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Trayanova NA, Pashakhanloo F, Wu KC, Halperin HR. Imaging-Based Simulations for Predicting Sudden Death and Guiding Ventricular Tachycardia Ablation. Circ Arrhythm Electrophysiol 2019; 10:CIRCEP.117.004743. [PMID: 28696219 DOI: 10.1161/circep.117.004743] [Citation(s) in RCA: 36] [Impact Index Per Article: 7.2] [Reference Citation Analysis] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/23/2017] [Accepted: 06/08/2017] [Indexed: 11/16/2022]
Affiliation(s)
- Natalia A Trayanova
- From the Institute for Computational Medicine and Department of Biomedical Engineering (N.A.T., F.P.) and Departments of Radiology and Biomedical Engineering (H.R.H.), Johns Hopkins University, Baltimore, MD; and Division of Cardiology, Department of Medicine, Johns Hopkins Medical Institutions, Baltimore, MD (K.C.W., H.R.H.).
| | - Farhad Pashakhanloo
- From the Institute for Computational Medicine and Department of Biomedical Engineering (N.A.T., F.P.) and Departments of Radiology and Biomedical Engineering (H.R.H.), Johns Hopkins University, Baltimore, MD; and Division of Cardiology, Department of Medicine, Johns Hopkins Medical Institutions, Baltimore, MD (K.C.W., H.R.H.)
| | - Katherine C Wu
- From the Institute for Computational Medicine and Department of Biomedical Engineering (N.A.T., F.P.) and Departments of Radiology and Biomedical Engineering (H.R.H.), Johns Hopkins University, Baltimore, MD; and Division of Cardiology, Department of Medicine, Johns Hopkins Medical Institutions, Baltimore, MD (K.C.W., H.R.H.)
| | - Henry R Halperin
- From the Institute for Computational Medicine and Department of Biomedical Engineering (N.A.T., F.P.) and Departments of Radiology and Biomedical Engineering (H.R.H.), Johns Hopkins University, Baltimore, MD; and Division of Cardiology, Department of Medicine, Johns Hopkins Medical Institutions, Baltimore, MD (K.C.W., H.R.H.)
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Abstract
The treatment of individual patients in cardiology practice increasingly relies on advanced imaging, genetic screening and devices. As the amount of imaging and other diagnostic data increases, paralleled by the greater capacity to personalize treatment, the difficulty of using the full array of measurements of a patient to determine an optimal treatment seems also to be paradoxically increasing. Computational models are progressively addressing this issue by providing a common framework for integrating multiple data sets from individual patients. These models, which are based on physiology and physics rather than on population statistics, enable computational simulations to reveal diagnostic information that would have otherwise remained concealed and to predict treatment outcomes for individual patients. The inherent need for patient-specific models in cardiology is clear and is driving the rapid development of tools and techniques for creating personalized methods to guide pharmaceutical therapy, deployment of devices and surgical interventions.
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Affiliation(s)
- Steven A Niederer
- Department of Biomedical Engineering, School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK.
| | - Joost Lumens
- Department of Biomedical Engineering, Cardiovascular Research Institute Maastricht (CARIM), Maastricht University Medical Center, Maastricht, Netherlands
- IHU LIRYC, Electrophysiology and Heart Modeling Institute, Fondation Bordeaux Université, Pessac, France
| | - Natalia A Trayanova
- Department of Biomedical Engineering and the Institute for Computational Medicine, Johns Hopkins University, Baltimore, MD, USA
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Deng D, Nikolov P, Arevalo HJ, Trayanova NA. Optimal contrast-enhanced MRI image thresholding for accurate prediction of ventricular tachycardia using ex-vivo high resolution models. Comput Biol Med 2018; 102:426-432. [PMID: 30301573 PMCID: PMC6218273 DOI: 10.1016/j.compbiomed.2018.09.031] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2018] [Revised: 09/12/2018] [Accepted: 09/30/2018] [Indexed: 11/23/2022]
Abstract
Patient specific models created from contrast-enhanced (i.e. late-gadolinium, LGE) MRI images can be used for prediction of reentry location and clinical ablation planning. However, there is still a need for direct and systematic comparison between characteristics of ventricular tachycardia (VT) morphologies predicted in computational models and those acquired in clinical or experimental protocols. In this study, we aimed to: 1) assess the differences in VT morphologies predicted by modeling and recorded in experiments in terms of patterns and location of reentries, earliest and latest activation sites, and cycle lengths; and 2) define the optimal range of infarct tissue threshold values which provide best match between simulation and experimental results. To achieve these goals, we utilized LGE-MRI images from 4 swine hearts with inducible monomorphic VT. The images were segmented to identify non-infarcted myocardium, semi viable gray zone (GZ), and core scar based on pixel intensity. Several models were reconstructed from each LGE-MRI scan, with voxels of intensity between that of non-infarcted myocardium and 20-50% of the maximum intensity (in 10% increments) in the infarct region classified as GZ. VT induction was simulated in each model. Our simulation results showed that using GZ intensity thresholds of 20% or 30% resulted in the best match of simulated propagation patterns and reentry locations with those from the experiment. Overall, we matched 70% (7/10) morphologies for all the hearts. Our simulation shows that MRI-based computational models of hearts with myocardial infarction can accurately reproduce the majority of experimentally recorded post-infarction VTs.
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Affiliation(s)
- Dongdong Deng
- Institute for Computational Medicine, Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA
| | - Plamen Nikolov
- Institute for Computational Medicine, Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA
| | - Hermenegild J Arevalo
- Institute for Computational Medicine, Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA; Cardiac Modelling Department, Simula Research Laboratory, Fornebu, Norway
| | - Natalia A Trayanova
- Institute for Computational Medicine, Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA.
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Lerma C, Glass L. Predicting the risk of sudden cardiac death. J Physiol 2016; 594:2445-58. [PMID: 26660287 DOI: 10.1113/jp270535] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2015] [Accepted: 12/07/2015] [Indexed: 12/18/2022] Open
Abstract
Sudden cardiac death (SCD) is the result of a change of cardiac activity from normal (typically sinus) rhythm to a rhythm that does not pump adequate blood to the brain. The most common rhythms leading to SCD are ventricular tachycardia (VT) or ventricular fibrillation (VF). These result from an accelerated ventricular pacemaker or ventricular reentrant waves. Despite significant efforts to develop accurate predictors for the risk of SCD, current methods for risk stratification still need to be improved. In this article we briefly review current approaches to risk stratification. Then we discuss the mathematical basis for dynamical transitions (called bifurcations) that may lead to VT and VF. One mechanism for transition to VT or VF involves a perturbation by a premature ventricular complex (PVC) during sinus rhythm. We describe the main mechanisms of PVCs (reentry, independent pacemakers and abnormal depolarizations). An emerging approach to risk stratification for SCD involves the development of individualized dynamical models of a patient based on measured anatomy and physiology. Careful analysis and modelling of dynamics of ventricular arrhythmia on an individual basis will be essential in order to improve risk stratification for SCD and to lay a foundation for personalized (precision) medicine in cardiology.
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Affiliation(s)
- Claudia Lerma
- Departamento de Instrumentación Electromecánica, Instituto Nacional de Cardiología Ignacio Chávez, México, Distrito Federal, México, 14080
| | - Leon Glass
- Department of Physiology, McGill University, Montreal, Quebec, Canada, H3G 1Y6
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Trayanova NA, Chang KC. How computer simulations of the human heart can improve anti-arrhythmia therapy. J Physiol 2016; 594:2483-502. [PMID: 26621489 DOI: 10.1113/jp270532] [Citation(s) in RCA: 35] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2015] [Accepted: 11/25/2015] [Indexed: 01/26/2023] Open
Abstract
Over the last decade, the state-of-the-art in cardiac computational modelling has progressed rapidly. The electrophysiological function of the heart can now be simulated with a high degree of detail and accuracy, opening the doors for simulation-guided approaches to anti-arrhythmic drug development and patient-specific therapeutic interventions. In this review, we outline the basic methodology for cardiac modelling, which has been developed and validated over decades of research. In addition, we present several recent examples of how computational models of the human heart have been used to address current clinical problems in cardiac electrophysiology. We will explore the use of simulations to improve anti-arrhythmic pacing and defibrillation interventions; to predict optimal sites for clinical ablation procedures; and to aid in the understanding and selection of arrhythmia risk markers. Together, these studies illustrate how the tremendous advances in cardiac modelling are poised to revolutionize medical treatment and prevention of arrhythmia.
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Affiliation(s)
- Natalia A Trayanova
- Johns Hopkins School of Medicine, Baltimore, MD, 21205, USA.,Johns Hopkins University, Baltimore, MD, 21218, USA
| | - Kelly C Chang
- Johns Hopkins School of Medicine, Baltimore, MD, 21205, USA
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Deng D, Arevalo H, Pashakhanloo F, Prakosa A, Ashikaga H, McVeigh E, Halperin H, Trayanova N. Accuracy of prediction of infarct-related arrhythmic circuits from image-based models reconstructed from low and high resolution MRI. Front Physiol 2015; 6:282. [PMID: 26528188 PMCID: PMC4602125 DOI: 10.3389/fphys.2015.00282] [Citation(s) in RCA: 43] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/15/2015] [Accepted: 09/22/2015] [Indexed: 11/13/2022] Open
Abstract
Identification of optimal ablation sites in hearts with infarct-related ventricular tachycardia (VT) remains difficult to achieve with the current catheter-based mapping techniques. Limitations arise from the ambiguities in determining the reentrant pathways location(s). The goal of this study was to develop experimentally validated, individualized computer models of infarcted swine hearts, reconstructed from high-resolution ex-vivo MRI and to examine the accuracy of the reentrant circuit location prediction when models of the same hearts are instead reconstructed from low clinical-resolution MRI scans. To achieve this goal, we utilized retrospective data obtained from four pigs ~10 weeks post infarction that underwent VT induction via programmed stimulation and epicardial activation mapping via a multielectrode epicardial sock. After the experiment, high-resolution ex-vivo MRI with late gadolinium enhancement was acquired. The Hi-res images were downsampled into two lower resolutions (Med-res and Low-res) in order to replicate image quality obtainable in the clinic. The images were segmented and models were reconstructed from the three image stacks for each pig heart. VT induction similar to what was performed in the experiment was simulated. Results of the reconstructions showed that the geometry of the ventricles including the infarct could be accurately obtained from Med-res and Low-res images. Simulation results demonstrated that induced VTs in the Med-res and Low-res models were located close to those in Hi-res models. Importantly, all models, regardless of image resolution, accurately predicted the VT morphology and circuit location induced in the experiment. These results demonstrate that MRI-based computer models of hearts with ischemic cardiomyopathy could provide a unique opportunity to predict and analyze VT resulting for from specific infarct architecture, and thus may assist in clinical decisions to identify and ablate the reentrant circuit(s).
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Affiliation(s)
- Dongdong Deng
- Department of Biomedical Engineering, Institute for Computational Medicine, Johns Hopkins University Baltimore, MD, USA
| | - Hermenegild Arevalo
- Department of Biomedical Engineering, Institute for Computational Medicine, Johns Hopkins University Baltimore, MD, USA
| | - Farhad Pashakhanloo
- Department of Biomedical Engineering, Institute for Computational Medicine, Johns Hopkins University Baltimore, MD, USA
| | - Adityo Prakosa
- Department of Biomedical Engineering, Institute for Computational Medicine, Johns Hopkins University Baltimore, MD, USA
| | - Hiroshi Ashikaga
- Division of Cardiology, Department of Medicine, Johns Hopkins Medical Institute Baltimore, MD, USA ; Department of Biomedical Engineering, Johns Hopkins University Baltimore, MD, USA
| | - Elliot McVeigh
- Department of Biomedical Engineering, Johns Hopkins University Baltimore, MD, USA
| | - Henry Halperin
- Division of Cardiology, Department of Medicine, Johns Hopkins Medical Institute Baltimore, MD, USA
| | - Natalia Trayanova
- Department of Biomedical Engineering, Institute for Computational Medicine, Johns Hopkins University Baltimore, MD, USA
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