Vascular and Intravascular Imaging Trends, Analysis, and Challenges: Stent Applications

PRELIMS.pdf
Preface
Editor biographies
Petia Radeva
Jasjit S Suri
List of contributors
CH001.pdf
Chapter 1 OCT in the evaluation of late stent pathology: restenosis, neoatherosclerosis and late malapposition
1.1 Stent evolution and late stent pathology
1.2 OCT characterization of late stent pathology
1.2.1 Stent coverage: re-endothelialization
1.2.2 Restenosis
1.2.3 Neoatherosclerosis
1.2.4 Incomplete stent apposition (malapposition)
1.2.5 Stent thrombosis
1.3 OCT evaluation of bioresorbable vascular scaffolds
1.3.1 OCT in the evaluation of long-term BVS performance
1.3.2 Current pitfalls of BVSs
1.4 Future perspectives
References
CH002.pdf
Chapter 2 Bioresorbable eluting scaffolds in the era of optical coherence tomography: real-world clinical practice
2.1 Introduction
2.2 Historical background and the search for the ideal bioresorbable scaffold
2.3 Bioresorbable scaffolds: current clinical evidence
2.3.1 The Absorb® scaffold
2.3.2 Metallic magnesium BRSs
2.3.3 Other resorbable scaffolds
2.4 The clinical utility of optical coherence tomography in the optimization of bioresorbable scaffolds
2.5 Bioresorbable scaffolds in real-world clinical settings
2.5.1 Case 1—the need for state-of-the-art peri-procedural intravascular imaging
2.5.2 Case 2—a careful OCT interpretation
2.5.3 Case 3—BRS in calcified vessels. Does OCT have a role?
2.5.4 Case 4—BRS in ST-elevation myocardial infarction and long-term evaluation by OCT
2.5.5 Case 5—different devices for different lesions
2.6 Conclusions
References
CH003.pdf
Chapter 3 Computer modeling of blood flow and plaque progression in the stented coronary artery
3.1 Introduction
3.2 Methods
3.2.1 Geometrical stent modeling
3.2.2 Blood flow simulation
3.2.3 Modeling the deformation of blood vessels
3.2.4 Plaque formation and progression modeling—continuum approach
3.2.5 Discrete approach
3.2.6 DPD modeling of oxidized LDL particle adhesion to the wall
3.3 Results
3.3.1 Coupled method for modeling of atherosclerosis
3.3.2 Stent deployment modeling
3.3.3 Deformable artery wall
3.3.4 Nitinol material model
3.3.5 Stress analysis for stent deployment
3.3.6 Plaque concentration for stented arteries
3.4 Discussion and conclusions
Acknowledgment
References
CH004.pdf
Chapter 4 Current status of computational fluid dynamics for modeling of diseased vessels
4.1 Introduction
4.1.1 Disease vessel classification
4.2 Constitutive equation of blood flow in a diseased vessel
4.2.1 Mass conservation equation
4.2.2 Momentum conservation equations
4.3 Viscoelastic models of diseased blood
4.3.1 Carreau model
4.3.2 Power-law model
4.3.3 Quemada model
4.4 CFD modeling of blood flow in a diseased vessel
4.4.1 Laminar flow model
4.5 Evaluation of the shear index on the vascular wall
4.5.1 Oscillatory shear index
4.5.2 Relative residual time
4.6 Conclusion
References
CH005.pdf
Chapter 5 Fast virtual endovascular stenting: technique, validation and applications in computational haemodynamics
5.1 Motivation
5.2 Virtual stenting
5.3 The fast virtual stenting method
5.4 Validation—how accurate is accurate enough?
5.4.1 FVS versus FEM—mechanics
5.4.2 FVS versus FEM—fluid dynamics
5.4.3 FVS—real versus virtual angiographies
5.5 Discussion and future work
5.5.1 Comparison of steady-state and transient blood flow simulations of intracranial aneurysms
5.5.2 Haemodynamic alterations of intracranial aneurysms induced by virtual stent deployment
5.5.3 Reproducibility of virtual angiographies by computational haemodynamics simulations in a stented aneurysm model
5.5.4 Effect of vascular morphology on haemodynamics after flow diverter placement in intracranial aneurysms
5.5.5 Flow diverter length change and future research
References
CH006.pdf
Chapter 6 Graph-based cross-sectional intravascular image segmentation
6.1 Introduction
6.2 Pre-processing
6.3 Feature extraction
6.3.1 Steerable filter
6.3.2 The log-Gabor filter
6.3.3 Local phase
6.3.4 Circulation density features
6.4 Single- and double-interface segmentation
6.4.1 Graph construction
6.4.2 Cost function
6.4.3 Compute the minimum closed set
6.4.4 Post-processing
6.5 Results: IVUS
6.5.1 Single-interface segmentation
6.5.2 Double-interface segmentation
6.6 Results: OCT
6.7 Conclusion
References
CH007.pdf
Chapter 7 Blind inpainting and outlier detection using logarithmic transformation and total variation
7.1 Introduction
7.1.1 Related work
7.1.2 Contributions and organization
7.2 Blind inpainting
7.2.1 Blind inpainting for additive noise
7.2.2 Blind inpainting for Rayleigh multiplicative noise
7.3 Experimental results
7.3.1 Blind inpainting
7.3.2 Outlier maps for lumen segmentation
7.4 Conclusions and future work
Acknowledgments
References
CH008.pdf
Chapter 8 Differential imaging for the detection of extra-luminal blood perfusion due to the vasa vasorum
8.1 Introduction
8.1.1 The vasa vasorum
8.1.2 Intravascular ultrasound
8.2 Methods
8.2.1 Data acquisition protocol
8.2.2 Computer-aided detection of perfusion
8.3 Results
8.3.1 Human cases
8.3.2 Animal cases
8.4 Discussion
8.5 Conclusion
References
CH009.pdf
Chapter 9 Assessment of atherosclerosis in large arteries from PET images
9.1 Introduction
9.2 The formation of atherosclerosis
9.3 Management of atherosclerosis
9.4 Detection of atherosclerosis
9.4.1 Biomarkers
9.4.2 Imaging
9.5 Imaging of atherosclerosis with PET/CT
9.5.1 Fast quantitative assessment
9.5.2 Kinetic modeling
9.5.3 Multiple approaches in atherosclerosis quantitation with PET
9.6 Discussion
9.7 Conclusions
References
CH010.pdf
Chapter 10 3D–2D registration of vascular structures
10.1 Clinical interventions and 3D–2D registration
10.2 Mathematical definition of 3D–2D registration
10.3 Classification of 3D–2D registration
10.3.1 Image modality
10.3.2 Spatial transformation
10.3.3 Dimensional correspondence
10.3.4 Number of views
10.3.5 Registration basis
10.4 Review of registration bases
10.4.1 Calibration-based methods
10.4.2 Extrinsic methods
10.4.3 Intensity-based methods
10.4.4 Feature-based methods
10.4.5 Gradient-based methods
10.5 Review of transformation estimation approaches
10.5.1 Iterative methods
10.5.2 Stratified methods
10.5.3 Regression-based methods
10.6 Validation procedures
10.6.1 Gold standard creation
10.6.2 Registration error
10.6.3 Performance evaluation
10.7 Validation of 3D–2D registration on cerebral angiograms
10.7.1 Experimental set-up
10.7.2 Evaluation based on failure criteria
10.7.3 Evaluation without a failure criterion
10.8 Challenges in translation to clinical application
References
CH011.pdf
Chapter 11 Endovascular navigation with intravascular imaging
11.1 Introduction
11.2 Existing research into intravascular imaging for navigation
11.2.1 IVUS
11.2.2 OCT
11.2.3 Intravascular magnetic resonance imaging
11.2.4 Other sensing
11.3 IVUS for navigation
11.3.1 IVUS and EM sensing
11.3.2 Vessel navigation and retargeting
11.4 The future of intravascular imaging for navigation
11.5 Conclusion
Acknowledgements
References
CH012.pdf
Chapter 12 A cloud-based smart IMT measurement tool for multi-center clinical trial and stroke risk stratification in carotid ultrasound
12.1 Introduction
12.2 Patient demographics and data acquisition
12.2.1 Patient demographics
12.2.2 Ultrasound image data acquisition
12.2.3 Sonographer’s cIMT readings
12.2.4 Manual cIMT readings
12.3 Methodology and cloud-based workflow
12.3.1 Workflow architecture of the AtheroCloud™ 1.0 system
12.3.2 Engineering component design of the AtheroCloud™ 1.0 system
12.3.3 General features of the AtheroCloud™ 1.0 system
12.3.4 Two application modes of AtheroCloud™: the Routine mode and Pharma mode
12.4 Results: measurements and visualization
12.4.1 Carotid intima–media thickness (cIMT) reading
12.4.2 Display of LI/MA interfaces using AtheroCloud™ and manual methods
12.5 Performance evaluation of the AtheroCloud™ system
12.5.1 Precision-of-merit
12.5.2 Coefficient of correlation between the three methods
12.5.3 Bland–Altman plots between the different methods
12.5.4 Coefficient of correlation between age and cIMT
12.5.5 Cumulative distribution of cIMT errors and LI/MA errors
12.5.6 Statistical tests
12.5.7 Receiver operating characteristic (ROC)
12.5.8 Risk stratification
12.5.9 Framingham risk score
12.6 Discussion
12.6.1 Our system
12.6.2 Benchmarking AtheroCloud™ against AtheroEdge™
12.6.3 A brief survey of previous techniques
12.6.4 A note on PoM, cross-correlation and ROC analysis
12.6.5 Risk stratification
12.6.6 Strengths, weaknesses and extensions
12.7 Conclusion
Acknowledgments
Funding
Conflicts of interest
Appendix A Polyline distance metric and precision-of-merit for AtheroCloud™ cIMT measurements
A.1. Polyline distance metric
A.2 Precision-of-merit for AtheroCloud™ cIMT measurements
Appendix B Tables
References
CH013.pdf
Chapter 13 Stroke risk stratification and its validation using ultrasonic echolucent carotid wall plaque morphology: a machine learning paradigm
13.1 Introduction
13.1.1 Small changes in the wall leading to cIMT
13.1.2 The role of the lumen diameter
13.1.3 The role of grayscale morphological-based tissue characterization
13.1.4 The importance of near wall and tissue characterization
13.1.5 A sRAS for the near and far walls using a machine learning paradigm
13.2 Demographics, data acquisition and data preparation
13.2.1 Patient demographics
13.2.2 Data acquisition
13.2.3 Ground truth data preparation
13.2.4 Stratification of manual LD into high risk and low risk
13.3 Methodology
13.3.1 Wall segmentation
13.3.2 Stroke risk assessment system (sRAS)
13.3.3 Texture features
13.4 Experimental protocol
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
13.5 Results
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
13.6.1 Precision-of-merit (PoM) analysis
13.6.2 ROC analysis
13.7 Discussion
13.7.1 Our system
13.7.2 Parameters of the machine learning system
13.7.3 A note on wall segmentation validation
13.7.4 Tissue characterization for risk assessment
13.7.5 Benchmarking
13.7.6 Strengths and weaknesses
13.8 Conclusions
Conflict of interest
Contributions
Acknowledgements
Appendix A Grayscale features
Appendix B Statistical results
References
CH014.pdf
Chapter 14 An improved framework for IVUS-based coronary artery disease risk stratification by fusing wall-based and texture-based features during a machine learning paradigm
14.1 Introduction
14.2 Patient demographics and data acquisition
14.2.1 Patient demographics
14.2.2 Data acquisition
14.3 Methodology
14.3.1 IVUS data preparation
14.3.2 Wall region of interest estimation
14.3.3 Wall- and texture-based feature computation
14.3.4 Principal component analysis with polling contribution
14.3.5 Support vector machine
14.3.6 Machine learning (ML) paradigm for class prediction
14.4 Results
14.4.1 Dominant feature selection
14.4.2 Selection of the best kernel function
14.4.3 Memorization versus generalization
14.5 Performance evaluation
14.5.1 Dominant feature retaining power of the cRAS
14.5.2 Receiver operating characteristics
14.5.3 Reliability index of the cRAS
14.5.4 Stability of the cRAS
14.6 Discussion
14.6.1 Our system
14.6.2 A note on population size
14.6.3 A note on kernel functions
14.6.4 A note on performance evaluation of our cRAS
14.6.5 Comparison against current literature and benchmarking
14.6.6 Carotid plaque burden as a gold standard for the training phase in ML design
14.6.7 A note on time computation for online risk prediction
14.6.8 Strength, weakness and extensions
14.7 Conclusion
Acknowledgments
Funding
Conflicts of interest
References