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

Description

As one of the most prominent diseases in our society, Cardio Vascular Disease (CVD) requires dedicated analysis and investigation to reduce the increasing mortality rate worldwide. Scholars, biomedical engineers and medical practitioners will greatly benefit from the detailed information in this book which gives a better understanding of the causes, diagnosis and treatment of CVD. 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