of 3D–2D registration on cerebral angiograms 10-22
10.7.1 Experimental set-up 10-23
10.7.2 Evaluation based on failure criteria 10-23
10.7.3 Evaluation without a failure criterion 10-26
10.8 Challenges in translation to clinical application 10-26
References 10-29
11 Endovascular navigation with intravascular imaging 11-1
11.1 Introduction 11-1
11.2 Existing research into intravascular imaging for navigation 11-2
11.2.1 IVUS 11-2
11.2.2 OCT 11-4
11.2.3 Intravascular magnetic resonance imaging 11-5
11.2.4 Other sensing 11-5
11.3 IVUS for navigation 11-7
11.3.1 IVUS and EM sensing 11-7
11.3.2 Vessel navigation and retargeting 11-12
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11.4 The future of intravascular imaging for navigation 11-17
11.5 Conclusion 11-20
References 11-21
Section IV Risk stratification in carotid and coronary artery
12 A cloud-based smart IMT measurement tool for multi-center
12-1
clinical trial and stroke risk stratification in carotid ultrasound
12.1 Introduction 12-2
12.2 Patient demographics and data acquisition 12-4
12.2.1 Patient demographics 12-4
12.2.2 Ultrasound image data acquisition 12-5
12.2.3 Sonographer’s cIMT readings 12-5
12.2.4 Manual cIMT readings 12-6
12.3 Methodology and cloud-based workflow 12-7
12.3.1 Workflow architecture of the AtheroCloud™ 1.0 system 12-7
12.3.2 Engineering component design of the AtheroCloud™
1.0 system
12.3.3 General features of the AtheroCloud™ 1.0 system 12-9
12.3.4 Two application modes of AtheroCloud™: the Routine
mode and Pharma mode
12.4 Results: measurements and visualization 12-9
12.4.1 Carotid intima–media thickness (cIMT) reading 12-9
12.4.2 Display of LI/MA interfaces using AtheroCloud™
and manual methods
12.5 Performance evaluation of the AtheroCloud™ system 12-11
12.5.1 Precision-of-merit 12-13
12.5.2 Coefficient of correlation between the three methods 12-14
12.5.3 Bland–Altman plots between the different methods 12-14
12.5.4 Coefficient of correlation between age and cIMT 12-15
12.5.5 Cumulative distribution of cIMT errors and LI/MA errors 12-16
12.5.6 Statistical tests 12-17
12.5.7 Receiver operating characteristic (ROC) 12-18
12.5.8 Risk stratification 12-23
12.5.9 Framingham risk score 12-23
12-8
12-9
12-11
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12.6 Discussion 12-24
12.6.1 Our system 12-24
12.6.2 Benchmarking AtheroCloud™ against AtheroEdge™ 12-25
12.6.3 A brief survey of previous techniques 12-26
12.6.4 A note on PoM, cross-correlation and ROC analysis 12-29
12.6.5 Risk stratification 12-30
12.6.6 Strengths, weaknesses and extensions 12-30
12.7 Conclusion 12-31
References 12-35
13 Stroke risk stratification and its validation using ultrasonic
13-1
echolucent carotid wall plaque morphology: a machine
learning paradigm
13.1 Introduction 13-2
13.1.1 Small changes in the wall leading to cIMT 13-2
13.1.2 The role of the lumen diameter 13-3
13.1.3 The role of grayscale morphological-based
tissue characterization
13.1.4 The importance of near wall and tissue characterization 13-4
13.1.5 A sRAS for the near and far walls using a machine learning
paradigm
13.2 Demographics, data acquisition and data preparation 13-5
13.2.1 Patient demographics 13-5
13.2.2 Data acquisition 13-5
13.2.3 Ground truth data preparation 13-5
13.2.4 Stratification of manual LD into high risk and low risk 13-6
13.3 Methodology 13-7
13.3.1 Wall segmentation 13-8
13.3.2 Stroke risk assessment system (sRAS) 13-9
13.3.3 Texture features 13-10
13.4 Experimental protocol 13-11
13.4.1 Experiment 1: Kernel optimization during machine learning
training phase
13.4.2 Experiment 2: The effect of dominant features on
classification accuracy
13.4.3 Experiment 3: The effect of data size on machine learning
performance
13-3
13-4
13-11
13-12
13-12
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13.5 Results 13-12
13.5.1 Experiment 1—Results: Kernel optimization during the
machine learning training phase
13.5.2 Experiment 2—Results: The effect of dominant features
on classification accuracy
13.5.3 Experiment 3—Results: The effect of data size on machine
learning performance
13.6 Performance evaluation 13-17
13.6.1 Precision-of-merit (PoM) analysis 13-17
13.6.2 ROC analysis 13-18
13.7 Discussion 13-18
13.7.1 Our system 13-18
13.7.2 Parameters of the machine learning system 13-20
13.7.3 A note on wall segmentation validation 13-20
13.7.4 Tissue characterization for risk assessment 13-20
13.7.5 Benchmarking 13-20
13.7.6 Strengths and weaknesses 13-22
13.8 Conclusions 13-23
References 13-33
13-12
13-12
13-16
14 An improved framework for IVUS-based coronary
14-1
artery disease risk stratification by fusing wall-based
and texture-based features during a machine learning paradigm
14.1 Introduction 14-2
14.2 Patient demographics and data acquisition 14-4
14.2.1 Patient demographics 14-4
14.2.2 Data acquisition 14-6
14.3 Methodology 14-6
14.3.1 IVUS data preparation 14-6
14.3.2 Wall region of interest estimation 14-6
14.3.3 Wall- and texture-based feature computation 14-7
14.3.4 Principal component analysis with polling contribution 14-13
14.3.5 Support vector machine 14-14
14.3.6 Machine learning (ML) paradigm for class prediction 14-15
14.4 Results 14-16
14.4.1 Dominant feature selection 14-18
14.4.2 Selection of the best kernel function 14-18
14.4.3 Memorization versus generalization 14-20
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14.5 Performance evaluation 14-21
14.5.1 Dominant feature retaining power of the cRAS 14-21
14.5.2 Receiver operating characteristics 14-22
14.5.3 Reliability index of the cRAS 14-23
14.5.4 Stability of the cRAS 14-26
14.6 Discussion 14-26
14.6.1 Our system 14-26
14.6.2 A note on population size 14-27
14.6.3 A note on kernel functions 14-28
14.6.4 A note on performance evaluation of our cRAS 14-28
14.6.5 Comparison against current literature and benchmarking 14-28
14.6.6 Carotid plaque burden as a gold standard for the training
14-30
phase in ML design
14.6.7 A note on time computation for online risk prediction 14-31
14.6.8 Strength, weakness and extensions 14-31
14.7 Conclusion 14-31
References 14-32
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Preface
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Cardiovascular diseases (CVDs) are responsible for a third of all deaths in women
worldwide and more than a half in men. Mortality from coronary heart disease
(CHD) is falling due to the continuous improvement in treatment devices and
imaging, but morbidity appears to be rising every year. The aetiology of the CVDs
is multifactorial; different factors play a role, such as environment, lifestyle and
genetics. As they are the l eading cause of morbidity and mortality worldwide,
healthcare costs for the management of CVDs are predicted to increase by more
than 40% by 2040 in developed countries (e.g. the USA). This fact underlines the
importance of addressing different open questions, such as the following. What are
the most appropriate diagnosis and interventional strategies? What are the optimal
devices for treatment and how can they avoid secondary effects? Which imaging
technique gives the most information about the morphology and the dynamics of
the coronary vessels? How should one combine complementary information from
multi-modal imaging? How does one best evaluate coronary interventions,
perform follow-up on coronary lesion evolution, predict the outcomes of interventions, and make possible the retrieval and construction of clinical atlases on a
huge scale, etc?
In this book, we are pleased to present several advanced clinical and medical
imaging studies that cover a wide spectrum of clinical disease issues, clinical
intervention techniques, imaging modalities for plaque visualization and inspection,
automatic analysis and clinical parameter extraction techniques, and advanced tools
for the navigation of and intervention for coronary lesions.
This book is organized into four sections. The first comprises two clinical papers
that discuss the most commonly used clinical imaging techniques for coronary
plaque detection and analysis (angiography, intravascular ultrasound, optical
coherence tomography (OCT), etc) with their advantages and disadvantages.
Special attention is paid to late stent pathology, restenosis, neoatherosclerosis and
late malapposition, and their diagnosis in OCT images.
The second section is devoted to computer modeling and computational fluid
hemodynamics for nonlinear stent deployment, modeling plaque formation and
progression. Continuum-based methods for modeling the evolution of plaque are
derived. Low-density lipoprotein (LDL) penetration is defined using a convection–
diffusion equation, while endothelial permeability is shear stress dependent. The
inflammatory process is modeled using reaction–diffusion partial differential equations. The predictive value of computational models is of high interest in the clinical
context. The ability to plan one or more treatment alternatives and being able to
assess their outcomes can help in identifying potentially harmful or dangerous
situations. Also, the fact that such tools can be used within the intervention room or,
equivalently, obtain a response in real time, opens the possibility of their being used
in day-to-day clinical practice.
The third section covers different works on image analysis of coronary and
carotid vessels: segmentation of vessels and stents in intravascular ultrasound
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(IVUS) and OCT using advanced computer vision techniques such as graph-cuts
and active shape models; advanced medical imaging and computer vision techniques
for automatic plaque characterization in coronary and carotid vessels; computer
models for blood and plaque growth, robust techniques for IVUS, and histological
tissue characterization and registration; calcium real-time analysis; novel methods to
combine dynamic and morphological features for robust plaque characterization in
the carotid; and robust methods for 2D and 3D image registration followed by
different strategies to be used for better image-guided intervention. Here we can also
find methods that go beyond the more ‘classical’ problems to analyze coronary
lesions, proposing models for extraluminal blood perfusion, studying the relation
between carotid–coronary plaque progression and extending the discussions to
neural aneurysm, proposing a complete overview from neurovascular images to
morphology analysis, diagnosis and treatment.
The last section is devoted to disease risk stratification, which is considered from
different sources such as the intima–media thickness of the carotid, the wall
morphology of the carotid, as well as fusing wall-based and texture-based features
within a machine learning paradigm. All these techniques and methodologies are
very important in order to predict the risk of an increase in stenosis and stress on the
fibrous cap thickness, which can cause the risk of rupture leading to myocardial
infarction. Rupture of the arterial wall cap can cause calcium to dislodge, blocking
the oxygen-rich blood flow in the arteries, leading to myocardial infarction or stroke.
Prior to stenting and percutaneous interventional procedures, cardiologists can be
aided by performing pre-screening and risk stratification of coronary artery disease.
Therefore, automated machine learning and computer vision systems are being
adopted and becoming popular for clinical use in cardiovascular imaging laboratories, leading to more precise diagnosis and image-guided intervention and, hence,
a much higher quality of clinical care.
In summary, this collection of studies gives an overview of different research on
vascular and intravascular analysis, discusses different scientific and clinical questions in detail, and proposes advances in clinical treatment and the automatic
analysis of medical imaging. We aim to give an overview of the active topics and
problems in this field and encourage the community to continue in their search for
scientific and clinical answers as to which are the most precise, objective, effective
and efficient strategies for atherosclerotic diagnosis, treatment and follow-up, as
CVD remains one of the most important health problems of humanity.
Petia Radeva
Jasjit S Suri
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Editor biographies
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Petia Radeva
Dr Petia Radeva (PhD 1993, Universitat Autònoma de Barcelona,
Spain) is a senior researcher and full professor at the University of
Barcelona. She received her PhD degree from the Universitat
Autònoma de Barcelona in 1998. She is the head of the Computer
Vision and Machine Learning Consolidated Research Group at the
University of Barcelona and the head of MiLab of the Computer
Vision Center (www.cvc.uab.es). Her current research interests
include the development of learning-based approaches (in particular, deep learning
methods) for computer vision and image analysis. Radeva has been an AIPR Fellow
since 2015, and became an ICREA Academia researcher in 2014 for her outstanding
research achievements. In 2015 she received the Aurora Pons Porrata award for her
scientific merits as well as the Antonio Caparros award for the best technology
transfer.
Jasjit S Suri
Jasjit S Suri, PhD, MBA, is an innovator, visionary, scientist and an
internationally known world leader in the field of biomedical
imaging and healthcare management. Dr Suri is a recipient of the
Director General’s Gold Medal (1980), was named a Fellow of
the American Institute of Medical and Biological Engineering by the
National Academy of Sciences, Washington, DC (2004), and
received a Marquis Life Time Achievement Award (2018). Dr Suri is
a board member in several organizations.
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