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Vascular and IntravascularImaging Trends, Analysis, and

Challenges, Volume 1Stent applications

Vascular and IntravascularImaging Trends, Analysis, and

Challenges, Volume 1Stent applications

Petia RadevaUniversitat de Barcelona, Barcelona, Spain

andComputer Vision Center, Bellaterra (Barcelona), Spain

Jasjit S SuriATHEROPOINT, California, USA

IOP Publishing, Bristol, UK

ª IOP Publishing Ltd 2019

All rights reserved. No part of this publication may be reproduced, stored in a retrieval systemor transmitted in any form or by any means, electronic, mechanical, photocopying, recordingor otherwise, without the prior permission of the publisher, or as expressly permitted by law orunder terms agreed with the appropriate rights organization. Multiple copying is permitted inaccordance with the terms of licences issued by the Copyright Licensing Agency, the CopyrightClearance Centre and other reproduction rights organizations.

Permission to make use of IOP Publishing content other than as set out above may be soughtat [email protected].

Petia Radeva and Jasjit S Suri have asserted their right to be identified as the authors of this workin accordance with sections 77 and 78 of the Copyright, Designs and Patents Act 1988.

ISBN 978-0-7503-1997-3 (ebook)ISBN 978-0-7503-1995-9 (print)ISBN 978-0-7503-1996-6 (mobi)

DOI 10.1088/2053-2563/ab01fa

Version: 20190801

IOP Expanding PhysicsISSN 2053-2563 (online)ISSN 2054-7315 (print)

British Library Cataloguing-in-Publication Data: A catalogue record for this book is availablefrom the British Library.

Published by IOP Publishing, wholly owned by The Institute of Physics, London

IOP Publishing, Temple Circus, Temple Way, Bristol, BS1 6HG, UK

US Office: IOP Publishing, Inc., 190 North Independence Mall West, Suite 601, Philadelphia,PA 19106, USA

To our families and friends for their infinite patience, love and support.

Contents

Preface xvii

Editor biographies xix

List of contributors xx

Section I Vascular and intravascular clinical analysis

1 OCT in the evaluation of late stent pathology: restenosis,neoatherosclerosis and late malapposition

1-1

1.1 Stent evolution and late stent pathology 1-1

1.2 OCT characterization of late stent pathology 1-2

1.2.1 Stent coverage: re-endothelialization 1-2

1.2.2 Restenosis 1-7

1.2.3 Neoatherosclerosis 1-11

1.2.4 Incomplete stent apposition (malapposition) 1-14

1.2.5 Stent thrombosis 1-20

1.3 OCT evaluation of bioresorbable vascular scaffolds 1-24

1.3.1 OCT in the evaluation of long-term BVS performance 1-25

1.3.2 Current pitfalls of BVSs 1-26

1.4 Future perspectives 1-26

References 1-27

2 Bioresorbable eluting scaffolds in the era of optical coherencetomography: real-world clinical practice

2-1

2.1 Introduction 2-2

2.2 Historical background and the search for the ideal bioresorbablescaffold

2-3

2.3 Bioresorbable scaffolds: current clinical evidence 2-4

2.3.1 The Absorb® scaffold 2-5

2.3.2 Metallic magnesium BRSs 2-8

2.3.3 Other resorbable scaffolds 2-9

2.4 The clinical utility of optical coherence tomography in theoptimization of bioresorbable scaffolds

2-10

2.5 Bioresorbable scaffolds in real-world clinical settings 2-12

vii

2.5.1 Case 1—the need for state-of-the-art peri-proceduralintravascular imaging

2-12

2.5.2 Case 2—a careful OCT interpretation 2-14

2.5.3 Case 3—BRS in calcified vessels. Does OCT have a role? 2-18

2.5.4 Case 4—BRS in ST-elevation myocardial infarction andlong-term evaluation by OCT

2-20

2.5.5 Case 5—different devices for different lesions 2-21

2.6 Conclusions 2-22

References 2-24

Section II Computer modeling and computational fluidhemodynamics

3 Computer modeling of blood flow and plaque progressionin the stented coronary artery

3-1


3.2 Methods 3-5

3.2.1 Geometrical stent modeling 3-5

3.2.2 Blood flow simulation 3-8

3.2.3 Modeling the deformation of blood vessels 3-10

3.2.4 Plaque formation and progression modeling—continuumapproach

3-11

3.2.5 Discrete approach 3-13

3.2.6 DPD modeling of oxidized LDL particle adhesion to the wall 3-14

3.3 Results 3-14

3.3.1 Coupled method for modeling of atherosclerosis 3-14

3.3.2 Stent deployment modeling 3-15

3.3.3 Deformable artery wall 3-16

3.3.4 Nitinol material model 3-18

3.3.5 Stress analysis for stent deployment 3-19

3.3.6 Plaque concentration for stented arteries 3-20

3.4 Discussion and conclusions 3-21

References 3-23

Vascular and Intravascular Imaging Trends, Analysis, and Challenges, Volume 1

viii

4 Current status of computational fluid dynamics for modelingof diseased vessels

4-1


4.1.1 Disease vessel classification 4-1

4.2 Constitutive equation of blood flow in a diseased vessel 4-4

4.2.1 Mass conservation equation 4-4

4.2.2 Momentum conservation equations 4-5

4.3 Viscoelastic models of diseased blood 4-6

4.3.1 Carreau model 4-6

4.3.2 Power-law model 4-7

4.3.3 Quemada model 4-7

4.4 CFD modeling of blood flow in a diseased vessel 4-8

4.4.1 Laminar flow model 4-8

4.5 Evaluation of the shear index on the vascular wall 4-11

4.5.1 Oscillatory shear index 4-14

4.5.2 Relative residual time 4-17

4.6 Conclusion 4-17

References 4-19

5 Fast virtual endovascular stenting: technique, validation andapplications in computational haemodynamics

5-1

5.1 Motivation 5-1

5.2 Virtual stenting 5-2

5.3 The fast virtual stenting method 5-3

5.4 Validation—how accurate is accurate enough? 5-5

5.4.1 FVS versus FEM—mechanics 5-5

5.4.2 FVS versus FEM—fluid dynamics 5-8

5.4.3 FVS—real versus virtual angiographies 5-10

5.5 Discussion and future work 5-10

5.5.1 Comparison of steady-state and transient blood flowsimulations of intracranial aneurysms

5-11

5.5.2 Haemodynamic alterations of intracranial aneurysmsinduced by virtual stent deployment

5-12

5.5.3 Reproducibility of virtual angiographies by computationalhaemodynamics simulations in a stented aneurysm model

5-13

5.5.4 Effect of vascular morphology on haemodynamics after flowdiverter placement in intracranial aneurysms

5-14


ix

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


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

7 Blind inpainting and outlier detection using logarithmictransformation and total variation

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


x

7.4 Conclusions and future work 7-15

References 7-16

8 Differential imaging for the detection of extra-luminal bloodperfusion due to the vasa vasorum

8-1


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


9.2 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


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


xi

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


11.2 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


xii

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-centerclinical trial and stroke risk stratification in carotid ultrasound

12-1


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

12-8

12.3.3 General features of the AtheroCloud™ 1.0 system 12-9

12.3.4 Two application modes of AtheroCloud™: the Routinemode and Pharma mode

12-9

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

12-11

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


xiii

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


References 12-35

13 Stroke risk stratification and its validation using ultrasonicecholucent carotid wall plaque morphology: a machinelearning paradigm

13-1


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-basedtissue characterization

13-3

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 learningparadigm

13-4

13.2 Demographics, data acquisition and data preparation 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 learningtraining phase

13-11

13.4.2 Experiment 2: The effect of dominant features onclassification accuracy

13-12

13.4.3 Experiment 3: The effect of data size on machine learningperformance

13-12


xiv

13.5 Results 13-12

13.5.1 Experiment 1—Results: Kernel optimization during themachine learning training phase

13-12

13.5.2 Experiment 2—Results: The effect of dominant featureson classification accuracy

13-12

13.5.3 Experiment 3—Results: The effect of data size on machinelearning performance

13-16

13.6 Performance evaluation 13-17

13.6.1 Precision-of-merit (PoM) analysis 13-17

13.6.2 ROC analysis 13-18


13.7.1 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


References 13-33

14 An improved framework for IVUS-based coronaryartery disease risk stratification by fusing wall-basedand texture-based features during a machine learning paradigm

14-1


14.2 Patient demographics and data acquisition 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


xv

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


14.6.1 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 trainingphase in ML design

14-30

14.6.7 A note on time computation for online risk prediction 14-31

14.6.8 Strength, weakness and extensions 14-31


References 14-32


xvi

Preface

Cardiovascular diseases (CVDs) are responsible for a third of all deaths in womenworldwide and more than a half in men. Mortality from coronary heart disease(CHD) is falling due to the continuous improvement in treatment devices andimaging, but morbidity appears to be rising every year. The aetiology of the CVDsis multifactorial; different factors play a role, such as environment, lifestyle andgenetics. As they are the leading cause of morbidity and mortality worldwide,healthcare costs for the management of CVDs are predicted to increase by morethan 40% by 2040 in developed countries (e.g. the USA). This fact underlines theimportance of addressing different open questions, such as the following. What arethe most appropriate diagnosis and interventional strategies? What are the optimaldevices for treatment and how can they avoid secondary effects? Which imagingtechnique gives the most information about the morphology and the dynamics ofthe coronary vessels? How should one combine complementary information frommulti-modal imaging? How does one best evaluate coronary interventions,perform follow-up on coronary lesion evolution, predict the outcomes of inter-ventions, and make possible the retrieval and construction of clinical atlases on ahuge scale, etc?

In this book, we are pleased to present several advanced clinical and medicalimaging studies that cover a wide spectrum of clinical disease issues, clinicalintervention techniques, imaging modalities for plaque visualization and inspection,automatic analysis and clinical parameter extraction techniques, and advanced toolsfor the navigation of and intervention for coronary lesions.

This book is organized into four sections. The first comprises two clinical papersthat discuss the most commonly used clinical imaging techniques for coronaryplaque detection and analysis (angiography, intravascular ultrasound, opticalcoherence tomography (OCT), etc) with their advantages and disadvantages.Special attention is paid to late stent pathology, restenosis, neoatherosclerosis andlate malapposition, and their diagnosis in OCT images.

The second section is devoted to computer modeling and computational fluidhemodynamics for nonlinear stent deployment, modeling plaque formation andprogression. Continuum-based methods for modeling the evolution of plaque arederived. Low-density lipoprotein (LDL) penetration is defined using a convection–diffusion equation, while endothelial permeability is shear stress dependent. Theinflammatory process is modeled using reaction–diffusion partial differential equa-tions. The predictive value of computational models is of high interest in the clinicalcontext. The ability to plan one or more treatment alternatives and being able toassess their outcomes can help in identifying potentially harmful or dangeroussituations. 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 usedin day-to-day clinical practice.

The third section covers different works on image analysis of coronary andcarotid vessels: segmentation of vessels and stents in intravascular ultrasound

xvii

(IVUS) and OCT using advanced computer vision techniques such as graph-cutsand active shape models; advanced medical imaging and computer vision techniquesfor automatic plaque characterization in coronary and carotid vessels; computermodels for blood and plaque growth, robust techniques for IVUS, and histologicaltissue characterization and registration; calcium real-time analysis; novel methods tocombine dynamic and morphological features for robust plaque characterization inthe carotid; and robust methods for 2D and 3D image registration followed bydifferent strategies to be used for better image-guided intervention. Here we can alsofind methods that go beyond the more ‘classical’ problems to analyze coronarylesions, proposing models for extraluminal blood perfusion, studying the relationbetween carotid–coronary plaque progression and extending the discussions toneural aneurysm, proposing a complete overview from neurovascular images tomorphology analysis, diagnosis and treatment.

The last section is devoted to disease risk stratification, which is considered fromdifferent sources such as the intima–media thickness of the carotid, the wallmorphology of the carotid, as well as fusing wall-based and texture-based featureswithin a machine learning paradigm. All these techniques and methodologies arevery important in order to predict the risk of an increase in stenosis and stress on thefibrous cap thickness, which can cause the risk of rupture leading to myocardialinfarction. Rupture of the arterial wall cap can cause calcium to dislodge, blockingthe oxygen-rich blood flow in the arteries, leading to myocardial infarction or stroke.Prior to stenting and percutaneous interventional procedures, cardiologists can beaided by performing pre-screening and risk stratification of coronary artery disease.Therefore, automated machine learning and computer vision systems are beingadopted and becoming popular for clinical use in cardiovascular imaging labora-tories, 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 onvascular and intravascular analysis, discusses different scientific and clinical ques-tions in detail, and proposes advances in clinical treatment and the automaticanalysis of medical imaging. We aim to give an overview of the active topics andproblems in this field and encourage the community to continue in their search forscientific and clinical answers as to which are the most precise, objective, effectiveand efficient strategies for atherosclerotic diagnosis, treatment and follow-up, asCVD remains one of the most important health problems of humanity.

Petia RadevaJasjit S Suri


xviii

Editor biographies

Petia Radeva

Dr Petia Radeva (PhD 1993, Universitat Autònoma de Barcelona,Spain) is a senior researcher and full professor at the University ofBarcelona. She received her PhD degree from the UniversitatAutònoma de Barcelona in 1998. She is the head of the ComputerVision and Machine Learning Consolidated Research Group at theUniversity of Barcelona and the head of MiLab of the ComputerVision Center (www.cvc.uab.es). Her current research interests

include the development of learning-based approaches (in particular, deep learningmethods) for computer vision and image analysis. Radeva has been an AIPR Fellowsince 2015, and became an ICREA Academia researcher in 2014 for her outstandingresearch achievements. In 2015 she received the Aurora Pons Porrata award for herscientific merits as well as the Antonio Caparros award for the best technologytransfer.

Jasjit S Suri

Jasjit S Suri, PhD, MBA, is an innovator, visionary, scientist and aninternationally known world leader in the field of biomedicalimaging and healthcare management. Dr Suri is a recipient of theDirector General’s Gold Medal (1980), was named a Fellow ofthe American Institute of Medical and Biological Engineering by theNational Academy of Sciences, Washington, DC (2004), andreceived a Marquis Life Time Achievement Award (2018). Dr Suri isa board member in several organizations.

xix

http://www.cvc.uab.es

List of contributors

Sergio García-BlasHospital Clínico Universitario de Valencia, Spain

Santiago Jiménez-ValeroHospital Clínico Universitario de Valencia, Spain

Clara BonanadHospital Clínico Universitario de Valencia, Spain

Juan SanchisHospital Clínico Universitario de Valencia, Spain

Vicente BodíHospital Clínico Universitario de Valencia, Spain

Joana Delgado SilvaCoimbra’s Hospital and University Centre—General Hospital, Portugal

Marco CostaCoimbra’s Hospital and University Centre—General Hospital, Portugal

Lino GonçalvesCoimbra’s Hospital and University Centre—General Hospital, Portugal

Nenad FilipovicFaculty of Engineering, University of Kragujevac, Serbia

Arindam BitNational Institute of Technology, Raipur, India

Himadri ChattopadhyayJadavpur University, India

Ignacio LabarrideNational University of Central Buenos Aires, Argentina

Ehab EssaSwansea University, UK

Xianghua XieSwansea University, UK

xx

Huaizhong ZhangEdge Hill University, UK

James CottonRoyal Wolverhampton NHS Trust, UK

Dave SmithABMU, UK

Manya V AfonsoWageningen University and Research, Netherlands

J Miguel SanchesInstitute for Systems and Robotics, Portugal

E Gerardo Mendizabal-RuizUniversity of Guadalajara, Mexico

M’hamed BentourkiaUniversity of Sherbrooke, Canada

Ioannis A KakadiarisUniversity of Houston, TX, USA

Timur AksoySabanci University, Turkey

Gozde UnalIstanbul Technical University, Turkey

Franjo PernusUniversity of Ljubljana, Slovenia

Ziga SpiclinUniversity of Ljubljana, Slovenia

Su-Lin LeeImperial College London, UK

Angelos KarlasTechnical University of Munich, Germany

Alessio DoreImperial College London, UK


xxi

Luca SabaUniversity of Cagliari, Italy

Sumit K BanchhorNational Institute of Technology, India

Harman S SuriMonitoring and Diagnostic Division, AtheroPoint™, USA

Narendra D LondheNational Institute of Technology, India

Tadashi ArakiToho University Medical Center Omori Hospital, Japan

Nobutaka IkedaNational Center for Global Health and Medicine, Japan

Klaudija ViskovicUniversity Hospital for Infectious Disease, Croatia

Shoaib ShafiqueCorVasc MDs PC, IN, USA

John R LairdSt Helena Hospital, CA, USA

Ajay GuptaWeill Cornell Medical College, NY, USA

Andrew NicolaidesVascular Screening and Diagnostic Centre, UK

Pankaj K JainIndian Institute of Technology Varanasi (BHU), India

Ayman El-BazUniversity of Louisville, KY, USA

Vimal K ShrivastavaKalinga Institute of Industrial Technology, India

Shoaib ShafiqueCorVasc Vascular Laboratory, IN, USA


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