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Fandaros M, Kwok C, Wolf Z, Labropoulos N, Yin W. Patient-Specific Numerical Simulations of Coronary Artery Hemodynamics and Biomechanics: A Pathway to Clinical Use. Cardiovasc Eng Technol 2024; 15:503-521. [PMID: 38710896 DOI: 10.1007/s13239-024-00731-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/08/2023] [Accepted: 04/29/2024] [Indexed: 05/08/2024]
Abstract
PURPOSE Numerical models that simulate the behaviors of the coronary arteries have been greatly improved by the addition of fluid-structure interaction (FSI) methods. Although computationally demanding, FSI models account for the movement of the arterial wall and more adequately describe the biomechanical conditions at and within the arterial wall. This offers greater physiological relevance over Computational Fluid Dynamics (CFD) models, which assume the walls do not move or deform. Numerical simulations of patient-specific cases have been greatly bolstered by the use of imaging modalities such as Computed Tomography Angiography (CTA), Magnetic Resonance Imaging (MRI), Optical Coherence Tomography (OCT), and Intravascular Ultrasound (IVUS) to reconstruct accurate 2D and 3D representations of artery geometries. The goal of this study was to conduct a comprehensive review on CFD and FSI models on coronary arteries, and evaluate their translational potential. METHODS This paper reviewed recent work on patient-specific numerical simulations of coronary arteries that describe the biomechanical conditions associated with atherosclerosis using CFD and FSI models. Imaging modality for geometry collection and clinical applications were also discussed. RESULTS Numerical models using CFD and FSI approaches are commonly used to study biomechanics within the vasculature. At high temporal and spatial resolution (compared to most cardiac imaging modalities), these numerical models can generate large amount of biomechanics data. CONCLUSIONS Physiologically relevant FSI models can more accurately describe atherosclerosis pathogenesis, and help to translate biomechanical assessment to clinical evaluation.
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Affiliation(s)
- Marina Fandaros
- Department of Biomedical Engineering, Stony Brook University, Bioengineering Building, Room 109, 11794, Stony Brook, NY, USA
| | - Chloe Kwok
- Department of Biomedical Engineering, Stony Brook University, Bioengineering Building, Room 109, 11794, Stony Brook, NY, USA
| | - Zachary Wolf
- Department of Biomedical Engineering, Stony Brook University, Bioengineering Building, Room 109, 11794, Stony Brook, NY, USA
| | - Nicos Labropoulos
- Department of Surgery, Stony Brook Medicine, 11794, Stony Brook, NY, USA
| | - Wei Yin
- Department of Biomedical Engineering, Stony Brook University, Bioengineering Building, Room 109, 11794, Stony Brook, NY, USA.
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2
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Straughan R, Kadry K, Parikh SA, Edelman ER, Nezami FR. Fully Automated Construction of Three-dimensional Finite Element Simulations from Optical Coherence Tomography. ARXIV 2024:arXiv:2405.13643v1. [PMID: 38827462 PMCID: PMC11142312] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Download PDF] [Subscribe] [Scholar Register] [Indexed: 06/04/2024]
Abstract
Despite recent advances in diagnosis and treatment, atherosclerotic coronary artery diseases remain a leading cause of death worldwide. Various imaging modalities and metrics can detect lesions and predict patients at risk; however, identifying unstable lesions is still difficult. Current techniques cannot fully capture the complex morphology-modulated mechanical responses that affect plaque stability, leading to catastrophic failure and mute the benefit of device and drug interventions. Finite Element (FE) simulations utilizing intravascular imaging OCT (Optical Coherence Tomography) are effective in defining physiological stress distributions. However, creating 3D FE simulations of coronary arteries from OCT images is challenging to fully automate given OCT frame sparsity, limited material contrast, and restricted penetration depth. To address such limitations, we developed an algorithmic approach to automatically produce 3D FE-ready digital twins from labeled OCT images. The 3D models are anatomically faithful and recapitulate mechanically relevant tissue lesion components, automatically producing morphologies structurally similar to manually constructed models whilst including more minute details. A mesh convergence study highlighted the ability to reach stress and strain convergence with average errors of just 5.9% and 1.6% respectively in comparison to FE models with approximately twice the number of elements in areas of refinement. Such an automated procedure will enable analysis of large clinical cohorts at a previously unattainable scale and opens the possibility for in-silico methods for patient specific diagnoses and treatment planning for coronary artery disease.
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Affiliation(s)
- Ross Straughan
- Cardiac Surgery Division, Brigham and Women’s Hospital, Harvard Medical School, Boston, 02115, MA, USA
- Department of Mechanical and Process Engineering, ETH Zurich, Leonhardstrasse 21, 8092 Zurich, Switzerland
| | - Karim Kadry
- Institute for Medical Engineering and Science, Massachusetts Institute of Technology, 77 Massachusetts Ave, Cambridge, 02139, MA, USA
| | - Sahil A. Parikh
- Cardiovascular Division, Division of Cardiology, Columbia University Irving Medical Center, New York, 10032, NY, USA
| | - Elazer R. Edelman
- Institute for Medical Engineering and Science, Massachusetts Institute of Technology, 77 Massachusetts Ave, Cambridge, 02139, MA, USA
- Cardiovascular Division, Brigham and Women’s Hospital, Harvard Medical School, Boston, 02115, MA, USA
| | - Farhad R. Nezami
- Cardiac Surgery Division, Brigham and Women’s Hospital, Harvard Medical School, Boston, 02115, MA, USA
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3
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Straughan R, Kadry K, Parikh SA, Edelman ER, Nezami FR. Fully automated construction of three-dimensional finite element simulations from Optical Coherence Tomography. Comput Biol Med 2023; 165:107341. [PMID: 37611423 PMCID: PMC10528179 DOI: 10.1016/j.compbiomed.2023.107341] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/01/2023] [Revised: 07/18/2023] [Accepted: 08/07/2023] [Indexed: 08/25/2023]
Abstract
Despite recent advances in diagnosis and treatment, atherosclerotic coronary artery diseases remain a leading cause of death worldwide. Various imaging modalities and metrics can detect lesions and predict patients at risk; however, identifying unstable lesions is still difficult. Current techniques cannot fully capture the complex morphology-modulated mechanical responses that affect plaque stability, leading to catastrophic failure and mute the benefit of device and drug interventions. Finite Element (FE) simulations utilizing intravascular imaging OCT (Optical Coherence Tomography) are effective in defining physiological stress distributions. However, creating 3D FE simulations of coronary arteries from OCT images is challenging to fully automate given OCT frame sparsity, limited material contrast, and restricted penetration depth. To address such limitations, we developed an algorithmic approach to automatically produce 3D FE-ready digital twins from labeled OCT images. The 3D models are anatomically faithful and recapitulate mechanically relevant tissue lesion components, automatically producing morphologies structurally similar to manually constructed models whilst including more minute details. A mesh convergence study highlighted the ability to reach stress and strain convergence with average errors of just 5.9% and 1.6% respectively in comparison to FE models with approximately twice the number of elements in areas of refinement. Such an automated procedure will enable analysis of large clinical cohorts at a previously unattainable scale and opens the possibility for in-silico methods for patient specific diagnoses and treatment planning for coronary artery disease.
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Affiliation(s)
- Ross Straughan
- Cardiac Surgery Division, Brigham and Women's Hospital, Harvard Medical School, Boston, 02115, MA, USA; Department of Mechanical and Process Engineering, ETH Zurich, Leonhardstrasse 21, 8092 Zurich, Switzerland.
| | - Karim Kadry
- Institute for Medical Engineering and Science, Massachusetts Institute of Technology, 77 Massachusetts Ave, Cambridge, 02139, MA, USA.
| | - Sahil A Parikh
- Division of Cardiology, Columbia University Irving Medical Center, New York, 10032, NY, USA.
| | - Elazer R Edelman
- Institute for Medical Engineering and Science, Massachusetts Institute of Technology, 77 Massachusetts Ave, Cambridge, 02139, MA, USA; Cardiovascular Division, Brigham and Women's Hospital, Harvard Medical School, Boston, 02115, MA, USA.
| | - Farhad R Nezami
- Cardiac Surgery Division, Brigham and Women's Hospital, Harvard Medical School, Boston, 02115, MA, USA.
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Khodaei S, Garber L, Bauer J, Emadi A, Keshavarz-Motamed Z. Long-term prognostic impact of paravalvular leakage on coronary artery disease requires patient-specific quantification of hemodynamics. Sci Rep 2022; 12:21357. [PMID: 36494362 PMCID: PMC9734172 DOI: 10.1038/s41598-022-21104-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2022] [Accepted: 09/22/2022] [Indexed: 12/13/2022] Open
Abstract
Transcatheter aortic valve replacement (TAVR) is a frequently used minimally invasive intervention for patient with aortic stenosis across a broad risk spectrum. While coronary artery disease (CAD) is present in approximately half of TAVR candidates, correlation of post-TAVR complications such as paravalvular leakage (PVL) or misalignment with CAD are not fully understood. For this purpose, we developed a multiscale computational framework based on a patient-specific lumped-parameter algorithm and a 3-D strongly-coupled fluid-structure interaction model to quantify metrics of global circulatory function, metrics of global cardiac function and local cardiac fluid dynamics in 6 patients. Based on our findings, PVL limits the benefits of TAVR and restricts coronary perfusion due to the lack of sufficient coronary blood flow during diastole phase (e.g., maximum coronary flow rate reduced by 21.73%, 21.43% and 21.43% in the left anterior descending (LAD), left circumflex (LCX) and right coronary artery (RCA) respectively (N = 6)). Moreover, PVL may increase the LV load (e.g., LV load increased by 17.57% (N = 6)) and decrease the coronary wall shear stress (e.g., maximum wall shear stress reduced by 20.62%, 21.92%, 22.28% and 25.66% in the left main coronary artery (LMCA), left anterior descending (LAD), left circumflex (LCX) and right coronary artery (RCA) respectively (N = 6)), which could promote atherosclerosis development through loss of the physiological flow-oriented alignment of endothelial cells. This study demonstrated that a rigorously developed personalized image-based computational framework can provide vital insights into underlying mechanics of TAVR and CAD interactions and assist in treatment planning and patient risk stratification in patients.
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Affiliation(s)
- Seyedvahid Khodaei
- Department of Mechanical Engineering (Mail to JHE-310), McMaster University, Hamilton, ON, L8S 4L7, Canada
| | - Louis Garber
- School of Biomedical Engineering, McMaster University, Hamilton, ON, Canada
| | - Julia Bauer
- Department of Mechanical Engineering (Mail to JHE-310), McMaster University, Hamilton, ON, L8S 4L7, Canada
| | - Ali Emadi
- Department of Mechanical Engineering (Mail to JHE-310), McMaster University, Hamilton, ON, L8S 4L7, Canada
- Department of Electrical and Computer Engineering, McMaster University, Hamilton, ON, Canada
| | - Zahra Keshavarz-Motamed
- Department of Mechanical Engineering (Mail to JHE-310), McMaster University, Hamilton, ON, L8S 4L7, Canada.
- School of Biomedical Engineering, McMaster University, Hamilton, ON, Canada.
- School of Computational Science and Engineering, McMaster University, Hamilton, ON, Canada.
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He L, Cai Y, Feng Y, Wang W, Feng T, Shen E, Yang S. Utility of vector flow mapping technology in quantitative assessment of carotid wall shear stress in hypertensive patients: A preliminary study. Front Cardiovasc Med 2022; 9:967763. [PMID: 36386366 PMCID: PMC9649775 DOI: 10.3389/fcvm.2022.967763] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2022] [Accepted: 10/11/2022] [Indexed: 01/18/2024] Open
Abstract
BACKGROUND Blood flowing in the arterial lumen acts on the surface of the vessel wall to form wall shear stress (WSS). To date, there has been limited research on the utility of non-invasive technology in the accurate quantification of carotid WSS in patients with hypertension (HP). OBJECTIVE The present study aimed to explore the usage of vascular vector flow mapping (VFM) in the quantitative assessment of carotid WSS in hypertensive patients at an early stage and to validate its clinical utility. METHODS A total of 50 individuals confirmed without carotid plaques were grouped into a HP group (n = 25) and a control (CON) group (n = 25) according to blood pressure. An ALOKA LISENDO 880 Color Doppler Ultrasound with a L441 3-15 MHZ probe was used to obtain a longitudinal section scan to determine the regions of interests (ROIs) of the common carotid artery. VFM-based WSS measurements were obtained by selecting the ROI with optimal image quality from three full cardiac cycles. WSS-derived measurements, including WSSmax, WSSmin, and WSSmean, were analyzed and compared between the HP and CON groups. In addition, the correlations between WSS-derived measurements and the carotid artery intima-media thickness (IMT) were also analyzed. RESULTS There were significant statistical differences in WSSmax and WSSmean between patients in the HP and CON groups. Specifically, the HP group had significantly decreased WSSmax and WSSmean compared to the CON group (WSSmax: 1.781 ± 0.305 Pa vs. 2.286 ± 0.257 Pa; WSSmean: 1.276 ± 0.333 Pa vs. 1.599 ± 0.293 Pa, both p < 0.001). However, there was no statistical difference in WSSmin between the groups (0.79 ± 0.36 vs. 0.99 ± 0.42, p = 0.080). Additionally, Spearman's correlation analysis indicated that the WSS-derived parameters were negatively correlated with the IMT (p < 0.001). CONCLUSION Vascular VFM technology shows promising results in the quantitative assessment of difference in hemodynamics of the vascular flow field between patients with HP and normal controls. Difference in WSS may serve as a potential predictor for the development of arteriosclerosis risks.
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Affiliation(s)
- Lan He
- Department of Ultrasound Medicine, Shanghai Eighth People’s Hospital, Shanghai, China
| | - Yundan Cai
- Department of Ultrasound Medicine, Shanghai Sixth People’s Hospital, Shanghai, China
| | - Yuhong Feng
- FUJIFILM Healthcare (Guangzhou), Co., Ltd., Guangzhou, China
| | - Wenwen Wang
- Department of Ultrasound Medicine, Shanghai Eighth People’s Hospital, Shanghai, China
| | - Tienan Feng
- Clinical Research Institute, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - E. Shen
- Department of Ultrasound Medicine, Chest Hospital Affiliated to Shanghai Jiao Tong University, Shanghai, China
| | - Shaoling Yang
- Department of Ultrasound Medicine, Shanghai Eighth People’s Hospital, Shanghai, China
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Hemodynamic Effect of Pulsatile on Blood Flow Distribution with VA ECMO: A Numerical Study. Bioengineering (Basel) 2022; 9:bioengineering9100487. [PMID: 36290455 PMCID: PMC9598990 DOI: 10.3390/bioengineering9100487] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2022] [Revised: 08/20/2022] [Accepted: 09/09/2022] [Indexed: 11/20/2022] Open
Abstract
The pulsatile properties of arterial flow and pressure have been thought to be important. Nevertheless, a gap still exists in the hemodynamic effect of pulsatile flow in improving blood flow distribution of veno-arterial extracorporeal membrane oxygenation (VA ECMO) supported by the circulatory system. The finite-element models, consisting of the aorta, VA ECMO, and intra-aortic balloon pump (IABP) are proposed for fluid-structure interaction calculation of the mechanical response. Group A is cardiogenic shock with 1.5 L/min of cardiac output. Group B is cardiogenic shock with VA ECMO. Group C is added to IABP based on Group B. The sum of the blood flow of cardiac output and VA ECMO remains constant at 4.5 L/min in Group B and Group C. With the recovery of the left ventricular, the flow of VA ECMO declines, and the effective blood of IABP increases. IABP plays the function of balancing blood flow between left arteria femoralis and right arteria femoralis compared with VA ECMO only. The difference of the equivalent energy pressure (dEEP) is crossed at 2.0 L/min to 1.5 L/min of VA ECMO. PPI’ (the revised pulse pressure index) with IABP is twice as much as without IABP. The intersection with two opposing blood generates the region of the aortic arch for the VA ECMO (Group B). In contrast to the VA ECMO, the blood intersection appears from the descending aorta to the renal artery with VA ECMO and IABP. The maximum time-averaged wall shear stress (TAWSS) of the renal artery is a significant difference with or not IABP (VA ECMO: 2.02 vs. 1.98 vs. 2.37 vs. 2.61 vs. 2.86 Pa; VA ECMO and IABP: 8.02 vs. 6.99 vs. 6.62 vs. 6.30 vs. 5.83 Pa). In conclusion, with the recovery of the left ventricle, the flow of VA ECMO declines and the effective blood of IABP increases. The difference between the equivalent energy pressure (EEP) and the surplus hemodynamic energy (SHE) indicates the loss of pulsation from the left ventricular to VA ECMO. 2.0 L/min to 1.5 L/min of VA ECMO showing a similar hemodynamic energy loss with the weak influence of IABP.
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8
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He Y, Northrup H, Le H, Cheung AK, Berceli SA, Shiu YT. Medical Image-Based Computational Fluid Dynamics and Fluid-Structure Interaction Analysis in Vascular Diseases. Front Bioeng Biotechnol 2022; 10:855791. [PMID: 35573253 PMCID: PMC9091352 DOI: 10.3389/fbioe.2022.855791] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2022] [Accepted: 04/08/2022] [Indexed: 01/17/2023] Open
Abstract
Hemodynamic factors, induced by pulsatile blood flow, play a crucial role in vascular health and diseases, such as the initiation and progression of atherosclerosis. Computational fluid dynamics, finite element analysis, and fluid-structure interaction simulations have been widely used to quantify detailed hemodynamic forces based on vascular images commonly obtained from computed tomography angiography, magnetic resonance imaging, ultrasound, and optical coherence tomography. In this review, we focus on methods for obtaining accurate hemodynamic factors that regulate the structure and function of vascular endothelial and smooth muscle cells. We describe the multiple steps and recent advances in a typical patient-specific simulation pipeline, including medical imaging, image processing, spatial discretization to generate computational mesh, setting up boundary conditions and solver parameters, visualization and extraction of hemodynamic factors, and statistical analysis. These steps have not been standardized and thus have unavoidable uncertainties that should be thoroughly evaluated. We also discuss the recent development of combining patient-specific models with machine-learning methods to obtain hemodynamic factors faster and cheaper than conventional methods. These critical advances widen the use of biomechanical simulation tools in the research and potential personalized care of vascular diseases.
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Affiliation(s)
- Yong He
- Division of Vascular Surgery and Endovascular Therapy, University of Florida, Gainesville, FL, United States
| | - Hannah Northrup
- Department of Biomedical Engineering, University of Utah, Salt Lake City, UT, United States
- Division of Nephrology and Hypertension, Department of Internal Medicine, University of Utah, Salt Lake City, UT, United States
| | - Ha Le
- Division of Nephrology and Hypertension, Department of Internal Medicine, University of Utah, Salt Lake City, UT, United States
| | - Alfred K. Cheung
- Division of Nephrology and Hypertension, Department of Internal Medicine, University of Utah, Salt Lake City, UT, United States
- Veterans Affairs Salt Lake City Healthcare System, Salt Lake City, UT, United States
| | - Scott A. Berceli
- Division of Vascular Surgery and Endovascular Therapy, University of Florida, Gainesville, FL, United States
- Vascular Surgery Section, Malcom Randall Veterans Affairs Medical Center, Gainesville, FL, United States
| | - Yan Tin Shiu
- Division of Nephrology and Hypertension, Department of Internal Medicine, University of Utah, Salt Lake City, UT, United States
- Veterans Affairs Salt Lake City Healthcare System, Salt Lake City, UT, United States
- *Correspondence: Yan Tin Shiu,
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Cai Y, Li Z. Mathematical modeling of plaque progression and associated microenvironment: How far from predicting the fate of atherosclerosis? COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2021; 211:106435. [PMID: 34619601 DOI: 10.1016/j.cmpb.2021.106435] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/09/2021] [Accepted: 09/16/2021] [Indexed: 06/13/2023]
Abstract
Mathematical modeling contributes to pathophysiological research of atherosclerosis by helping to elucidate mechanisms and by providing quantitative predictions that can be validated. In turn, the complexity of atherosclerosis is well suited to quantitative approaches as it provides challenges and opportunities for new developments of modeling. In this review, we summarize the current 'state of the art' on the mathematical modeling of the effects of biomechanical factors and microenvironmental factors on the plaque progression, and its potential help in prediction of plaque development. We begin with models that describe the biomechanical environment inside and outside the plaque and its influence on its growth and rupture. We then discuss mathematical models that describe the dynamic evolution of plaque microenvironmental factors, such as lipid deposition, inflammation, smooth muscle cells migration and intraplaque hemorrhage, followed by studies on plaque growth and progression using these modelling approaches. Moreover, we present several key questions for future research. Mathematical models can complement experimental and clinical studies, but also challenge current paradigms, redefine our understanding of mechanisms driving plaque vulnerability and propose future potential direction in therapy for cardiovascular disease.
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Affiliation(s)
- Yan Cai
- School of Biological Sciences and Medical Engineering, Southeast University, Nanjing 210096, China.
| | - Zhiyong Li
- School of Biological Sciences and Medical Engineering, Southeast University, Nanjing 210096, China; School of Mechanical, Medical and Process Engineering, Queensland University of Technology, Brisbane, QLD 4001, Australia
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10
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Kadry K, Olender ML, Marlevi D, Edelman ER, Nezami FR. A platform for high-fidelity patient-specific structural modelling of atherosclerotic arteries: from intravascular imaging to three-dimensional stress distributions. J R Soc Interface 2021; 18:20210436. [PMID: 34583562 DOI: 10.1098/rsif.2021.0436] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022] Open
Abstract
The pathophysiology of atherosclerotic lesions, including plaque rupture triggered by mechanical failure of the vessel wall, depends directly on the plaque morphology-modulated mechanical response. The complex interplay between lesion morphology and structural behaviour can be studied with high-fidelity computational modelling. However, construction of three-dimensional (3D) and heterogeneous models is challenging, with most previous work focusing on two-dimensional geometries or on single-material lesion compositions. Addressing these limitations, we here present a semi-automatic computational platform, leveraging clinical optical coherence tomography images to effectively reconstruct a 3D patient-specific multi-material model of atherosclerotic plaques, for which the mechanical response is obtained by structural finite-element simulations. To demonstrate the importance of including multi-material plaque components when recovering the mechanical response, a computational case study was conducted in which systematic variation of the intraplaque lipid and calcium was performed. The study demonstrated that the inclusion of various tissue components greatly affected the lesion mechanical response, illustrating the importance of multi-material formulations. This platform accordingly provides a viable foundation for studying how plaque micro-morphology affects plaque mechanical response, allowing for patient-specific assessments and extension into clinically relevant patient cohorts.
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Affiliation(s)
- Karim Kadry
- Institute for Medical Engineering and Science, Massachusetts Institute of Technology, Cambridge, MA, USA.,Laboratory of Hemodynamics and Cardiovascular Technology, Swiss Federal Institute of Technology, MED 3.2922, 1015 Lausanne, Switzerland
| | - Max L Olender
- Institute for Medical Engineering and Science, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - David Marlevi
- Institute for Medical Engineering and Science, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Elazer R Edelman
- Institute for Medical Engineering and Science, Massachusetts Institute of Technology, Cambridge, MA, USA.,Cardiovascular Division, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Farhad R Nezami
- Thoracic and Cardiac Surgery Division, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
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Wang L, He L, Jia H, Lv R, Guo X, Yang C, Giddens DP, Samady H, Maehara A, Mintz GS, Yu B, Tang D. Optical Coherence Tomography-Based Patient-Specific Residual Multi-Thrombus Coronary Plaque Models With Fluid-Structure Interaction for Better Treatment Decisions: A Biomechanical Modeling Case Study. J Biomech Eng 2021; 143:091003. [PMID: 33876192 DOI: 10.1115/1.4050911] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2020] [Indexed: 11/08/2022]
Abstract
Intracoronary thrombus from plaque erosion could cause fatal acute coronary syndrome (ACS). A conservative antithrombotic therapy has been proposed to treat ACS patients in lieu of stenting. It is speculated that the residual thrombus after aspiration thrombectomy would influence the prognosis of this treatment. However, biomechanical mechanisms affecting intracoronary thrombus remodeling and clinical outcome remain largely unknown. in vivo optical coherence tomography (OCT) data of a coronary plaque with two residual thrombi after antithrombotic therapy were acquired from an ACS patient with consent obtained. Three OCT-based fluid-structure interaction (FSI) models with different thrombus volumes, fluid-only, and structure-only models were constructed to simulate and compare the biomechanical interplay among blood flow, residual thrombus, and vessel wall mimicking different clinical situations. Our results showed that residual thrombus would decrease coronary volumetric flow rate by 9.3%, but elevate wall shear stress (WSS) by 29.4% and 75.5% at thrombi 1 and 2, respectively. WSS variations in a cardiac cycle from structure-only model were 12.1% and 13.5% higher at the two thrombus surfaces than those from FSI model. Intracoronary thrombi were subjected to compressive forces indicated by negative thrombus stress. Tandem intracoronary thrombus might influence coronary hemodynamics and solid mechanics differently. Computational modeling could be used to quantify biomechanical conditions under which patients could receive patient-specific treatment plan with optimized outcome after antithrombotic therapy. More patient studies with follow-up data are needed to continue the investigation and better understand mechanisms governing thrombus remodeling process.
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Affiliation(s)
- Liang Wang
- School of Biological Science and Medical Engineering, Southeast University, Nanjing 210096, China
| | - Luping He
- Department of Cardiology, The Second Affiliated Hospital of Harbin Medical University, Harbin 150086, China
| | - Haibo Jia
- Department of Cardiology, The Second Affiliated Hospital of Harbin Medical University, Harbin 150086, China
| | - Rui Lv
- School of Biological Science and Medical Engineering, Southeast University, Nanjing 210096, China
| | - Xiaoya Guo
- School of Science, Nanjing University of Posts and Telecommunications, Nanjing 210023, China
| | - Chun Yang
- Network Technology Research Institute, China United Network Comm. Co., Ltd., Beijing 100048, China; Mathematical Sciences Department, Worcester Polytechnic Institute, Worcester, MA 01609
| | - Don P Giddens
- Department of Medicine, Emory University School of Medicine, Atlanta, GA 30307; The Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology, Atlanta, GA 30332
| | - Habib Samady
- Department of Medicine, Emory University School of Medicine, Atlanta, GA 30307
| | - Akiko Maehara
- The Cardiovascular Research Foundation, Columbia University, New York, NY 10022
| | - Gary S Mintz
- The Cardiovascular Research Foundation, Columbia University, New York, NY 10022
| | - Bo Yu
- Department of Cardiology, The Second Affiliated Hospital of Harbin Medical University, Harbin 150086, China
| | - Dalin Tang
- School of Biological Science and Medical Engineering, Southeast University, Nanjing 210096, China;Mathematical Sciences Department, Worcester Polytechnic Institute, Worcester, MA 01609
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Lv R, Maehara A, Matsumura M, Wang L, Zhang C, Huang M, Guo X, Samady H, Giddens DP, Zheng J, Mintz GS, Tang D. Using Optical Coherence Tomography and Intravascular Ultrasound Imaging to Quantify Coronary Plaque Cap Stress/Strain and Progression: A Follow-Up Study Using 3D Thin-Layer Models. Front Bioeng Biotechnol 2021; 9:713525. [PMID: 34497800 PMCID: PMC8419245 DOI: 10.3389/fbioe.2021.713525] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2021] [Accepted: 07/20/2021] [Indexed: 11/13/2022] Open
Abstract
Accurate plaque cap thickness quantification and cap stress/strain calculations are of fundamental importance for vulnerable plaque research. To overcome uncertainties due to intravascular ultrasound (IVUS) resolution limitation, IVUS and optical coherence tomography (OCT) coronary plaque image data were combined together to obtain accurate and reliable cap thickness data, stress/strain calculations, and reliable plaque progression predictions. IVUS, OCT, and angiography baseline and follow-up data were collected from nine patients (mean age: 69; m: 5) at Cardiovascular Research Foundation with informed consent obtained. IVUS and OCT slices were coregistered and merged to form IVUS + OCT (IO) slices. A total of 114 matched slices (IVUS and OCT, baseline and follow-up) were obtained, and 3D thin-layer models were constructed to obtain stress and strain values. A generalized linear mixed model (GLMM) and least squares support vector machine (LSSVM) method were used to predict cap thickness change using nine morphological and mechanical risk factors. Prediction accuracies by all combinations (511) of those predictors with both IVUS and IO data were compared to identify optimal predictor(s) with their best accuracies. For the nine patients, the average of minimum cap thickness from IVUS was 0.17 mm, which was 26.08% lower than that from IO data (average = 0.23 mm). Patient variations of the individual errors ranged from ‒58.11 to 20.37%. For maximum cap stress between IO and IVUS, patient variations of the individual errors ranged from ‒30.40 to 46.17%. Patient variations of the individual errors of maximum cap strain values ranged from ‒19.90 to 17.65%. For the GLMM method, the optimal combination predictor using IO data had AUC (area under the ROC curve) = 0.926 and highest accuracy = 90.8%, vs. AUC = 0.783 and accuracy = 74.6% using IVUS data. For the LSSVM method, the best combination predictor using IO data had AUC = 0.838 and accuracy = 75.7%, vs. AUC = 0.780 and accuracy = 69.6% using IVUS data. This preliminary study demonstrated improved plaque cap progression prediction accuracy using accurate cap thickness data from IO slices and the differences in cap thickness, stress/strain values, and prediction results between IVUS and IO data. Large-scale studies are needed to verify our findings.
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Affiliation(s)
- Rui Lv
- School of Biological Science and Medical Engineering, Southeast University, Nanjing, China
| | - Akiko Maehara
- The Cardiovascular Research Foundation, Columbia University, New York, NY, United States
| | - Mitsuaki Matsumura
- The Cardiovascular Research Foundation, Columbia University, New York, NY, United States
| | - Liang Wang
- School of Biological Science and Medical Engineering, Southeast University, Nanjing, China
| | - Caining Zhang
- School of Biological Science and Medical Engineering, Southeast University, Nanjing, China
| | - Mengde Huang
- School of Biological Science and Medical Engineering, Southeast University, Nanjing, China
| | - Xiaoya Guo
- School of Science, Nanjing University of Posts and Telecommunications, Nanjing, China
| | - Habib Samady
- Department of Medicine, Emory University School of Medicine, Atlanta, GA, United States
| | - Don. P. Giddens
- Department of Medicine, Emory University School of Medicine, Atlanta, GA, United States
- The Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology, Atlanta, GA, United States
| | - Jie Zheng
- Mallinckrodt Institute of Radiology, Washington University, St. Louis, MO, United States
| | - Gary S. Mintz
- The Cardiovascular Research Foundation, Columbia University, New York, NY, United States
| | - Dalin Tang
- School of Biological Science and Medical Engineering, Southeast University, Nanjing, China
- Mathematical Sciences Department, Worcester Polytechnic Institute, Worcester, MA, United States
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13
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Guo X, Maehara A, Matsumura M, Wang L, Zheng J, Samady H, Mintz GS, Giddens DP, Tang D. Predicting plaque vulnerability change using intravascular ultrasound + optical coherence tomography image-based fluid-structure interaction models and machine learning methods with patient follow-up data: a feasibility study. Biomed Eng Online 2021; 20:34. [PMID: 33823858 PMCID: PMC8025351 DOI: 10.1186/s12938-021-00868-6] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/28/2020] [Accepted: 03/13/2021] [Indexed: 02/07/2023] Open
Abstract
BACKGROUND Coronary plaque vulnerability prediction is difficult because plaque vulnerability is non-trivial to quantify, clinically available medical image modality is not enough to quantify thin cap thickness, prediction methods with high accuracies still need to be developed, and gold-standard data to validate vulnerability prediction are often not available. Patient follow-up intravascular ultrasound (IVUS), optical coherence tomography (OCT) and angiography data were acquired to construct 3D fluid-structure interaction (FSI) coronary models and four machine-learning methods were compared to identify optimal method to predict future plaque vulnerability. METHODS Baseline and 10-month follow-up in vivo IVUS and OCT coronary plaque data were acquired from two arteries of one patient using IRB approved protocols with informed consent obtained. IVUS and OCT-based FSI models were constructed to obtain plaque wall stress/strain and wall shear stress. Forty-five slices were selected as machine learning sample database for vulnerability prediction study. Thirteen key morphological factors from IVUS and OCT images and biomechanical factors from FSI model were extracted from 45 slices at baseline for analysis. Lipid percentage index (LPI), cap thickness index (CTI) and morphological plaque vulnerability index (MPVI) were quantified to measure plaque vulnerability. Four machine learning methods (least square support vector machine, discriminant analysis, random forest and ensemble learning) were employed to predict the changes of three indices using all combinations of 13 factors. A standard fivefold cross-validation procedure was used to evaluate prediction results. RESULTS For LPI change prediction using support vector machine, wall thickness was the optimal single-factor predictor with area under curve (AUC) 0.883 and the AUC of optimal combinational-factor predictor achieved 0.963. For CTI change prediction using discriminant analysis, minimum cap thickness was the optimal single-factor predictor with AUC 0.818 while optimal combinational-factor predictor achieved an AUC 0.836. Using random forest for predicting MPVI change, minimum cap thickness was the optimal single-factor predictor with AUC 0.785 and the AUC of optimal combinational-factor predictor achieved 0.847. CONCLUSION This feasibility study demonstrated that machine learning methods could be used to accurately predict plaque vulnerability change based on morphological and biomechanical factors from multi-modality image-based FSI models. Large-scale studies are needed to verify our findings.
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Affiliation(s)
- Xiaoya Guo
- School of Science, Nanjing University of Posts and Telecommunications, Nanjing, 210023, China.
- Department of Mathematics, Southeast University, Nanjing, 210096, China.
| | - Akiko Maehara
- The Cardiovascular Research Foundation, Columbia University, New York, NY, 10022, USA
| | - Mitsuaki Matsumura
- The Cardiovascular Research Foundation, Columbia University, New York, NY, 10022, USA
| | - Liang Wang
- School of Biological Science and Medical Engineering, Southeast University, Nanjing, 210096, China
| | - Jie Zheng
- Mallinckrodt Institute of Radiology, Washington University, St. Louis, MO, 63110, USA
| | - Habib Samady
- Department of Medicine, Emory University School of Medicine, Atlanta, GA, 30307, USA
| | - Gary S Mintz
- The Cardiovascular Research Foundation, Columbia University, New York, NY, 10022, USA
| | - Don P Giddens
- Department of Medicine, Emory University School of Medicine, Atlanta, GA, 30307, USA
- The Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology, Atlanta, GA, 30332, USA
| | - Dalin Tang
- Department of Mathematics, Southeast University, Nanjing, 210096, China.
- Mathematical Sciences Department, Worcester Polytechnic Institute, Worcester, MA, 01609, USA.
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14
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Bajaj R, Garcia-Garcia HM, Courtney BK, Ramasamy A, Tufaro V, Erdogan E, Khan AH, Alves N, Rathod KS, Onuma Y, Serruys PW, Mathur A, Baumbach A, Bourantas C. Multi-modality intravascular imaging for guiding coronary intervention and assessing coronary atheroma: the Novasight Hybrid IVUS-OCT system. Minerva Cardiol Angiol 2021; 69:655-670. [PMID: 33703857 DOI: 10.23736/s2724-5683.21.05532-0] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
Intravascular imaging has evolved alongside interventional cardiology as an adjunctive tool for assessing plaque pathology and for guiding and optimising percutaneous coronary intervention (PCI) in challenging lesions. The two modalities which have dominated the field are intravascular ultrasound (IVUS), which relies on sound waves and optical coherence tomography (OCT), relying on light waves. These approaches however have limited efficacy in assessing plaque morphology and vulnerability that are essential for guiding PCI in complex lesions and identifying patient at risk that will benefit from emerging therapies targeting plaque evolution. These limitations are complementary and, in this context, it has been recognised and demonstrated in multi-modality studies that the concurrent use of IVUS and OCT can help overcome these deficits enabling a more complete and accurate plaque assessment. The Conavi Novasight Hybrid IVUS-OCT catheter is the first commercially available device that is capable of invasive clinical coronary assessment with simultaneously acquired and co-registered IVUS and OCT imaging. It represents a significant evolution in the field and is expected to have broad application in clinical practice and research. In this review article we present the limitations of standalone intravascular imaging techniques, summarise the data supporting the value of multimodality imaging in clinical practice and research, describe the Novasight Hybrid IVUS-OCT system and highlight the potential utility of this technology in coronary intervention and in the study of atherosclerosis.
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Affiliation(s)
- Retesh Bajaj
- Department of Cardiology, Barts Heart Centre, Barts Health NHS Trust, London, UK.,Cardiovascular Devices Hub, Centre for Cardiovascular Medicine and Devices, William Harvey Research Institute, Queen Mary University of London, London, UK
| | | | - Brian K Courtney
- Sunnybrook Research Institute, Schulich Heart Program, University of Toronto, Toronto, ON, Canada.,Conavi Medical, North York, ON, Canada
| | - Anantharaman Ramasamy
- Department of Cardiology, Barts Heart Centre, Barts Health NHS Trust, London, UK.,Cardiovascular Devices Hub, Centre for Cardiovascular Medicine and Devices, William Harvey Research Institute, Queen Mary University of London, London, UK
| | - Vincenzo Tufaro
- Department of Cardiology, Barts Heart Centre, Barts Health NHS Trust, London, UK.,Cardiovascular Devices Hub, Centre for Cardiovascular Medicine and Devices, William Harvey Research Institute, Queen Mary University of London, London, UK
| | - Emrah Erdogan
- Department of Cardiology, Barts Heart Centre, Barts Health NHS Trust, London, UK.,Cardiovascular Devices Hub, Centre for Cardiovascular Medicine and Devices, William Harvey Research Institute, Queen Mary University of London, London, UK
| | - Ameer H Khan
- Department of Cardiology, Barts Heart Centre, Barts Health NHS Trust, London, UK.,Cardiovascular Devices Hub, Centre for Cardiovascular Medicine and Devices, William Harvey Research Institute, Queen Mary University of London, London, UK
| | - Natasha Alves
- Sunnybrook Research Institute, Schulich Heart Program, University of Toronto, Toronto, ON, Canada
| | - Krishnaraj S Rathod
- Department of Cardiology, Barts Heart Centre, Barts Health NHS Trust, London, UK.,Cardiovascular Devices Hub, Centre for Cardiovascular Medicine and Devices, William Harvey Research Institute, Queen Mary University of London, London, UK
| | - Yoshinobu Onuma
- School of Medicine, National University of Ireland Galway, Galway, Ireland
| | - Patrick W Serruys
- School of Medicine, National University of Ireland Galway, Galway, Ireland.,National Heart & Lung Institute, Imperial College London, London, UK
| | - Anthony Mathur
- Department of Cardiology, Barts Heart Centre, Barts Health NHS Trust, London, UK.,Cardiovascular Devices Hub, Centre for Cardiovascular Medicine and Devices, William Harvey Research Institute, Queen Mary University of London, London, UK
| | - Andreas Baumbach
- Department of Cardiology, Barts Heart Centre, Barts Health NHS Trust, London, UK.,Cardiovascular Devices Hub, Centre for Cardiovascular Medicine and Devices, William Harvey Research Institute, Queen Mary University of London, London, UK
| | - Christos Bourantas
- Department of Cardiology, Barts Heart Centre, Barts Health NHS Trust, London, UK - .,Cardiovascular Devices Hub, Centre for Cardiovascular Medicine and Devices, William Harvey Research Institute, Queen Mary University of London, London, UK
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15
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Bajaj R, Huang X, Kilic Y, Jain A, Ramasamy A, Torii R, Moon J, Koh T, Crake T, Parker MK, Tufaro V, Serruys PW, Pugliese F, Mathur A, Baumbach A, Dijkstra J, Zhang Q, Bourantas CV. A deep learning methodology for the automated detection of end-diastolic frames in intravascular ultrasound images. Int J Cardiovasc Imaging 2021; 37:1825-1837. [PMID: 33590430 PMCID: PMC8255253 DOI: 10.1007/s10554-021-02162-x] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/06/2020] [Accepted: 01/07/2021] [Indexed: 12/13/2022]
Abstract
Coronary luminal dimensions change during the cardiac cycle. However, contemporary volumetric intravascular ultrasound (IVUS) analysis is performed in non-gated images as existing methods to acquire gated or to retrospectively gate IVUS images have failed to dominate in research. We developed a novel deep learning (DL)-methodology for end-diastolic frame detection in IVUS and compared its efficacy against expert analysts and a previously established methodology using electrocardiographic (ECG)-estimations as reference standard. Near-infrared spectroscopy-IVUS (NIRS-IVUS) data were prospectively acquired from 20 coronary arteries and co-registered with the concurrent ECG-signal to identify end-diastolic frames. A DL-methodology which takes advantage of changes in intensity of corresponding pixels in consecutive NIRS-IVUS frames and consists of a network model designed in a bidirectional gated-recurrent-unit (Bi-GRU) structure was trained to detect end-diastolic frames. The efficacy of the DL-methodology in identifying end-diastolic frames was compared with two expert analysts and a conventional image-based (CIB)-methodology that relies on detecting vessel movement to estimate phases of the cardiac cycle. A window of ± 100 ms from the ECG estimations was used to define accurate end-diastolic frames detection. The ECG-signal identified 3,167 end-diastolic frames. The mean difference between DL and ECG estimations was 3 ± 112 ms while the mean differences between the 1st-analyst and ECG, 2nd-analyst and ECG and CIB-methodology and ECG were 86 ± 192 ms, 78 ± 183 ms and 59 ± 207 ms, respectively. The DL-methodology was able to accurately detect 80.4%, while the two analysts and the CIB-methodology detected 39.0%, 43.4% and 42.8% of end-diastolic frames, respectively (P < 0.05). The DL-methodology can identify NIRS-IVUS end-diastolic frames accurately and should be preferred over expert analysts and CIB-methodologies, which have limited efficacy.
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Affiliation(s)
- Retesh Bajaj
- Department of Cardiology, Barts Heart Centre, Barts Health NHS Trust, West Smithfield, London, EC1A 7BE, UK.,Centre for Cardiovascular Medicine and Devices, William Harvey Research Institute, Queen Mary University of London, London, UK
| | - Xingru Huang
- School of Electronic Engineering and Computer Science, Queen Mary University of London, London, UK
| | - Yakup Kilic
- Department of Cardiology, Barts Heart Centre, Barts Health NHS Trust, West Smithfield, London, EC1A 7BE, UK
| | - Ajay Jain
- Department of Cardiology, Barts Heart Centre, Barts Health NHS Trust, West Smithfield, London, EC1A 7BE, UK
| | - Anantharaman Ramasamy
- Department of Cardiology, Barts Heart Centre, Barts Health NHS Trust, West Smithfield, London, EC1A 7BE, UK.,Centre for Cardiovascular Medicine and Devices, William Harvey Research Institute, Queen Mary University of London, London, UK
| | - Ryo Torii
- Department of Mechanical Engineering, University College London, London, UK
| | - James Moon
- Department of Cardiology, Barts Heart Centre, Barts Health NHS Trust, West Smithfield, London, EC1A 7BE, UK.,Institute of Cardiovascular Sciences, University College London, London, UK
| | - Tat Koh
- Department of Cardiology, Barts Heart Centre, Barts Health NHS Trust, West Smithfield, London, EC1A 7BE, UK
| | - Tom Crake
- Department of Cardiology, Barts Heart Centre, Barts Health NHS Trust, West Smithfield, London, EC1A 7BE, UK
| | - Maurizio K Parker
- Centre for Cardiovascular Medicine and Devices, William Harvey Research Institute, Queen Mary University of London, London, UK
| | - Vincenzo Tufaro
- Department of Cardiology, Barts Heart Centre, Barts Health NHS Trust, West Smithfield, London, EC1A 7BE, UK.,Centre for Cardiovascular Medicine and Devices, William Harvey Research Institute, Queen Mary University of London, London, UK
| | - Patrick W Serruys
- Faculty of Medicine, National Heart & Lung Institute, Imperial College London, London, UK
| | - Francesca Pugliese
- Department of Cardiology, Barts Heart Centre, Barts Health NHS Trust, West Smithfield, London, EC1A 7BE, UK.,Centre for Cardiovascular Medicine and Devices, William Harvey Research Institute, Queen Mary University of London, London, UK
| | - Anthony Mathur
- Department of Cardiology, Barts Heart Centre, Barts Health NHS Trust, West Smithfield, London, EC1A 7BE, UK.,Centre for Cardiovascular Medicine and Devices, William Harvey Research Institute, Queen Mary University of London, London, UK
| | - Andreas Baumbach
- Department of Cardiology, Barts Heart Centre, Barts Health NHS Trust, West Smithfield, London, EC1A 7BE, UK.,Centre for Cardiovascular Medicine and Devices, William Harvey Research Institute, Queen Mary University of London, London, UK
| | - Jouke Dijkstra
- Department of Radiology, Division of Image Processing, Leiden University Medical Center, Leiden, The Netherlands
| | - Qianni Zhang
- School of Electronic Engineering and Computer Science, Queen Mary University of London, London, UK
| | - Christos V Bourantas
- Department of Cardiology, Barts Heart Centre, Barts Health NHS Trust, West Smithfield, London, EC1A 7BE, UK. .,Centre for Cardiovascular Medicine and Devices, William Harvey Research Institute, Queen Mary University of London, London, UK. .,Institute of Cardiovascular Sciences, University College London, London, UK.
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16
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Lv R, Maehara A, Matsumura M, Wang L, Wang Q, Zhang C, Guo X, Samady H, Giddens DP, Zheng J, Mintz GS, Tang D. Using optical coherence tomography and intravascular ultrasound imaging to quantify coronary plaque cap thickness and vulnerability: a pilot study. Biomed Eng Online 2020; 19:90. [PMID: 33256759 PMCID: PMC7706023 DOI: 10.1186/s12938-020-00832-w] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2020] [Accepted: 11/17/2020] [Indexed: 11/11/2022] Open
Abstract
Background Detecting coronary vulnerable plaques in vivo and assessing their vulnerability have been great challenges for clinicians and the research community. Intravascular ultrasound (IVUS) is commonly used in clinical practice for diagnosis and treatment decisions. However, due to IVUS limited resolution (about 150–200 µm), it is not sufficient to detect vulnerable plaques with a threshold cap thickness of 65 µm. Optical Coherence Tomography (OCT) has a resolution of 15–20 µm and can measure fibrous cap thickness more accurately. The aim of this study was to use OCT as the benchmark to obtain patient-specific coronary plaque cap thickness and evaluate the differences between OCT and IVUS fibrous cap quantifications. A cap index with integer values 0–4 was also introduced as a quantitative measure of plaque vulnerability to study plaque vulnerability. Methods Data from 10 patients (mean age: 70.4; m: 6; f: 4) with coronary heart disease who underwent IVUS, OCT, and angiography were collected at Cardiovascular Research Foundation (CRF) using approved protocol with informed consent obtained. 348 slices with lipid core and fibrous caps were selected for study. Convolutional Neural Network (CNN)-based and expert-based data segmentation were performed using established methods previously published. Cap thickness data were extracted to quantify differences between IVUS and OCT measurements. Results For the 348 slices analyzed, the mean value difference between OCT and IVUS cap thickness measurements was 1.83% (p = 0.031). However, mean value of point-to-point differences was 35.76%. Comparing minimum cap thickness for each plaque, the mean value of the 20 plaque IVUS-OCT differences was 44.46%, ranging from 2.36% to 91.15%. For cap index values assigned to the 348 slices, the disagreement between OCT and IVUS assignments was 25%. However, for the OCT cap index = 2 and 3 groups, the disagreement rates were 91% and 80%, respectively. Furthermore, the observation of cap index changes from baseline to follow-up indicated that IVUS results differed from OCT by 80%. Conclusions These preliminary results demonstrated that there were significant differences between IVUS and OCT plaque cap thickness measurements. Large-scale patient studies are needed to confirm our findings.
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Affiliation(s)
- Rui Lv
- School of Biological Science and Medical Engineering, Southeast University, #2 SiPailou, Nanjing, China
| | - Akiko Maehara
- The Cardiovascular Research Foundation, Columbia University, New York, USA
| | - Mitsuaki Matsumura
- The Cardiovascular Research Foundation, Columbia University, New York, USA
| | - Liang Wang
- School of Biological Science and Medical Engineering, Southeast University, #2 SiPailou, Nanjing, China
| | - Qingyu Wang
- School of Biological Science and Medical Engineering, Southeast University, #2 SiPailou, Nanjing, China
| | - Caining Zhang
- School of Biological Science and Medical Engineering, Southeast University, #2 SiPailou, Nanjing, China
| | - Xiaoya Guo
- School of Science, Nanjing University of Posts and Telecommunications, Nanjing, China
| | - Habib Samady
- Department of Medicine, Emory University School of Medicine, Atlanta, GA, USA
| | - Don P Giddens
- Department of Medicine, Emory University School of Medicine, Atlanta, GA, USA.,The Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology, Atlanta, GA, USA
| | - Jie Zheng
- Mallinckrodt Institute of Radiology, Washington University, St. Louis, MO, USA
| | - Gary S Mintz
- The Cardiovascular Research Foundation, Columbia University, New York, USA
| | - Dalin Tang
- School of Biological Science and Medical Engineering, Southeast University, #2 SiPailou, Nanjing, China. .,Mathematical Sciences Department, Worcester Polytechnic Institute, 100 Institute Road, Worcester, MA, 01609, USA.
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17
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Gijsen F, Katagiri Y, Barlis P, Bourantas C, Collet C, Coskun U, Daemen J, Dijkstra J, Edelman E, Evans P, van der Heiden K, Hose R, Koo BK, Krams R, Marsden A, Migliavacca F, Onuma Y, Ooi A, Poon E, Samady H, Stone P, Takahashi K, Tang D, Thondapu V, Tenekecioglu E, Timmins L, Torii R, Wentzel J, Serruys P. Expert recommendations on the assessment of wall shear stress in human coronary arteries: existing methodologies, technical considerations, and clinical applications. Eur Heart J 2020; 40:3421-3433. [PMID: 31566246 PMCID: PMC6823616 DOI: 10.1093/eurheartj/ehz551] [Citation(s) in RCA: 184] [Impact Index Per Article: 46.0] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/19/2018] [Revised: 04/09/2019] [Accepted: 09/23/2019] [Indexed: 01/09/2023] Open
Affiliation(s)
- Frank Gijsen
- Department of Cardiology, Erasmus MC, University Medical Center Rotterdam, Rotterdam, the Netherlands
| | - Yuki Katagiri
- Amsterdam University Medical Centre, University of Amsterdam, Amsterdam, the Netherlands
| | - Peter Barlis
- Department of Medicine and Radiology, Melbourne Medical School, Faculty of Medicine, Dentistry and Health Sciences, The University of Melbourne, Melbourne, VIC, Australia.,Department of Cardiology, Northern Hospital, 185 Cooper Street, Epping, Australia.,St Vincent's Heart Centre, Building C, 41 Victoria Parade, Fitzroy, Australia
| | - Christos Bourantas
- Institute of Cardiovascular Sciences, University College of London, London, UK.,Department of Cardiology, Barts Heart Centre, London, UK.,School of Medicine and Dentistry, Queen Mary University London, London, UK
| | - Carlos Collet
- Amsterdam University Medical Centre, University of Amsterdam, Amsterdam, the Netherlands
| | - Umit Coskun
- Division of Cardiovascular Medicine, Brigham & Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Joost Daemen
- Department of Cardiology, Erasmus MC, University Medical Center Rotterdam, Rotterdam, the Netherlands
| | - Jouke Dijkstra
- LKEB-Division of Image Processing, Department of Radiology, Leiden University Medical Centre, Leiden, the Netherlands
| | - Elazer Edelman
- Division of Cardiovascular Medicine, Brigham & Women's Hospital, Harvard Medical School, Boston, MA, USA.,Institute for Medical Engineering and Science, MIT, Cambridge, MA, USA
| | - Paul Evans
- Department of Infection, Immunity and Cardiovascular Disease, University of Sheffield, UK
| | - Kim van der Heiden
- Department of Cardiology, Erasmus MC, University Medical Center Rotterdam, Rotterdam, the Netherlands
| | - Rod Hose
- Department of Infection, Immunity and Cardiovascular Disease, University of Sheffield, UK.,Department of Circulation and Imaging, NTNU, Trondheim, Norway
| | - Bon-Kwon Koo
- Department of Internal Medicine and Cardiovascular Center, Seoul National University Hospital, Seoul, Korea.,Institute of Aging, Seoul National University, Seoul, Korea
| | - Rob Krams
- School of Engineering and Materials Science Queen Mary University of London, London, UK
| | - Alison Marsden
- Departments of Bioengineering and Pediatrics, Institute of Computational and Mathematical Engineering, Stanford University, Stanford, CA, USA
| | - Francesco Migliavacca
- Laboratory of Biological Structure Mechanics (LaBS), Department of Chemistry, Materials and Chemical Engineering "Giulio Natta", Politecnico di Milano, Milan, Italy
| | - Yoshinobu Onuma
- Erasmus University Medical Center, Rotterdam, the Netherlands
| | - Andrew Ooi
- Department of Mechanical Engineering, Melbourne School of Engineering, The University of Melbourne, Melbourne, VIC, Australia
| | - Eric Poon
- Department of Mechanical Engineering, Melbourne School of Engineering, The University of Melbourne, Melbourne, VIC, Australia
| | - Habib Samady
- Department of Medicine, Emory University School of Medicine, Atlanta, GA, USA
| | - Peter Stone
- Division of Cardiovascular Medicine, Brigham & Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Kuniaki Takahashi
- Amsterdam University Medical Centre, University of Amsterdam, Amsterdam, the Netherlands
| | - Dalin Tang
- Department of Mathematics, Southeast University, Nanjing, China; Mathematical Sciences Department, Worcester Polytechnic Institute, Worcester, MA, USA
| | - Vikas Thondapu
- Department of Medicine and Radiology, Melbourne Medical School, Faculty of Medicine, Dentistry and Health Sciences, The University of Melbourne, Melbourne, VIC, Australia.,Department of Mechanical Engineering, Melbourne School of Engineering, The University of Melbourne, Melbourne, VIC, Australia.,Cardiology Division, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Erhan Tenekecioglu
- Department of Interventional Cardiology, Thoraxcentre, Erasmus Medical Centre, Rotterdam, the Netherlands
| | - Lucas Timmins
- Department of Biomedical Engineering, University of Utah, Salt Lake City, UT.,Scientific Computing and Imaging Institute, University of Utah, Salt Lake City, UT
| | - Ryo Torii
- Department of Mechanical Engineering, University College London, UK
| | - Jolanda Wentzel
- Department of Cardiology, Erasmus MC, University Medical Center Rotterdam, Rotterdam, the Netherlands
| | - Patrick Serruys
- Erasmus University Medical Center, Rotterdam, the Netherlands.,Imperial College London, London, UK.,Melbourne School of Engineering, University of Melbourne, Melbourne, Australia
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18
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Wang L, Tang D, Maehara A, Wu Z, Yang C, Muccigrosso D, Matsumura M, Zheng J, Bach R, Billiar KL, Stone GW, Mintz GS. Using intravascular ultrasound image-based fluid-structure interaction models and machine learning methods to predict human coronary plaque vulnerability change. Comput Methods Biomech Biomed Engin 2020; 23:1267-1276. [PMID: 32696674 DOI: 10.1080/10255842.2020.1795838] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/23/2023]
Abstract
Plaque vulnerability prediction is of great importance in cardiovascular research. In vivo follow-up intravascular ultrasound (IVUS) coronary plaque data were acquired from nine patients to construct fluid-structure interaction models to obtain plaque biomechanical conditions. Morphological plaque vulnerability index (MPVI) was defined to measure plaque vulnerability. The generalized linear mixed regression model (GLMM), support vector machine (SVM) and random forest (RF) were introduced to predict MPVI change (ΔMPVI = MPVIfollow-up‒MPVIbaseline) using ten risk factors at baseline. The combination of mean wall thickness, lumen area, plaque area, critical plaque wall stress, and MPVI was the best predictor using RF with the highest prediction accuracy 91.47%, compared to 90.78% from SVM, and 85.56% from GLMM. Machine learning method (RF) improved the prediction accuracy by 5.91% over that from GLMM. MPVI was the best single risk factor using both GLMM (82.09%) and RF (78.53%) while plaque area was the best using SVM (81.29%).
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Affiliation(s)
- Liang Wang
- School of Biological Science and Medical Engineering, Southeast University, Nanjing, China.,Mathematical Sciences Department, Worcester Polytechnic Institute, Worcester, MA, USA
| | - Dalin Tang
- School of Biological Science and Medical Engineering, Southeast University, Nanjing, China.,Mathematical Sciences Department, Worcester Polytechnic Institute, Worcester, MA, USA
| | - Akiko Maehara
- The Cardiovascular Research Foundation, Columbia University, New York, NY, USA
| | - Zheyang Wu
- Mathematical Sciences Department, Worcester Polytechnic Institute, Worcester, MA, USA
| | - Chun Yang
- Mathematical Sciences Department, Worcester Polytechnic Institute, Worcester, MA, USA
| | - David Muccigrosso
- Mallinckrodt Institute of Radiology, Washington University, St. Louis, MO, USA
| | - Mitsuaki Matsumura
- The Cardiovascular Research Foundation, Columbia University, New York, NY, USA
| | - Jie Zheng
- Mallinckrodt Institute of Radiology, Washington University, St. Louis, MO, USA
| | - Richard Bach
- Cardiovascular Division, Washington University School of Medicine, St. Louis, MO, USA
| | - Kristen L Billiar
- Department of Biomedical Engineering, Worcester Polytechnic Institute, Worcester, MA, USA
| | - Gregg W Stone
- The Cardiovascular Research Foundation, Columbia University, New York, NY, USA
| | - Gary S Mintz
- The Cardiovascular Research Foundation, Columbia University, New York, NY, USA
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Karanasiou GS, Tsobou PI, Tachos NS, Antonini L, Petrini L, Pennati G, Gijsen F, Nezami FR, Tzafiri R, Vaughan T, Fawdry M, Fotiadis DI. Design and implementation of in silico clinical trial for Bioresorbable Vascular Scaffolds. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2020; 2020:2675-2678. [PMID: 33018557 DOI: 10.1109/embc44109.2020.9176317] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/11/2023]
Abstract
In the recent years, Bioresorbable Vascular Scaffolds (BVS) for the treatment of atherosclerosis have been introduced. InSilc is a cloud based in silico clinical trial (ISCT) platform for drug-eluting BVS. The platform integrates multidisciplinary and multiscale models predicting the BVS performance. In this study, we present a use case scenario and demonstrate the functioning of the individual modules and of the whole pipeline and the ability to predict BVS short, medium, long-term outcomes.
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Gu K, Guan Z, Lin X, Feng Y, Feng J, Yang Y, Zhang Z, Chang Y, Ling Y, Wan F. Numerical analysis of aortic hemodynamics under the support of venoarterial extracorporeal membrane oxygenation and intra-aortic balloon pump. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2019; 182:105041. [PMID: 31465978 DOI: 10.1016/j.cmpb.2019.105041] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/20/2019] [Revised: 08/04/2019] [Accepted: 08/18/2019] [Indexed: 06/10/2023]
Abstract
BACKGROUND AND OBJECTIVE A gap still exists in the hemodynamic effect of intra-aortic balloon pump (IABP), venoarterial extracorporeal membrane oxygenation (VA-ECMO), and VA-ECMO plus IABP on the blood perfusion of the coronary artery, brain, and lower limb; the relation between heart flow and ECMO flow; and the wall stress of vessels. METHODS A finite-element model of the aorta, ECMO, and IABP was proposed to calculate the mechanical response via fluid-structure interaction. Heart failure (HF), IABP, ECMO, and ECMO plus IABP were utilized to study the effect of support models. RESULTS For the pressure curve, VA-ECMO weakened the dicrotic notch of pressure compared with HF and the pulsatile index (0.494 vs. 0.706 vs. 0.471 vs. 0.613). IABP, ECMO, and ECMO plus IABP increased the perfusion of the coronary, brain, and renal artery compared with HF. However, ECMO and ECMO plus IABP clearly reduced the blood flow of the left arteria femoralis compared to that of the right arteria femoralis (ECMO: 194.04 vs. 730.80 mL/min; ECMO plus IABP: 342.15 vs. 947.22 mL/min). In addition, the flow of ECMO accessed the renal artery more than the left ventricular flow. Greater ventricular flow perfused to the renal artery at a diastolic period for ECMO plus IABP, especially at the time points of 2.192 s and 2.304 s. Compared to the velocity distribution with ECMO, the flow of the right arteria femoralis was increased in the process of IABP-on. According to these four cases, the stress of the vascular wall was increased for ECMO support at the systolic period. The peak wall stress of ECMO is increased by 20% at 1.68 s. CONCLUSIONS ECMO plus IABP is more conducive to the blood supply than other cases from the result of numerical simulation. The location of blood intersection was generated in the region of the renal artery, which is estimated carefully.
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Affiliation(s)
- Kaiyun Gu
- Peking University Third Hospital, 49 North Garden Rd., Haidian District, Beijing 100191, China
| | - Zhiyuan Guan
- Peking University Third Hospital, 49 North Garden Rd., Haidian District, Beijing 100191, China
| | - Xuanqi Lin
- College of Life Science and Bioengineering, Beijing University of Technology, 100 Pingleyuan, Chaoyang District, Beijing 200120, China
| | - Yunzhen Feng
- Shanghai East Hospital, Tongji University, 150 Jimo Rd., Pudong District, Shanghai 100124, China
| | - Jieli Feng
- Peking University Third Hospital, 49 North Garden Rd., Haidian District, Beijing 100191, China
| | - Yujie Yang
- Peking University Third Hospital, 49 North Garden Rd., Haidian District, Beijing 100191, China
| | - Zhe Zhang
- Peking University Third Hospital, 49 North Garden Rd., Haidian District, Beijing 100191, China.
| | - Yu Chang
- College of Life Science and Bioengineering, Beijing University of Technology, 100 Pingleyuan, Chaoyang District, Beijing 200120, China.
| | - Yunpeng Ling
- Peking University Third Hospital, 49 North Garden Rd., Haidian District, Beijing 100191, China
| | - Feng Wan
- Shanghai East Hospital, Tongji University, 150 Jimo Rd., Pudong District, Shanghai 100124, China
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21
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Wang J, Paritala PK, Mendieta JB, Komori Y, Raffel OC, Gu Y, Li Z. Optical coherence tomography-based patient-specific coronary artery reconstruction and fluid–structure interaction simulation. Biomech Model Mechanobiol 2019; 19:7-20. [DOI: 10.1007/s10237-019-01191-9] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/04/2019] [Accepted: 06/21/2019] [Indexed: 01/14/2023]
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22
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Guo X, Giddens D, Molony D, Yang C, Samady H, Zheng J, Matsumura M, Mintz G, Maehara A, Wang L, Tang D. A Multi-Modality Image-Based FSI Modeling Approach for Prediction of Coronary Plaque Progression Using IVUS and OCT Data with Follow-Up. J Biomech Eng 2019; 141:2735312. [PMID: 31141591 DOI: 10.1115/1.4043866] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2019] [Indexed: 11/08/2022]
Abstract
Medical image resolution has been a serious limitation in plaque progression research. A modeling approach combining intravascular ultrasound (IVUS) and optical coherence tomography (OCT) was introduced and patient follow-up IVUS and OCT data were acquired to construct 3D coronary models for plaque progression investigations. Baseline and follow-up in vivo IVUS and OCT coronary plaque data were acquired from one patient with 105 matched slices selected for model construction. 3D FSI models based on IVUS and OCT data (denoted as IVUS+OCT model) were constructed to obtain stress/strain and wall shear stress (WSS) for plaque progression prediction. IVUS-based IVUS50 and IVUS200 models were constructed for comparison with cap thickness set as 50 and 200 microns, respectively. Lumen area increase (LAI), plaque area increase (PAI) and plaque burden increase (PBI) were chosen to measure plaque progression. The least squares support vector machine method was employed for plaque progression prediction using 19 risk factors. For IVUS+OCT model with LAI, PAI and PBI, the best single predictor was plaque strain, local plaque stress, and minimal cap thickness, with prediction accuracy as 0.766, 0.838 and 0.890, respectively; The prediction accuracy using best combinations of 19 factors was 0.911, 0.881 and 0.905, respectively. Compared to IVUS+OCT model, IVUS50 and IVUS200 models had errors ranging from 1% to 66.5% in quantifying cap thickness, stress, strain and prediction accuracies. WSS showed relatively lower prediction accuracy compared to other predictors in all 9 prediction studies.
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Affiliation(s)
- Xiaoya Guo
- Department of Mathematics, Southeast University, Nanjing, 210096, China
| | - Don Giddens
- Department of Medicine, Emory University School of Medicine, Atlanta, GA, 30307, USA; The Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology, Atlanta, GA, 30332 USA
| | - David Molony
- Department of Medicine, Emory University School of Medicine, Atlanta, GA, 30307, USA
| | - Chun Yang
- Mathematical Sciences Department, Worcester Polytechnic Institute, Worcester, MA 01609 USA
| | - Habib Samady
- Department of Medicine, Emory University School of Medicine, Atlanta, GA, 30307, USA
| | - Jie Zheng
- Mallinckrodt Institute of Radiology, Washington University, St. Louis, MO, 63110, USA
| | - Mitsuaki Matsumura
- The Cardiovascular Research Foundation, Columbia University, New York, NY 10022, USA
| | - Gary Mintz
- The Cardiovascular Research Foundation, Columbia University, New York, NY 10022, USA
| | - Akiko Maehara
- The Cardiovascular Research Foundation, Columbia University, New York, NY 10022, USA
| | - Liang Wang
- Mathematical Sciences Department, Worcester Polytechnic Institute, Worcester, MA 01609 USA
| | - Dalin Tang
- Department of Mathematics, Southeast University, Nanjing, 210096, China; Mathematical Sciences Department, Worcester Polytechnic Institute, Worcester, MA 01609 USA
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23
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Multi-factor decision-making strategy for better coronary plaque burden increase prediction: a patient-specific 3D FSI study using IVUS follow-up data. Biomech Model Mechanobiol 2019; 18:1269-1280. [DOI: 10.1007/s10237-019-01143-3] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/31/2018] [Accepted: 03/22/2019] [Indexed: 10/27/2022]
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24
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Barocas VH, Winkelstein BA. Editors' Choice Papers for 2018. J Biomech Eng 2019; 141:2728069. [PMID: 30840049 DOI: 10.1115/1.4043072] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/31/2018] [Indexed: 02/28/2024]
Abstract
No abstract.
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Affiliation(s)
- Victor H Barocas
- 7-105 Nils Hasselmo Hall 312 Church Street SE Minneapolis, MN 55455
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