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Trayanova NA, Lyon A, Shade J, Heijman J. Computational modeling of cardiac electrophysiology and arrhythmogenesis: toward clinical translation. Physiol Rev 2024; 104:1265-1333. [PMID: 38153307 DOI: 10.1152/physrev.00017.2023] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2023] [Revised: 12/19/2023] [Accepted: 12/21/2023] [Indexed: 12/29/2023] Open
Abstract
The complexity of cardiac electrophysiology, involving dynamic changes in numerous components across multiple spatial (from ion channel to organ) and temporal (from milliseconds to days) scales, makes an intuitive or empirical analysis of cardiac arrhythmogenesis challenging. Multiscale mechanistic computational models of cardiac electrophysiology provide precise control over individual parameters, and their reproducibility enables a thorough assessment of arrhythmia mechanisms. This review provides a comprehensive analysis of models of cardiac electrophysiology and arrhythmias, from the single cell to the organ level, and how they can be leveraged to better understand rhythm disorders in cardiac disease and to improve heart patient care. Key issues related to model development based on experimental data are discussed, and major families of human cardiomyocyte models and their applications are highlighted. An overview of organ-level computational modeling of cardiac electrophysiology and its clinical applications in personalized arrhythmia risk assessment and patient-specific therapy of atrial and ventricular arrhythmias is provided. The advancements presented here highlight how patient-specific computational models of the heart reconstructed from patient data have achieved success in predicting risk of sudden cardiac death and guiding optimal treatments of heart rhythm disorders. Finally, an outlook toward potential future advances, including the combination of mechanistic modeling and machine learning/artificial intelligence, is provided. As the field of cardiology is embarking on a journey toward precision medicine, personalized modeling of the heart is expected to become a key technology to guide pharmaceutical therapy, deployment of devices, and surgical interventions.
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Affiliation(s)
- Natalia A Trayanova
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, Maryland, United States
- Alliance for Cardiovascular Diagnostic and Treatment Innovation, Johns Hopkins University, Baltimore, Maryland, United States
| | - Aurore Lyon
- Department of Biomedical Engineering, CARIM School for Cardiovascular Diseases, Maastricht University, Maastricht, The Netherlands
- Division of Heart and Lungs, Department of Medical Physiology, University Medical Centre Utrecht, Utrecht, The Netherlands
| | - Julie Shade
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, Maryland, United States
- Alliance for Cardiovascular Diagnostic and Treatment Innovation, Johns Hopkins University, Baltimore, Maryland, United States
| | - Jordi Heijman
- Department of Cardiology, CARIM School for Cardiovascular Diseases, Maastricht University, Maastricht, The Netherlands
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2
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Bhagirath P, Campos FO, Zaidi HA, Chen Z, Elliott M, Gould J, Kemme MJB, Wilde AAM, Götte MJW, Postema PG, Prassl AJ, Neic A, Plank G, Rinaldi CA, Bishop MJ. Predicting postinfarct ventricular tachycardia by integrating cardiac MRI and advanced computational reentrant pathway analysis. Heart Rhythm 2024:S1547-5271(24)02507-4. [PMID: 38670247 DOI: 10.1016/j.hrthm.2024.04.077] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/12/2024] [Revised: 03/26/2024] [Accepted: 04/15/2024] [Indexed: 04/28/2024]
Abstract
BACKGROUND Implantable cardiac defibrillator (ICD) implantation can protect against sudden cardiac death after myocardial infarction. However, improved risk stratification for device requirement is still needed. OBJECTIVE The purpose of this study was to improve assessment of postinfarct ventricular electropathology and prediction of appropriate ICD therapy by combining late gadolinium enhancement (LGE) and advanced computational modeling. METHODS ADAS 3D LV (ADAS LV Medical, Barcelona, Spain) and custom-made software were used to generate 3-dimensional patient-specific ventricular models in a prospective cohort of patients with a myocardial infarction (N = 40) having undergone LGE imaging before ICD implantation. Corridor metrics and 3-dimensional surface features were computed from LGE images. The Virtual Induction and Treatment of Arrhythmias (VITA) framework was applied to patient-specific models to comprehensively probe the vulnerability of the scar substrate to sustaining reentrant circuits. Imaging and VITA metrics, related to the numbers of induced ventricular tachycardias and their corresponding round trip times (RTTs), were compared with ICD therapy during follow-up. RESULTS Patients with an event (n = 17) had a larger interface between healthy myocardium and scar and higher VITA metrics. Cox regression analysis demonstrated a significant independent association with an event: interface (hazard ratio [HR] 2.79; 95% confidence interval [CI] 1.44-5.44; P < .01), unique ventricular tachycardias (HR 1.67; 95% CI 1.04-2.68; P = .03), mean RTT (HR 2.14; 95% CI 1.11-4.12; P = .02), and maximum RTT (HR 2.13; 95% CI 1.19-3.81; P = .01). CONCLUSION A detailed quantitative analysis of LGE-based scar maps, combined with advanced computational modeling, can accurately predict ICD therapy and could facilitate the early identification of high-risk patients in addition to left ventricular ejection fraction.
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Affiliation(s)
- Pranav Bhagirath
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom; Department of Cardiology, Amsterdam University Medical Center, Amsterdam, The Netherlands.
| | - Fernando O Campos
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
| | - Hassan A Zaidi
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
| | - Zhong Chen
- Department of Cardiology, Royal Brompton & Harefield NHS Foundation Trust, London, United Kingdom
| | - Mark Elliott
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom; Department of Cardiology, St. Thomas' Hospital, London, United Kingdom
| | - Justin Gould
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom; Department of Cardiology, St. Thomas' Hospital, London, United Kingdom
| | - Michiel J B Kemme
- Department of Cardiology, Amsterdam University Medical Center, Amsterdam, The Netherlands
| | - Arthur A M Wilde
- Department of Cardiology, Amsterdam University Medical Center, Amsterdam, The Netherlands
| | - Marco J W Götte
- Department of Cardiology, Amsterdam University Medical Center, Amsterdam, The Netherlands
| | - Pieter G Postema
- Department of Cardiology, Amsterdam University Medical Center, Amsterdam, The Netherlands
| | - Anton J Prassl
- Gottfried Schatz Research Center, Division of Biophysics, Medical University of Graz, Graz, Austria
| | | | - Gernot Plank
- Gottfried Schatz Research Center, Division of Biophysics, Medical University of Graz, Graz, Austria; NumeriCor GmbH, Graz, Austria
| | | | - Martin J Bishop
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
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Sethi Y, Padda I, Sebastian SA, Moinuddin A, Johal G. Computational Cardiology: The Door to the Future of Interventional Cardiology. JACC. ADVANCES 2023; 2:100625. [PMID: 38938342 PMCID: PMC11198715 DOI: 10.1016/j.jacadv.2023.100625] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 06/29/2024]
Affiliation(s)
- Yashendra Sethi
- PearResearch, Dehradun, India
- Government Doon Medical College, HNB Uttarakhand Medical Education University, Dehradun, Uttarakhand, India
| | - Inderbir Padda
- Department of Medicine, Richmond University Medical Center, Staten Island, New York, USA
| | | | - Arsalan Moinuddin
- Vascular Health Researcher, School of Sport and Exercise, University of Gloucestershire, Gloucester, United Kingdom
| | - Gurpreet Johal
- Valley Medical Center, University of Washington, Seattle, Washington, USA
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4
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Moinuddin A, Ali SY, Goel A, Sethi Y, Patel N, Kaka N, Satapathy P, Sah R, Barboza JJ, Suhail MK. The age of computational cardiology and future of long-term ablation target prediction for ventricular tachycardia. Front Cardiovasc Med 2023; 10:1233991. [PMID: 37817867 PMCID: PMC10561379 DOI: 10.3389/fcvm.2023.1233991] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/03/2023] [Accepted: 08/30/2023] [Indexed: 10/12/2023] Open
Abstract
Ventricular arrhythmias, particularly ventricular tachycardia, are ubiquitously linked to 300,000 deaths annually. However, the current interventional procedure-the cardiac ablation-predict only short-term responses to treatment as the heart constantly remodels itself post-arrhythmia. To assist in the design of computational methods which focuses on long-term arrhythmia prediction, this review postulates three interdependent prospectives. The main objective is to propose computational methods for predicting long-term heart response to interventions in ventricular tachycardia Following a general discussion on the importance of devising simulations predicting long-term heart response to interventions, each of the following is discussed: (i) application of "metabolic sink theory" to elucidate the "re-entry" mechanism of ventricular tachycardia; (ii) application of "growth laws" to explain "mechanical load" translation in ventricular tachycardia; (iii) derivation of partial differential equations (PDE) to establish a pipeline to predict long-term clinical outcomes in ventricular tachycardia.
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Affiliation(s)
- Arsalan Moinuddin
- School of Sport and Exercise, University of Gloucestershire, Gloucester, United Kingdom
| | - Syed Yusuf Ali
- Department of Biomedical Engineering, Johns Hopkins University, Balimore, MD, United States
| | - Ashish Goel
- Department of Medicine, Government Doon Medical College, Dehradun, India
| | - Yashendra Sethi
- Department of Medicine, Government Doon Medical College, Dehradun, India
- PearResearch, Dehradun, India
| | - Neil Patel
- PearResearch, Dehradun, India
- Department of Medicine, GMERS Medical College, Himmatnagar, India
| | - Nirja Kaka
- PearResearch, Dehradun, India
- Department of Medicine, GMERS Medical College, Himmatnagar, India
| | - Prakasini Satapathy
- Global Center for Evidence Synthesis, Chandigarh, India
- Center for Global Health Research, Saveetha Medical College and Hospital, Saveetha Institute of Medical and Technical Sciences, Saveetha University, Chennai, India
| | - Ranjit Sah
- Department of Microbiology, Tribhuvan University Teaching Hospital, Institute of Medicine, Kathmandu, Nepal
- Department of Microbiology, Dr. D. Y. Patil Medical College, Hospital and Research Centre, Dr. D. Y. Patil Vidyapeeth, Pune, India
- Department of Public Health Dentistry, Dr. D.Y. Patil Dental College and Hospital, Dr. D.Y. Patil Vidyapeeth, Pune, India
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5
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Gibbs CE, Marchianó S, Zhang K, Yang X, Murry CE, Boyle PM. Graft-host coupling changes can lead to engraftment arrhythmia: a computational study. J Physiol 2023; 601:2733-2749. [PMID: 37014103 PMCID: PMC10901678 DOI: 10.1113/jp284244] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/06/2023] [Accepted: 03/30/2023] [Indexed: 04/05/2023] Open
Abstract
After myocardial infarction (MI), a significant portion of heart muscle is replaced with scar tissue, progressively leading to heart failure. Human pluripotent stem cell-derived cardiomyocytes (hPSC-CM) offer a promising option for improving cardiac function after MI. However, hPSC-CM transplantation can lead to engraftment arrhythmia (EA). EA is a transient phenomenon arising shortly after transplantation then spontaneously resolving after a few weeks. The underlying mechanism of EA is unknown. We hypothesize that EA may be explained partially by time-varying, spatially heterogeneous, graft-host electrical coupling. Here, we created computational slice models derived from histological images that reflect different configuration of grafts in the infarcted ventricle. We ran simulations with varying degrees of connection imposed upon the graft-host perimeter to assess how heterogeneous electrical coupling affected EA with non-conductive scar, slow-conducting scar and scar replaced by host myocardium. We also quantified the effect of variation in intrinsic graft conductivity. Susceptibility to EA initially increased and subsequently decreased with increasing graft-host coupling, suggesting the waxing and waning of EA is regulated by progressive increases in graft-host coupling. Different spatial distributions of graft, host and scar yielded markedly different susceptibility curves. Computationally replacing non-conductive scar with host myocardium or slow-conducting scar, and increasing intrinsic graft conductivity both demonstrated potential means to blunt EA vulnerability. These data show how graft location, especially relative to scar, along with its dynamic electrical coupling to host, can influence EA burden; moreover, they offer a rational base for further studies aimed to define the optimal delivery of hPSC-CM injection. KEY POINTS: Human pluripotent stem cell-derived cardiomyocytes (hPSC-CM) hold great cardiac regenerative potential but can also cause engraftment arrhythmias (EA). Spatiotemporal evolution in the pattern of electrical coupling between injected hPSC-CMs and surrounding host myocardium may explain the dynamics of EA observed in large animal models. We conducted simulations in histology-derived 2D slice computational models to assess the effects of heterogeneous graft-host electrical coupling on EA propensity, with or without scar tissue. Our findings suggest spatiotemporally heterogeneous graft-host coupling can create an electrophysiological milieu that favours graft-initiated host excitation, a surrogate metric of EA susceptibility. Removing scar from our models reduced but did not abolish the propensity for this phenomenon. Conversely, reduced intra-graft electrical connectedness increased the incidence of graft-initiated host excitation. The computational framework created for this study can be used to generate new hypotheses, targeted delivery of hPSC-CMs.
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Affiliation(s)
- Chelsea E Gibbs
- Department of Bioengineering, University of Washington, Seattle, WA, USA
| | - Silvia Marchianó
- Institute for Stem Cell and Regenerative Medicine, University of Washington, Seattle, WA, USA
- Department of Laboratory Medicine & Pathology, University of Washington, Seattle, WA, USA
- Center for Cardiovascular Biology, University of Washington, Seattle, WA, USA
| | - Kelly Zhang
- Department of Bioengineering, University of Washington, Seattle, WA, USA
| | - Xiulan Yang
- Institute for Stem Cell and Regenerative Medicine, University of Washington, Seattle, WA, USA
- Department of Laboratory Medicine & Pathology, University of Washington, Seattle, WA, USA
- Center for Cardiovascular Biology, University of Washington, Seattle, WA, USA
| | - Charles E Murry
- Department of Bioengineering, University of Washington, Seattle, WA, USA
- Institute for Stem Cell and Regenerative Medicine, University of Washington, Seattle, WA, USA
- Department of Laboratory Medicine & Pathology, University of Washington, Seattle, WA, USA
- Center for Cardiovascular Biology, University of Washington, Seattle, WA, USA
- Division of Cardiology, University of Washington, Seattle, WA, USA
| | - Patrick M Boyle
- Department of Bioengineering, University of Washington, Seattle, WA, USA
- Institute for Stem Cell and Regenerative Medicine, University of Washington, Seattle, WA, USA
- Center for Cardiovascular Biology, University of Washington, Seattle, WA, USA
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Moinuddin A, Sethi Y, Goel A, Uniyal N. Predicting long-term ablation targets for ventricular arrhythmia; the evolution with computational cardiology - Correspondence. Int J Surg 2022; 108:106987. [PMID: 36356824 DOI: 10.1016/j.ijsu.2022.106987] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2022] [Accepted: 11/03/2022] [Indexed: 11/09/2022]
Affiliation(s)
- Arsalan Moinuddin
- School of Sports and Exercise, University of Gloucestershire, UK Government Doon Medical College, H.N.B. Uttarakhand Medical Education University, Dehradun, Uttarakhand, India Gautam Buddha Chikitsa Mahavidyalaya, Ras Bihari Bose Subharti University, Dehradun, Uttarakhand, India
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7
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Rabbat MG, Kwong RY, Heitner JF, Young AA, Shanbhag SM, Petersen SE, Selvanayagam JB, Berry C, Nagel E, Heydari B, Maceira AM, Shenoy C, Dyke C, Bilchick KC. The Future of Cardiac Magnetic Resonance Clinical Trials. JACC Cardiovasc Imaging 2022; 15:2127-2138. [PMID: 34922874 DOI: 10.1016/j.jcmg.2021.07.029] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/06/2020] [Revised: 05/17/2021] [Accepted: 07/27/2021] [Indexed: 01/13/2023]
Abstract
Over the past 2 decades, cardiac magnetic resonance (CMR) has become an essential component of cardiovascular clinical care and contributed to imaging-guided diagnosis and management of coronary artery disease, cardiomyopathy, congenital heart disease, cardio-oncology, valvular, and vascular disease, amongst others. The widespread availability, safety, and capability of CMR to provide corresponding anatomical, physiological, and functional data in 1 imaging session can improve the design and conduct of clinical trials through both a reduction of sample size and provision of important mechanistic data that may augment clinical trial findings. Moreover, prospective imaging-guided strategies using CMR can enhance safety, efficacy, and cost-effectiveness of cardiovascular pathways in clinical practice around the world. As the future of large-scale clinical trial design evolves to integrate personalized medicine, cost-effectiveness, and mechanistic insights of novel therapies, the integration of CMR will continue to play a critical role. In this document, the attributes, limitations, and challenges of CMR's integration into the future design and conduct of clinical trials will also be covered, and recommendations for trialists will be explored. Several prominent examples of clinical trials that test the efficacy of CMR-imaging guided pathways will also be discussed.
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Affiliation(s)
- Mark G Rabbat
- Division of Cardiology, Loyola University Chicago, Chicago, Illinois, USA; Division of Cardiology, Edward Hines Jr VA Hospital, Hines, Illinois, USA
| | - Raymond Y Kwong
- Cardiovascular Division, Department of Medicine, Brigham and Women's Hospital, Boston, Massachusetts, USA.
| | - John F Heitner
- Department of Medicine, New York-Presbyterian Brooklyn Methodist Hospital, Brooklyn, New York, USA
| | - Alistair A Young
- Department of Biomedical Engineering, King's College London, London, United Kingdom
| | - Sujata M Shanbhag
- National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, Maryland, USA
| | - Steffen E Petersen
- Barts Heart Centre, St Bartholomew's Hospital, Barts Health NHS Trust, West Smithfield, London, United Kingdom; National Institute for Health Research Barts Biomedical Research Centre, William Harvey Research Institute, Queen Mary University of London, London, United Kingdom
| | - Joseph B Selvanayagam
- College of Medicine, Flinders University of South Australia, Department of Cardiovascular Medicine, Flinders Medical Centre, Southern Adelaide Local Health Network, and Cardiac Imaging Research Group, South Australian Health and Medical Research Institute, Adelaide, South Australia
| | - Colin Berry
- West of Scotland Heart and Lung Centre, Golden Jubilee National Hospital, and British Heart Foundation Glasgow Cardiovascular Research Centre, Institute of Cardiovascular and Medical Sciences, University of Glasgow, Scotland, United Kingdom
| | - Eike Nagel
- Institute for Experimental and Translational Cardiovascular Imaging, Klinikum der Johann Wolfgang Goethe-Universitat Frankfurt, Frankfurt am Main, Germany
| | - Bobak Heydari
- Stephenson Cardiac Imaging Centre and Department of Cardiac Sciences, Libin Cardiovascular Institute of Alberta, and Department of Cardiac Sciences, University of Calgary, Calgary, Alberta, Canada
| | - Alicia M Maceira
- Cardiovascular Unit, Ascires Biomedical Group, and Department of Medicine, Health Sciences School, UCH-CEU University, Valencia, Spain
| | - Chetan Shenoy
- Cardiovascular Division, Department of Medicine, University of Minnesota Medical School, Minneapolis, Minnesota, USA
| | - Christopher Dyke
- Division of Cardiology, National Jewish Health, Denver, Colorado, USA
| | - Kenneth C Bilchick
- Division of Cardiovascular Medicine, University of Virginia Health System, Charlottesville, Virginia, USA
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8
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Xie E, Sung E, Saad E, Trayanova N, Wu KC, Chrispin J. Advanced imaging for risk stratification for ventricular arrhythmias and sudden cardiac death. Front Cardiovasc Med 2022; 9:884767. [PMID: 36072882 PMCID: PMC9441865 DOI: 10.3389/fcvm.2022.884767] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2022] [Accepted: 08/02/2022] [Indexed: 11/13/2022] Open
Abstract
Sudden cardiac death (SCD) is a leading cause of mortality, comprising approximately half of all deaths from cardiovascular disease. In the US, the majority of SCD (85%) occurs in patients with ischemic cardiomyopathy (ICM) and a subset in patients with non-ischemic cardiomyopathy (NICM), who tend to be younger and whose risk of mortality is less clearly delineated than in ischemic cardiomyopathies. The conventional means of SCD risk stratification has been the determination of the ejection fraction (EF), typically via echocardiography, which is currently a means of determining candidacy for primary prevention in the form of implantable cardiac defibrillators (ICDs). Advanced cardiac imaging methods such as cardiac magnetic resonance imaging (CMR), single-photon emission computerized tomography (SPECT) and positron emission tomography (PET), and computed tomography (CT) have emerged as promising and non-invasive means of risk stratification for sudden death through their characterization of the underlying myocardial substrate that predisposes to SCD. Late gadolinium enhancement (LGE) on CMR detects myocardial scar, which can inform ICD decision-making. Overall scar burden, region-specific scar burden, and scar heterogeneity have all been studied in risk stratification. PET and SPECT are nuclear methods that determine myocardial viability and innervation, as well as inflammation. CT can be used for assessment of myocardial fat and its association with reentrant circuits. Emerging methodologies include the development of "virtual hearts" using complex electrophysiologic modeling derived from CMR to attempt to predict arrhythmic susceptibility. Recent developments have paired novel machine learning (ML) algorithms with established imaging techniques to improve predictive performance. The use of advanced imaging to augment risk stratification for sudden death is increasingly well-established and may soon have an expanded role in clinical decision-making. ML could help shift this paradigm further by advancing variable discovery and data analysis.
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Affiliation(s)
- Eric Xie
- Division of Cardiology, Department of Medicine, Section of Cardiac Electrophysiology, Johns Hopkins University School of Medicine, Baltimore, MD, United States
| | - Eric Sung
- Division of Cardiology, Department of Medicine, Section of Cardiac Electrophysiology, Johns Hopkins University School of Medicine, Baltimore, MD, United States
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, United States
| | - Elie Saad
- Division of Cardiology, Department of Medicine, Section of Cardiac Electrophysiology, Johns Hopkins University School of Medicine, Baltimore, MD, United States
| | - Natalia Trayanova
- Division of Cardiology, Department of Medicine, Section of Cardiac Electrophysiology, Johns Hopkins University School of Medicine, Baltimore, MD, United States
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, United States
| | - Katherine C. Wu
- Division of Cardiology, Department of Medicine, Section of Cardiac Electrophysiology, Johns Hopkins University School of Medicine, Baltimore, MD, United States
| | - Jonathan Chrispin
- Division of Cardiology, Department of Medicine, Section of Cardiac Electrophysiology, Johns Hopkins University School of Medicine, Baltimore, MD, United States
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Dawkins JF, Ehdaie A, Rogers R, Soetkamp D, Valle J, Holm K, Sanchez L, Tremmel I, Nawaz A, Shehata M, Wang X, Prakosa A, Yu J, Van Eyk JE, Trayanova N, Marbán E, Cingolani E. Biological substrate modification suppresses ventricular arrhythmias in a porcine model of chronic ischaemic cardiomyopathy. Eur Heart J 2022; 43:2139-2156. [PMID: 35262692 PMCID: PMC9649918 DOI: 10.1093/eurheartj/ehac042] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/14/2021] [Revised: 01/25/2022] [Accepted: 01/26/2022] [Indexed: 08/15/2023] Open
Abstract
AIMS Cardiomyopathy patients are prone to ventricular arrhythmias (VA) and sudden cardiac death. Current therapies to prevent VA include radiofrequency ablation to destroy slowly conducting pathways of viable myocardium which support re-entry. Here, we tested the reverse concept, namely that boosting local tissue viability in zones of slow conduction might eliminate slow conduction and suppress VA in ischaemic cardiomyopathy. METHODS AND RESULTS Exosomes are extracellular vesicles laden with bioactive cargo. Exosomes secreted by cardiosphere-derived cells (CDCEXO) reduce scar and improve heart function after intramyocardial delivery. In a VA-prone porcine model of ischaemic cardiomyopathy, we injected CDCEXO or vehicle into zones of delayed conduction defined by electroanatomic mapping. Up to 1-month post-injection, CDCEXO, but not the vehicle, decreased myocardial scar, suppressed slowly conducting electrical pathways, and inhibited VA induction by programmed electrical stimulation. In silico reconstruction of electrical activity based on magnetic resonance images accurately reproduced the suppression of VA inducibility by CDCEXO. Strong anti-fibrotic effects of CDCEXO, evident histologically and by proteomic analysis from pig hearts, were confirmed in a co-culture assay of cardiomyocytes and fibroblasts. CONCLUSION Biological substrate modification by exosome injection may be worth developing as a non-destructive alternative to conventional ablation for the prevention of recurrent ventricular tachyarrhythmias.
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Affiliation(s)
- James F. Dawkins
- Smidt Heart Institute, Cedars-Sinai Medical Center, 8700 Beverly Blvd, Los Angeles, CA 90048, USA
| | - Ashkan Ehdaie
- Smidt Heart Institute, Cedars-Sinai Medical Center, 8700 Beverly Blvd, Los Angeles, CA 90048, USA
| | - Russell Rogers
- Smidt Heart Institute, Cedars-Sinai Medical Center, 8700 Beverly Blvd, Los Angeles, CA 90048, USA
| | - Daniel Soetkamp
- Smidt Heart Institute, Cedars-Sinai Medical Center, 8700 Beverly Blvd, Los Angeles, CA 90048, USA
| | - Jackelyn Valle
- Smidt Heart Institute, Cedars-Sinai Medical Center, 8700 Beverly Blvd, Los Angeles, CA 90048, USA
| | - Kevin Holm
- Smidt Heart Institute, Cedars-Sinai Medical Center, 8700 Beverly Blvd, Los Angeles, CA 90048, USA
| | - Lizbeth Sanchez
- Smidt Heart Institute, Cedars-Sinai Medical Center, 8700 Beverly Blvd, Los Angeles, CA 90048, USA
| | - Ileana Tremmel
- Smidt Heart Institute, Cedars-Sinai Medical Center, 8700 Beverly Blvd, Los Angeles, CA 90048, USA
| | - Asma Nawaz
- Smidt Heart Institute, Cedars-Sinai Medical Center, 8700 Beverly Blvd, Los Angeles, CA 90048, USA
| | - Michael Shehata
- Smidt Heart Institute, Cedars-Sinai Medical Center, 8700 Beverly Blvd, Los Angeles, CA 90048, USA
| | - Xunzhang Wang
- Smidt Heart Institute, Cedars-Sinai Medical Center, 8700 Beverly Blvd, Los Angeles, CA 90048, USA
| | - Adityo Prakosa
- Alliance for Cardiovascular Diagnostic and Treatment Innovation, Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA
| | - Joseph Yu
- Alliance for Cardiovascular Diagnostic and Treatment Innovation, Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA
| | - Jennifer E Van Eyk
- Smidt Heart Institute, Cedars-Sinai Medical Center, 8700 Beverly Blvd, Los Angeles, CA 90048, USA
| | - Natalia Trayanova
- Alliance for Cardiovascular Diagnostic and Treatment Innovation, Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA
| | - Eduardo Marbán
- Smidt Heart Institute, Cedars-Sinai Medical Center, 8700 Beverly Blvd, Los Angeles, CA 90048, USA
| | - Eugenio Cingolani
- Smidt Heart Institute, Cedars-Sinai Medical Center, 8700 Beverly Blvd, Los Angeles, CA 90048, USA
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10
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Jian K, Li C, Hancox JC, Zhang H. Pro-Arrhythmic Effects of Discontinuous Conduction at the Purkinje Fiber-Ventricle Junction Arising From Heart Failure-Induced Ionic Remodeling - Insights From Computational Modelling. Front Physiol 2022; 13:877428. [PMID: 35547576 PMCID: PMC9081695 DOI: 10.3389/fphys.2022.877428] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2022] [Accepted: 03/18/2022] [Indexed: 11/18/2022] Open
Abstract
Heart failure is associated with electrical remodeling of the electrical properties and kinetics of the ion channels and transporters that are responsible for cardiac action potentials. However, it is still unclear whether heart failure-induced ionic remodeling can affect the conduction of excitation waves at the Purkinje fiber-ventricle junction contributing to pro-arrhythmic effects of heart failure, as the complexity of the heart impedes a detailed experimental analysis. The aim of this study was to employ computational models to investigate the pro-arrhythmic effects of heart failure-induced ionic remodeling on the cardiac action potentials and excitation wave conduction at the Purkinje fiber-ventricle junction. Single cell models of canine Purkinje fiber and ventricular myocytes were developed for control and heart failure. These single cell models were then incorporated into one-dimensional strand and three-dimensional wedge models to investigate the effects of heart failure-induced remodeling on propagation of action potentials in Purkinje fiber and ventricular tissue and at the Purkinje fiber-ventricle junction. This revealed that heart failure-induced ionic remodeling of Purkinje fiber and ventricular tissue reduced conduction safety and increased tissue vulnerability to the genesis of the unidirectional conduction block. This was marked at the Purkinje fiber-ventricle junction, forming a potential substrate for the genesis of conduction failure that led to re-entry. This study provides new insights into proarrhythmic consequences of heart failure-induced ionic remodeling.
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Affiliation(s)
- Kun Jian
- Biological Physics Group, Department of Physics and Astronomy, The University of Manchester, Manchester, United Kingdom
| | - Chen Li
- Biological Physics Group, Department of Physics and Astronomy, The University of Manchester, Manchester, United Kingdom
| | - Jules C. Hancox
- Biological Physics Group, Department of Physics and Astronomy, The University of Manchester, Manchester, United Kingdom
- School of Physiology, Pharmacology and Neuroscience, Medical Sciences Building, University Walk, Bristol, United Kingdom
| | - Henggui Zhang
- Biological Physics Group, Department of Physics and Astronomy, The University of Manchester, Manchester, United Kingdom
- Key Laboratory of Medical Electrophysiology of Ministry of Education and Medical Electrophysiological Key Laboratory of Sichuan Province, Institute of Cardiovascular Research, Southwest Medical University, Luzhou, China
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11
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An Automata-Based Cardiac Electrophysiology Simulator to Assess Arrhythmia Inducibility. MATHEMATICS 2022. [DOI: 10.3390/math10081293] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
Personalized cardiac electrophysiology simulations have demonstrated great potential to study cardiac arrhythmias and help in therapy planning of radio-frequency ablation. Its application to analyze vulnerability to ventricular tachycardia and sudden cardiac death in infarcted patients has been recently explored. However, the detailed multi-scale biophysical simulations used in these studies are very demanding in terms of memory and computational resources, which prevents their clinical translation. In this work, we present a fast phenomenological system based on cellular automata (CA) to simulate personalized cardiac electrophysiology. The system is trained on biophysical simulations to reproduce cellular and tissue dynamics in healthy and pathological conditions, including action potential restitution, conduction velocity restitution and cell safety factor. We show that a full ventricular simulation can be performed in the order of seconds, emulate the results of a biophysical simulation and reproduce a patient’s ventricular tachycardia in a model that includes a heterogeneous scar region. The system could be used to study the risk of arrhythmia in infarcted patients for a large number of scenarios.
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12
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Brahim K, Arega TW, Boucher A, Bricq S, Sakly A, Meriaudeau F. An Improved 3D Deep Learning-Based Segmentation of Left Ventricular Myocardial Diseases from Delayed-Enhancement MRI with Inclusion and Classification Prior Information U-Net (ICPIU-Net). SENSORS (BASEL, SWITZERLAND) 2022; 22:2084. [PMID: 35336258 PMCID: PMC8954140 DOI: 10.3390/s22062084] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/29/2021] [Revised: 02/18/2022] [Accepted: 03/01/2022] [Indexed: 06/14/2023]
Abstract
Accurate segmentation of the myocardial scar may supply relevant advancements in predicting and controlling deadly ventricular arrhythmias in subjects with cardiovascular disease. In this paper, we propose the architecture of inclusion and classification of prior information U-Net (ICPIU-Net) to efficiently segment the left ventricle (LV) myocardium, myocardial infarction (MI), and microvascular-obstructed (MVO) tissues from late gadolinium enhancement magnetic resonance (LGE-MR) images. Our approach was developed using two subnets cascaded to first segment the LV cavity and myocardium. Then, we used inclusion and classification constraint networks to improve the resulting segmentation of the diseased regions within the pre-segmented LV myocardium. This network incorporates the inclusion and classification information of the LGE-MRI to maintain topological constraints of pathological areas. In the testing stage, the outputs of each segmentation network obtained with specific estimated parameters from training were fused using the majority voting technique for the final label prediction of each voxel in the LGE-MR image. The proposed method was validated by comparing its results to manual drawings by experts from 50 LGE-MR images. Importantly, compared to various deep learning-based methods participating in the EMIDEC challenge, the results of our approach have a more significant agreement with manual contouring in segmenting myocardial diseases.
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Affiliation(s)
- Khawla Brahim
- ImViA EA 7535 Laboratory, University of Burgundy, 21078 Dijon, France; (K.B.); (T.W.A.); (A.B.); (S.B.)
- National Engineering School of Sousse, University of Sousse, Sousse 4054, Tunisia
- LASEE Laboratory, National Engineering School of Monastir, University of Monastir, Monastir 5000, Tunisia;
| | | | - Arnaud Boucher
- ImViA EA 7535 Laboratory, University of Burgundy, 21078 Dijon, France; (K.B.); (T.W.A.); (A.B.); (S.B.)
| | - Stephanie Bricq
- ImViA EA 7535 Laboratory, University of Burgundy, 21078 Dijon, France; (K.B.); (T.W.A.); (A.B.); (S.B.)
| | - Anis Sakly
- LASEE Laboratory, National Engineering School of Monastir, University of Monastir, Monastir 5000, Tunisia;
| | - Fabrice Meriaudeau
- ImViA EA 7535 Laboratory, University of Burgundy, 21078 Dijon, France; (K.B.); (T.W.A.); (A.B.); (S.B.)
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13
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Monaci S, Gillette K, Puyol-Antón E, Rajani R, Plank G, King A, Bishop M. Automated Localization of Focal Ventricular Tachycardia From Simulated Implanted Device Electrograms: A Combined Physics-AI Approach. Front Physiol 2021; 12:682446. [PMID: 34276403 PMCID: PMC8281305 DOI: 10.3389/fphys.2021.682446] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2021] [Accepted: 05/31/2021] [Indexed: 11/13/2022] Open
Abstract
Background: Focal ventricular tachycardia (VT) is a life-threating arrhythmia, responsible for high morbidity rates and sudden cardiac death (SCD). Radiofrequency ablation is the only curative therapy against incessant VT; however, its success is dependent on accurate localization of its source, which is highly invasive and time-consuming. Objective: The goal of our study is, as a proof of concept, to demonstrate the possibility of utilizing electrogram (EGM) recordings from cardiac implantable electronic devices (CIEDs). To achieve this, we utilize fast and accurate whole torso electrophysiological (EP) simulations in conjunction with convolutional neural networks (CNNs) to automate the localization of focal VTs using simulated EGMs. Materials and Methods: A highly detailed 3D torso model was used to simulate ∼4000 focal VTs, evenly distributed across the left ventricle (LV), utilizing a rapid reaction-eikonal environment. Solutions were subsequently combined with lead field computations on the torso to derive accurate electrocardiograms (ECGs) and EGM traces, which were used as inputs to CNNs to localize focal sources. We compared the localization performance of a previously developed CNN architecture (Cartesian probability-based) with our novel CNN algorithm utilizing universal ventricular coordinates (UVCs). Results: Implanted device EGMs successfully localized VT sources with localization error (8.74 mm) comparable to ECG-based localization (6.69 mm). Our novel UVC CNN architecture outperformed the existing Cartesian probability-based algorithm (errors = 4.06 mm and 8.07 mm for ECGs and EGMs, respectively). Overall, localization was relatively insensitive to noise and changes in body compositions; however, displacements in ECG electrodes and CIED leads caused performance to decrease (errors 16-25 mm). Conclusion: EGM recordings from implanted devices may be used to successfully, and robustly, localize focal VT sources, and aid ablation planning.
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Affiliation(s)
| | - Karli Gillette
- Division of Biophysics, Medical University of Graz, Graz, Austria
| | | | | | - Gernot Plank
- Division of Biophysics, Medical University of Graz, Graz, Austria
| | - Andrew King
- King’s College London, London, United Kingdom
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14
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Al-Owais MM, Steele DS, Holden AV, Benson AP. Deterministic and Stochastic Cellular Mechanisms Contributing to Carbon Monoxide Induced Ventricular Arrhythmias. Front Pharmacol 2021; 12:651050. [PMID: 33995065 PMCID: PMC8113948 DOI: 10.3389/fphar.2021.651050] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/08/2021] [Accepted: 04/12/2021] [Indexed: 11/26/2022] Open
Abstract
Chronic exposure to low levels of Carbon Monoxide is associated with an increased risk of cardiac arrhythmia. Microelectrode recordings from rat and guinea pig single isolated ventricular myocytes exposed to CO releasing molecule CORM-2 and excited at 0.2/s show repolarisation changes that develop over hundreds of seconds: action potential prolongation by delayed repolarisation, EADs, multiple EADs and oscillations around the plateau, leading to irreversible repolarisation failure. The measured direct effects of CO on currents in these cells, and ion channels expressed in mammalian systems showed an increase in prolonged late Na+, and a decrease in the maximal T- and L-type Ca++. peak and late Na+, ultra-rapid delayed, delayed rectifier, and the inward rectifier K+ currents. Incorporation of these CO induced changes in maximal currents in ventricular cell models; (Gattoni et al., J. Physiol., 2016, 594, 4193-4224) (rat) and (Luo and Rudy, Circ. Res., 1994, 74, 1071-1096) (guinea-pig) and human endo-, mid-myo- and epi-cardial (O'Hara et al., PLoS Comput. Biol., 2011, 7, e1002061) models, by changes in maximal ionic conductance reproduces these repolarisation abnormalities. Simulations of cell populations with Gaussian distributions of maximal conductance parameters predict a CO induced increase in APD and its variability. Incorporation of these predicted CO induced conductance changes in human ventricular cell electrophysiology into ventricular tissue and wall models give changes in indices for the probability of the initiation of re-entrant arrhythmia.
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Affiliation(s)
- Moza M. Al-Owais
- School of Biomedical Sciences, University of Leeds, Leeds, United Kingdom
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15
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Martonová D, Holz D, Duong MT, Leyendecker S. Towards the simulation of active cardiac mechanics using a smoothed finite element method. J Biomech 2020; 115:110153. [PMID: 33388486 DOI: 10.1016/j.jbiomech.2020.110153] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/17/2020] [Revised: 11/19/2020] [Accepted: 11/23/2020] [Indexed: 01/31/2023]
Abstract
In the last decades, various computational models have been developed to simulate cardiac electromechanics. The most common numerical tool is the finite element method (FEM). However, this method crucially depends on the mesh quality. For complex geometries such as cardiac structures, it is convenient to use tetrahedral discretisations which can be generated automatically. On the other hand, such automatic meshing with tetrahedrons together with large deformations often lead to elements distortion and volumetric locking. To overcome these difficulties, different smoothed finite element methods (S-FEMs) have been proposed in the recent years. They are known to be volumetric locking free, less sensitive to mesh distortion and so far have been used e.g. in simulation of passive cardiac mechanics. In this work, we extend for the first time node-based S-FEM (NS-FEM) towards active cardiac mechanics. Firstly, the sensitivity to mesh distortion is tested and compared to that of FEM. Secondly, an active contraction in circumferentially aligned fibre direction is modelled in the healthy and the infarcted case. We show, that the proposed method is more robust with respect to mesh distortion and computationally more efficient than standard FEM. Being furthermore free of volumetric locking problems makes S-FEM a promising alternative in modelling of active cardiac mechanics, respectively electromechanics.
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Affiliation(s)
- Denisa Martonová
- Friedrich-Alexander-Universität Erlangen-Nürnberg, Institute of Applied Dynamics, Immerwahrstraße 1, 91058 Erlangen, Germany.
| | - David Holz
- Friedrich-Alexander-Universität Erlangen-Nürnberg, Institute of Applied Dynamics, Immerwahrstraße 1, 91058 Erlangen, Germany
| | - Minh Tuan Duong
- Friedrich-Alexander-Universität Erlangen-Nürnberg, Institute of Applied Dynamics, Immerwahrstraße 1, 91058 Erlangen, Germany; Hanoi University of Science and Technology, School of Mechanical Engineering, 1 Dai Co Viet Road, Ha Noi, Viet Nam
| | - Sigrid Leyendecker
- Friedrich-Alexander-Universität Erlangen-Nürnberg, Institute of Applied Dynamics, Immerwahrstraße 1, 91058 Erlangen, Germany
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16
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Nguyen TD, Kadri OE, Voronov RS. An Introductory Overview of Image-Based Computational Modeling in Personalized Cardiovascular Medicine. Front Bioeng Biotechnol 2020; 8:529365. [PMID: 33102452 PMCID: PMC7546862 DOI: 10.3389/fbioe.2020.529365] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2020] [Accepted: 08/31/2020] [Indexed: 02/05/2023] Open
Abstract
Cardiovascular diseases account for the number one cause of deaths in the world. Part of the reason for such grim statistics is our limited understanding of the underlying mechanisms causing these devastating pathologies, which is made difficult by the invasiveness of the procedures associated with their diagnosis (e.g., inserting catheters into the coronal artery to measure blood flow to the heart). Likewise, it is also difficult to design and test assistive devices without implanting them in vivo. However, with the recent advancements made in biomedical scanning technologies and computer simulations, image-based modeling (IBM) has arisen as the next logical step in the evolution of non-invasive patient-specific cardiovascular medicine. Yet, due to its novelty, it is still relatively unknown outside of the niche field. Therefore, the goal of this manuscript is to review the current state-of-the-art and the limitations of the methods used in this area of research, as well as their applications to personalized cardiovascular investigations and treatments. Specifically, the modeling of three different physics – electrophysiology, biomechanics and hemodynamics – used in the cardiovascular IBM is discussed in the context of the physiology that each one of them describes and the mechanisms of the underlying cardiac diseases that they can provide insight into. Only the “bare-bones” of the modeling approaches are discussed in order to make this introductory material more accessible to an outside observer. Additionally, the imaging methods, the aspects of the unique cardiac anatomy derived from them, and their relation to the modeling algorithms are reviewed. Finally, conclusions are drawn about the future evolution of these methods and their potential toward revolutionizing the non-invasive diagnosis, virtual design of treatments/assistive devices, and increasing our understanding of these lethal cardiovascular diseases.
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Affiliation(s)
- Thanh Danh Nguyen
- Otto H. York Department of Chemical and Materials Engineering, Newark College of Engineering, New Jersey Institute of Technology, Newark, NJ, United States
| | - Olufemi E Kadri
- Otto H. York Department of Chemical and Materials Engineering, Newark College of Engineering, New Jersey Institute of Technology, Newark, NJ, United States.,UC-P&G Simulation Center, University of Cincinnati, Cincinnati, OH, United States
| | - Roman S Voronov
- Otto H. York Department of Chemical and Materials Engineering, Newark College of Engineering, New Jersey Institute of Technology, Newark, NJ, United States.,Department of Biomedical Engineering, Newark College of Engineering, New Jersey Institute of Technology, Newark, NJ, United States
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17
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Monaci S, Strocchi M, Rodero C, Gillette K, Whitaker J, Rajani R, Rinaldi CA, O'Neill M, Plank G, King A, Bishop MJ. In-silico pace-mapping using a detailed whole torso model and implanted electronic device electrograms for more efficient ablation planning. Comput Biol Med 2020; 125:104005. [PMID: 32971325 DOI: 10.1016/j.compbiomed.2020.104005] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2020] [Revised: 09/07/2020] [Accepted: 09/07/2020] [Indexed: 10/23/2022]
Abstract
BACKGROUND Pace-mapping is a commonly used electrophysiological (EP) procedure which aims to identify exit sites of ventricular tachycardia (VT) by matching ventricular activation patterns (assessed by QRS morphology) at specific pacing locations with activation during VT. However, long procedure durations and the need for VT induction render this technique non-optimal. To demonstrate the potential of in-silico pace-mapping, using stored electrogram (EGM) recordings of clinical VT from implanted devices to guide pre-procedural ablation planning. METHOD Six scar-related VT episodes were simulated in a 3D torso model reconstructed from computed tomography (CT) imaging data, including three different infarct anatomies mapped from infarcted porcine imaging data. In-silico pace-mapping was performed to localise VT exit sites and isthmuses by using 12-lead electrocardiogram (ECG) signals and different combinations of EGM sensing vectors from implanted devices, through the creation of conventional correlation maps and reference-less maps. RESULTS Our in-silico platform was successful in identifying VT exit sites for a variety of different VT morphologies from both ECG correlation maps and corresponding EGM maps, with the latter dependent upon the number of sensing vectors used. We also showed the added utility of both ECG and EGM reference-less pace-mapping for the identification of slow-conducting isthmuses, uncovering the optimal algorithm parameters. Finally, EGM-based pace-mapping was shown to be more dependent upon the mapped surface (epicardial/endocardial), relative to the VT origin. CONCLUSIONS In-silico pace-mapping can be used along with EGMs from implanted devices to localise VT ablation targets in pre-procedural planning.
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Affiliation(s)
| | | | | | | | | | - Ronak Rajani
- King's College London, London, United Kingdom; Guy's and St Thomas' Hospital, London, United Kingdom
| | - Christopher A Rinaldi
- King's College London, London, United Kingdom; Guy's and St Thomas' Hospital, London, United Kingdom
| | | | | | - Andrew King
- King's College London, London, United Kingdom
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18
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Zabihollahy F, Rajan S, Ukwatta E. Machine Learning-Based Segmentation of Left Ventricular Myocardial Fibrosis from Magnetic Resonance Imaging. Curr Cardiol Rep 2020; 22:65. [PMID: 32562100 DOI: 10.1007/s11886-020-01321-1] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/05/2023]
Abstract
PURPOSE OF REVIEW Myocardial fibrosis (MF) arises due to myocardial infarction and numerous cardiac diseases. MF may lead to several heart disorders, such as heart failure, arrhythmias, and ischemia. Cardiac magnetic resonance (CMR) imaging techniques, such as late gadolinium enhancement (LGE) CMR, enable non-invasive assessment of MF in the left ventricle (LV). Manual assessment of MF on CMR is a tedious and time-consuming task that is subject to high observer variability. Automated segmentation and quantification of MF is important for risk stratification and treatment planning in patients with heart disorders. This article aims to review the machine learning (ML)-based methodologies developed for MF quantification in the LV using CMR images. RECENT FINDINGS With the availability of relatively large labeled datasets supervised learning methods based on both conventional ML and state-of-the-art deep learning (DL) methods have been successfully applied for automated segmentation of MF. The incorporation of ML algorithms into imaging techniques such as 3D LGE CMR permits fast characterization of MF on CMR imaging and may enhance the diagnosis and prognosis of patients with heart disorders. Concurrently, the studies using cine CMR images have revealed that accurate segmentation of MF on non-contrast CMR imaging might be possible. The application of ML/DL tools in CMR image interpretation is likely to result in accurate and efficient quantification of MF.
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Affiliation(s)
- Fatemeh Zabihollahy
- Department of Systems and Computer Engineering, Carleton University, Ottawa, ON, Canada.
| | - S Rajan
- Department of Systems and Computer Engineering, Carleton University, Ottawa, ON, Canada
| | - E Ukwatta
- School of Engineering, University of Guelph, Guelph, ON, Canada
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19
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Smirnov D, Pikunov A, Syunyaev R, Deviatiiarov R, Gusev O, Aras K, Gams A, Koppel A, Efimov IR. Genetic algorithm-based personalized models of human cardiac action potential. PLoS One 2020; 15:e0231695. [PMID: 32392258 PMCID: PMC7213718 DOI: 10.1371/journal.pone.0231695] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2019] [Accepted: 03/31/2020] [Indexed: 11/21/2022] Open
Abstract
We present a novel modification of genetic algorithm (GA) which determines personalized parameters of cardiomyocyte electrophysiology model based on set of experimental human action potential (AP) recorded at different heart rates. In order to find the steady state solution, the optimized algorithm performs simultaneous search in the parametric and slow variables spaces. We demonstrate that several GA modifications are required for effective convergence. Firstly, we used Cauchy mutation along a random direction in the parametric space. Secondly, relatively large number of elite organisms (6-10% of the population passed on to new generation) was required for effective convergence. Test runs with synthetic AP as input data indicate that algorithm error is low for high amplitude ionic currents (1.6±1.6% for IKr, 3.2±3.5% for IK1, 3.9±3.5% for INa, 8.2±6.3% for ICaL). Experimental signal-to-noise ratio above 28 dB was required for high quality GA performance. GA was validated against optical mapping recordings of human ventricular AP and mRNA expression profile of donor hearts. In particular, GA output parameters were rescaled proportionally to mRNA levels ratio between patients. We have demonstrated that mRNA-based models predict the AP waveform dependence on heart rate with high precision. The latter also provides a novel technique of model personalization that makes it possible to map gene expression profile to cardiac function.
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Affiliation(s)
- Dmitrii Smirnov
- Moscow Institute of Physics and Technology, Dolgoprudny, Russia
| | - Andrey Pikunov
- Moscow Institute of Physics and Technology, Dolgoprudny, Russia
| | - Roman Syunyaev
- Moscow Institute of Physics and Technology, Dolgoprudny, Russia
- The George Washington University, Washington, DC, United States of America
- Sechenov University, Moscow, Russia
| | | | | | - Kedar Aras
- The George Washington University, Washington, DC, United States of America
| | - Anna Gams
- The George Washington University, Washington, DC, United States of America
| | - Aaron Koppel
- The George Washington University, Washington, DC, United States of America
| | - Igor R. Efimov
- Moscow Institute of Physics and Technology, Dolgoprudny, Russia
- The George Washington University, Washington, DC, United States of America
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20
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Shade JK, Cartoski MJ, Nikolov P, Prakosa A, Doshi A, Binka E, Olivieri L, Boyle PM, Spevak PJ, Trayanova NA. Ventricular arrhythmia risk prediction in repaired Tetralogy of Fallot using personalized computational cardiac models. Heart Rhythm 2020; 17:408-414. [PMID: 31589989 PMCID: PMC7056519 DOI: 10.1016/j.hrthm.2019.10.002] [Citation(s) in RCA: 32] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/16/2019] [Indexed: 12/29/2022]
Abstract
BACKGROUND Adults with repaired tetralogy of Fallot (rTOF) are at increased risk for ventricular tachycardia (VT) due to fibrotic remodeling of the myocardium. However, the current clinical guidelines for VT risk stratification and subsequent implantable cardioverter-defibrillator deployment for primary prevention of sudden cardiac death in rTOF remain inadequate. OBJECTIVE The purpose of this study was to determine the feasibility of using an rTOF-specific virtual-heart approach to identify patients stratified incorrectly as being at low VT risk by current clinical criteria. METHODS This multicenter retrospective pilot study included 7 adult rTOF patients who were considered low risk for VT based on clinical criteria. Patient-specific computational heart models were generated from late gadolinium enhanced magnetic resonance imaging (LGE-MRI), incorporating the individual distribution of rTOF fibrotic remodeling in both ventricles. Simulations of rapid pacing determined VT inducibility. Model creation and simulations were performed by operators blinded to clinical outcome. RESULTS Two patients in the study experienced clinical VT. The virtual hearts constructed from LGE-MRI scans of 7 rTOF patients correctly predicted reentrant VT in the models from VT-positive patients and no arrhythmia in those from VT-negative patients. There were no statistically significant differences in clinical criteria commonly used to assess VT risk, including QRS duration and age, between patients who did and those who did not experience clinical VT. CONCLUSION This study demonstrates the feasibility of image-based virtual-heart modeling in patients with congenital heart disease and structurally abnormal hearts. It highlights the potential of the methodology to improve VT risk stratification in patients with rTOF.
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Affiliation(s)
- Julie K Shade
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, Maryland; Alliance for Cardiovascular Diagnostic and Treatment Innovation, Whiting School of Engineering and School of Medicine, Johns Hopkins University, Baltimore, Maryland
| | - Mark J Cartoski
- Division of Pediatric Cardiology, Department of Pediatrics, Johns Hopkins University School of Medicine, Baltimore, Maryland
| | - Plamen Nikolov
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, Maryland
| | - Adityo Prakosa
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, Maryland; Alliance for Cardiovascular Diagnostic and Treatment Innovation, Whiting School of Engineering and School of Medicine, Johns Hopkins University, Baltimore, Maryland
| | - Ashish Doshi
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, Maryland; Alliance for Cardiovascular Diagnostic and Treatment Innovation, Whiting School of Engineering and School of Medicine, Johns Hopkins University, Baltimore, Maryland
| | - Edem Binka
- Alliance for Cardiovascular Diagnostic and Treatment Innovation, Whiting School of Engineering and School of Medicine, Johns Hopkins University, Baltimore, Maryland; Division of Pediatric Cardiology, Department of Pediatrics, Johns Hopkins University School of Medicine, Baltimore, Maryland
| | - Laura Olivieri
- Division of Cardiology, Children's National Medical Center, Washington, DC
| | - Patrick M Boyle
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, Maryland
| | - Philip J Spevak
- Division of Pediatric Cardiology, Department of Pediatrics, Johns Hopkins University School of Medicine, Baltimore, Maryland
| | - Natalia A Trayanova
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, Maryland; Alliance for Cardiovascular Diagnostic and Treatment Innovation, Whiting School of Engineering and School of Medicine, Johns Hopkins University, Baltimore, Maryland; Division of Cardiology, Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, Maryland.
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21
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Whitaker J, Neji R, Byrne N, Puyol-Antón E, Mukherjee RK, Williams SE, Chubb H, O’Neill L, Razeghi O, Connolly A, Rhode K, Niederer S, King A, Tschabrunn C, Anter E, Nezafat R, Bishop MJ, O’Neill M, Razavi R, Roujol S. Improved co-registration of ex-vivo and in-vivo cardiovascular magnetic resonance images using heart-specific flexible 3D printed acrylic scaffold combined with non-rigid registration. J Cardiovasc Magn Reson 2019; 21:62. [PMID: 31597563 PMCID: PMC6785908 DOI: 10.1186/s12968-019-0574-z] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/11/2018] [Accepted: 09/02/2019] [Indexed: 01/01/2023] Open
Abstract
BACKGROUND Ex-vivo cardiovascular magnetic resonance (CMR) imaging has played an important role in the validation of in-vivo CMR characterization of pathological processes. However, comparison between in-vivo and ex-vivo imaging remains challenging due to shape changes occurring between the two states, which may be non-uniform across the diseased heart. A novel two-step process to facilitate registration between ex-vivo and in-vivo CMR was developed and evaluated in a porcine model of chronic myocardial infarction (MI). METHODS Seven weeks after ischemia-reperfusion MI, 12 swine underwent in-vivo CMR imaging with late gadolinium enhancement followed by ex-vivo CMR 1 week later. Five animals comprised the control group, in which ex-vivo imaging was undertaken without any support in the LV cavity, 7 animals comprised the experimental group, in which a two-step registration optimization process was undertaken. The first step involved a heart specific flexible 3D printed scaffold generated from in-vivo CMR, which was used to maintain left ventricular (LV) shape during ex-vivo imaging. In the second step, a non-rigid co-registration algorithm was applied to align in-vivo and ex-vivo data. Tissue dimension changes between in-vivo and ex-vivo imaging were compared between the experimental and control group. In the experimental group, tissue compartment volumes and thickness were compared between in-vivo and ex-vivo data before and after non-rigid registration. The effectiveness of the alignment was assessed quantitatively using the DICE similarity coefficient. RESULTS LV cavity volume changed more in the control group (ratio of cavity volume between ex-vivo and in-vivo imaging in control and experimental group 0.14 vs 0.56, p < 0.0001) and there was a significantly greater change in the short axis dimensions in the control group (ratio of short axis dimensions in control and experimental group 0.38 vs 0.79, p < 0.001). In the experimental group, prior to non-rigid co-registration the LV cavity contracted isotropically in the ex-vivo condition by less than 20% in each dimension. There was a significant proportional change in tissue thickness in the healthy myocardium (change = 29 ± 21%), but not in dense scar (change = - 2 ± 2%, p = 0.034). Following the non-rigid co-registration step of the process, the DICE similarity coefficients for the myocardium, LV cavity and scar were 0.93 (±0.02), 0.89 (±0.01) and 0.77 (±0.07) respectively and the myocardial tissue and LV cavity volumes had a ratio of 1.03 and 1.00 respectively. CONCLUSIONS The pattern of the morphological changes seen between the in-vivo and the ex-vivo LV differs between scar and healthy myocardium. A 3D printed flexible scaffold based on the in-vivo shape of the LV cavity is an effective strategy to minimize morphological changes in the ex-vivo LV. The subsequent non-rigid registration step further improved the co-registration and local comparison between in-vivo and ex-vivo data.
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Affiliation(s)
- John Whitaker
- School of Biomedical Engineering and Imaging Sciences, King’s College, London, Fourth Floor Lambeth Wing, St Thomas’ Hospital, London, SE1 7EH UK
| | - Radhouene Neji
- School of Biomedical Engineering and Imaging Sciences, King’s College, London, Fourth Floor Lambeth Wing, St Thomas’ Hospital, London, SE1 7EH UK
- Siemens Healthcare Limited, Frimley, UK
| | - Nicholas Byrne
- School of Biomedical Engineering and Imaging Sciences, King’s College, London, Fourth Floor Lambeth Wing, St Thomas’ Hospital, London, SE1 7EH UK
- Medical Physics, Guy’s and St. Thomas’ NHS Foundation Trust, London, UK
| | - Esther Puyol-Antón
- School of Biomedical Engineering and Imaging Sciences, King’s College, London, Fourth Floor Lambeth Wing, St Thomas’ Hospital, London, SE1 7EH UK
| | - Rahul K. Mukherjee
- School of Biomedical Engineering and Imaging Sciences, King’s College, London, Fourth Floor Lambeth Wing, St Thomas’ Hospital, London, SE1 7EH UK
| | - Steven E. Williams
- School of Biomedical Engineering and Imaging Sciences, King’s College, London, Fourth Floor Lambeth Wing, St Thomas’ Hospital, London, SE1 7EH UK
| | - Henry Chubb
- School of Biomedical Engineering and Imaging Sciences, King’s College, London, Fourth Floor Lambeth Wing, St Thomas’ Hospital, London, SE1 7EH UK
| | - Louisa O’Neill
- School of Biomedical Engineering and Imaging Sciences, King’s College, London, Fourth Floor Lambeth Wing, St Thomas’ Hospital, London, SE1 7EH UK
| | - Orod Razeghi
- School of Biomedical Engineering and Imaging Sciences, King’s College, London, Fourth Floor Lambeth Wing, St Thomas’ Hospital, London, SE1 7EH UK
| | - Adam Connolly
- School of Biomedical Engineering and Imaging Sciences, King’s College, London, Fourth Floor Lambeth Wing, St Thomas’ Hospital, London, SE1 7EH UK
| | - Kawal Rhode
- School of Biomedical Engineering and Imaging Sciences, King’s College, London, Fourth Floor Lambeth Wing, St Thomas’ Hospital, London, SE1 7EH UK
| | - Steven Niederer
- School of Biomedical Engineering and Imaging Sciences, King’s College, London, Fourth Floor Lambeth Wing, St Thomas’ Hospital, London, SE1 7EH UK
| | - Andrew King
- School of Biomedical Engineering and Imaging Sciences, King’s College, London, Fourth Floor Lambeth Wing, St Thomas’ Hospital, London, SE1 7EH UK
| | - Cory Tschabrunn
- Cardiology Department, University of Pennsylvania, Philadelphia, PA USA
| | - Elad Anter
- Cardiology Department, Beth Israel Deaconess Medical Centre / Harvard Medical School, Boston, MA USA
| | - Reza Nezafat
- Cardiology Department, Beth Israel Deaconess Medical Centre / Harvard Medical School, Boston, MA USA
| | - Martin J. Bishop
- School of Biomedical Engineering and Imaging Sciences, King’s College, London, Fourth Floor Lambeth Wing, St Thomas’ Hospital, London, SE1 7EH UK
| | - Mark O’Neill
- School of Biomedical Engineering and Imaging Sciences, King’s College, London, Fourth Floor Lambeth Wing, St Thomas’ Hospital, London, SE1 7EH UK
| | - Reza Razavi
- School of Biomedical Engineering and Imaging Sciences, King’s College, London, Fourth Floor Lambeth Wing, St Thomas’ Hospital, London, SE1 7EH UK
| | - Sébastien Roujol
- School of Biomedical Engineering and Imaging Sciences, King’s College, London, Fourth Floor Lambeth Wing, St Thomas’ Hospital, London, SE1 7EH UK
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22
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Cedilnik N, Duchateau J, Dubois R, Sacher F, Jaïs P, Cochet H, Sermesant M. Fast personalized electrophysiological models from computed tomography images for ventricular tachycardia ablation planning. Europace 2019; 20:iii94-iii101. [PMID: 30476056 DOI: 10.1093/europace/euy228] [Citation(s) in RCA: 27] [Impact Index Per Article: 5.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2018] [Accepted: 09/18/2018] [Indexed: 11/12/2022] Open
Abstract
Aims Clinical application of patient-specific cardiac computer models requires fast and robust processing pipelines that can be seamlessly integrated into clinical workflows. We aim at building such a pipeline from computed tomography (CT) images to personalized cardiac electrophysiology (EP) model. The simulation output could be useful in the context of post-infarct ventricular tachycardia (VT) radiofrequency ablation (RFA) planning for pre-operative targets prediction. Methods and results The support for model personalization is a patient-specific virtual three-dimensional heart obtained from CT images. Here, the scar is identified as thinning of the myocardial wall on automatically computed thickness maps. We then use an Eikonal model of wave front propagation with reduced velocity in the damaged areas. An image-based vessel enhancement algorithm can automatically identify VT isthmuses. The personalized model is used for virtual pacing. We obtained a very fast pipeline that enables simulations in only a few minutes. It is fully automated starting from the semi-automated image segmentation phase. The computational time frame is compatible with the construction of a virtual pacing tool. In this tool, onset points and an optional directional block could be interactively selected. The directional block is a simple way to model tissue refractoriness. Output activation maps are compared with EP data acquired pre-operatively. We show that this framework allows the reproduction of recorded re-entrant VT activation patterns. Conclusion Our simulation framework has an application in VT RFA intervention planning. It could be used to guide EP explorations and even predict ablation targets pre-operatively. This could reduce intervention duration and improve success rate.
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Affiliation(s)
- Nicolas Cedilnik
- Université Côte d'Azur, Inria, Epione, Sophia Antipolis, France & Liryc Institute, Bordeaux, France
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23
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Pashakhanloo F, Herzka DA, Halperin H, McVeigh ER, Trayanova NA. Role of 3-Dimensional Architecture of Scar and Surviving Tissue in Ventricular Tachycardia: Insights From High-Resolution Ex Vivo Porcine Models. Circ Arrhythm Electrophysiol 2019; 11:e006131. [PMID: 29880529 DOI: 10.1161/circep.117.006131] [Citation(s) in RCA: 27] [Impact Index Per Article: 5.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/23/2017] [Accepted: 04/05/2018] [Indexed: 11/16/2022]
Abstract
BACKGROUND An improved knowledge of the spatial organization of infarct structure and its contribution to ventricular tachycardia (VT) is important for designing optimal treatments. This study explores the relationship between the 3-dimensional structure of the healed infarct and the VT reentrant pathways in high-resolution models of infarcted porcine hearts. METHODS Structurally detailed models of infarcted ventricles were reconstructed from ex vivo late gadolinium enhancement and diffusion tensor magnetic resonance imaging data of 8 chronically infarcted porcine hearts at submillimeter resolution (0.25×0.25×0.5 mm3). To characterize the 3-dimensional structure of surviving tissue in the zone of infarct, a novel scar-mapped thickness metric was introduced. Further, using the ventricular models, electrophysiological simulations were conducted to determine and analyze the 3-dimensional VT pathways that were established in each of the complex infarct morphologies. RESULTS The scar-mapped thickness metric revealed the heterogeneous organization of infarct and enabled us to systematically characterize the distribution of surviving tissue thickness in 8 hearts. Simulation results demonstrated the involvement of a subendocardial tissue layer of varying thickness in the majority of VT pathways. Importantly, they revealed that VT pathways are most frequently established within thin surviving tissue structures of thickness ≤2.2 mm (90th percentile) surrounding the scar. CONCLUSIONS The combination of high-resolution imaging data and ventricular simulations revealed the 3-dimensional distribution of surviving tissue surrounding the scar and demonstrated its involvement in VT pathways. The new knowledge obtained in this study contributes toward a better understanding of infarct-related VT.
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Affiliation(s)
| | - Daniel A Herzka
- Department of Biomedical Engineering (F.P., D.A.H., E.R.M., N.A.T.)
| | | | - Elliot R McVeigh
- Department of Biomedical Engineering (F.P., D.A.H., E.R.M., N.A.T.).,Johns Hopkins University, Baltimore, MD. Departments of Bioengineering, Medicine, and Radiology, University of California, San Diego, La Jolla (E.R.M.)
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24
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Factors Promoting Conduction Slowing as Substrates for Block and Reentry in Infarcted Hearts. Biophys J 2019; 117:2361-2374. [PMID: 31521328 PMCID: PMC6990374 DOI: 10.1016/j.bpj.2019.08.008] [Citation(s) in RCA: 28] [Impact Index Per Article: 5.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/16/2019] [Revised: 07/03/2019] [Accepted: 08/05/2019] [Indexed: 01/11/2023] Open
Abstract
The development of effective and safe therapies for scar-related ventricular tachycardias requires a detailed understanding of the mechanisms underlying the conduction block that initiates electrical re-entries associated with these arrhythmias. Conduction block has been often associated with electrophysiological changes that prolong action potential duration (APD) within the border zone (BZ) of chronically infarcted hearts. However, experimental evidence suggests that remodeling processes promoting conduction slowing as opposed to APD prolongation mark the chronic phase. In this context, the substrate for the initial block at the mouth of an isthmus/diastolic channel leading to ventricular tachycardia is unclear. The goal of this study was to determine whether electrophysiological parameters associated with conduction slowing can cause block and re-entry in the BZ. In silico experiments were conducted on two-dimensional idealized infarct tissue as well as on a cohort of postinfarction porcine left ventricular models constructed from ex vivo magnetic resonance imaging scans. Functional conduction slowing in the BZ was modeled by reducing sodium current density, whereas structural conduction slowing was represented by decreasing tissue conductivity and including fibrosis. The arrhythmogenic potential of APD prolongation was also tested as a basis for comparison. Within all models, the combination of reduced sodium current with structural remodeling more often degenerated into re-entry and, if so, was more likely to be sustained for more cycles. Although re-entries were also detected in experiments with prolonged APD, they were often not sustained because of the subsequent block caused by long-lasting repolarization. Functional and structural conditions associated with slow conduction rather than APD prolongation form a potent substrate for arrhythmogenesis at the isthmus/BZ of chronically infarcted hearts. Reduced excitability led to block while slow conduction shortened the wavelength of propagation, facilitating the sustenance of re-entries. These findings provide important insights for models of patient-specific risk stratification and therapy planning.
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25
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Okada DR, Wu KC. Applications of Cardiac MR Imaging in Electrophysiology. Magn Reson Imaging Clin N Am 2019; 27:465-473. [PMID: 31279450 DOI: 10.1016/j.mric.2019.04.006] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/22/2022]
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26
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A comprehensive, multiscale framework for evaluation of arrhythmias arising from cell therapy in the whole post-myocardial infarcted heart. Sci Rep 2019; 9:9238. [PMID: 31239508 PMCID: PMC6592890 DOI: 10.1038/s41598-019-45684-0] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/14/2018] [Accepted: 06/12/2019] [Indexed: 12/19/2022] Open
Abstract
Direct remuscularization approaches to cell-based heart repair seek to restore ventricular contractility following myocardial infarction (MI) by introducing new cardiomyocytes (CMs) to replace lost or injured ones. However, despite promising improvements in cardiac function, high incidences of ventricular arrhythmias have been observed in animal models of MI injected with pluripotent stem cell-derived cardiomyocytes (PSC-CMs). The mechanisms of arrhythmogenesis remain unclear. Here, we present a comprehensive framework for computational modeling of direct remuscularization approaches to cell therapy. Our multiscale 3D whole-heart modeling framework integrates realistic representations of cell delivery and transdifferentiation therapy modalities as well as representation of spatial distributions of engrafted cells, enabling simulation of clinical therapy and the prediction of emergent electrophysiological behavior and arrhythmogenensis. We employ this framework to explore how varying parameters of cell delivery and transdifferentiation could result in three mechanisms of arrhythmogenesis: focal ectopy, heart block, and reentry.
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27
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Lopez-Perez A, Sebastian R, Izquierdo M, Ruiz R, Bishop M, Ferrero JM. Personalized Cardiac Computational Models: From Clinical Data to Simulation of Infarct-Related Ventricular Tachycardia. Front Physiol 2019; 10:580. [PMID: 31156460 PMCID: PMC6531915 DOI: 10.3389/fphys.2019.00580] [Citation(s) in RCA: 41] [Impact Index Per Article: 8.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/08/2018] [Accepted: 04/25/2019] [Indexed: 12/20/2022] Open
Abstract
In the chronic stage of myocardial infarction, a significant number of patients develop life-threatening ventricular tachycardias (VT) due to the arrhythmogenic nature of the remodeled myocardium. Radiofrequency ablation (RFA) is a common procedure to isolate reentry pathways across the infarct scar that are responsible for VT. Unfortunately, this strategy show relatively low success rates; up to 50% of patients experience recurrent VT after the procedure. In the last decade, intensive research in the field of computational cardiac electrophysiology (EP) has demonstrated the ability of three-dimensional (3D) cardiac computational models to perform in-silico EP studies. However, the personalization and modeling of certain key components remain challenging, particularly in the case of the infarct border zone (BZ). In this study, we used a clinical dataset from a patient with a history of infarct-related VT to build an image-based 3D ventricular model aimed at computational simulation of cardiac EP, including detailed patient-specific cardiac anatomy and infarct scar geometry. We modeled the BZ in eight different ways by combining the presence or absence of electrical remodeling with four different levels of image-based patchy fibrosis (0, 10, 20, and 30%). A 3D torso model was also constructed to compute the ECG. Patient-specific sinus activation patterns were simulated and validated against the patient's ECG. Subsequently, the pacing protocol used to induce reentrant VTs in the EP laboratory was reproduced in-silico. The clinical VT was induced with different versions of the model and from different pacing points, thus identifying the slow conducting channel responsible for such VT. Finally, the real patient's ECG recorded during VT episodes was used to validate our simulation results and to assess different strategies to model the BZ. Our study showed that reduced conduction velocities and heterogeneity in action potential duration in the BZ are the main factors in promoting reentrant activity. Either electrical remodeling or fibrosis in a degree of at least 30% in the BZ were required to initiate VT. Moreover, this proof-of-concept study confirms the feasibility of developing 3D computational models for cardiac EP able to reproduce cardiac activation in sinus rhythm and during VT, using exclusively non-invasive clinical data.
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Affiliation(s)
- Alejandro Lopez-Perez
- Center for Research and Innovation in Bioengineering (Ci2B), Universitat Politècnica de València, Valencia, Spain
| | - Rafael Sebastian
- Computational Multiscale Simulation Lab (CoMMLab), Universitat de València, Valencia, Spain
| | - M Izquierdo
- INCLIVA Health Research Institute, Valencia, Spain.,Arrhythmia Unit, Cardiology Department, Hospital Clínico Universitario de Valencia, Valencia, Spain
| | - Ricardo Ruiz
- INCLIVA Health Research Institute, Valencia, Spain.,Arrhythmia Unit, Cardiology Department, Hospital Clínico Universitario de Valencia, Valencia, Spain
| | - Martin Bishop
- Division of Imaging Sciences & Biomedical Engineering, Department of Biomedical Engineering, King's College London, London, United Kingdom
| | - Jose M Ferrero
- Center for Research and Innovation in Bioengineering (Ci2B), Universitat Politècnica de València, Valencia, Spain
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28
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Hsia HH, Xiong N. Infusion Needle Ablation Catheter: An Evolution of Needs. J Am Coll Cardiol 2019; 73:1426-1429. [PMID: 30922473 DOI: 10.1016/j.jacc.2018.12.069] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/15/2018] [Revised: 12/21/2018] [Accepted: 12/21/2018] [Indexed: 10/27/2022]
Affiliation(s)
- Henry H Hsia
- Cardiac Electrophysiology Service, University of California, San Francisco, San Francisco, California.
| | - Nanqing Xiong
- Department of Cardiology, Husham Hospital Furan University, Shanghai, China
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29
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Cartoski MJ, Nikolov PP, Prakosa A, Boyle PM, Spevak PJ, Trayanova NA. Computational Identification of Ventricular Arrhythmia Risk in Pediatric Myocarditis. Pediatr Cardiol 2019; 40:857-864. [PMID: 30840104 PMCID: PMC6451890 DOI: 10.1007/s00246-019-02082-7] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/21/2018] [Accepted: 02/27/2019] [Indexed: 12/11/2022]
Abstract
Children with myocarditis have increased risk of ventricular tachycardia (VT) due to myocardial inflammation and remodeling. There is currently no accepted method for VT risk stratification in this population. We hypothesized that personalized models developed from cardiac late gadolinium enhancement magnetic resonance imaging (LGE-MRI) could determine VT risk in patients with myocarditis using a previously-validated protocol. Personalized three-dimensional computational cardiac models were reconstructed from LGE-MRI scans of 12 patients diagnosed with myocarditis. Four patients with clinical VT and eight patients without VT were included in this retrospective analysis. In each model, we incorporated a personalized spatial distribution of fibrosis and myocardial fiber orientations. Then, VT inducibility was assessed in each model by pacing rapidly from 26 sites distributed throughout both ventricles. Sustained reentrant VT was induced from multiple pacing sites in all patients with clinical VT. In the eight patients without clinical VT, we were unable to induce sustained reentry in our simulations using rapid ventricular pacing. Application of our non-invasive approach in children with myocarditis has the potential to correctly identify those at risk for developing VT.
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Affiliation(s)
- Mark J. Cartoski
- Divison of Pediatric Cardiology, Department of Pediatrics, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Plamen P. Nikolov
- Institute for Computational Medicine and Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA
| | - Adityo Prakosa
- Institute for Computational Medicine and Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA
| | - Patrick M. Boyle
- Institute for Computational Medicine and Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA
| | - Philip J. Spevak
- Divison of Pediatric Cardiology, Department of Pediatrics, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Natalia A. Trayanova
- Institute for Computational Medicine and Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA,Division of Cardiology, Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, USA
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30
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Zabihollahy F, White JA, Ukwatta E. Convolutional neural network-based approach for segmentation of left ventricle myocardial scar from 3D late gadolinium enhancement MR images. Med Phys 2019; 46:1740-1751. [PMID: 30734937 DOI: 10.1002/mp.13436] [Citation(s) in RCA: 33] [Impact Index Per Article: 6.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/04/2018] [Revised: 01/10/2019] [Accepted: 01/31/2019] [Indexed: 02/04/2023] Open
Abstract
PURPOSE Accurate three-dimensional (3D) segmentation of myocardial replacement fibrosis (i.e., scar) is emerging as a potentially valuable tool for risk stratification and procedural planning in patients with ischemic cardiomyopathy. The main purpose of this study was to develop a semiautomated method using a 3D convolutional neural network (CNN)-based for the segmentation of left ventricle (LV) myocardial scar from 3D late gadolinium enhancement magnetic resonance (LGE-MR) images. METHODS Our proposed CNN is built upon several convolutional and pooling layers aimed at choosing appropriate features from LGE-MR images to distinguish between myocardial scar and healthy tissues of the left ventricle. In contrast to previous methods that consider image intensity as the sole feature, CNN-based algorithms have the potential to improve the accuracy of scar segmentation through the creation of unconventional features that separate scar from normal myocardium in the feature space. The first step of our pipeline was to manually delineate the left ventricular myocardium, which was used as the region of interest for scar segmentation. Our developed algorithm was trained using 265,220 volume patches extracted from ten 3D LGE-MR images, then was validated on 450,454 patches from a testing dataset of 24 3D LGE-MR images, all obtained from patients with chronic myocardial infarction. We evaluated our method in the context of several alternative methods by comparing algorithm-generated segmentations to manual delineations performed by experts. RESULTS Our CNN-based method reported an average Dice similarity coefficient (DSC) and Jaccard Index (JI) of 93.63% ± 2.6% and 88.13% ± 4.70%. In comparison to several previous methods, including K-nearest neighbor (KNN), hierarchical max flow (HMF), full width at half maximum (FWHM), and signal threshold to reference mean (STRM), the developed algorithm reported significantly higher accuracy for DSC with a P-value less than 0.0001. CONCLUSIONS Our experimental results demonstrated that our CNN-based proposed method yielded the highest accuracy of all contemporary LV myocardial scar segmentation methodologies, inclusive of the most widely used signal intensity-based methods, such as FWHM and STRM. To our knowledge, this is the first description of LV myocardial scar tissue segmentation from 3D LGE-MR images using a CNN-based method.
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Affiliation(s)
- Fatemeh Zabihollahy
- Department of Systems and Computer Engineering, Carleton University, Ottawa, ON, Canada
| | - James A White
- Stephenson Cardiac Imaging Centre, Libin Cardiovascular Institute of Alberta, University of Calgary, Calgary, AB, USA
| | - Eranga Ukwatta
- Department of Systems and Computer Engineering, Carleton University, Ottawa, ON, Canada
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31
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Ukwatta E, Nikolov P, Zabihollahy F, Trayanova NA, Wright GA. Virtual electrophysiological study as a tool for evaluating efficacy of MRI techniques in predicting adverse arrhythmic events in ischemic patients. Phys Med Biol 2018; 63:225008. [PMID: 30412472 DOI: 10.1088/1361-6560/aae8b2] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/26/2022]
Abstract
Myocardial infarct (MI) related indices determined by late gadolinium enhancement (LGE) MRI have been widely investigated in determining patients suitable for implantable cardiovascular-defibrillator (ICD) therapy to complement left ventricular ejection fraction (LV EF). In comparison to LGE-MRI using inversion-recovery fast-gradient-echo (IR-FGRE), T1 mapping techniques, such as multi contrast late enhancement (MCLE), have been shown to provide more quantitative and reproducible estimates of infarct regions. The objective of this study is to use individualized heart computer models in determining the efficacy of IR-FGRE and MCLE techniques in predicting the occurrence of post-MI ventricular tachycardia (VT). Twenty-seven patients with MI underwent LGE-MRI using IR-FGRE and MCLE prior to ICD implantation and were followed up for 6-46 months. Individualized image-based computational models were built separately for each imaging technique; simulations of propensity to VT were conducted with each model. The imaging methods were evaluated by comparing simulated inducibility of VT to clinical outcome (appropriate ICD therapy) in patients. Twelve patients had at least one appropriate ICD therapy for VT at follow-up. For both MCLE and IR-FGRE, the outcomes of the simulations of VT were significantly associated with the events of appropriate ICD therapy. This indicates that, as compared to conventional measurements such as LV EF, the simulations of VT corresponding to both MCLE and IR-FGRE were more sensitive in predicting appropriate ICD therapy in post-MI patients.
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Affiliation(s)
- Eranga Ukwatta
- School of Engineering, University of Guelph, Guelph, ON, Canada. Author to whom any correspondence should be addressed
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32
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Determinants of atrial bipolar voltage: Inter electrode distance and wavefront angle. Comput Biol Med 2018; 102:449-457. [DOI: 10.1016/j.compbiomed.2018.07.011] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/20/2018] [Revised: 07/16/2018] [Accepted: 07/16/2018] [Indexed: 11/18/2022]
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McGillivray MF, Cheng W, Peters NS, Christensen K. Machine learning methods for locating re-entrant drivers from electrograms in a model of atrial fibrillation. ROYAL SOCIETY OPEN SCIENCE 2018; 5:172434. [PMID: 29765687 PMCID: PMC5936952 DOI: 10.1098/rsos.172434] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/03/2018] [Accepted: 03/13/2018] [Indexed: 05/14/2023]
Abstract
Mapping resolution has recently been identified as a key limitation in successfully locating the drivers of atrial fibrillation (AF). Using a simple cellular automata model of AF, we demonstrate a method by which re-entrant drivers can be located quickly and accurately using a collection of indirect electrogram measurements. The method proposed employs simple, out-of-the-box machine learning algorithms to correlate characteristic electrogram gradients with the displacement of an electrogram recording from a re-entrant driver. Such a method is less sensitive to local fluctuations in electrical activity. As a result, the method successfully locates 95.4% of drivers in tissues containing a single driver, and 95.1% (92.6%) for the first (second) driver in tissues containing two drivers of AF. Additionally, we demonstrate how the technique can be applied to tissues with an arbitrary number of drivers. In its current form, the techniques presented are not refined enough for a clinical setting. However, the methods proposed offer a promising path for future investigations aimed at improving targeted ablation for AF.
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Affiliation(s)
- Max Falkenberg McGillivray
- The Blackett Laboratory, Imperial College London, London SW7 2AZ, UK
- Centre for Complexity Science, Imperial College London, London SW7 2AZ, UK
| | - William Cheng
- The Blackett Laboratory, Imperial College London, London SW7 2AZ, UK
- Centre for Complexity Science, Imperial College London, London SW7 2AZ, UK
| | - Nicholas S Peters
- ElectroCardioMaths Programme, Imperial Centre for Cardiac Engineering, Imperial College London, London W12 0NN, UK
| | - Kim Christensen
- The Blackett Laboratory, Imperial College London, London SW7 2AZ, UK
- Centre for Complexity Science, Imperial College London, London SW7 2AZ, UK
- ElectroCardioMaths Programme, Imperial Centre for Cardiac Engineering, Imperial College London, London W12 0NN, UK
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35
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Trayanova NA, Boyle PM, Nikolov PP. Personalized Imaging and Modeling Strategies for Arrhythmia Prevention and Therapy. CURRENT OPINION IN BIOMEDICAL ENGINEERING 2018; 5:21-28. [PMID: 29546250 PMCID: PMC5847279 DOI: 10.1016/j.cobme.2017.11.007] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
Abstract
The goal of this article is to review advances in computational modeling of the heart, with a focus on recent non-invasive clinical imaging- and simulation-based strategies aimed at improving the diagnosis and treatment of patients with arrhythmias and structural heart disease. Following a brief overview of the field of computational cardiology, we present recent applications of the personalized virtual-heart approach in predicting the optimal targets for infarct-related ventricular tachycardia and atrial fibrillation ablation, and in determining risk of sudden cardiac death in myocardial infarction patients. The hope is that with such models at the patient bedside, therapies could be improved, invasiveness of diagnostic procedures minimized, and health-care costs reduced.
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Affiliation(s)
- Natalia A Trayanova
- Institute for Computational Medicine and Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD
| | - Patrick M Boyle
- Institute for Computational Medicine and Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD
| | - Plamen P Nikolov
- Institute for Computational Medicine and Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD
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36
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Shinbane JS, Saxon LA. Virtual medicine: Utilization of the advanced cardiac imaging patient avatar for procedural planning and facilitation. J Cardiovasc Comput Tomogr 2017; 12:16-27. [PMID: 29198733 DOI: 10.1016/j.jcct.2017.11.004] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/25/2017] [Revised: 11/08/2017] [Accepted: 11/12/2017] [Indexed: 01/17/2023]
Abstract
Advances in imaging technology have led to a paradigm shift from planning of cardiovascular procedures and surgeries requiring the actual patient in a "brick and mortar" hospital to utilization of the digitalized patient in the virtual hospital. Cardiovascular computed tomographic angiography (CCTA) and cardiovascular magnetic resonance (CMR) digitalized 3-D patient representation of individual patient anatomy and physiology serves as an avatar allowing for virtual delineation of the most optimal approaches to cardiovascular procedures and surgeries prior to actual hospitalization. Pre-hospitalization reconstruction and analysis of anatomy and pathophysiology previously only accessible during the actual procedure could potentially limit the intrinsic risks related to time in the operating room, cardiac procedural laboratory and overall hospital environment. Although applications are specific to areas of cardiovascular specialty focus, there are unifying themes related to the utilization of technologies. The virtual patient avatar computer can also be used for procedural planning, computational modeling of anatomy, simulation of predicted therapeutic result, printing of 3-D models, and augmentation of real time procedural performance. Examples of the above techniques are at various stages of development for application to the spectrum of cardiovascular disease processes, including percutaneous, surgical and hybrid minimally invasive interventions. A multidisciplinary approach within medicine and engineering is necessary for creation of robust algorithms for maximal utilization of the virtual patient avatar in the digital medical center. Utilization of the virtual advanced cardiac imaging patient avatar will play an important role in the virtual health care system. Although there has been a rapid proliferation of early data, advanced imaging applications require further assessment and validation of accuracy, reproducibility, standardization, safety, efficacy, quality, cost effectiveness, and overall value to medical care.
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Affiliation(s)
- Jerold S Shinbane
- Division of Cardiovascular Medicine/USC Center for Body Computing, Keck School of Medicine of the University of Southern California, Los Angeles, CA, United States.
| | - Leslie A Saxon
- Division of Cardiovascular Medicine/USC Center for Body Computing, Keck School of Medicine of the University of Southern California, Los Angeles, CA, United States
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