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Attention-Based UNet Deep Learning Model for Plaque Segmentation in Carotid Ultrasound for Stroke Risk Stratification: An Artificial Intelligence Paradigm. J Cardiovasc Dev Dis 2022; 9:jcdd9100326. [PMID: 36286278 PMCID: PMC9604424 DOI: 10.3390/jcdd9100326] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/21/2022] [Revised: 09/06/2022] [Accepted: 09/14/2022] [Indexed: 11/17/2022] Open
Abstract
Stroke and cardiovascular diseases (CVD) significantly affect the world population. The early detection of such events may prevent the burden of death and costly surgery. Conventional methods are neither automated nor clinically accurate. Artificial Intelligence-based methods of automatically detecting and predicting the severity of CVD and stroke in their early stages are of prime importance. This study proposes an attention-channel-based UNet deep learning (DL) model that identifies the carotid plaques in the internal carotid artery (ICA) and common carotid artery (CCA) images. Our experiments consist of 970 ICA images from the UK, 379 CCA images from diabetic Japanese patients, and 300 CCA images from post-menopausal women from Hong Kong. We combined both CCA images to form an integrated database of 679 images. A rotation transformation technique was applied to 679 CCA images, doubling the database for the experiments. The cross-validation K5 (80% training: 20% testing) protocol was applied for accuracy determination. The results of the Attention-UNet model are benchmarked against UNet, UNet++, and UNet3P models. Visual plaque segmentation showed improvement in the Attention-UNet results compared to the other three models. The correlation coefficient (CC) value for Attention-UNet is 0.96, compared to 0.93, 0.96, and 0.92 for UNet, UNet++, and UNet3P models. Similarly, the AUC value for Attention-UNet is 0.97, compared to 0.964, 0.966, and 0.965 for other models. Conclusively, the Attention-UNet model is beneficial in segmenting very bright and fuzzy plaque images that are hard to diagnose using other methods. Further, we present a multi-ethnic, multi-center, racial bias-free study of stroke risk assessment.
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Suri JS, Agarwal S, Elavarthi P, Pathak R, Ketireddy V, Columbu M, Saba L, Gupta SK, Faa G, Singh IM, Turk M, Chadha PS, Johri AM, Khanna NN, Viskovic K, Mavrogeni S, Laird JR, Pareek G, Miner M, Sobel DW, Balestrieri A, Sfikakis PP, Tsoulfas G, Protogerou A, Misra DP, Agarwal V, Kitas GD, Teji JS, Al-Maini M, Dhanjil SK, Nicolaides A, Sharma A, Rathore V, Fatemi M, Alizad A, Krishnan PR, Ferenc N, Ruzsa Z, Gupta A, Naidu S, Kalra MK. Inter-Variability Study of COVLIAS 1.0: Hybrid Deep Learning Models for COVID-19 Lung Segmentation in Computed Tomography. Diagnostics (Basel) 2021; 11:2025. [PMID: 34829372 PMCID: PMC8625039 DOI: 10.3390/diagnostics11112025] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2021] [Revised: 10/26/2021] [Accepted: 10/27/2021] [Indexed: 02/05/2023] Open
Abstract
Background: For COVID-19 lung severity, segmentation of lungs on computed tomography (CT) is the first crucial step. Current deep learning (DL)-based Artificial Intelligence (AI) models have a bias in the training stage of segmentation because only one set of ground truth (GT) annotations are evaluated. We propose a robust and stable inter-variability analysis of CT lung segmentation in COVID-19 to avoid the effect of bias. Methodology: The proposed inter-variability study consists of two GT tracers for lung segmentation on chest CT. Three AI models, PSP Net, VGG-SegNet, and ResNet-SegNet, were trained using GT annotations. We hypothesized that if AI models are trained on the GT tracings from multiple experience levels, and if the AI performance on the test data between these AI models is within the 5% range, one can consider such an AI model robust and unbiased. The K5 protocol (training to testing: 80%:20%) was adapted. Ten kinds of metrics were used for performance evaluation. Results: The database consisted of 5000 CT chest images from 72 COVID-19-infected patients. By computing the coefficient of correlations (CC) between the output of the two AI models trained corresponding to the two GT tracers, computing their differences in their CC, and repeating the process for all three AI-models, we show the differences as 0%, 0.51%, and 2.04% (all < 5%), thereby validating the hypothesis. The performance was comparable; however, it had the following order: ResNet-SegNet > PSP Net > VGG-SegNet. Conclusions: The AI models were clinically robust and stable during the inter-variability analysis on the CT lung segmentation on COVID-19 patients.
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Affiliation(s)
- Jasjit S. Suri
- Stroke Diagnostic and Monitoring Division, AtheroPoint™, Roseville, CA 95661, USA; (I.M.S.); (P.S.C.)
- Advanced Knowledge Engineering Centre, GBTI, Roseville, CA 95661, USA; (S.A.); (P.E.)
| | - Sushant Agarwal
- Advanced Knowledge Engineering Centre, GBTI, Roseville, CA 95661, USA; (S.A.); (P.E.)
- Department of Computer Science Engineering, PSIT, Kanpur 209305, India
| | - Pranav Elavarthi
- Advanced Knowledge Engineering Centre, GBTI, Roseville, CA 95661, USA; (S.A.); (P.E.)
- Thomas Jefferson High School for Science and Technology, Alexandria, VA 22312, USA
| | - Rajesh Pathak
- Department of Computer Science Engineering, Rawatpura Sarkar University, Raipur 492001, India;
| | | | - Marta Columbu
- Department of Radiology, Azienda Ospedaliero Universitaria (A.O.U.), 10015 Cagliari, Italy; (M.C.); (L.S.); (A.B.)
| | - Luca Saba
- Department of Radiology, Azienda Ospedaliero Universitaria (A.O.U.), 10015 Cagliari, Italy; (M.C.); (L.S.); (A.B.)
| | - Suneet K. Gupta
- Department of Computer Science, Bennett University, Noida 201310, India;
| | - Gavino Faa
- Department of Pathology, Azienda Ospedaliero Universitaria (A.O.U.), 10015 Cagliari, Italy;
| | - Inder M. Singh
- Stroke Diagnostic and Monitoring Division, AtheroPoint™, Roseville, CA 95661, USA; (I.M.S.); (P.S.C.)
| | - Monika Turk
- The Hanse-Wissenschaftskolleg Institute for Advanced Study, 27753 Delmenhorst, Germany;
| | - Paramjit S. Chadha
- Stroke Diagnostic and Monitoring Division, AtheroPoint™, Roseville, CA 95661, USA; (I.M.S.); (P.S.C.)
| | - Amer M. Johri
- Department of Medicine, Division of Cardiology, Queen’s University, Kingston, ON K7L 3N6, Canada;
| | - Narendra N. Khanna
- Department of Cardiology, Indraprastha APOLLO Hospitals, New Delhi 110076, India;
| | | | - Sophie Mavrogeni
- Cardiology Clinic, Onassis Cardiac Surgery Center, 10558 Athens, Greece;
| | - John R. Laird
- Heart and Vascular Institute, Adventist Health St. Helena, St. Helena, CA 94574, USA;
| | - Gyan Pareek
- Minimally Invasive Urology Institute, Brown University, Providence, RI 02912, USA; (G.P.); (D.W.S.)
| | - Martin Miner
- Men’s Health Center, Miriam Hospital, Providence, RI 02906, USA;
| | - David W. Sobel
- Minimally Invasive Urology Institute, Brown University, Providence, RI 02912, USA; (G.P.); (D.W.S.)
| | - Antonella Balestrieri
- Department of Radiology, Azienda Ospedaliero Universitaria (A.O.U.), 10015 Cagliari, Italy; (M.C.); (L.S.); (A.B.)
| | - Petros P. Sfikakis
- Rheumatology Unit, National & Kapodistrian University of Athens, 10679 Athens, Greece;
| | - George Tsoulfas
- Aristoteleion University of Thessaloniki, 54636 Thessaloniki, Greece;
| | | | - Durga Prasanna Misra
- Department of Immunology, Sanjay Gandhi Postgraduate Institute of Medical Sciences, Lucknow 226014, India; (D.P.M.); (V.A.)
| | - Vikas Agarwal
- Department of Immunology, Sanjay Gandhi Postgraduate Institute of Medical Sciences, Lucknow 226014, India; (D.P.M.); (V.A.)
| | - George D. Kitas
- Academic Affairs, Dudley Group NHS Foundation Trust, Dudley DY1 2HQ, UK;
- Arthritis Research UK Epidemiology Unit, Manchester University, Manchester M13 9PT, UK
| | - Jagjit S. Teji
- Ann and Robert H. Lurie Children’s Hospital of Chicago, Chicago, IL 60611, USA;
| | - Mustafa Al-Maini
- Allergy, Clinical Immunology and Rheumatology Institute, Toronto, ON L4Z 4C4, Canada;
| | | | - Andrew Nicolaides
- Vascular Screening and Diagnostic Centre, University of Nicosia Medical School, Nicosia 2368, Cyprus;
| | - Aditya Sharma
- Division of Cardiovascular Medicine, University of Virginia, Charlottesville, VA 22904, USA;
| | - Vijay Rathore
- AtheroPoint LLC, Roseville, CA 95611, USA; (S.K.D.); (V.R.)
| | - Mostafa Fatemi
- Department of Physiology & Biomedical Engineering, Mayo Clinic College of Medicine and Science, Rochester, MN 55905, USA;
| | - Azra Alizad
- Department of Radiology, Mayo Clinic College of Medicine and Science, Rochester, MN 55905, USA;
| | | | - Nagy Ferenc
- Internal Medicine Department, University of Szeged, 6725 Szeged, Hungary;
| | - Zoltan Ruzsa
- Zoltan Invasive Cardiology Division, University of Szeged, 6725 Szeged, Hungary;
| | - Archna Gupta
- Radiology Department, Sanjay Gandhi Postgraduate Institute of Medical Sciences, Lucknow 226014, India;
| | - Subbaram Naidu
- Electrical Engineering Department, University of Minnesota, Duluth, MN 55812, USA;
| | - Mannudeep K. Kalra
- Department of Radiology, Massachusetts General Hospital, 55 Fruit Street, Boston, MA 02114, USA;
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3
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Liu H, Wingert A, Wang X, Zhang J, Sun J, Chen F, Khalid SG, Gong Y, Xia L, Jiang J, Wang J, Zheng D. Consistency in Geometry Among Coronary Atherosclerotic Plaques Extracted From Computed Tomography Angiography. Front Physiol 2021; 12:715265. [PMID: 34712147 PMCID: PMC8546263 DOI: 10.3389/fphys.2021.715265] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2021] [Accepted: 09/03/2021] [Indexed: 11/25/2022] Open
Abstract
Background: The three-dimensional (3D) geometry of coronary atherosclerotic plaques is associated with plaque growth and the occurrence of coronary artery disease. However, there is a lack of studies on the 3D geometric properties of coronary plaques. We aim to investigate if coronary plaques of different sizes are consistent in geometric properties. Methods: Nineteen cases with symptomatic stenosis caused by atherosclerotic plaques in the left coronary artery were included. Based on attenuation values on computed tomography angiography images, coronary atherosclerotic plaques and calcifications were identified, 3D reconstructed, and manually revised. Multidimensional geometric parameters were measured on the 3D models of plaques and calcifications. Linear and non-linear (i.e., power function) fittings were used to investigate the relationship between multidimensional geometric parameters (length, surface area, volume, etc.). Pearson correlation coefficient (r), R-squared, and p-values were used to evaluate the significance of the relationship. The analysis was performed based on cases and plaques, respectively. Significant linear relationship was defined as R-squared > 0.25 and p < 0.05. Results: In total, 49 atherosclerotic plaques and 56 calcifications were extracted. In the case-based analysis, significant linear relationships were found between number of plaques and number of calcifications (r = 0.650, p = 0.003) as well as total volume of plaques (r = 0.538, p = 0.018), between number of calcifications and total volume of plaques (r = 0.703, p = 0.001) as well as total volume of calcification (r = 0.646, p = 0.003), and between the total volumes of plaques and calcifications (r = 0.872, p < 0.001). In plaque-based analysis, the power function showed higher R-squared values than the linear function in fitting the relationships of multidimensional geometric parameters. Two presumptions of plaque geometry in different growth stages were proposed with simplified geometric models developed. In the proposed models, the exponents in the power functions of geometric parameters were in accordance with the fitted values. Conclusion: In patients with coronary artery disease, coronary plaques and calcifications are positively related in number and volume. Different coronary plaques are consistent in the relationship between geometry parameters in different dimensions.
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Affiliation(s)
- Haipeng Liu
- Research Centre for Intelligent Healthcare, Coventry University, Coventry, United Kingdom.,Faculty of Health, Education, Medicine, and Social Care, Anglia Ruskin University, Chelmsford, United Kingdom
| | - Aleksandra Wingert
- Faculty of Health, Education, Medicine, and Social Care, Anglia Ruskin University, Chelmsford, United Kingdom
| | - Xinhong Wang
- Department of Radiology, The Second Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, China
| | - Jucheng Zhang
- Department of Clinical Engineering, School of Medicine, The Second Affiliated Hospital, Zhejiang University, Hangzhou, China
| | - Jianzhong Sun
- Department of Radiology, The Second Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, China
| | - Fei Chen
- Department of Electrical and Electronic Engineering, Southern University of Science and Technology, Shenzhen, China
| | - Syed Ghufran Khalid
- Research Centre for Intelligent Healthcare, Coventry University, Coventry, United Kingdom.,Department of Medical Physics, Guy's and St Thomas' NHS Foundation Trust, London, United Kingdom
| | - Yinglan Gong
- Key Laboratory for Biomedical Engineering of Ministry of Education, Institute of Biomedical Engineering, Zhejiang University, Hangzhou, China
| | - Ling Xia
- Key Laboratory for Biomedical Engineering of Ministry of Education, Institute of Biomedical Engineering, Zhejiang University, Hangzhou, China
| | - Jun Jiang
- Department of Cardiology, School of Medicine, The Second Affiliated Hospital, Zhejiang University, Hangzhou, China
| | - Jian'an Wang
- Department of Cardiology, School of Medicine, The Second Affiliated Hospital, Zhejiang University, Hangzhou, China
| | - Dingchang Zheng
- Research Centre for Intelligent Healthcare, Coventry University, Coventry, United Kingdom
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4
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Saba L, Sanagala SS, Gupta SK, Koppula VK, Johri AM, Khanna NN, Mavrogeni S, Laird JR, Pareek G, Miner M, Sfikakis PP, Protogerou A, Misra DP, Agarwal V, Sharma AM, Viswanathan V, Rathore VS, Turk M, Kolluri R, Viskovic K, Cuadrado-Godia E, Kitas GD, Sharma N, Nicolaides A, Suri JS. Multimodality carotid plaque tissue characterization and classification in the artificial intelligence paradigm: a narrative review for stroke application. ANNALS OF TRANSLATIONAL MEDICINE 2021; 9:1206. [PMID: 34430647 PMCID: PMC8350643 DOI: 10.21037/atm-20-7676] [Citation(s) in RCA: 36] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/26/2020] [Accepted: 02/25/2021] [Indexed: 12/12/2022]
Abstract
Cardiovascular disease (CVD) is one of the leading causes of morbidity and mortality in the United States of America and globally. Carotid arterial plaque, a cause and also a marker of such CVD, can be detected by various non-invasive imaging modalities such as magnetic resonance imaging (MRI), computer tomography (CT), and ultrasound (US). Characterization and classification of carotid plaque-type in these imaging modalities, especially into symptomatic and asymptomatic plaque, helps in the planning of carotid endarterectomy or stenting. It can be challenging to characterize plaque components due to (I) partial volume effect in magnetic resonance imaging (MRI) or (II) varying Hausdorff values in plaque regions in CT, and (III) attenuation of echoes reflected by the plaque during US causing acoustic shadowing. Artificial intelligence (AI) methods have become an indispensable part of healthcare and their applications to the non-invasive imaging technologies such as MRI, CT, and the US. In this narrative review, three main types of AI models (machine learning, deep learning, and transfer learning) are analyzed when applied to MRI, CT, and the US. A link between carotid plaque characteristics and the risk of coronary artery disease is presented. With regard to characterization, we review tools and techniques that use AI models to distinguish carotid plaque types based on signal processing and feature strengths. We conclude that AI-based solutions offer an accurate and robust path for tissue characterization and classification for carotid artery plaque imaging in all three imaging modalities. Due to cost, user-friendliness, and clinical effectiveness, AI in the US has dominated the most.
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Affiliation(s)
- Luca Saba
- Department of Radiology, Azienda Ospedaliero Universitaria (AOU), Cagliari, Italy
| | - Skandha S Sanagala
- CSE Department, CMR College of Engineering & Technology, Hyderabad, India.,CSE Department, Bennett University, Greater Noida, UP, India
| | - Suneet K Gupta
- CSE Department, Bennett University, Greater Noida, UP, India
| | - Vijaya K Koppula
- CSE Department, CMR College of Engineering & Technology, Hyderabad, India
| | - Amer M Johri
- Department of Medicine, Division of Cardiology, Queen's University, Kingston, Ontario, Canada
| | - Narendra N Khanna
- Department of Cardiology, Indraprastha APOLLO Hospitals, New Delhi, India
| | - Sophie Mavrogeni
- Cardiology Clinic, Onassis Cardiac Surgery Center, Athens, Greece
| | - John R Laird
- Heart and Vascular Institute, Adventist Health St. Helena, St Helena, CA, USA
| | - Gyan Pareek
- Minimally Invasive Urology Institute, Brown University, Providence, Rhode Island, USA
| | - Martin Miner
- Men's Health Center, Miriam Hospital Providence, Rhode Island, USA
| | - Petros P Sfikakis
- Rheumatology Unit, National Kapodistrian University of Athens, Greece
| | - Athanasios Protogerou
- Department of Cardiovascular Prevention, National and Kapodistrian University of Athens, Athens, Greece
| | - Durga P Misra
- Department of Clinical Immunology and Rheumatology, SGPGIMS, Lucknow, India
| | - Vikas Agarwal
- Department of Clinical Immunology and Rheumatology, SGPGIMS, Lucknow, India
| | - Aditya M Sharma
- Division of Cardiovascular Medicine, University of Virginia, VA, USA
| | - Vijay Viswanathan
- MV Hospital for Diabetes & Professor M Viswanathan Diabetes Research Centre, Chennai, India
| | - Vijay S Rathore
- Nephrology Department, Kaiser Permanente, Sacramento, CA, USA
| | - Monika Turk
- The Hanse-Wissenschaftskolleg Institute for Advanced Study, Delmenhorst, Germany
| | | | | | | | - George D Kitas
- R & D Academic Affairs, Dudley Group NHS Foundation Trust, Dudley, UK
| | - Neeraj Sharma
- Department of Biomedical Engineering, IIT-BHU, Banaras, UP, India
| | - Andrew Nicolaides
- Vascular Screening and Diagnostic Centre, University of Nicosia, Nicosia, Cyprus
| | - Jasjit S Suri
- Stroke Monitoring and Diagnostic Division, AtheroPoint™, Roseville, CA, USA
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5
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Jamthikar A, Gupta D, Cuadrado-Godia E, Puvvula A, Khanna NN, Saba L, Viskovic K, Mavrogeni S, Turk M, Laird JR, Pareek G, Miner M, Sfikakis PP, Protogerou A, Kitas GD, Shankar C, Nicolaides A, Viswanathan V, Sharma A, Suri JS. Ultrasound-based stroke/cardiovascular risk stratification using Framingham Risk Score and ASCVD Risk Score based on "Integrated Vascular Age" instead of "Chronological Age": a multi-ethnic study of Asian Indian, Caucasian, and Japanese cohorts. Cardiovasc Diagn Ther 2020; 10:939-954. [PMID: 32968652 DOI: 10.21037/cdt.2020.01.16] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Abstract
Background Vascular age (VA) has recently emerged for CVD risk assessment and can either be computed using conventional risk factors (CRF) or by using carotid intima-media thickness (cIMT) derived from carotid ultrasound (CUS). This study investigates a novel method of integrating both CRF and cIMT for estimating VA [so-called integrated VA (IVA)]. Further, the study analyzes and compares CVD/stroke risk using the Framingham Risk Score (FRS)-based risk calculator when adapting IVA against VA. Methods The system follows a four-step process: (I) VA using cIMT based using linear-regression (LR) model and its coefficients; (II) VA prediction using ten CRF using a multivariate linear regression (MLR)-based model with gender adjustment; (III) coefficients from the LR-based model and MLR-based model are combined using a linear model to predict the final IVA; (IV) the final step consists of FRS-based risk stratification with IVA as inputs and benchmarked against FRS using conventional method of CA. Area-under-the-curve (AUC) is computed using IVA and benchmarked against CA while taking the response variable as a standardized combination of cIMT and glycated hemoglobin. Results The study recruited 648 patients, 202 were Japanese, 314 were Asian Indian, and 132 were Caucasians. Both left and right common carotid arteries (CCA) of all the population were scanned, thus a total of 1,287 ultrasound scans. The 10-year FRS using IVA reported higher AUC (AUC =0.78) compared with 10-year FRS using CA (AUC =0.66) by ~18%. Conclusions IVA is an efficient biomarker for risk stratifications for patients in routine practice.
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Affiliation(s)
- Ankush Jamthikar
- Department of Electronics and Communications Engineering, Visvesvaraya National Institute of Technology, Nagpur, Maharashtra, India
| | - Deep Gupta
- Department of Electronics and Communications Engineering, Visvesvaraya National Institute of Technology, Nagpur, Maharashtra, India
| | | | - Anudeep Puvvula
- Annu's Hospitals for Skin and Diabetes, Nellore, Andra Pradesh, India
| | - Narendra N Khanna
- Department of Cardiology, Indraprastha APOLLO Hospitals, New Delhi, India
| | - Luca Saba
- Department of Radiology, University of Cagliari, Italy
| | - Klaudija Viskovic
- Department of Radiology and Ultrasound, University Hospital for Infectious Diseases, Zagreb, Croatia
| | - Sophie Mavrogeni
- Cardiology Clinic, Onassis Cardiac Surgery Center, Athens, Greece
| | - Monika Turk
- Department of Neurology, University Medical Centre Maribor, Maribor, Slovenia
| | - John R Laird
- Heart and Vascular Institute, Adventist Health St. Helena, St Helena, CA, USA
| | - Gyan Pareek
- Minimally Invasive Urology Institute, Brown University, Providence, Rhode Island, USA
| | - Martin Miner
- Men's Health Center, Miriam Hospital Providence, Rhode Island, USA
| | - Petros P Sfikakis
- Rheumatology Unit, National Kapodistrian University of Athens, Athens, Greece
| | - Athanasios Protogerou
- Department of Cardiovascular Prevention & Research Unit Clinic & Laboratory of Pathophysiology, National and Kapodistrian Univ. of Athens, Athens, Greece
| | - George D Kitas
- R & D Academic Affairs, Dudley Group NHS Foundation Trust, Dudley, UK
| | | | - Andrew Nicolaides
- Vascular Screening and Diagnostic Centre and University of Nicosia Medical School, Nicosia, Cyprus
| | - Vijay Viswanathan
- MV Hospital for Diabetes and Professor M Viswanathan Diabetes Research Centre, Chennai, India
| | - Aditya Sharma
- Division of Cardiovascular Medicine, University of Virginia, Charlottesville, VA, USA
| | - Jasjit S Suri
- Stroke Monitoring and Diagnostic Division, AtheroPoint™, Roseville, CA, USA
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6
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Saba L, Jamthikar A, Gupta D, Khanna NN, Viskovic K, Suri HS, Gupta A, Mavrogeni S, Turk M, Laird JR, Pareek G, Miner M, Sfikakis PP, Protogerou A, Kitas GD, Viswanathan V, Nicolaides A, Bhatt DL, Suri JS. Global perspective on carotid intima-media thickness and plaque: should the current measurement guidelines be revisited? INT ANGIOL 2019; 38:451-465. [PMID: 31782286 DOI: 10.23736/s0392-9590.19.04267-6] [Citation(s) in RCA: 26] [Impact Index Per Article: 5.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Abstract
Carotid intima-media thickness (cIMT) and carotid plaque (CP) currently act as risk predictors for CVD/Stroke risk assessment. Over 2000 articles have been published that cover either use cIMT/CP or alterations of cIMT/CP and additional image-based phenotypes to associate cIMT related markers with CVD/Stroke risk. These articles have shown variable results, which likely reflect a lack of standardization in the tools for measurement, risk stratification, and risk assessment. Guidelines for cIMT/CP measurement are influenced by major factors like the atherosclerosis disease itself, conventional risk factors, 10-year measurement tools, types of CVD/Stroke risk calculators, incomplete validation of measurement tools, and the fast pace of computer technology advancements. This review discusses the following major points: 1) the American Society of Echocardiography and Mannheim guidelines for cIMT/CP measurements; 2) forces that influence the guidelines; and 3) calculators for risk stratification and assessment under the influence of advanced intelligence methods. The review also presents the knowledge-based learning strategies such as machine and deep learning which may play a future role in CVD/stroke risk assessment. We conclude that both machine learning and non-machine learning strategies will flourish for current and 10-year CVD/Stroke risk prediction as long as they integrate image-based phenotypes with conventional risk factors.
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Affiliation(s)
- Luca Saba
- Department of Radiology, University of Cagliari, Cagliari, Italy
| | - Ankush Jamthikar
- Department of Electronics and Communication Engineering, Visvesvaraya National Institute of Technology, Nagpur, India
| | - Deep Gupta
- Department of Electronics and Communication Engineering, Visvesvaraya National Institute of Technology, Nagpur, India
| | - Narendra N Khanna
- Department of Cardiology, Indraprastha APOLLO Hospitals, New Delhi, India
| | - Klaudija Viskovic
- Department of Radiology and Ultrasound, University Hospital for Infectious Diseases, Zagreb, Croatia
| | | | - Ajay Gupta
- Department of Radiology, Weill Cornell Medicine, New York, NY, USA
| | - Sophie Mavrogeni
- Cardiology Clinic, Onassis Cardiac Surgery Center, Athens, Greece
| | - Monika Turk
- Department of Neurology, University Medical Center Maribor, Maribor, Slovenia
| | - John R Laird
- Heart and Vascular Institute, Adventist Health St. Helena, St Helena, CA, USA
| | - Gyan Pareek
- Minimally Invasive Urology Institute, Brown University, Providence, RI, USA
| | - Martin Miner
- Men's Health Center, Miriam Hospital, Providence, RI, USA
| | - Petros P Sfikakis
- Unit of Rheumatology, National Kapodistrian University of Athens, Athens, Greece
| | - Athanasios Protogerou
- Department of Cardiovascular Prevention and Research, Clinic and Laboratory of Pathophysiology, National and Kapodistrian, University of Athens, Athens, Greece
| | - George D Kitas
- R and D Academic Affairs, Dudley Group NHS Foundation Trust, Dudley, UK
| | - Vijay Viswanathan
- MV Hospital for Diabete, Professor M Viswanathan Diabetes Research Center, Chennai, India
| | - Andrew Nicolaides
- Vascular Screening and Diagnostic Center, University of Nicosia Medical School, Nicosia, Cyprus
| | - Deepak L Bhatt
- Brigham and Women's Hospital Heart, Vascular Center, Harvard Medical School, Boston, MA, USA
| | - Jasjit S Suri
- Stroke Monitoring and Diagnostic Division, AtheroPoint™, Roseville, CA, USA -
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7
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Datong C, Minghui L, Cheng J, Yue S, Dongbin X, Yueming L. Coronary Calcium Detection Based on Improved Deep Residual Network in Mimics. J Med Syst 2019; 43:119. [PMID: 30911850 DOI: 10.1007/s10916-019-1218-4] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2018] [Accepted: 02/20/2019] [Indexed: 12/19/2022]
Abstract
Coronary calcium detection in medicine image processing is a hot research topic. According to the low resolution and complex background in medicine image, an improved coronary calcium detection algorithm based on the Single Shot MultiBox Detector (SSD) in Mimics is proposed in this paper. The algorithm firstly uses the aggregate channel feature model to preprocess the image to obtain the suspected calcium area, which greatly reduces the time of single-frame image detection. The basic network VGG-16 is replaced by Resnet-50, which introduces the identity mapping to solve the problem of reducing the detection accuracy when the number of network layers are increased. Finally, the powerful and flexible two-parameter loss function is used to optimize the training deep network and improve the network model generalization ability. Qualitative and quantitative experiments show that the performance of the proposed detection algorithm exceeds the existing calcium detection algorithms, and the detection efficiency is improved while ensuring the accuracy of calcium detection.
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Affiliation(s)
- Chen Datong
- School of Medical Technology, Qiqihar Medical University, Qiqihar, 161006, Heilongjiang, China.
| | - Liang Minghui
- School of Medical Technology, Qiqihar Medical University, Qiqihar, 161006, Heilongjiang, China
| | - Jin Cheng
- School of Medical Technology, Qiqihar Medical University, Qiqihar, 161006, Heilongjiang, China
| | - Sun Yue
- Third Affiliated Hospital of Qiqihar Medical University, Qiqihar, 161006, Heilongjiang, China
| | - Xu Dongbin
- School of Medical Technology, Qiqihar Medical University, Qiqihar, 161006, Heilongjiang, China
| | - Lin Yueming
- School of Medical Technology, Qiqihar Medical University, Qiqihar, 161006, Heilongjiang, China
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8
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Banchhor SK, Londhe ND, Araki T, Saba L, Radeva P, Khanna NN, Suri JS. Calcium detection, its quantification, and grayscale morphology-based risk stratification using machine learning in multimodality big data coronary and carotid scans: A review. Comput Biol Med 2018; 101:184-198. [DOI: 10.1016/j.compbiomed.2018.08.017] [Citation(s) in RCA: 27] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2018] [Revised: 08/14/2018] [Accepted: 08/14/2018] [Indexed: 01/04/2023]
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9
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Alizadehsani R, Hosseini MJ, Khosravi A, Khozeimeh F, Roshanzamir M, Sarrafzadegan N, Nahavandi S. Non-invasive detection of coronary artery disease in high-risk patients based on the stenosis prediction of separate coronary arteries. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2018; 162:119-127. [PMID: 29903478 DOI: 10.1016/j.cmpb.2018.05.009] [Citation(s) in RCA: 40] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/18/2017] [Revised: 04/24/2018] [Accepted: 05/03/2018] [Indexed: 05/10/2023]
Abstract
BACKGROUND AND OBJECTIVE Cardiovascular diseases are an extremely widespread sickness and account for 17 million deaths in the world per annum. Coronary artery disease (CAD) is one of such diseases with an annual mortality rate of about 7 million. Thus, early diagnosis of CAD is of vital importance. Angiography is currently the modality of choice for the detection of CAD. However, its complications and costs have prompted researchers to seek alternative methods via machine learning algorithms. METHODS The present study proposes a novel machine learning algorithm. The proposed algorithm uses three classifiers for detection of the stenosis of three coronary arteries, i.e., left anterior descending (LAD), left circumflex (LCX) and right coronary artery (RCA) to get higher accuracy for CAD diagnosis. RESULTS This method was applied on the extension of Z-Alizadeh Sani dataset which contains demographic, examination, ECG, and laboratory and echo data of 500 patients. This method achieves an accuracy, sensitivity and specificity rates of 96.40%, 100% and 88.1%, respectively for the detection of CAD. To our knowledge, such high rates of accuracy and sensitivity have not been attained elsewhere before. CONCLUSION This new algorithm reliably distinguishes those with normal coronary arteries from those with CAD which may obviate the need for angiography in the normal group.
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Affiliation(s)
- Roohallah Alizadehsani
- Institute for Intelligent Systems Research and Innovation (IISRI), Deakin University, Victoria 3217, Australia
| | - Mohammad Javad Hosseini
- Department of Computer Science and Engineering, University of Washington, Seattle, United States
| | - Abbas Khosravi
- Institute for Intelligent Systems Research and Innovation (IISRI), Deakin University, Victoria 3217, Australia.
| | - Fahime Khozeimeh
- Faculty of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran
| | - Mohamad Roshanzamir
- Department of Electrical and Computer Engineering, Isfahan University of Technology, Isfahan, Iran
| | - Nizal Sarrafzadegan
- Isfahan Cardiovascular Research Center, Cardiovascular Research Institute, Isfahan University of Medical Sciences,Isfahan,Iran & Faculty of Medicine, SPPH, University of British Columbia, Vancouver,BC, Canada
| | - Saeid Nahavandi
- Institute for Intelligent Systems Research and Innovation (IISRI), Deakin University, Victoria 3217, Australia
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Manterola HL, Lo Vercio L, Díaz A, Del Fresno M, Larrabide I. Validation of an Open-Source Tool for Measuring Carotid Lumen Diameter and Intima-Media Thickness. ULTRASOUND IN MEDICINE & BIOLOGY 2018; 44:1873-1881. [PMID: 29773245 DOI: 10.1016/j.ultrasmedbio.2018.04.001] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/10/2017] [Revised: 02/20/2018] [Accepted: 04/02/2018] [Indexed: 06/08/2023]
Abstract
In low- and middle-income regions, a relatively large number of deaths occur from cardiovascular disease or stroke. Carotid intima-media thickness (cIMT) and carotid lumen diameter (cLD) are strong indicators of cardiovascular event risk and stenosis severity, respectively. The interactive open-source software described here, Cimtool, is based on active contours for measuring these indicators in clinical practice and thus helping in preventive diagnosis and treatment. Cimtool was validated using carotid phantoms and real images obtained using ultrasound. Expert users measured cIMT and cLD in regular practice and also with Cimtool. The results obtained with Cimtool were then compared with the results for the manual approach in terms of measurement agreement, time spent on the measurements and usability. Intra-observer variability when using Cimtool was also analyzed. Statistical analysis revealed strong agreement between the manual method and Cimtool (p > 0.01 for cIMT and cLD). The correlation coefficient for both cIMT and cLD measurements was r > 0.9. Moreover, this software allowed the users to spend considerably less time on each measurement (3.5 min per study versus 50 s with Cimtool on average). An open-source, interactive, validated tool for measuring cIMT and cLD clinically was thus developed. Compared with the manual approach, Cimtool's straightforward measurement flow allows the user to spend less time per measurement and has less standard deviation. The coefficients of variation for measurements and intra-observer variability were lower than those reported for recent automated approaches, even with low-quality images.
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Affiliation(s)
- Hugo Luis Manterola
- Pladema Institute, Universidad Nacional del Centro de la Provincia de Buenos Aires, Tandil, Argentina; Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Buenos Aires, Argentina.
| | - Lucas Lo Vercio
- Pladema Institute, Universidad Nacional del Centro de la Provincia de Buenos Aires, Tandil, Argentina; Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Buenos Aires, Argentina
| | - Alejandro Díaz
- Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Buenos Aires, Argentina
| | - Mariana Del Fresno
- Pladema Institute, Universidad Nacional del Centro de la Provincia de Buenos Aires, Tandil, Argentina; Comisión de Investigaciones Científicas de la Provincia de Buenos Aires (CICPBA), Buenos Aires, Argentina
| | - Ignacio Larrabide
- Pladema Institute, Universidad Nacional del Centro de la Provincia de Buenos Aires, Tandil, Argentina; Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Buenos Aires, Argentina
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11
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Deep learning strategy for accurate carotid intima-media thickness measurement: An ultrasound study on Japanese diabetic cohort. Comput Biol Med 2018; 98:100-117. [DOI: 10.1016/j.compbiomed.2018.05.014] [Citation(s) in RCA: 51] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2018] [Revised: 05/10/2018] [Accepted: 05/10/2018] [Indexed: 01/06/2023]
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12
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Banchhor SK, Londhe ND, Araki T, Saba L, Radeva P, Laird JR, Suri JS. Wall-based measurement features provides an improved IVUS coronary artery risk assessment when fused with plaque texture-based features during machine learning paradigm. Comput Biol Med 2017; 91:198-212. [PMID: 29100114 DOI: 10.1016/j.compbiomed.2017.10.019] [Citation(s) in RCA: 31] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/04/2017] [Revised: 10/19/2017] [Accepted: 10/19/2017] [Indexed: 12/31/2022]
Abstract
BACKGROUND Planning of percutaneous interventional procedures involves a pre-screening and risk stratification of the coronary artery disease. Current screening tools use stand-alone plaque texture-based features and therefore lack the ability to stratify the risk. METHOD This IRB approved study presents a novel strategy for coronary artery disease risk stratification using an amalgamation of IVUS plaque texture-based and wall-based measurement features. Due to common genetic plaque makeup, carotid plaque burden was chosen as a gold standard for risk labels during training-phase of machine learning (ML) paradigm. Cross-validation protocol was adopted to compute the accuracy of the ML framework. A set of 59 plaque texture-based features was padded with six wall-based measurement features to show the improvement in stratification accuracy. The ML system was executed using principle component analysis-based framework for dimensionality reduction and uses support vector machine classifier for training and testing-phases. RESULTS The ML system produced a stratification accuracy of 91.28%, demonstrating an improvement of 5.69% when wall-based measurement features were combined with plaque texture-based features. The fused system showed an improvement in mean sensitivity, specificity, positive predictive value, and area under the curve by: 6.39%, 4.59%, 3.31% and 5.48%, respectively when compared to the stand-alone system. While meeting the stability criteria of 5%, the ML system also showed a high average feature retaining power and mean reliability of 89.32% and 98.24%, respectively. CONCLUSIONS The ML system showed an improvement in risk stratification accuracy when the wall-based measurement features were fused with the plaque texture-based features.
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Affiliation(s)
| | | | - Tadashi Araki
- Division of Cardiovascular Medicine, Toho University Ohashi Medical Center, Tokyo, Japan
| | - Luca Saba
- Department of Radiology, University of Cagliari, Italy
| | - Petia Radeva
- Department of Mathematics and Computer Science, University of Barcelona, Barcelona, Spain
| | | | - Jasjit S Suri
- Monitoring and Diagnostic Division, AtheroPoint™, Roseville, CA, USA.
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Banchhor SK, Londhe ND, Saba L, Radeva P, Laird JR, Suri JS. Relationship between Automated Coronary Calcium Volumes and a Set of Manual Coronary Lumen Volume, Vessel Volume and Atheroma Volume in Japanese Diabetic Cohort. J Clin Diagn Res 2017; 11:TC09-TC14. [PMID: 28764262 DOI: 10.7860/jcdr/2017/26336.10030] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/27/2016] [Accepted: 03/22/2017] [Indexed: 11/24/2022]
Abstract
INTRODUCTION A high degree of correlation exists between Coronary Artery Diseases (CAD) and calcification of the vessel wall. For Percutaneous Coronary Interventional (PCI) planning, it is essential to have an exact understanding of the extent to which calcium volume is correlated to the lumen, vessel, and atheroma volume regions in the coronary artery, which is unclear in recent studies. AIM Four automated Coronary Calcium Volume (aCCV) measurement methods {threshold, Fuzzy c-Means (FCM), K-means, and Hidden Markov Random Field (HMRF)} and its correlation with three manual (experts) coronary parameters namely: Coronary Vessel Volume (mCVV), Coronary Lumen Volume (mCLV), and Coronary Atheroma Volume (mCAV), was determined in a Japanese diabetic cohort. MATERIALS AND METHODS Intravascular Ultrasound (IVUS) image dataset from 19 patients (around 40,090 frames) was collected using 40 MHz IVUS catheter (Atlantis® SR Pro, Boston Scientific®, pullback speed of 0.5 mm/sec). The methodology consisted of automatically computing the calcium volume in the entire IVUS coronary videos using FCM, K-means, and HMRF based pixel classification and comparing it against the previously published threshold-based method. The Coefficient of Correlation (CC) was then established between the four aCCV and three manually (experts) coronary parameters: mCVV, mCLV, and mCAV computed using iMAP software Boston Scientific®. Statistical tests (Two-tailed paired Student t-test, Wilcoxon signed rank test, Mann-Whitney test, Chi-square test, and Kolmogorov-Smirnov KS-test) were performed to demonstrate consistency, reliability, and accuracy of the proposed work. RESULTS Correlation coefficient of: (a) automated threshold-based volume; (b) automated FCM based volume; (c) automated K-means based volume; and (d) automated HMRF based volume and corresponding three manually (expert's) coronary parameters (mCLV, mCVV, mCAV) were: (0.51, 0.40, 0.48), (0.52, 0.38, 0.49), (0.56, 0.45, 0.52), and (0.57, 0.42, 0.56), respectively. The CC between age and haemoglobin was 0.50. CONCLUSION Automated coronary volume measurement using HMRF method is more accurate compared to threshold, FCM, and K-means-based method, since it is more strongly correlated with three expert's readings.
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Affiliation(s)
- Sumit K Banchhor
- Research Scholar, Department of Electrical Engineering, National Institute of Technology, Raipur, Chhattisgarh, India
| | - Narendra D Londhe
- Assistant Professor, Department of Electrical Engineering, National Institute of Technology, Raipur, Chhattisgarh, India
| | - Luca Saba
- Neurologist, Department of Radiology, Azienda Ospedaliero Universitaria (A.O.U.), di Cagliari - Polo di Monserrato, Cagliari, Italy
| | - Petia Radeva
- Associate Professor, Department of Applied Mathematics, University of Barcelona, Barcelona 08007, Spain
| | - John R Laird
- Cardiologist, UC Davis Vascular Centre, University of California, Davis, CA, USA
| | - Jasjit S Suri
- Professor, Fellow AIMBE, Monitoring and Diagnostic Division, AtheroPoint™, Roseville, CA, USA. Point-of-Care Devices, Global Biomedical Technologies, Inc., Roseville, CA, USA. Department of Electrical Engineering, University of Idaho (Aff.), ID, USA
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Chakraborty S, Chatterjee S, Dey N, Ashour AS, Ashour AS, Shi F, Mali K. Modified cuckoo search algorithm in microscopic image segmentation of hippocampus. Microsc Res Tech 2017; 80:1051-1072. [PMID: 28557041 DOI: 10.1002/jemt.22900] [Citation(s) in RCA: 75] [Impact Index Per Article: 10.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2017] [Revised: 05/05/2017] [Accepted: 05/09/2017] [Indexed: 11/06/2022]
Abstract
Microscopic image analysis is one of the challenging tasks due to the presence of weak correlation and different segments of interest that may lead to ambiguity. It is also valuable in foremost meadows of technology and medicine. Identification and counting of cells play a vital role in features extraction to diagnose particular diseases precisely. Different segments should be identified accurately in order to identify and to count cells in a microscope image. Consequently, in the current work, a novel method for cell segmentation and identification has been proposed that incorporated marking cells. Thus, a novel method based on cuckoo search after pre-processing step is employed. The method is developed and evaluated on light microscope images of rats' hippocampus which used as a sample for the brain cells. The proposed method can be applied on the color images directly. The proposed approach incorporates the McCulloch's method for lévy flight production in cuckoo search (CS) algorithm. Several objective functions, namely Otsu's method, Kapur entropy and Tsallis entropy are used for segmentation. In the cuckoo search process, the Otsu's between class variance, Kapur's entropy and Tsallis entropy are employed as the objective functions to be optimized. Experimental results are validated by different metrics, namely the peak signal to noise ratio (PSNR), mean square error, feature similarity index and CPU running time for all the test cases. The experimental results established that the Kapur's entropy segmentation method based on the modified CS required the least computational time compared to Otsu's between-class variance segmentation method and the Tsallis entropy segmentation method. Nevertheless, Tsallis entropy method with optimized multi-threshold levels achieved superior performance compared to the other two segmentation methods in terms of the PSNR.
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Affiliation(s)
- Shouvik Chakraborty
- Department of Computer Science and Engineering, University of Kalyani, Kolkata, India
| | - Sankhadeep Chatterjee
- Department of Computer Science and Engineering, University of Calcutta, Kolkata, India
| | - Nilanjan Dey
- Department of Information Technology, Techno India College of Technology, Kolkata, India
| | - Amira S Ashour
- Department of Electronics and Electrical Communication Engineering, Faculty of Engineering, Tanta University, Tanta, Egypt
| | - Ahmed S Ashour
- Department of Biomedical Dental Sciences, Faculty of Dentistry, University of Dammam, Dammam, Saudi Arabia
| | - Fuqian Shi
- College of Information and Engineering, Wenzhou Medical University, Wenzhou, People's Republic of China
| | - Kalyani Mali
- Department of Computer Science and Engineering, University of Kalyani, Kolkata, India
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Banchhor SK, Londhe ND, Araki T, Saba L, Radeva P, Laird JR, Suri JS. Well-balanced system for coronary calcium detection and volume measurement in a low resolution intravascular ultrasound videos. Comput Biol Med 2017; 84:168-181. [DOI: 10.1016/j.compbiomed.2017.03.026] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2017] [Revised: 03/12/2017] [Accepted: 03/27/2017] [Indexed: 01/22/2023]
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16
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Chavan SS, Talbar SN. Multimodality Medical Image Fusion Using M-Band Wavelet and Daubechies Complex Wavelet Transform for Radiation Therapy. Oncology 2017. [DOI: 10.4018/978-1-5225-0549-5.ch018] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
The process of enriching the important details from various modality medical images by combining them into single image is called multimodality medical image fusion. It aids physicians in terms of better visualization, more accurate diagnosis and appropriate treatment plan for the cancer patient. The combined fused image is the result of merging of anatomical and physiological variations. It allows accurate localization of cancer tissues and more helpful for estimation of target volume for radiation. The details from both modalities (CT and MRI) are extracted in frequency domain by applying various transforms and combined them using variety of fusion rules to achieve the best quality of images. The performance and effectiveness of each transform on fusion results is evaluated subjectively as well as objectively. The fused images by algorithms in which feature extraction is achieved by M-Band Wavelet Transform and Daubechies Complex Wavelet Transform are superior over other frequency domain algorithms as per subjective and objective analysis.
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Araki T, Jain PK, Suri HS, Londhe ND, Ikeda N, El-Baz A, Shrivastava VK, Saba L, Nicolaides A, Shafique S, Laird JR, Gupta A, Suri JS. Stroke Risk Stratification and its Validation using Ultrasonic Echolucent Carotid Wall Plaque Morphology: A Machine Learning Paradigm. Comput Biol Med 2016; 80:77-96. [PMID: 27915126 DOI: 10.1016/j.compbiomed.2016.11.011] [Citation(s) in RCA: 47] [Impact Index Per Article: 5.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/09/2016] [Revised: 11/20/2016] [Accepted: 11/25/2016] [Indexed: 01/26/2023]
Abstract
Stroke risk stratification based on grayscale morphology of the ultrasound carotid wall has recently been shown to have a promise in classification of high risk versus low risk plaque or symptomatic versus asymptomatic plaques. In previous studies, this stratification has been mainly based on analysis of the far wall of the carotid artery. Due to the multifocal nature of atherosclerotic disease, the plaque growth is not restricted to the far wall alone. This paper presents a new approach for stroke risk assessment by integrating assessment of both the near and far walls of the carotid artery using grayscale morphology of the plaque. Further, this paper presents a scientific validation system for stroke risk assessment. Both these innovations have never been presented before. The methodology consists of an automated segmentation system of the near wall and far wall regions in grayscale carotid B-mode ultrasound scans. Sixteen grayscale texture features are computed, and fed into the machine learning system. The training system utilizes the lumen diameter to create ground truth labels for the stratification of stroke risk. The cross-validation procedure is adapted in order to obtain the machine learning testing classification accuracy through the use of three sets of partition protocols: (5, 10, and Jack Knife). The mean classification accuracy over all the sets of partition protocols for the automated system in the far and near walls is 95.08% and 93.47%, respectively. The corresponding accuracies for the manual system are 94.06% and 92.02%, respectively. The precision of merit of the automated machine learning system when compared against manual risk assessment system are 98.05% and 97.53% for the far and near walls, respectively. The ROC of the risk assessment system for the far and near walls is close to 1.0 demonstrating high accuracy.
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Affiliation(s)
- Tadashi Araki
- Division of Cardiovascular Medicine, Toho University Ohashi Medical Center, Tokyo, Japan
| | - Pankaj K Jain
- Point-of-Care Devices, Global Biomedical Technologies, Inc., Roseville, CA, USA
| | - Harman S Suri
- Monitoring and Diagnostic Division, AtheroPoint™, Roseville, CA, USA
| | - Narendra D Londhe
- Department of Electrical Engineering, NIT Raipur, Chhattisgarh, India
| | - Nobutaka Ikeda
- Cardiovascular Medicine, National Center for Global Health and Medicine, Tokyo, Japan
| | - Ayman El-Baz
- Department of Bioengineering, University of Louisville, USA
| | | | - Luca Saba
- Department of Radiology, University of Cagliari, Italy
| | - Andrew Nicolaides
- Vascular Screening and Diagnostic Centre, London, England; Vascular Diagnostic Centre, University of Cyprus, Nicosia, Cyprus
| | - Shoaib Shafique
- CorVasc Vascular Laboratory, 8433 Harcourt Rd #100, Indianapolis, IN, USA
| | - John R Laird
- UC Davis Vascular Centre, University of California, Davis, CA, USA
| | - Ajay Gupta
- Brain and Mind Research Institute and Department of Radiology, Weill Cornell Medical College, NY, USA
| | - Jasjit S Suri
- Point-of-Care Devices, Global Biomedical Technologies, Inc., Roseville, CA, USA; Monitoring and Diagnostic Division, AtheroPoint™, Roseville, CA, USA; Department of Electrical Engineering, University of Idaho (Affl.), ID, USA.
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Satapathy SC, Sri Madhava Raja N, Rajinikanth V, Ashour AS, Dey N. Multi-level image thresholding using Otsu and chaotic bat algorithm. Neural Comput Appl 2016. [DOI: 10.1007/s00521-016-2645-5] [Citation(s) in RCA: 69] [Impact Index Per Article: 8.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
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19
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Banchhor SK, Araki T, Londhe ND, Ikeda N, Radeva P, Elbaz A, Saba L, Nicolaides A, Shafique S, Laird JR, Suri JS. Five multiresolution-based calcium volume measurement techniques from coronary IVUS videos: A comparative approach. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2016; 134:237-258. [PMID: 27480747 DOI: 10.1016/j.cmpb.2016.07.009] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/03/2016] [Revised: 06/13/2016] [Accepted: 07/01/2016] [Indexed: 06/06/2023]
Abstract
BACKGROUND AND OBJECTIVE Fast intravascular ultrasound (IVUS) video processing is required for calcium volume computation during the planning phase of percutaneous coronary interventional (PCI) procedures. Nonlinear multiresolution techniques are generally applied to improve the processing time by down-sampling the video frames. METHODS This paper presents four different segmentation methods for calcium volume measurement, namely Threshold-based, Fuzzy c-Means (FCM), K-means, and Hidden Markov Random Field (HMRF) embedded with five different kinds of multiresolution techniques (bilinear, bicubic, wavelet, Lanczos, and Gaussian pyramid). This leads to 20 different kinds of combinations. IVUS image data sets consisting of 38,760 IVUS frames taken from 19 patients were collected using 40 MHz IVUS catheter (Atlantis® SR Pro, Boston Scientific®, pullback speed of 0.5 mm/sec.). The performance of these 20 systems is compared with and without multiresolution using the following metrics: (a) computational time; (b) calcium volume; (c) image quality degradation ratio; and (d) quality assessment ratio. RESULTS Among the four segmentation methods embedded with five kinds of multiresolution techniques, FCM segmentation combined with wavelet-based multiresolution gave the best performance. FCM and wavelet experienced the highest percentage mean improvement in computational time of 77.15% and 74.07%, respectively. Wavelet interpolation experiences the highest mean precision-of-merit (PoM) of 94.06 ± 3.64% and 81.34 ± 16.29% as compared to other multiresolution techniques for volume level and frame level respectively. Wavelet multiresolution technique also experiences the highest Jaccard Index and Dice Similarity of 0.7 and 0.8, respectively. Multiresolution is a nonlinear operation which introduces bias and thus degrades the image. The proposed system also provides a bias correction approach to enrich the system, giving a better mean calcium volume similarity for all the multiresolution-based segmentation methods. After including the bias correction, bicubic interpolation gives the largest increase in mean calcium volume similarity of 4.13% compared to the rest of the multiresolution techniques. The system is automated and can be adapted in clinical settings. CONCLUSIONS We demonstrated the time improvement in calcium volume computation without compromising the quality of IVUS image. Among the 20 different combinations of multiresolution with calcium volume segmentation methods, the FCM embedded with wavelet-based multiresolution gave the best performance.
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Affiliation(s)
- Sumit K Banchhor
- Department of Electrical Engineering, NIT Raipur, Chhattisgarh, India
| | - Tadashi Araki
- Division of Cardiovascular Medicine, Toho University Ohashi Medical Center, Tokyo, Japan
| | - Narendra D Londhe
- Department of Electrical Engineering, NIT Raipur, Chhattisgarh, India
| | - Nobutaka Ikeda
- Cardiovascular Medicine, National Center for Global Health and Medicine, Tokyo, Japan
| | - Petia Radeva
- Dept. MAIA, Computer Vision Centre, Cerdanyola del Vallés, University of Barcelona, Spain
| | - Ayman Elbaz
- Department of Bioengineering, University of Louisville, USA
| | - Luca Saba
- Department of Radiology, University of Cagliari, Italy
| | - Andrew Nicolaides
- Vascular Screening and Diagnostic Centre, London, UK; Vascular Diagnostic Centre, University of Cyprus, Nicosia, Cyprus
| | - Shoaib Shafique
- CorVasc Vascular Laboratory, 8433 Harcourt Rd #100, Indianapolis, IN, USA
| | - John R Laird
- UC Davis Vascular Centre, University of California, Davis, CA, USA
| | - Jasjit S Suri
- Monitoring and Diagnostic Division, AtheroPoint™, Roseville, CA, USA; Point-of-Care Devices, Global Biomedical Technologies, Inc., Roseville, CA, USA; Department of Electrical Engineering, University of Idaho (Affl.), ID, USA.
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Saba L, Banchhor SK, Suri HS, Londhe ND, Araki T, Ikeda N, Viskovic K, Shafique S, Laird JR, Gupta A, Nicolaides A, Suri JS. Accurate cloud-based smart IMT measurement, its validation and stroke risk stratification in carotid ultrasound: A web-based point-of-care tool for multicenter clinical trial. Comput Biol Med 2016; 75:217-34. [PMID: 27318571 DOI: 10.1016/j.compbiomed.2016.06.010] [Citation(s) in RCA: 34] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/16/2016] [Revised: 06/05/2016] [Accepted: 06/07/2016] [Indexed: 11/29/2022]
Abstract
This study presents AtheroCloud™ - a novel cloud-based smart carotid intima-media thickness (cIMT) measurement tool using B-mode ultrasound for stroke/cardiovascular risk assessment and its stratification. This is an anytime-anywhere clinical tool for routine screening and multi-center clinical trials. In this pilot study, the physician can upload ultrasound scans in one of the following formats (DICOM, JPEG, BMP, PNG, GIF or TIFF) directly into the proprietary cloud of AtheroPoint from the local server of the physician's office. They can then run the intelligent and automated AtheroCloud™ cIMT measurements in point-of-care settings in less than five seconds per image, while saving the vascular reports in the cloud. We statistically benchmark AtheroCloud™ cIMT readings against sonographer (a registered vascular technologist) readings and manual measurements derived from the tracings of the radiologist. One hundred patients (75 M/25 F, mean age: 68±11 years), IRB approved, Toho University, Japan, consisted of Left/Right common carotid artery (CCA) artery (200 ultrasound scans), (Toshiba, Tokyo, Japan) were collected using a 7.5MHz transducer. The measured cIMTs for L/R carotid were as follows (in mm): (i) AtheroCloud™ (0.87±0.20, 0.77±0.20); (ii) sonographer (0.97±0.26, 0.89±0.29) and (iii) manual (0.90±0.20, 0.79±0.20), respectively. The coefficient of correlation (CC) between sonographer and manual for L/R cIMT was 0.74 (P<0.0001) and 0.65 (P<0.0001), while, between AtheroCloud™ and manual was 0.96 (P<0.0001) and 0.97 (P<0.0001), respectively. We observed that 91.15% of the population in AtheroCloud™ had a mean cIMT error less than 0.11mm compared to sonographer's 68.31%. The area under curve for receiving operating characteristics was 0.99 for AtheroCloud™ against 0.81 for sonographer. Our Framingham Risk Score stratified the population into three bins as follows: 39% in low-risk, 70.66% in medium-risk and 10.66% in high-risk bins. Statistical tests were performed to demonstrate consistency, reliability and accuracy of the results. The proposed AtheroCloud™ system is completely reliable, automated, fast (3-5 seconds depending upon the image size having an internet speed of 180Mbps), accurate, and an intelligent, web-based clinical tool for multi-center clinical trials and routine telemedicine clinical care.
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Affiliation(s)
- Luca Saba
- Department of Radiology, University of Cagliari, Italy
| | - Sumit K Banchhor
- Department of Electrical Engineering, NIT Raipur, Chhattisgarh, India
| | - Harman S Suri
- Monitoring and Diagnostic Division, AtheroPoint™, Roseville, CA, USA
| | - Narendra D Londhe
- Department of Electrical Engineering, NIT Raipur, Chhattisgarh, India
| | - Tadashi Araki
- Division of Cardiovascular Medicine, Toho University Ohashi Medical Center, Tokyo, Japan
| | - Nobutaka Ikeda
- Cardiovascular Medicine, National Center for Global Health and Medicine, Tokyo, Japan
| | | | - Shoaib Shafique
- CorVasc Vascular Laboratory, 8433 Harcourt Rd #100, Indianapolis, IN, USA
| | - John R Laird
- UC Davis Vascular Centre, University of California, Davis, CA, USA
| | - Ajay Gupta
- Brain and Mind Research Institute, Weill Cornell Medical College, NY, USA
| | - Andrew Nicolaides
- Vascular Screening and Diagnostic Centre, London, England; Vascular Diagnostic Centre, University of Cyprus, Nicosia, Cyprus
| | - Jasjit S Suri
- Monitoring and Diagnostic Division, AtheroPoint™, Roseville, CA, USA; Point-of-Care Devices, Global Biomedical Technologies, Inc., Roseville, CA, USA; Department of Electrical Engineering, University of Idaho (Affl.), ID, USA.
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Araki T, Ikeda N, Shukla D, Jain PK, Londhe ND, Shrivastava VK, Banchhor SK, Saba L, Nicolaides A, Shafique S, Laird JR, Suri JS. PCA-based polling strategy in machine learning framework for coronary artery disease risk assessment in intravascular ultrasound: A link between carotid and coronary grayscale plaque morphology. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2016; 128:137-158. [PMID: 27040838 DOI: 10.1016/j.cmpb.2016.02.004] [Citation(s) in RCA: 46] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/20/2015] [Accepted: 02/16/2016] [Indexed: 06/05/2023]
Abstract
BACKGROUND AND OBJECTIVE Percutaneous coronary interventional procedures need advance planning prior to stenting or an endarterectomy. Cardiologists use intravascular ultrasound (IVUS) for screening, risk assessment and stratification of coronary artery disease (CAD). We hypothesize that plaque components are vulnerable to rupture due to plaque progression. Currently, there are no standard grayscale IVUS tools for risk assessment of plaque rupture. This paper presents a novel strategy for risk stratification based on plaque morphology embedded with principal component analysis (PCA) for plaque feature dimensionality reduction and dominant feature selection technique. The risk assessment utilizes 56 grayscale coronary features in a machine learning framework while linking information from carotid and coronary plaque burdens due to their common genetic makeup. METHOD This system consists of a machine learning paradigm which uses a support vector machine (SVM) combined with PCA for optimal and dominant coronary artery morphological feature extraction. Carotid artery proven intima-media thickness (cIMT) biomarker is adapted as a gold standard during the training phase of the machine learning system. For the performance evaluation, K-fold cross validation protocol is adapted with 20 trials per fold. For choosing the dominant features out of the 56 grayscale features, a polling strategy of PCA is adapted where the original value of the features is unaltered. Different protocols are designed for establishing the stability and reliability criteria of the coronary risk assessment system (cRAS). RESULTS Using the PCA-based machine learning paradigm and cross-validation protocol, a classification accuracy of 98.43% (AUC 0.98) with K=10 folds using an SVM radial basis function (RBF) kernel was achieved. A reliability index of 97.32% and machine learning stability criteria of 5% were met for the cRAS. CONCLUSIONS This is the first Computer aided design (CADx) system of its kind that is able to demonstrate the ability of coronary risk assessment and stratification while demonstrating a successful design of the machine learning system based on our assumptions.
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Affiliation(s)
- Tadashi Araki
- Division of Cardiovascular Medicine, Toho University Ohashi Medical Center, Tokyo, Japan
| | - Nobutaka Ikeda
- Cardiovascular Medicine, National Center for Global Health and Medicine, Tokyo, Japan
| | - Devarshi Shukla
- Department of Electrical Engineering, NIT Raipur, Chhattisgarh, India
| | - Pankaj K Jain
- Department of Electrical Engineering, NIT Raipur, Chhattisgarh, India
| | - Narendra D Londhe
- Department of Electrical Engineering, NIT Raipur, Chhattisgarh, India
| | | | - Sumit K Banchhor
- Department of Electrical Engineering, NIT Raipur, Chhattisgarh, India
| | - Luca Saba
- Department of Radiology, University of Cagliari, Italy
| | - Andrew Nicolaides
- Vascular Screening and Diagnostic Centre, London, England, United Kingdom; Vascular Diagnostic Center, University of Cyprus, Nicosia, Cyprus
| | - Shoaib Shafique
- CorVasc Vascular Laboratory, 8433 Harcourt Rd #100, Indianapolis, IN, USA
| | - John R Laird
- UC Davis Vascular Center, University of California, Davis, CA, USA
| | - Jasjit S Suri
- Monitoring and Diagnostic Division, AtheroPoint™, Roseville, CA, USA; Point-Of-Care Devices, Global Biomedical Technologies, Inc., Roseville, CA, USA; Department of Electrical Engineering, University of Idaho (Affl.), Pocatello, ID, USA.
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Araki T, Ikeda N, Shukla D, Londhe ND, Shrivastava VK, Banchhor SK, Saba L, Nicolaides A, Shafique S, Laird JR, Suri JS. A new method for IVUS-based coronary artery disease risk stratification: A link between coronary & carotid ultrasound plaque burdens. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2016; 124:161-179. [PMID: 26707374 DOI: 10.1016/j.cmpb.2015.10.022] [Citation(s) in RCA: 33] [Impact Index Per Article: 4.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/22/2015] [Revised: 10/12/2015] [Accepted: 10/21/2015] [Indexed: 06/05/2023]
Abstract
Interventional cardiologists have a deep interest in risk stratification prior to stenting and percutaneous coronary intervention (PCI) procedures. Intravascular ultrasound (IVUS) is most commonly adapted for screening, but current tools lack the ability for risk stratification based on grayscale plaque morphology. Our hypothesis is based on the genetic makeup of the atherosclerosis disease, that there is evidence of a link between coronary atherosclerosis disease and carotid plaque built up. This novel idea is explored in this study for coronary risk assessment and its classification of patients between high risk and low risk. This paper presents a strategy for coronary risk assessment by combining the IVUS grayscale plaque morphology and carotid B-mode ultrasound carotid intima-media thickness (cIMT) - a marker of subclinical atherosclerosis. Support vector machine (SVM) learning paradigm is adapted for risk stratification, where both the learning and testing phases use tissue characteristics derived from six feature combinational spaces, which are then used by the SVM classifier with five different kernels sets. These six feature combinational spaces are designed using 56 novel feature sets. K-fold cross validation protocol with 10 trials per fold is used for optimization of best SVM-kernel and best feature combination set. IRB approved coronary IVUS and carotid B-mode ultrasound were jointly collected on 15 patients (2 days apart) via: (a) 40MHz catheter utilizing iMap (Boston Scientific, Marlborough, MA, USA) with 2865 frames per patient (42,975 frames) and (b) linear probe B-mode carotid ultrasound (Toshiba scanner, Japan). Using the above protocol, the system shows the classification accuracy of 94.95% and AUC of 0.95 using optimized feature combination. This is the first system of its kind for risk stratification as a screening tool to prevent excessive cost burden and better patients' cardiovascular disease management, while validating our two hypotheses.
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Affiliation(s)
- Tadashi Araki
- Division of Cardiovascular Medicine, Toho University Ohashi Medical Center, Tokyo, Japan
| | - Nobutaka Ikeda
- Cardiovascular Medicine, National Center for Global Health and Medicine, Tokyo, Japan
| | - Devarshi Shukla
- Department of Electrical Engineering, NIT Raipur, Chhattisgarh, India; Department of Electrical Engineering, University of Idaho (Affl.), ID, USA
| | - Narendra D Londhe
- Department of Electrical Engineering, NIT Raipur, Chhattisgarh, India; Department of Electrical Engineering, University of Idaho (Affl.), ID, USA
| | | | - Sumit K Banchhor
- Department of Electrical Engineering, NIT Raipur, Chhattisgarh, India; Department of Electrical Engineering, University of Idaho (Affl.), ID, USA
| | - Luca Saba
- Department of Radiology, University of Cagliari, Italy
| | - Andrew Nicolaides
- Vascular Screening and Diagnostic Centre, London, England, United Kingdom; Vascular Diagnostic Center, University of Cyprus, Nicosia, Cyprus
| | | | - John R Laird
- UC Davis Vascular Center, University of California, Davis, CA, USA
| | - Jasjit S Suri
- Monitoring and Diagnostic Division, AtheroPoint™, Roseville, CA, USA; Department of Electrical Engineering, University of Idaho (Affl.), ID, USA.
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Karaa WBA, Ashour AS, Sassi DB, Roy P, Kausar N, Dey N. MEDLINE Text Mining: An Enhancement Genetic Algorithm Based Approach for Document Clustering. INTELLIGENT SYSTEMS REFERENCE LIBRARY 2016. [DOI: 10.1007/978-3-319-21212-8_12] [Citation(s) in RCA: 41] [Impact Index Per Article: 5.1] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/11/2023]
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Araki T, Banchhor SK, Londhe ND, Ikeda N, Radeva P, Shukla D, Saba L, Balestrieri A, Nicolaides A, Shafique S, Laird JR, Suri JS. Reliable and Accurate Calcium Volume Measurement in Coronary Artery Using Intravascular Ultrasound Videos. J Med Syst 2015; 40:51. [PMID: 26643081 DOI: 10.1007/s10916-015-0407-z] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/26/2015] [Accepted: 11/16/2015] [Indexed: 11/29/2022]
Abstract
Quantitative assessment of calcified atherosclerotic volume within the coronary artery wall is vital for cardiac interventional procedures. The goal of this study is to automatically measure the calcium volume, given the borders of coronary vessel wall for all the frames of the intravascular ultrasound (IVUS) video. Three soft computing fuzzy classification techniques were adapted namely Fuzzy c-Means (FCM), K-means, and Hidden Markov Random Field (HMRF) for automated segmentation of calcium regions and volume computation. These methods were benchmarked against previously developed threshold-based method. IVUS image data sets (around 30,600 IVUS frames) from 15 patients were collected using 40 MHz IVUS catheter (Atlantis® SR Pro, Boston Scientific®, pullback speed of 0.5 mm/s). Calcium mean volume for FCM, K-means, HMRF and threshold-based method were 37.84 ± 17.38 mm(3), 27.79 ± 10.94 mm(3), 46.44 ± 19.13 mm(3) and 35.92 ± 16.44 mm(3) respectively. Cross-correlation, Jaccard Index and Dice Similarity were highest between FCM and threshold-based method: 0.99, 0.92 ± 0.02 and 0.95 + 0.02 respectively. Student's t-test, z-test and Wilcoxon-test are also performed to demonstrate consistency, reliability and accuracy of the results. Given the vessel wall region, the system reliably and automatically measures the calcium volume in IVUS videos. Further, we validated our system against a trained expert using scoring: K-means showed the best performance with an accuracy of 92.80%. Out procedure and protocol is along the line with method previously published clinically.
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Affiliation(s)
- Tadashi Araki
- Division of Cardiovascular Medicine, Toho University Ohashi Medical Center, Tokyo, Japan
| | - Sumit K Banchhor
- Department of Electrical Engineering, NIT Raipur, Chhattisgarh, India.,Monitoring and Diagnostic Division, AtheroPoint™, Roseville, CA, USA
| | - Narendra D Londhe
- Department of Electrical Engineering, NIT Raipur, Chhattisgarh, India.,Monitoring and Diagnostic Division, AtheroPoint™, Roseville, CA, USA
| | - Nobutaka Ikeda
- Cardiovascular Medicine, National Center for Global Health and Medicine, Tokyo, Japan
| | - Petia Radeva
- Department MAIA, Computer Vision Centre, Cerdanyola del Vallés, University of Barcelona, Barcelona, Spain
| | - Devarshi Shukla
- Department of Electrical Engineering, NIT Raipur, Chhattisgarh, India.,Monitoring and Diagnostic Division, AtheroPoint™, Roseville, CA, USA
| | - Luca Saba
- Department of Radiology, University of Cagliari, Cagliari, Italy
| | | | - Andrew Nicolaides
- Vascular Screening and Diagnostic Centre, London, UK.,Vascular Diagnostic Centre, University of Cyprus, Nicosia, Cyprus
| | - Shoaib Shafique
- CorVasc Vascular Laboratory, 8433 Harcourt Rd #100, Indianapolis, IN, USA
| | - John R Laird
- UC Davis Vascular Centre, University of California, Davis, CA, USA
| | - Jasjit S Suri
- Monitoring and Diagnostic Division, AtheroPoint™, Roseville, CA, USA. .,Point-of-Care Devices, Global Biomedical Technologies, Inc., Roseville, CA, USA. .,Department of Electrical Engineering, University of Idaho (Affl.), Moscow, ID, USA.
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