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Arnold R, Prassl AJ, Neic A, Thaler F, Augustin CM, Gsell MAF, Gillette K, Manninger M, Scherr D, Plank G. pyCEPS: A cross-platform electroanatomic mapping data to computational model conversion platform for the calibration of digital twin models of cardiac electrophysiology. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2024; 254:108299. [PMID: 38959599 DOI: 10.1016/j.cmpb.2024.108299] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/18/2024] [Revised: 06/10/2024] [Accepted: 06/19/2024] [Indexed: 07/05/2024]
Abstract
BACKGROUND AND OBJECTIVE Data from electro-anatomical mapping (EAM) systems are playing an increasingly important role in computational modeling studies for the patient-specific calibration of digital twin models. However, data exported from commercial EAM systems are challenging to access and parse. Converting to data formats that are easily amenable to be viewed and analyzed with commonly used cardiac simulation software tools such as openCARP remains challenging. We therefore developed an open-source platform, pyCEPS, for parsing and converting clinical EAM data conveniently to standard formats widely adopted within the cardiac modeling community. METHODS AND RESULTS pyCEPS is an open-source Python-based platform providing the following functions: (i) access and interrogate the EAM data exported from clinical mapping systems; (ii) efficient browsing of EAM data to preview mapping procedures, electrograms (EGMs), and electro-cardiograms (ECGs); (iii) conversion to modeling formats according to the openCARP standard, to be amenable to analysis with standard tools and advanced workflows as used for in silico EAM data. Documentation and training material to facilitate access to this complementary research tool for new users is provided. We describe the technological underpinnings and demonstrate the capabilities of pyCEPS first, and showcase its use in an exemplary modeling application where we use clinical imaging data to build a patient-specific anatomical model. CONCLUSION With pyCEPS we offer an open-source framework for accessing EAM data, and converting these to cardiac modeling standard formats. pyCEPS provides the core functionality needed to integrate EAM data in cardiac modeling research. We detail how pyCEPS could be integrated into model calibration workflows facilitating the calibration of a computational model based on EAM data.
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Affiliation(s)
- Robert Arnold
- Gottfried Schatz Research Center, Division of Medical Physics and Biophysics, Medical University of Graz, Graz, Austria
| | - Anton J Prassl
- Gottfried Schatz Research Center, Division of Medical Physics and Biophysics, Medical University of Graz, Graz, Austria
| | | | - Franz Thaler
- Gottfried Schatz Research Center, Division of Medical Physics and Biophysics, Medical University of Graz, Graz, Austria; Institute of Computer Graphics and Vision, Graz University of Technology, Graz, Austria
| | - Christoph M Augustin
- Gottfried Schatz Research Center, Division of Medical Physics and Biophysics, Medical University of Graz, Graz, Austria
| | - Matthias A F Gsell
- Gottfried Schatz Research Center, Division of Medical Physics and Biophysics, Medical University of Graz, Graz, Austria
| | - Karli Gillette
- Gottfried Schatz Research Center, Division of Medical Physics and Biophysics, Medical University of Graz, Graz, Austria; BioTechMed-Graz, Graz, Austria
| | - Martin Manninger
- Division of Cardiology, Department of Medicine, Medical University of Graz, Graz, Austria
| | - Daniel Scherr
- Division of Cardiology, Department of Medicine, Medical University of Graz, Graz, Austria
| | - Gernot Plank
- Gottfried Schatz Research Center, Division of Medical Physics and Biophysics, Medical University of Graz, Graz, Austria; NumeriCor GmbH, Graz, Austria; BioTechMed-Graz, Graz, Austria.
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2
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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 PMCID: PMC11381036 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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Gillette K, Gsell MAF, Nagel C, Bender J, Winkler B, Williams SE, Bär M, Schäffter T, Dössel O, Plank G, Loewe A. MedalCare-XL: 16,900 healthy and pathological synthetic 12 lead ECGs from electrophysiological simulations. Sci Data 2023; 10:531. [PMID: 37553349 PMCID: PMC10409805 DOI: 10.1038/s41597-023-02416-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2023] [Accepted: 07/25/2023] [Indexed: 08/10/2023] Open
Abstract
Mechanistic cardiac electrophysiology models allow for personalized simulations of the electrical activity in the heart and the ensuing electrocardiogram (ECG) on the body surface. As such, synthetic signals possess known ground truth labels of the underlying disease and can be employed for validation of machine learning ECG analysis tools in addition to clinical signals. Recently, synthetic ECGs were used to enrich sparse clinical data or even replace them completely during training leading to improved performance on real-world clinical test data. We thus generated a novel synthetic database comprising a total of 16,900 12 lead ECGs based on electrophysiological simulations equally distributed into healthy control and 7 pathology classes. The pathological case of myocardial infraction had 6 sub-classes. A comparison of extracted features between the virtual cohort and a publicly available clinical ECG database demonstrated that the synthetic signals represent clinical ECGs for healthy and pathological subpopulations with high fidelity. The ECG database is split into training, validation, and test folds for development and objective assessment of novel machine learning algorithms.
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Affiliation(s)
- Karli Gillette
- Gottfried Schatz Research Center: Division of Medical Physics and Biophysics, Medical University of Graz, Graz, Austria
- BioTechMed-Graz, Graz, Austria
| | - Matthias A F Gsell
- Gottfried Schatz Research Center: Division of Medical Physics and Biophysics, Medical University of Graz, Graz, Austria
| | - Claudia Nagel
- Institute of Biomedical Engineering, Karlsruhe Institute of Technology (KIT), Karlsruhe, Germany
| | - Jule Bender
- Institute of Biomedical Engineering, Karlsruhe Institute of Technology (KIT), Karlsruhe, Germany
| | - Benjamin Winkler
- Physikalisch-Technische Bundesanstalt, National Metrology Institute, Berlin, Germany
| | - Steven E Williams
- King's College London, London, United Kingdom
- University of Edinburgh, Edinburgh, United Kingdom
| | - Markus Bär
- Physikalisch-Technische Bundesanstalt, National Metrology Institute, Berlin, Germany
| | - Tobias Schäffter
- Physikalisch-Technische Bundesanstalt, National Metrology Institute, Berlin, Germany
- King's College London, London, United Kingdom
- Biomedical Engineering, Technische Universität Berlin, Einstein Centre Digital Future, Berlin, Germany
| | - Olaf Dössel
- Institute of Biomedical Engineering, Karlsruhe Institute of Technology (KIT), Karlsruhe, Germany
| | - Gernot Plank
- Gottfried Schatz Research Center: Division of Medical Physics and Biophysics, Medical University of Graz, Graz, Austria.
- BioTechMed-Graz, Graz, Austria.
| | - Axel Loewe
- Institute of Biomedical Engineering, Karlsruhe Institute of Technology (KIT), Karlsruhe, Germany.
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4
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Campos FO, Neic A, Mendonca Costa C, Whitaker J, O'Neill M, Razavi R, Rinaldi CA, DanielScherr, Niederer SA, Plank G, Bishop MJ. An automated near-real time computational method for induction and treatment of scar-related ventricular tachycardias. Med Image Anal 2022; 80:102483. [PMID: 35667328 PMCID: PMC10114098 DOI: 10.1016/j.media.2022.102483] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2021] [Revised: 04/22/2022] [Accepted: 05/20/2022] [Indexed: 02/05/2023]
Abstract
Catheter ablation is currently the only curative treatment for scar-related ventricular tachycardias (VTs). However, not only are ablation procedures long, with relatively high risk, but success rates are punitively low, with frequent VT recurrence. Personalized in-silico approaches have the opportunity to address these limitations. However, state-of-the-art reaction diffusion (R-D) simulations of VT induction and subsequent circuits used for in-silico ablation target identification require long execution times, along with vast computational resources, which are incompatible with the clinical workflow. Here, we present the Virtual Induction and Treatment of Arrhythmias (VITA), a novel, rapid and fully automated computational approach that uses reaction-Eikonal methodology to induce VT and identify subsequent ablation targets. The rationale for VITA is based on finding isosurfaces associated with an activation wavefront that splits in the ventricles due to the presence of an isolated isthmus of conduction within the scar; once identified, each isthmus may be assessed for their vulnerability to sustain a reentrant circuit, and the corresponding exit site automatically identified for potential ablation targeting. VITA was tested on a virtual cohort of 7 post-infarcted porcine hearts and the results compared to R-D simulations. Using only a standard desktop machine, VITA could detect all scar-related VTs, simulating activation time maps and ECGs (for clinical comparison) as well as computing ablation targets in 48 minutes. The comparable VTs probed by the R-D simulations took 68.5 hours on 256 cores of high-performance computing infrastructure. The set of lesions computed by VITA was shown to render the ventricular model VT-free. VITA could be used in near real-time as a complementary modality aiding in clinical decision-making in the treatment of post-infarction VTs.
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Affiliation(s)
- Fernando O Campos
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom.
| | | | - Caroline Mendonca Costa
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
| | - John Whitaker
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom; Guy's and St. Thomas' NHS Foundation Trust, Cardiovascular Directorate
| | - Mark O'Neill
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom; Guy's and St. Thomas' NHS Foundation Trust, Cardiovascular Directorate
| | - Reza Razavi
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
| | - Christopher A Rinaldi
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom; Guy's and St. Thomas' NHS Foundation Trust, Cardiovascular Directorate
| | - DanielScherr
- Division of Cardiology, Department of Internal Medicine, Medical University of Graz, Austria
| | - Steven A Niederer
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
| | - Gernot Plank
- Gottfried Schatz Research Center Biophysics, Medical University of Graz, Graz, Austria; BioTechMed-Graz, Graz, Austria
| | - Martin J Bishop
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
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Jung A, Gsell MAF, Augustin CM, Plank G. An Integrated Workflow for Building Digital Twins of Cardiac Electromechanics-A Multi-Fidelity Approach for Personalising Active Mechanics. MATHEMATICS (BASEL, SWITZERLAND) 2022; 10:823. [PMID: 35295404 PMCID: PMC7612499 DOI: 10.3390/math10050823] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 02/04/2023]
Abstract
Personalised computer models of cardiac function, referred to as cardiac digital twins, are envisioned to play an important role in clinical precision therapies of cardiovascular diseases. A major obstacle hampering clinical translation involves the significant computational costs involved in the personalisation of biophysically detailed mechanistic models that require the identification of high-dimensional parameter vectors. An important aspect to identify in electromechanics (EM) models are active mechanics parameters that govern cardiac contraction and relaxation. In this study, we present a novel, fully automated, and efficient approach for personalising biophysically detailed active mechanics models using a two-step multi-fidelity solution. In the first step, active mechanical behaviour in a given 3D EM model is represented by a purely phenomenological, low-fidelity model, which is personalised at the organ scale by calibration to clinical cavity pressure data. Then, in the second step, median traces of nodal cellular active stress, intracellular calcium concentration, and fibre stretch are generated and utilised to personalise the desired high-fidelity model at the cellular scale using a 0D model of cardiac EM. Our novel approach was tested on a cohort of seven human left ventricular (LV) EM models, created from patients treated for aortic coarctation (CoA). Goodness of fit, computational cost, and robustness of the algorithm against uncertainty in the clinical data and variations of initial guesses were evaluated. We demonstrate that our multi-fidelity approach facilitates the personalisation of a biophysically detailed active stress model within only a few (2 to 4) expensive 3D organ-scale simulations-a computational effort compatible with clinical model applications.
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Affiliation(s)
- Alexander Jung
- Gottfried Schatz Research Center for Cell Signaling, Metabolism and Aging—Division of Biophysics, Medical University Graz, 8010 Graz, Austria
| | - Matthias A. F. Gsell
- Gottfried Schatz Research Center for Cell Signaling, Metabolism and Aging—Division of Biophysics, Medical University Graz, 8010 Graz, Austria
- NAWI Graz, Institute of Mathematics and Scientific Computing, University of Graz, 8010 Graz, Austria
| | - Christoph M. Augustin
- Gottfried Schatz Research Center for Cell Signaling, Metabolism and Aging—Division of Biophysics, Medical University Graz, 8010 Graz, Austria
- BioTechMed-Graz, 8010 Graz, Austria
| | - Gernot Plank
- Gottfried Schatz Research Center for Cell Signaling, Metabolism and Aging—Division of Biophysics, Medical University Graz, 8010 Graz, Austria
- BioTechMed-Graz, 8010 Graz, Austria
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O'Hara RP, Binka E, Prakosa A, Zimmerman SL, Cartoski MJ, Abraham MR, Lu DY, Boyle PM, Trayanova NA. Personalized computational heart models with T1-mapped fibrotic remodeling predict sudden death risk in patients with hypertrophic cardiomyopathy. eLife 2022; 11:73325. [PMID: 35076018 PMCID: PMC8789259 DOI: 10.7554/elife.73325] [Citation(s) in RCA: 18] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2021] [Accepted: 01/07/2022] [Indexed: 11/13/2022] Open
Abstract
Hypertrophic cardiomyopathy (HCM) is associated with risk of sudden cardiac death (SCD) due to ventricular arrhythmias (VAs) arising from the proliferation of fibrosis in the heart. Current clinical risk stratification criteria inadequately identify at-risk patients in need of primary prevention of VA. Here, we use mechanistic computational modeling of the heart to analyze how HCM-specific remodeling promotes arrhythmogenesis and to develop a personalized strategy to forecast risk of VAs in these patients. We combine contrast-enhanced cardiac magnetic resonance imaging and T1 mapping data to construct digital replicas of HCM patient hearts that represent the patient-specific distribution of focal and diffuse fibrosis and evaluate the substrate propensity to VA. Our analysis indicates that the presence of diffuse fibrosis, which is rarely assessed in these patients, increases arrhythmogenic propensity. In forecasting future VA events in HCM patients, the imaging-based computational heart approach achieved 84.6%, 76.9%, and 80.1% sensitivity, specificity, and accuracy, respectively, and significantly outperformed current clinical risk predictors. This novel VA risk assessment may have the potential to prevent SCD and help deploy primary prevention appropriately in HCM patients.
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Affiliation(s)
- Ryan P O'Hara
- Department of Biomedical Engineering, Johns Hopkins University
| | - Edem Binka
- Division of Pediatric Cardiology, Johns Hopkins University
| | - Adityo Prakosa
- Department of Biomedical Engineering, Johns Hopkins University
| | | | - Mark J Cartoski
- Division of Pediatric Cardiology, Alfred I. duPont Hospital for Children
| | | | - Dai-Yin Lu
- Division of Cardiology, University of California, San Francisco
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7
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O’Hara RP, Prakosa A, Binka E, Lacy A, Trayanova NA. Arrhythmia in hypertrophic cardiomyopathy: Risk prediction using contrast enhanced MRI, T1 mapping, and personalized virtual heart technology. J Electrocardiol 2022; 74:122-127. [PMID: 36183522 PMCID: PMC9729380 DOI: 10.1016/j.jelectrocard.2022.09.004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2022] [Revised: 08/04/2022] [Accepted: 09/12/2022] [Indexed: 12/13/2022]
Abstract
BACKGROUND Hypertrophic cardiomyopathy (HCM), a disease with myocardial fibrosis manifestation, is a common cause of sudden cardiac death (SCD) due to ventricular arrhythmias (VA). Current clinical risk stratification criteria are inadequate in identifying patients who are at risk for VA and in need of an implantable cardioverter defibrillator (ICD) for primary prevention. OBJECTIVE We aimed to develop a risk prediction approach based on imaging biomarkers from the combination of late gadolinium contrast-enhanced (LGE) MRI and T1 mapping. We then aimed to compare the prediction to a virtual heart computational risk assessment approach based on LGE-T1 virtual heart models. METHODS The methodology involved combining short-axis LGE-MRI with post-contrast T1 maps to define personalized thresholds for diffuse and dense fibrosis. The combined LGE-T1 maps were used to evaluate imaging biomarkers for VA risk prediction. The risk prediction capability of the biomarkers was compared with that of the LGE-T1 virtual heart arrhythmia inducibility simulation. VA risk prediction performance from both approaches was compared to clinical outcome (presence of clinical VA). RESULTS Image-based biomarkers, including hypertrophy, signal intensity heterogeneity, and fibrotic border complexity, could not discriminate high vs low VA risk. LGE-T1 virtual heart technology outperformed all the image-based biomarker metrics and was statistically significant in predicting VA risk in HCM. CONCLUSIONS We combined two MR imaging techniques to analyze imaging biomarkers in HCM. Raw and processed image-based biomarkers cannot discriminate patients with VA from those without VA. Hybrid LGE-T1 virtual heart models could correctly predict VA risk for this cohort and may improve SCD risk stratification to better identify HCM patients for primary preventative ICD implantation.
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Affiliation(s)
- Ryan P. O’Hara
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD 21218, United States of America
| | - Adityo Prakosa
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD 21218, United States of America
| | - Edem Binka
- Division of Pediatric Cardiology, Department of Pediatrics, Johns Hopkins School of Medicine, Baltimore, MD 21205, United States of America
| | - Audrey Lacy
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD 21218, United States of America
| | - Natalia A. Trayanova
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD 21218, United States of America,Corresponding author at: 3400 N Charles Street, Hackerman Hall 216, Baltimore, MD 21218, United States of America. (N.A. Trayanova)
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Plank G, Loewe A, Neic A, Augustin C, Huang YL, Gsell MAF, Karabelas E, Nothstein M, Prassl AJ, Sánchez J, Seemann G, Vigmond EJ. The openCARP simulation environment for cardiac electrophysiology. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2021; 208:106223. [PMID: 34171774 DOI: 10.1016/j.cmpb.2021.106223] [Citation(s) in RCA: 70] [Impact Index Per Article: 23.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/15/2021] [Accepted: 05/28/2021] [Indexed: 06/13/2023]
Abstract
BACKGROUND AND OBJECTIVE Cardiac electrophysiology is a medical specialty with a long and rich tradition of computational modeling. Nevertheless, no community standard for cardiac electrophysiology simulation software has evolved yet. Here, we present the openCARP simulation environment as one solution that could foster the needs of large parts of this community. METHODS AND RESULTS openCARP and the Python-based carputils framework allow developing and sharing simulation pipelines which automate in silico experiments including all modeling and simulation steps to increase reproducibility and productivity. The continuously expanding openCARP user community is supported by tailored infrastructure. Documentation and training material facilitate access to this complementary research tool for new users. After a brief historic review, this paper summarizes requirements for a high-usability electrophysiology simulator and describes how openCARP fulfills them. We introduce the openCARP modeling workflow in a multi-scale example of atrial fibrillation simulations on single cell, tissue, organ and body level and finally outline future development potential. CONCLUSION As an open simulator, openCARP can advance the computational cardiac electrophysiology field by making state-of-the-art simulations accessible. In combination with the carputils framework, it offers a tailored software solution for the scientific community and contributes towards increasing use, transparency, standardization and reproducibility of in silico experiments.
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Affiliation(s)
- Gernot Plank
- Gottfried Schatz Research Center, Division of Biophysics, Medical University of Graz, Graz, Austria.
| | - Axel Loewe
- Institute of Biomedical Engineering, Karlsruhe Institute of Technology (KIT), Karlsruhe, Germany
| | | | - Christoph Augustin
- Gottfried Schatz Research Center, Division of Biophysics, Medical University of Graz, Graz, Austria
| | - Yung-Lin Huang
- Institute for Experimental Cardiovascular Medicine, University Heart Center Freiburg. Bad Krozingen, Medical Center - University of Freiburg, Freiburg, Germany; Faculty of Medicine, University of Freiburg, Freiburg, Germany
| | - Matthias A F Gsell
- Gottfried Schatz Research Center, Division of Biophysics, Medical University of Graz, Graz, Austria
| | - Elias Karabelas
- Institute of Mathematics and Scientific Computing, University of Graz, Graz, Austria
| | - Mark Nothstein
- Institute of Biomedical Engineering, Karlsruhe Institute of Technology (KIT), Karlsruhe, Germany
| | - Anton J Prassl
- Gottfried Schatz Research Center, Division of Biophysics, Medical University of Graz, Graz, Austria
| | - Jorge Sánchez
- Institute of Biomedical Engineering, Karlsruhe Institute of Technology (KIT), Karlsruhe, Germany
| | - Gunnar Seemann
- Institute for Experimental Cardiovascular Medicine, University Heart Center Freiburg. Bad Krozingen, Medical Center - University of Freiburg, Freiburg, Germany; Faculty of Medicine, University of Freiburg, Freiburg, Germany
| | - Edward J Vigmond
- IHU Liryc, Electrophysiology and Heart Modeling Institute, Fondation Bordeaux Université, F-33600 Pessac-Bordeaux, France; Université Bordeaux, IMB, UMR 5251, F-33400 Talence, France
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9
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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: 7] [Impact Index Per Article: 2.3] [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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Electro-Mechanical Whole-Heart Digital Twins: A Fully Coupled Multi-Physics Approach. MATHEMATICS 2021. [DOI: 10.3390/math9111247] [Citation(s) in RCA: 34] [Impact Index Per Article: 11.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
Abstract
Mathematical models of the human heart are evolving to become a cornerstone of precision medicine and support clinical decision making by providing a powerful tool to understand the mechanisms underlying pathophysiological conditions. In this study, we present a detailed mathematical description of a fully coupled multi-scale model of the human heart, including electrophysiology, mechanics, and a closed-loop model of circulation. State-of-the-art models based on human physiology are used to describe membrane kinetics, excitation-contraction coupling and active tension generation in the atria and the ventricles. Furthermore, we highlight ways to adapt this framework to patient specific measurements to build digital twins. The validity of the model is demonstrated through simulations on a personalized whole heart geometry based on magnetic resonance imaging data of a healthy volunteer. Additionally, the fully coupled model was employed to evaluate the effects of a typical atrial ablation scar on the cardiovascular system. With this work, we provide an adaptable multi-scale model that allows a comprehensive personalization from ion channels to the organ level enabling digital twin modeling.
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11
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Woodworth LA, Cansız B, Kaliske M. A numerical study on the effects of spatial and temporal discretization in cardiac electrophysiology. INTERNATIONAL JOURNAL FOR NUMERICAL METHODS IN BIOMEDICAL ENGINEERING 2021; 37:e3443. [PMID: 33522111 DOI: 10.1002/cnm.3443] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/30/2020] [Revised: 12/16/2020] [Accepted: 01/23/2021] [Indexed: 06/12/2023]
Abstract
Millions of degrees of freedom are often required to accurately represent the electrophysiology of the myocardium due to the presence of discretization effects. This study seeks to explore the influence of temporal and spatial discretization on the simulation of cardiac electrophysiology in conjunction with changes in modeling choices. Several finite element analyses are performed to examine how discretization affects solution time, conduction velocity and electrical excitation. Discretization effects are considered along with changes in the electrophysiology model and solution approach. Two action potential models are considered: the Aliev-Panfilov model and the ten Tusscher-Noble-Noble-Panfilov model. The solution approaches consist of two time integration schemes and different treatments for solving the local system of ordinary differential equations. The efficiency and stability of the calculation approaches are demonstrated to be dependent on the action potential model. The dependency of the conduction velocity on the element size and time step is shown to be different for changes in material parameters. Finally, the discrepancies between the wave propagation in coarse and fine meshes are analyzed based on the temporal evolution of the transmembrane potential at a node and its neighboring Gauss points. Insight obtained from this study can be used to suggest new methods to improve the efficiency of simulations in cardiac electrophysiology.
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Affiliation(s)
- Lucas A Woodworth
- Institute for Structural Analysis, Technische Universität Dresden, Dresden, Germany
| | - Barış Cansız
- Institute for Structural Analysis, Technische Universität Dresden, Dresden, Germany
| | - Michael Kaliske
- Institute for Structural Analysis, Technische Universität Dresden, Dresden, Germany
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A Framework for the generation of digital twins of cardiac electrophysiology from clinical 12-leads ECGs. Med Image Anal 2021; 71:102080. [PMID: 33975097 DOI: 10.1016/j.media.2021.102080] [Citation(s) in RCA: 43] [Impact Index Per Article: 14.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2020] [Revised: 02/15/2021] [Accepted: 04/06/2021] [Indexed: 12/21/2022]
Abstract
Cardiac digital twins (Cardiac Digital Twin (CDT)s) of human electrophysiology (Electrophysiology (EP)) are digital replicas of patient hearts derived from clinical data that match like-for-like all available clinical observations. Due to their inherent predictive potential, CDTs show high promise as a complementary modality aiding in clinical decision making and also in the cost-effective, safe and ethical testing of novel EP device therapies. However, current workflows for both the anatomical and functional twinning phases within CDT generation, referring to the inference of model anatomy and parameters from clinical data, are not sufficiently efficient, robust and accurate for advanced clinical and industrial applications. Our study addresses three primary limitations impeding the routine generation of high-fidelity CDTs by introducing; a comprehensive parameter vector encapsulating all factors relating to the ventricular EP; an abstract reference frame within the model allowing the unattended manipulation of model parameter fields; a novel fast-forward electrocardiogram (Electrocardiogram (ECG)) model for efficient and bio-physically-detailed simulation required for parameter inference. A novel workflow for the generation of CDTs is then introduced as an initial proof of concept. Anatomical twinning was performed within a reasonable time compatible with clinical workflows (<4h) for 12 subjects from clinically-attained magnetic resonance images. After assessment of the underlying fast forward ECG model against a gold standard bidomain ECG model, functional twinning of optimal parameters according to a clinically-attained 12 lead ECG was then performed using a forward Saltelli sampling approach for a single subject. The achieved results in terms of efficiency and fidelity demonstrate that our workflow is well-suited and viable for generating biophysically-detailed CDTs at scale.
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Fedele M, Quarteroni A. Polygonal surface processing and mesh generation tools for the numerical simulation of the cardiac function. INTERNATIONAL JOURNAL FOR NUMERICAL METHODS IN BIOMEDICAL ENGINEERING 2021; 37:e3435. [PMID: 33415829 PMCID: PMC8244076 DOI: 10.1002/cnm.3435] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/02/2020] [Revised: 01/01/2021] [Accepted: 01/02/2021] [Indexed: 06/05/2023]
Abstract
In order to simulate the cardiac function for a patient-specific geometry, the generation of the computational mesh is crucially important. In practice, the input is typically a set of unprocessed polygonal surfaces coming either from a template geometry or from medical images. These surfaces need ad-hoc processing to be suitable for a volumetric mesh generation. In this work we propose a set of new algorithms and tools aiming to facilitate the mesh generation process. In particular, we focus on different aspects of a cardiac mesh generation pipeline: (1) specific polygonal surface processing for cardiac geometries, like connection of different heart chambers or segmentation outputs; (2) generation of accurate boundary tags; (3) definition of mesh-size functions dependent on relevant geometric quantities; (4) processing and connecting together several volumetric meshes. The new algorithms-implemented in the open-source software vmtk-can be combined with each other allowing the creation of personalized pipelines, that can be optimized for each cardiac geometry or for each aspect of the cardiac function to be modeled. Thanks to these features, the proposed tools can significantly speed-up the mesh generation process for a large range of cardiac applications, from single-chamber single-physics simulations to multi-chambers multi-physics simulations. We detail all the proposed algorithms motivating them in the cardiac context and we highlight their flexibility by showing different examples of cardiac mesh generation pipelines.
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Affiliation(s)
- Marco Fedele
- MOX, Department of MathematicsPolitecnico di MilanoMilanItaly
| | - Alfio Quarteroni
- MOX, Department of MathematicsPolitecnico di MilanoMilanItaly
- Institute of MathematicsÉcole Polytechnique Fédérale de LausanneLausanneSwitzerland
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14
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Campos FO, Orini M, Arnold R, Whitaker J, O'Neill M, Razavi R, Plank G, Hanson B, Porter B, Rinaldi CA, Gill J, Lambiase PD, Taggart P, Bishop MJ. Assessing the ability of substrate mapping techniques to guide ventricular tachycardia ablation using computational modelling. Comput Biol Med 2021; 130:104214. [PMID: 33476992 DOI: 10.1016/j.compbiomed.2021.104214] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/05/2020] [Revised: 01/05/2021] [Accepted: 01/05/2021] [Indexed: 01/19/2023]
Abstract
BACKGROUND Identification of targets for ablation of post-infarction ventricular tachycardias (VTs) remains challenging, often requiring arrhythmia induction to delineate the reentrant circuit. This carries a risk for the patient and may not be feasible. Substrate mapping has emerged as a safer strategy to uncover arrhythmogenic regions. However, VT recurrence remains common. GOAL To use computer simulations to assess the ability of different substrate mapping approaches to identify VT exit sites. METHODS A 3D computational model of the porcine post-infarction heart was constructed to simulate VT and paced rhythm. Electroanatomical maps were constructed based on endocardial electrogram features and the reentry vulnerability index (RVI - a metric combining activation (AT) and repolarization timings to identify tissue susceptibility to reentry). Since scar transmurality in our model was not homogeneous, parameters derived from all signals (including dense scar regions) were used in the analysis. Potential ablation targets obtained from each electroanatomical map during pacing were compared to the exit site detected during VT mapping. RESULTS Simulation data showed that voltage cut-offs applied to bipolar electrograms could delineate the scar, but not the VT circuit. Electrogram fractionation had the highest correlation with scar transmurality. The RVI identified regions closest to VT exit site but was outperformed by AT gradients combined with voltage cut-offs. The performance of all metrics was affected by pacing location. CONCLUSIONS Substrate mapping could provide information about the infarct, but the directional dependency on activation should be considered. Activation-repolarization metrics have utility in safely identifying VT targets, even with non-transmural scars.
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Affiliation(s)
- Fernando O Campos
- School of Biomedical Engineering and Imaging Sciences, Rayne Institute, 4th Floor, Lambeth Wing, St. Thomas' Hospital, Westminster Bridge Road, London, SE1 7EH, United Kingdom.
| | - Michele Orini
- Institute of Cardiovascular Science, University College London, London, United Kingdom; Electrophysiology Department, Barts Heart Centre, St Bartholomew's Hospital, London, United Kingdom
| | - Robert Arnold
- Gottfried Schatz Research Center (for Cell Signaling, Metabolism and Aging), Division of Biophysics, Graz, Austria
| | - John Whitaker
- School of Biomedical Engineering and Imaging Sciences, Rayne Institute, 4th Floor, Lambeth Wing, St. Thomas' Hospital, Westminster Bridge Road, London, SE1 7EH, United Kingdom
| | - Mark O'Neill
- School of Biomedical Engineering and Imaging Sciences, Rayne Institute, 4th Floor, Lambeth Wing, St. Thomas' Hospital, Westminster Bridge Road, London, SE1 7EH, United Kingdom
| | - Reza Razavi
- School of Biomedical Engineering and Imaging Sciences, Rayne Institute, 4th Floor, Lambeth Wing, St. Thomas' Hospital, Westminster Bridge Road, London, SE1 7EH, United Kingdom
| | - Gernot Plank
- Gottfried Schatz Research Center (for Cell Signaling, Metabolism and Aging), Division of Biophysics, Graz, Austria
| | - Ben Hanson
- Department of Mechanical Engineering, University College London, London, United Kingdom
| | - Bradley Porter
- School of Biomedical Engineering and Imaging Sciences, Rayne Institute, 4th Floor, Lambeth Wing, St. Thomas' Hospital, Westminster Bridge Road, London, SE1 7EH, United Kingdom; Department of Cardiology, Guys and St Thomas' NHS Trust, London, United Kingdom
| | | | - Jaswinder Gill
- School of Biomedical Engineering and Imaging Sciences, Rayne Institute, 4th Floor, Lambeth Wing, St. Thomas' Hospital, Westminster Bridge Road, London, SE1 7EH, United Kingdom; Department of Cardiology, Guys and St Thomas' NHS Trust, London, United Kingdom
| | - Pier D Lambiase
- Institute of Cardiovascular Science, University College London, London, United Kingdom; Electrophysiology Department, Barts Heart Centre, St Bartholomew's Hospital, London, United Kingdom
| | - Peter Taggart
- Institute of Cardiovascular Science, University College London, London, United Kingdom; Electrophysiology Department, Barts Heart Centre, St Bartholomew's Hospital, London, United Kingdom
| | - Martin J Bishop
- School of Biomedical Engineering and Imaging Sciences, Rayne Institute, 4th Floor, Lambeth Wing, St. Thomas' Hospital, Westminster Bridge Road, London, SE1 7EH, United Kingdom
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15
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Grandits T, Gillette K, Neic A, Bayer J, Vigmond E, Pock T, Plank G. An Inverse Eikonal Method for Identifying Ventricular Activation Sequences from Epicardial Activation Maps. JOURNAL OF COMPUTATIONAL PHYSICS 2020; 419:109700. [PMID: 32952215 PMCID: PMC7116090 DOI: 10.1016/j.jcp.2020.109700] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/15/2023]
Abstract
A key mechanism controlling cardiac function is the electrical activation sequence of the heart's main pumping chambers termed the ventricles. As such, personalization of the ventricular activation sequences is of pivotal importance for the clinical utility of computational models of cardiac electrophysiology. However, a direct observation of the activation sequence throughout the ventricular volume is virtually impossible. In this study, we report on a novel method for identification of activation sequences from activation maps measured at the outer surface of the heart termed the epicardium. Conceptually, the method attempts to identify the key factors governing the ventricular activation sequence - the timing of earliest activation sites (EAS) and the velocity tensor field within the ventricular walls - from sparse and noisy activation maps sampled from the epicardial surface and fits an Eikonal model to the observations. Regularization methods are first investigated to overcome the severe ill-posedness of the inverse problem in a simplified 2D example. These methods are then employed in an anatomically accurate biventricular model with two realistic activation models of varying complexity - a simplified trifascicular model (3F) and a topologically realistic model of the His-Purkinje system (HPS). Using epicardial activation maps at full resolution, we first demonstrate that reconstructing the volumetric activation sequence is, in principle, feasible under the assumption of known location of EAS and later evaluate robustness of the method against noise and reduced spatial resolution of observations. Our results suggest that the FIMIN algorithm is able to robustly recover the full 3D activation sequence using epicardial activation maps at a spatial resolution achievable with current mapping systems and in the presence of noise. Comparing the accuracy achieved in the reconstructed activation maps with clinical data uncertainties suggests that the FIMIN method may be suitable for the patient- specific parameterization of activation models.
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Affiliation(s)
- Thomas Grandits
- Institute of Computer Graphics and Vision, Graz University of Technology
- BioTechMed-Graz, Austria
| | - Karli Gillette
- Institute of Biophysics, Medical University of Graz
- BioTechMed-Graz, Austria
| | - Aurel Neic
- Institute of Biophysics, Medical University of Graz
| | - Jason Bayer
- IHU Liryc, Electrophysiology and Heart Modeling Institute, fondation Bordeaux Université, Pessac-Bordeaux
| | - Edward Vigmond
- IHU Liryc, Electrophysiology and Heart Modeling Institute, fondation Bordeaux Université, Pessac-Bordeaux
| | - Thomas Pock
- Institute of Computer Graphics and Vision, Graz University of Technology
- BioTechMed-Graz, Austria
| | - Gernot Plank
- Institute of Biophysics, Medical University of Graz
- BioTechMed-Graz, Austria
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16
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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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17
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Gemmell PM, Gillette K, Balaban G, Rajani R, Vigmond EJ, Plank G, Bishop MJ. A computational investigation into rate-dependant vectorcardiogram changes due to specific fibrosis patterns in non-ischæmic dilated cardiomyopathy. Comput Biol Med 2020; 123:103895. [PMID: 32741753 PMCID: PMC7429989 DOI: 10.1016/j.compbiomed.2020.103895] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2020] [Revised: 06/12/2020] [Accepted: 06/27/2020] [Indexed: 01/13/2023]
Abstract
Patients with scar-associated fibrotic tissue remodelling are at greater risk of ventricular arrhythmic events, but current methods to detect the presence of such remodelling require invasive procedures. We present here a potential method to detect the presence, location and dimensions of scar using pacing-dependent changes in the vectorcardiogram (VCG). Using a clinically-derived whole-torso computational model, simulations were conducted at both slow and rapid pacing for a variety of scar patterns within the myocardium, with various VCG-derived metrics being calculated, with changes in these metrics being assessed for their ability to discern the presence and size of scar. Our results indicate that differences in the dipole angle at the end of the QRS complex and differences in the QRS area and duration may be used to predict scar properties. Using machine learning techniques, we were also able to predict the location of the scar to high accuracy, using only these VCG-derived rate-dependent changes as input. Such a non-invasive predictive tool for the presence of scar represents a potentially useful clinical tool for identifying patients at arrhythmic risk.
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Affiliation(s)
- Philip M Gemmell
- King's College London, St. Thomas' Hospital North Wing, London, SE1 7EH, UK.
| | - Karli Gillette
- Medical University of Graz, Division of Biophysics, Neue Stiftingtalstraße 6(MC1.D.)/IV, 8010 Graz, Austria
| | - Gabriel Balaban
- University of Oslo, Research Group for Biomedical Infomatics, Gaustadalléen 23B 0373 Oslo, Norway
| | - Ronak Rajani
- King's College London, St. Thomas' Hospital North Wing, London, SE1 7EH, UK
| | - Edward J Vigmond
- University of Bordeaux, IHU Liryc, Site Hopital Xavier Arnozan, Avenue de Haut-Leveque, 33604 Pessac, France
| | - Gernot Plank
- Medical University of Graz, Division of Biophysics, Neue Stiftingtalstraße 6(MC1.D.)/IV, 8010 Graz, Austria
| | - Martin J Bishop
- King's College London, St. Thomas' Hospital North Wing, London, SE1 7EH, UK
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18
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Ali RL, Hakim JB, Boyle PM, Zahid S, Sivasambu B, Marine JE, Calkins H, Trayanova NA, Spragg DD. Arrhythmogenic propensity of the fibrotic substrate after atrial fibrillation ablation: a longitudinal study using magnetic resonance imaging-based atrial models. Cardiovasc Res 2020; 115:1757-1765. [PMID: 30977811 DOI: 10.1093/cvr/cvz083] [Citation(s) in RCA: 30] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/06/2018] [Revised: 01/31/2019] [Accepted: 04/08/2019] [Indexed: 12/19/2022] Open
Abstract
AIMS Inadequate modification of the atrial fibrotic substrate necessary to sustain re-entrant drivers (RDs) may explain atrial fibrillation (AF) recurrence following failed pulmonary vein isolation (PVI). Personalized computational models of the fibrotic atrial substrate derived from late gadolinium enhanced (LGE)-magnetic resonance imaging (MRI) can be used to non-invasively determine the presence of RDs. The objective of this study is to assess the changes of the arrhythmogenic propensity of the fibrotic substrate after PVI. METHODS AND RESULTS Pre- and post-ablation individualized left atrial models were constructed from 12 AF patients who underwent pre- and post-PVI LGE-MRI, in six of whom PVI failed. Pre-ablation AF sustained by RDs was induced in 10 models. RDs in the post-ablation models were classified as either preserved or emergent. Pre-ablation models derived from patients for whom the procedure failed exhibited a higher number of RDs and larger areas defined as promoting RD formation when compared with atrial models from patients who had successful ablation, 2.6 ± 0.9 vs. 1.8 ± 0.2 and 18.9 ± 1.6% vs. 13.8 ± 1.5%, respectively. In cases of successful ablation, PVI eliminated completely the RDs sustaining AF. Preserved RDs unaffected by ablation were documented only in post-ablation models of patients who experienced recurrent AF (2/5 models); all of these models had also one or more emergent RDs at locations distinct from those of pre-ablation RDs. Emergent RDs occurred in regions that had the same characteristics of the fibrosis spatial distribution (entropy and density) as regions that harboured RDs in pre-ablation models. CONCLUSION Recurrent AF after PVI in the fibrotic atria may be attributable to both preserved RDs that sustain AF pre- and post-ablation, and the emergence of new RDs following ablation. The same levels of fibrosis entropy and density underlie the pro-RD propensity in both pre- and post-ablation substrates.
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Affiliation(s)
- Rheeda L Ali
- Institute for Computational Medicine, Johns Hopkins University, 3400 N Charles Street, 208 Hackerman, Baltimore, MD, USA
| | - Joe B Hakim
- Institute for Computational Medicine, Johns Hopkins University, 3400 N Charles Street, 208 Hackerman, Baltimore, MD, USA
| | - Patrick M Boyle
- Institute for Computational Medicine, Johns Hopkins University, 3400 N Charles Street, 208 Hackerman, Baltimore, MD, USA.,Department of Biomedical Engineering, Johns Hopkins University, 3400 N Charles Street, 208 Hackerman, Baltimore, MD, USA.,Department of Bioengineering, University of Washington, Seattle, WA, USA
| | - Sohail Zahid
- Department of Biomedical Engineering, Johns Hopkins University, 3400 N Charles Street, 208 Hackerman, Baltimore, MD, USA
| | - Bhradeev Sivasambu
- Division of Cardiology, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Joseph E Marine
- Division of Cardiology, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Hugh Calkins
- Division of Cardiology, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Natalia A Trayanova
- Institute for Computational Medicine, Johns Hopkins University, 3400 N Charles Street, 208 Hackerman, Baltimore, MD, USA.,Department of Biomedical Engineering, Johns Hopkins University, 3400 N Charles Street, 208 Hackerman, Baltimore, MD, USA.,Department of Medicine, Johns Hopkins University School of Medicine, USA
| | - David D Spragg
- Division of Cardiology, Johns Hopkins University School of Medicine, Baltimore, MD, USA
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Marx L, Gsell MAF, Rund A, Caforio F, Prassl AJ, Toth-Gayor G, Kuehne T, Augustin CM, Plank G. Personalization of electro-mechanical models of the pressure-overloaded left ventricle: fitting of Windkessel-type afterload models. PHILOSOPHICAL TRANSACTIONS. SERIES A, MATHEMATICAL, PHYSICAL, AND ENGINEERING SCIENCES 2020; 378:20190342. [PMID: 32448067 PMCID: PMC7287328 DOI: 10.1098/rsta.2019.0342] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Accepted: 04/01/2020] [Indexed: 05/21/2023]
Abstract
Computer models of left ventricular (LV) electro-mechanics (EM) show promise as a tool for assessing the impact of increased afterload upon LV performance. However, the identification of unique afterload model parameters and the personalization of EM LV models remains challenging due to significant clinical input uncertainties. Here, we personalized a virtual cohort of N = 17 EM LV models under pressure overload conditions. A global-local optimizer was developed to uniquely identify parameters of a three-element Windkessel (Wk3) afterload model. The sensitivity of Wk3 parameters to input uncertainty and of the EM LV model to Wk3 parameter uncertainty was analysed. The optimizer uniquely identified Wk3 parameters, and outputs of the personalized EM LV models showed close agreement with clinical data in all cases. Sensitivity analysis revealed a strong dependence of Wk3 parameters on input uncertainty. However, this had limited impact on outputs of EM LV models. A unique identification of Wk3 parameters from clinical data appears feasible, but it is sensitive to input uncertainty, thus depending on accurate invasive measurements. By contrast, the EM LV model outputs were less sensitive, with errors of less than 8.14% for input data errors of 10%, which is within the bounds of clinical data uncertainty. This article is part of the theme issue 'Uncertainty quantification in cardiac and cardiovascular modelling and simulation'.
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Affiliation(s)
- Laura Marx
- Gottfried Schatz Research Center for Cell Signaling, Metabolism and Aging - Division of Biophysics, Medical University Graz, Graz, Austria
| | - Matthias A. F. Gsell
- Gottfried Schatz Research Center for Cell Signaling, Metabolism and Aging - Division of Biophysics, Medical University Graz, Graz, Austria
| | - Armin Rund
- Institute for Mathematics and Scientific Computing, University of Graz, Graz, Austria
| | - Federica Caforio
- Gottfried Schatz Research Center for Cell Signaling, Metabolism and Aging - Division of Biophysics, Medical University Graz, Graz, Austria
| | - Anton J. Prassl
- Gottfried Schatz Research Center for Cell Signaling, Metabolism and Aging - Division of Biophysics, Medical University Graz, Graz, Austria
| | - Gabor Toth-Gayor
- Department of Cardiology, Medical University Graz, Graz, Austria
| | - Titus Kuehne
- Institute for Cardiovascular Computer-assisted Medicine (ICM), Charité - Universitätsmedizin Berlin, Berlin, Germany
- Department of Imaging and Congenital Heart Disease, German Heart Center Berlin, Berlin, Germany
| | - Christoph M. Augustin
- Gottfried Schatz Research Center for Cell Signaling, Metabolism and Aging - Division of Biophysics, Medical University Graz, Graz, Austria
| | - Gernot Plank
- Gottfried Schatz Research Center for Cell Signaling, Metabolism and Aging - Division of Biophysics, Medical University Graz, Graz, Austria
- e-mail:
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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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Trayanova NA, Doshi AN, Prakosa A. How personalized heart modeling can help treatment of lethal arrhythmias: A focus on ventricular tachycardia ablation strategies in post-infarction patients. WILEY INTERDISCIPLINARY REVIEWS-SYSTEMS BIOLOGY AND MEDICINE 2020; 12:e1477. [PMID: 31917524 DOI: 10.1002/wsbm.1477] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/12/2019] [Revised: 12/16/2019] [Accepted: 12/17/2019] [Indexed: 12/18/2022]
Abstract
Precision Cardiology is a targeted strategy for cardiovascular disease prevention and treatment that accounts for individual variability. Computational heart modeling is one of the novel approaches that have been developed under the umbrella of Precision Cardiology. Personalized computational modeling of patient hearts has made strides in the development of models that incorporate the individual geometry and structure of the heart as well as other patient-specific information. Of these developments, one of the potentially most impactful is the research aimed at noninvasively predicting the targets of ablation of lethal arrhythmia, ventricular tachycardia (VT), using patient-specific models. The approach has been successfully applied to patients with ischemic cardiomyopathy in proof-of-concept studies. The goal of this paper is to review the strategies for computational VT ablation guidance in ischemic cardiomyopathy patients, from model developments to the intricacies of the actual clinical application. To provide context in describing the road these computational modeling applications have undertaken, we first review the state of the art in VT ablation in the clinic, emphasizing the benefits that personalized computational prediction of ablation targets could bring to the clinical electrophysiology practice. This article is characterized under: Analytical and Computational Methods > Computational Methods Models of Systems Properties and Processes > Organ, Tissue, and Physiological Models Translational, Genomic, and Systems Medicine > Translational Medicine.
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Affiliation(s)
- Natalia A Trayanova
- Alliance for Cardiovascular Diagnostic and Treatment Innovation, Johns Hopkins University, Baltimore, Maryland.,Department of Biomedical Engineering, Johns Hopkins University, Baltimore, Maryland
| | - Ashish N Doshi
- Alliance for Cardiovascular Diagnostic and Treatment Innovation, Johns Hopkins University, Baltimore, Maryland
| | - Adityo Prakosa
- Alliance for Cardiovascular Diagnostic and Treatment Innovation, Johns Hopkins University, Baltimore, Maryland
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22
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Neic A, Gsell MA, Karabelas E, Prassl AJ, Plank G. Automating image-based mesh generation and manipulation tasks in cardiac modeling workflows using Meshtool. SOFTWAREX 2020; 11:100454. [PMID: 32607406 PMCID: PMC7326605 DOI: 10.1016/j.softx.2020.100454] [Citation(s) in RCA: 35] [Impact Index Per Article: 8.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/26/2023]
Abstract
Advanced cardiac modeling studies rely on the ability to generate and functionalize personalized in silico models from tomographic multi-label image stacks. Eventually, this is used for building virtual cohorts that capture the variability in size, shape, and morphology of individual hearts. Typical modeling workflows involve a multitude of interactive mesh manipulation steps, rendering model generation expensive. Meshtool is software specifically designed for automating all complex mesh manipulation tasks emerging in such workflows by implementing algorithms for tasks describable as operations on label fields and/or geometric features. We illustrate how Meshtool increases efficiency and reduces costs by offering an automatable, high performance mesh manipulation toolbox.
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Affiliation(s)
- Aurel Neic
- Gottfried Schatz Research Center: Division of Biophysics, Medical University of Graz, Graz, Austria
- NumeriCor GmbH, Graz, Austria
| | - Matthias A.F. Gsell
- Gottfried Schatz Research Center: Division of Biophysics, Medical University of Graz, Graz, Austria
| | - Elias Karabelas
- Gottfried Schatz Research Center: Division of Biophysics, Medical University of Graz, Graz, Austria
- Department of Biomedical Engineering, School of Biomedical Engineering & Imaging Sciences, King’s College London, London, United Kingdom
| | - 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
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23
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Plancke AM, Connolly A, Gemmell PM, Neic A, McSpadden LC, Whitaker J, O'Neill M, Rinaldi CA, Rajani R, Niederer SA, Plank G, Bishop MJ. Generation of a cohort of whole-torso cardiac models for assessing the utility of a novel computed shock vector efficiency metric for ICD optimisation. Comput Biol Med 2019; 112:103368. [PMID: 31352217 PMCID: PMC6873640 DOI: 10.1016/j.compbiomed.2019.103368] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2019] [Revised: 07/22/2019] [Accepted: 07/22/2019] [Indexed: 11/29/2022]
Abstract
Implanted cardiac defibrillators (ICDs) seek to automatically detect and terminate potentially lethal ventricular arrhythmias by applying strong internal electric shocks across the heart. However, the optimisation of the specific electrode design and configurations represents an intensive area of research in the pursuit of reduced shock strengths and fewer device complications and risks. Computational whole-torso simulations play an important role in this endeavour, although knowing which specific metric should be used to assess configuration efficacy and assessing the impact of different patient anatomies and pathologies, and the corresponding effect this may have on different metrics has not been investigated. We constructed a cohort of CT-derived high-resolution whole torso-cardiac computational models, including variants of cardiomyopathies and patients with differing torso dimensions. Simulations of electric shock application between electrode configurations corresponding to transveneous (TV-ICD) and subcutaneous (S-ICD) ICDs were modelled and conventional metrics such as defibrillation threshold (DFT) and impedance computed. In addition, we computed a novel metric termed the shock vector efficiency (η), which quantifies the fraction of electrical energy dissipated in the heart relative to the rest of the torso. Across the cohort, S-ICD configurations showed higher DFTs and impedances than TV-ICDs, as expected, although little consistent difference was seen between healthy and cardiomyopathy variants. η was consistently <2% for S-ICD configurations, becoming as high as 13% for TV-ICD setups. Simulations also suggested that a total torso height of approximately 20 cm is required for convergence in η. Overall, η was seen to be approximately negatively correlated with both DFT and impedance. However, important scenarios were identified in which certain values of DFT (or impedance) were associated with a range of η values, and vice-versa, highlighting the heterogeneity introduced by the different torsos and pathologies modelled. In conclusion, the shock vector efficiency represents a useful additional metric to be considered alongside DFT and impedance in the optimisation of ICD electrode configurations, particularly in the context of differing torso anatomies and cardiac pathologies, which can induce significant heterogeneity in conventional metrics of ICD efficacy.
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Affiliation(s)
- Anne-Marie Plancke
- Department of Biomedical Engineering, School of Biomedical Engineering & Imaging Sciences, King's College London, London, UK
| | - Adam Connolly
- Department of Biomedical Engineering, School of Biomedical Engineering & Imaging Sciences, King's College London, London, UK
| | - Philip M Gemmell
- Department of Biomedical Engineering, School of Biomedical Engineering & Imaging Sciences, King's College London, London, UK
| | - Aurel Neic
- Institute of Biophysics, Medical University of Graz, Austria
| | | | - John Whitaker
- Department of Biomedical Engineering, School of Biomedical Engineering & Imaging Sciences, King's College London, London, UK; Department of Cardiology, Guy's and St Thomas' Hospitals, London, UK
| | - Mark O'Neill
- Department of Biomedical Engineering, School of Biomedical Engineering & Imaging Sciences, King's College London, London, UK; Department of Cardiology, Guy's and St Thomas' Hospitals, London, UK
| | - Christopher A Rinaldi
- Department of Biomedical Engineering, School of Biomedical Engineering & Imaging Sciences, King's College London, London, UK; Department of Cardiology, Guy's and St Thomas' Hospitals, London, UK
| | - Ronak Rajani
- Cardiovascular Imaging Department, St Thomas' Hospital, London, UK
| | - Steven A Niederer
- Department of Biomedical Engineering, School of Biomedical Engineering & Imaging Sciences, King's College London, London, UK
| | - Gernot Plank
- Institute of Biophysics, Medical University of Graz, Austria
| | - Martin J Bishop
- Department of Biomedical Engineering, School of Biomedical Engineering & Imaging Sciences, King's College London, London, UK.
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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: 30] [Impact Index Per Article: 6.0] [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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Deng D, Prakosa A, Shade J, Nikolov P, Trayanova NA. Characterizing Conduction Channels in Postinfarction Patients Using a Personalized Virtual Heart. Biophys J 2019; 117:2287-2294. [PMID: 31447108 DOI: 10.1016/j.bpj.2019.07.024] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2019] [Revised: 06/25/2019] [Accepted: 07/10/2019] [Indexed: 01/22/2023] Open
Abstract
Patients with myocardial infarction have an abundance of conduction channels (CC); however, only a small subset of these CCs sustain ventricular tachycardia (VT). Identifying these critical CCs (CCCs) in the clinic so that they can be targeted by ablation remains a significant challenge. The objective of this study is to use a personalized virtual-heart approach to conduct a three-dimensional (3D) assessment of CCCs sustaining VTs of different morphologies in these patients, to investigate their 3D structural features, and to determine the optimal ablation strategy for each VT. To achieve these goals, ventricular models were constructed from contrast enhanced magnetic resonance imagings of six postinfarction patients. Rapid pacing induced VTs in each model. CCCs that sustained different VT morphologies were identified. CCCs' 3D structure and type and the resulting rotational electrical activity were examined. Ablation was performed at the optimal part of each CCC, aiming to terminate each VT with a minimal lesion size. Predicted ablation locations were compared to clinical. Analyzing the simulation results, we found that the observed VTs in each patient model were sustained by a limited number (2.7 ± 1.2) of CCCs. Further, we identified three types of CCCs sustaining VTs: I-type and T-type channels, with all channel branches bounded by scar, and functional reentry channels, which were fully or partially bounded by conduction block surfaces. The different types of CCCs accounted for 43.8, 18.8, and 37.4% of all CCCs, respectively. The mean narrowest width of CCCs or a branch of CCC was 9.7 ± 3.6 mm. Ablation of the narrowest part of each CCC was sufficient to terminate VT. Our results demonstrate that a personalized virtual-heart approach can determine the possible VT morphologies in each patient and identify the CCCs that sustain reentry. The approach can aid clinicians in identifying accurately the optimal VT ablation targets in postinfarction patients.
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Affiliation(s)
- Dongdong Deng
- School of Biomedical Engineering, Dalian University of Technology, Dalian, Liaoning, China; Department of Biomedical Engineering, Johns Hopkins University, Baltimore, Maryland
| | - Adityo Prakosa
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, Maryland
| | - Julie Shade
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, Maryland
| | - Plamen Nikolov
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, Maryland
| | - Natalia A Trayanova
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, Maryland.
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26
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Deng D, Prakosa A, Shade J, Nikolov P, Trayanova NA. Sensitivity of Ablation Targets Prediction to Electrophysiological Parameter Variability in Image-Based Computational Models of Ventricular Tachycardia in Post-infarction Patients. Front Physiol 2019; 10:628. [PMID: 31178758 PMCID: PMC6543853 DOI: 10.3389/fphys.2019.00628] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2018] [Accepted: 05/03/2019] [Indexed: 12/18/2022] Open
Abstract
Ventricular tachycardia (VT), which could lead to sudden cardiac death, occurs frequently in patients with myocardial infarction. Computational modeling has emerged as a powerful platform for the non-invasive investigation of lethal heart rhythm disorders in post-infarction patients and for guiding patient VT ablation. However, it remains unclear how VT dynamics and predicted ablation targets are influenced by inter-patient variability in action potential duration (APD) and conduction velocity (CV). The goal of this study was to systematically assess the effect of changes in the electrophysiological parameters on the induced VTs and predicted ablation targets in personalized models of post-infarction hearts. Simulations were conducted in 5 patient-specific left ventricular models reconstructed from late gadolinium-enhanced magnetic resonance imaging scans. We comprehensively characterized all possible pre-ablation and post-ablation VTs in simulations conducted with either an “average human VT”-based electrophysiological representation (i.e., EPavg) or with ±10% APD or CV (i.e., EPvar); additional simulations were also executed in some models for an extended range of these parameters. The results showed that: (1) a subset of reentries (76.2–100%, depending on EP parameter set) conducted with ±10% APD/CV was observed in approximately the same locations as reentries observed in EPavg cases; (2) emergent VTs could be induced sometimes after ablation in EPavg models, and these emergent VTs often corresponded to the pre-ablation reentries in simulations with EPvar parameter sets. These findings demonstrate that the VT ablation target uncertainty in patient-specific ventricular models with an average representation of VT-remodeled electrophysiology is relatively low and the ablation targets stable, as the localization of the induced VTs was primarily driven by the remodeled structural substrate. Thus, personalized ventricular modeling with an average representation of infarct-remodeled electrophysiology may uncover most targets for VT ablation.
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Affiliation(s)
- Dongdong Deng
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, United States.,School of Biomedical Engineering, Dalian University of Technology, Dalian, China
| | - Adityo Prakosa
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, United States
| | - Julie Shade
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, United States
| | - Plamen Nikolov
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, United States
| | - Natalia A Trayanova
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, United States
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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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Trayanova NA, Pashakhanloo F, Wu KC, Halperin HR. Imaging-Based Simulations for Predicting Sudden Death and Guiding Ventricular Tachycardia Ablation. Circ Arrhythm Electrophysiol 2019; 10:CIRCEP.117.004743. [PMID: 28696219 DOI: 10.1161/circep.117.004743] [Citation(s) in RCA: 36] [Impact Index Per Article: 7.2] [Reference Citation Analysis] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/23/2017] [Accepted: 06/08/2017] [Indexed: 11/16/2022]
Affiliation(s)
- Natalia A Trayanova
- From the Institute for Computational Medicine and Department of Biomedical Engineering (N.A.T., F.P.) and Departments of Radiology and Biomedical Engineering (H.R.H.), Johns Hopkins University, Baltimore, MD; and Division of Cardiology, Department of Medicine, Johns Hopkins Medical Institutions, Baltimore, MD (K.C.W., H.R.H.).
| | - Farhad Pashakhanloo
- From the Institute for Computational Medicine and Department of Biomedical Engineering (N.A.T., F.P.) and Departments of Radiology and Biomedical Engineering (H.R.H.), Johns Hopkins University, Baltimore, MD; and Division of Cardiology, Department of Medicine, Johns Hopkins Medical Institutions, Baltimore, MD (K.C.W., H.R.H.)
| | - Katherine C Wu
- From the Institute for Computational Medicine and Department of Biomedical Engineering (N.A.T., F.P.) and Departments of Radiology and Biomedical Engineering (H.R.H.), Johns Hopkins University, Baltimore, MD; and Division of Cardiology, Department of Medicine, Johns Hopkins Medical Institutions, Baltimore, MD (K.C.W., H.R.H.)
| | - Henry R Halperin
- From the Institute for Computational Medicine and Department of Biomedical Engineering (N.A.T., F.P.) and Departments of Radiology and Biomedical Engineering (H.R.H.), Johns Hopkins University, Baltimore, MD; and Division of Cardiology, Department of Medicine, Johns Hopkins Medical Institutions, Baltimore, MD (K.C.W., H.R.H.)
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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] [MESH Headings] [Grants] [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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Carpio EF, Gomez JF, Sebastian R, Lopez-Perez A, Castellanos E, Almendral J, Ferrero JM, Trenor B. Optimization of Lead Placement in the Right Ventricle During Cardiac Resynchronization Therapy. A Simulation Study. Front Physiol 2019; 10:74. [PMID: 30804805 PMCID: PMC6378298 DOI: 10.3389/fphys.2019.00074] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2018] [Accepted: 01/22/2019] [Indexed: 12/18/2022] Open
Abstract
Patients suffering from heart failure and left bundle branch block show electrical ventricular dyssynchrony causing an abnormal blood pumping. Cardiac resynchronization therapy (CRT) is recommended for these patients. Patients with positive therapy response normally present QRS shortening and an increased left ventricle (LV) ejection fraction. However, around one third do not respond favorably. Therefore, optimal location of pacing leads, timing delays between leads and/or choosing related biomarkers is crucial to achieve the best possible degree of ventricular synchrony during CRT application. In this study, computational modeling is used to predict the optimal location and delay of pacing leads to improve CRT response. We use a 3D electrophysiological computational model of the heart and torso to get insight into the changes in the activation patterns obtained when the heart is paced from different regions and for different atrioventricular and interventricular delays. The model represents a heart with left bundle branch block and heart failure, and allows a detailed and accurate analysis of the electrical changes observed simultaneously in the myocardium and in the QRS complex computed in the precordial leads. Computational simulations were performed using a modified version of the O'Hara et al. action potential model, the most recent mathematical model developed for human ventricular electrophysiology. The optimal location for the pacing leads was determined by QRS maximal reduction. Additionally, the influence of Purkinje system on CRT response was assessed and correlation analysis between several parameters of the QRS was made. Simulation results showed that the right ventricle (RV) upper septum near the outflow tract is an alternative location to the RV apical lead. Furthermore, LV endocardial pacing provided better results as compared to epicardial stimulation. Finally, the time to reach the 90% of the QRS area was a good predictor of the instant at which 90% of the ventricular tissue was activated. Thus, the time to reach the 90% of the QRS area is suggested as an additional index to assess CRT effectiveness to improve biventricular synchrony.
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Affiliation(s)
- Edison F Carpio
- Centre for Research and Innovation in Bioengineering (Ci2B), Universitat Politècnica de València, Valencia, Spain
| | - Juan F Gomez
- Centre for Research and Innovation in Bioengineering (Ci2B), Universitat Politècnica de València, Valencia, Spain
| | - Rafael Sebastian
- Computational Multiscale Simulation Lab (CoMMLab), Department of Computer Science, Universitat de València, Valencia, Spain
| | - Alejandro Lopez-Perez
- Centre for Research and Innovation in Bioengineering (Ci2B), Universitat Politècnica de València, Valencia, Spain
| | - Eduardo Castellanos
- Electrophysiology Laboratory and Arrhythmia Unit, Grupo HM Hospitales, Hospital Monteprincipe, University CEU-San Pablo, Madrid, Spain
| | - Jesus Almendral
- Electrophysiology Laboratory and Arrhythmia Unit, Grupo HM Hospitales, Hospital Monteprincipe, University CEU-San Pablo, Madrid, Spain
| | - Jose M Ferrero
- Centre for Research and Innovation in Bioengineering (Ci2B), Universitat Politècnica de València, Valencia, Spain
| | - Beatriz Trenor
- Centre for Research and Innovation in Bioengineering (Ci2B), Universitat Politècnica de València, Valencia, Spain
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31
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Deng D, Nikolov P, Arevalo HJ, Trayanova NA. Optimal contrast-enhanced MRI image thresholding for accurate prediction of ventricular tachycardia using ex-vivo high resolution models. Comput Biol Med 2018; 102:426-432. [PMID: 30301573 PMCID: PMC6218273 DOI: 10.1016/j.compbiomed.2018.09.031] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2018] [Revised: 09/12/2018] [Accepted: 09/30/2018] [Indexed: 11/23/2022]
Abstract
Patient specific models created from contrast-enhanced (i.e. late-gadolinium, LGE) MRI images can be used for prediction of reentry location and clinical ablation planning. However, there is still a need for direct and systematic comparison between characteristics of ventricular tachycardia (VT) morphologies predicted in computational models and those acquired in clinical or experimental protocols. In this study, we aimed to: 1) assess the differences in VT morphologies predicted by modeling and recorded in experiments in terms of patterns and location of reentries, earliest and latest activation sites, and cycle lengths; and 2) define the optimal range of infarct tissue threshold values which provide best match between simulation and experimental results. To achieve these goals, we utilized LGE-MRI images from 4 swine hearts with inducible monomorphic VT. The images were segmented to identify non-infarcted myocardium, semi viable gray zone (GZ), and core scar based on pixel intensity. Several models were reconstructed from each LGE-MRI scan, with voxels of intensity between that of non-infarcted myocardium and 20-50% of the maximum intensity (in 10% increments) in the infarct region classified as GZ. VT induction was simulated in each model. Our simulation results showed that using GZ intensity thresholds of 20% or 30% resulted in the best match of simulated propagation patterns and reentry locations with those from the experiment. Overall, we matched 70% (7/10) morphologies for all the hearts. Our simulation shows that MRI-based computational models of hearts with myocardial infarction can accurately reproduce the majority of experimentally recorded post-infarction VTs.
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Affiliation(s)
- Dongdong Deng
- Institute for Computational Medicine, Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA
| | - Plamen Nikolov
- Institute for Computational Medicine, Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA
| | - Hermenegild J Arevalo
- Institute for Computational Medicine, Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA; Cardiac Modelling Department, Simula Research Laboratory, Fornebu, Norway
| | - Natalia A Trayanova
- Institute for Computational Medicine, Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA.
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Prakosa A, Arevalo HJ, Deng D, Boyle PM, Nikolov PP, Ashikaga H, Blauer JJE, Ghafoori E, Park CJ, Blake RC, Han FT, MacLeod RS, Halperin HR, Callans DJ, Ranjan R, Chrispin J, Nazarian S, Trayanova NA. Personalized virtual-heart technology for guiding the ablation of infarct-related ventricular tachycardia. Nat Biomed Eng 2018; 2:732-740. [PMID: 30847259 PMCID: PMC6400313 DOI: 10.1038/s41551-018-0282-2] [Citation(s) in RCA: 161] [Impact Index Per Article: 26.8] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2017] [Accepted: 07/27/2018] [Indexed: 11/08/2022]
Abstract
Ventricular tachycardia (VT), which can lead to sudden cardiac death, occurs frequently in patients with myocardial infarction. Catheter-based radiofrequency ablation of cardiac tissue has achieved only modest efficacy, owing to the inaccurate identification of ablation targets by current electrical mapping techniques, which can lead to extensive lesions and to a prolonged, poorly tolerated procedure. Here we show that personalized virtual-heart technology based on cardiac imaging and computational modelling can identify optimal infarct-related VT ablation targets in retrospective animal (5 swine) and human studies (21 patients) and in a prospective feasibility study (5 patients). We first assessed in retrospective studies (one of which included a proportion of clinical images with artifacts) the capability of the technology to determine the minimum-size ablation targets for eradicating all VTs. In the prospective study, VT sites predicted by the technology were targeted directly, without relying on prior electrical mapping. The approach could improve infarct-related VT ablation guidance, where accurate identification of patient-specific optimal targets could be achieved on a personalized virtual heart prior to the clinical procedure.
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Affiliation(s)
- Adityo Prakosa
- Institute for Computational Medicine and Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA
| | - Hermenegild J Arevalo
- Institute for Computational Medicine and Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA
- Cardiac Modelling Department, Simula Research Laboratory, Fornebu, Norway
| | - Dongdong Deng
- 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
| | - Plamen P Nikolov
- Institute for Computational Medicine and Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA
| | - Hiroshi Ashikaga
- Division of Cardiology, Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Joshua J E Blauer
- Department of Bioengineering, University of Utah, Salt Lake City, UT, USA
| | - Elyar Ghafoori
- Department of Bioengineering, University of Utah, Salt Lake City, UT, USA
| | - Carolyn J Park
- Institute for Computational Medicine and Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA
| | - Robert C Blake
- Institute for Computational Medicine and Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA
| | - Frederick T Han
- University of Utah Health Sciences Center, Salt Lake City, UT, USA
| | - Rob S MacLeod
- Department of Bioengineering, University of Utah, Salt Lake City, UT, USA
| | - Henry R Halperin
- Division of Cardiology, Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - David J Callans
- Department of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Ravi Ranjan
- Department of Bioengineering, University of Utah, Salt Lake City, UT, USA
| | - Jonathan Chrispin
- Division of Cardiology, Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Saman Nazarian
- Department of Medicine, University of Pennsylvania, Philadelphia, PA, 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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33
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Connolly A, Robson MD, Schneider J, Burton R, Plank G, Bishop MJ. Highly trabeculated structure of the human endocardium underlies asymmetrical response to low-energy monophasic shocks. CHAOS (WOODBURY, N.Y.) 2017; 27:093913. [PMID: 28964115 PMCID: PMC5570597 DOI: 10.1063/1.4999609] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 03/13/2017] [Accepted: 08/03/2017] [Indexed: 06/07/2023]
Abstract
Novel low-energy defibrillation therapies are thought to be driven by virtual-electrodes (VEs), due to the interaction of applied monophasic electric shocks with fine-scale anatomical structures within the heart. Significant inter-species differences in the cardiac (micro)-anatomy exist, however, particularly with respect to the degree of endocardial trabeculations, which may underlie important differences in response to low-energy defibrillation protocols. Understanding the interaction of monophasic electric fields with the specific human micro-anatomy is therefore imperative in facilitating the translation and optimisation of these promising experimental therapies to the clinic. In this study, we sought to investigate how electric fields from implanted devices interact with the highly trabeculated human endocardial surface to better understand shock success in order to help optimise future clinical protocols. A bi-ventricular human computational model was constructed from high resolution (350 μm) ex-vivo MR data, including anatomically accurate endocardial structures. Monophasic shocks were applied between a basal right ventricular catheter and an exterior ground. Shocks of varying strengths were applied with both anodal [positive right ventricle (RV) electrode] and cathodal (negative RV electrode) polarities at different states of tissue refractoriness and during induced arrhythmias. Anodal shocks induced isolated positive VEs at the distal side of "detached" trabeculations, which rapidly spread into hyperpolarised tissue on the surrounding endocardial surfaces following the shock. Anodal shocks thus depolarised more tissue 10 ms after the shock than cathodal shocks where the propagation of activation from VEs induced on the proximal side of "detached" trabeculations was prevented due to refractory endocardium. Anodal shocks increased arrhythmia complexity more than cathodal shocks during failed anti-arrhythmia shocks. In conclusion, multiple detached trabeculations in the human ventricle interact with anodal stimuli to induce multiple secondary sources from VEs, facilitating more rapid shock-induced ventricular excitation compared to cathodal shocks. Such a mechanism may help explain inter-species differences in response to shocks and help to develop novel defibrillation strategies.
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Affiliation(s)
- Adam Connolly
- Department of Biomedical Engineering, Division of Imaging Sciences and Biomedical Engineering, King's College London, London, United Kingdom
| | - Matthew D Robson
- Division of Cardiovascular Medicine, University of Oxford, Oxford, United Kingdom
| | - Jürgen Schneider
- Division of Cardiovascular Medicine, University of Oxford, Oxford, United Kingdom
| | - Rebecca Burton
- Pharmacology Department, University of Oxford, Oxford, United Kingdom
| | - Gernot Plank
- Institute of Biophysics, Medical University of Graz, Graz, Austria
| | - Martin J Bishop
- Department of Biomedical Engineering, Division of Imaging Sciences and Biomedical Engineering, King's College London, London, United Kingdom
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34
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Deng D, Arevalo HJ, Prakosa A, Callans DJ, Trayanova NA. A feasibility study of arrhythmia risk prediction in patients with myocardial infarction and preserved ejection fraction. Europace 2017; 18:iv60-iv66. [PMID: 28011832 DOI: 10.1093/europace/euw351] [Citation(s) in RCA: 37] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2016] [Accepted: 08/17/2016] [Indexed: 12/27/2022] Open
Abstract
AIM To predict arrhythmia susceptibility in myocardial infarction (MI) patients with left ventricular ejection fraction (LVEF) >35% using a personalized virtual heart simulation approach. METHODS AND RESULTS A total of four contrast enhanced magnetic resonance imaging (MRI) datasets of patient hearts with MI and average LVEF of 44.0 ± 2.6% were used in this study. Because of the preserved LVEF, the patients were not indicated for implantable cardioverter defibrillator (ICD) insertion. One patient had spontaneous ventricular tachycardia (VT) prior to the MRI scan; the others had no arrhythmic events. Simulations of arrhythmia susceptibility were blind to clinical outcome. Models were constructed from patient MRI images segmented to identify myocardium, grey zone, and scar based on pixel intensity. Grey zone was modelled as having altered electrophysiology. Programmed electrical stimulation (PES) was performed to assess VT inducibility from 19 bi-ventricular sites in each heart model. Simulations successfully predicted arrhythmia risk in all four patients. For the patient with arrhythmic event, in-silico PES resulted in VT induction. Simulations correctly predicted that VT was non-inducible for the three patients with no recorded VT events. CONCLUSIONS Results demonstrate that the personalized virtual heart simulation approach may provide a novel risk stratification modality to non-invasively and effectively identify patients with LVEF >35% who could benefit from ICD implantation.
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Affiliation(s)
- Dongdong Deng
- Department of Biomedical Engineering, Institute for Computational Medicine, Johns Hopkins University, 3400 North Charles Street, Hackerman 216, Baltimore, MD 21218, USA
| | - Hermenegild J Arevalo
- Department of Biomedical Engineering, Institute for Computational Medicine, Johns Hopkins University, 3400 North Charles Street, Hackerman 216, Baltimore, MD 21218, USA
| | - Adityo Prakosa
- Department of Biomedical Engineering, Institute for Computational Medicine, Johns Hopkins University, 3400 North Charles Street, Hackerman 216, Baltimore, MD 21218, USA
| | - David J Callans
- Division of Cardiovascular Medicine, Electrophysiology Section, University of Pennsylvania, 3400 Spruce St, 9 Founders Pavillion, Philadelphia, PA 19104
| | - Natalia A Trayanova
- Department of Biomedical Engineering, Institute for Computational Medicine, Johns Hopkins University, 3400 North Charles Street, Hackerman 216, Baltimore, MD 21218, USA
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35
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Augustin CM, Crozier A, Neic A, Prassl AJ, Karabelas E, Ferreira da Silva T, Fernandes JF, Campos F, Kuehne T, Plank G. Patient-specific modeling of left ventricular electromechanics as a driver for haemodynamic analysis. Europace 2017; 18:iv121-iv129. [PMID: 28011839 PMCID: PMC5386137 DOI: 10.1093/europace/euw369] [Citation(s) in RCA: 22] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2016] [Accepted: 08/26/2016] [Indexed: 01/30/2023] Open
Abstract
Aims Models of blood flow in the left ventricle (LV) and aorta are an important tool for analysing the interplay between LV deformation and flow patterns. Typically, image-based kinematic models describing endocardial motion are used as an input to blood flow simulations. While such models are suitable for analysing the hemodynamic status quo, they are limited in predicting the response to interventions that alter afterload conditions. Mechano-fluidic models using biophysically detailed electromechanical (EM) models have the potential to overcome this limitation, but are more costly to build and compute. We report our recent advancements in developing an automated workflow for the creation of such CFD ready kinematic models to serve as drivers of blood flow simulations. Methods and results EM models of the LV and aortic root were created for four pediatric patients treated for either aortic coarctation or aortic valve disease. Using MRI, ECG and invasive pressure recordings, anatomy as well as electrophysiological, mechanical and circulatory model components were personalized. Results The implemented modeling pipeline was highly automated and allowed model construction and execution of simulations of a patient’s heartbeat within 1 day. All models reproduced clinical data with acceptable accuracy. Conclusion Using the developed modeling workflow, the use of EM LV models as driver of fluid flow simulations is becoming feasible. While EM models are costly to construct, they constitute an important and nontrivial step towards fully coupled electro-mechano-fluidic (EMF) models and show promise as a tool for predicting the response to interventions which affect afterload conditions.
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Affiliation(s)
- Christoph M Augustin
- Institute of Biophysics, Medical University of Graz, Harrachgasse 21/IV, 8010 Graz, Austria.,Department of Mechanical Engineering, University of California, 5126 Etcheverry Hall, Berkeley, CA 94720, USA
| | - Andrew Crozier
- Institute of Biophysics, Medical University of Graz, Harrachgasse 21/IV, 8010 Graz, Austria
| | - Aurel Neic
- Institute of Biophysics, Medical University of Graz, Harrachgasse 21/IV, 8010 Graz, Austria
| | - Anton J Prassl
- Institute of Biophysics, Medical University of Graz, Harrachgasse 21/IV, 8010 Graz, Austria
| | - Elias Karabelas
- Institute of Biophysics, Medical University of Graz, Harrachgasse 21/IV, 8010 Graz, Austria
| | - Tiago Ferreira da Silva
- Department of Congenital Heart Disease/Pediatric Cardiology, German Heart Institute Berlin, Augustenburger Platz 1, 13353 Berlin, Germany
| | - Joao F Fernandes
- Department of Congenital Heart Disease/Pediatric Cardiology, German Heart Institute Berlin, Augustenburger Platz 1, 13353 Berlin, Germany
| | - Fernando Campos
- Institute of Biophysics, Medical University of Graz, Harrachgasse 21/IV, 8010 Graz, Austria.,Department of Congenital Heart Disease/Pediatric Cardiology, German Heart Institute Berlin, Augustenburger Platz 1, 13353 Berlin, Germany
| | - Titus Kuehne
- Department of Congenital Heart Disease/Pediatric Cardiology, German Heart Institute Berlin, Augustenburger Platz 1, 13353 Berlin, Germany
| | - Gernot Plank
- Institute of Biophysics, Medical University of Graz, Harrachgasse 21/IV, 8010 Graz, Austria
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36
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Bruegmann T, Boyle PM, Vogt CC, Karathanos TV, Arevalo HJ, Fleischmann BK, Trayanova NA, Sasse P. Optogenetic defibrillation terminates ventricular arrhythmia in mouse hearts and human simulations. J Clin Invest 2016; 126:3894-3904. [PMID: 27617859 DOI: 10.1172/jci88950] [Citation(s) in RCA: 115] [Impact Index Per Article: 14.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/03/2016] [Accepted: 08/04/2016] [Indexed: 11/17/2022] Open
Abstract
Ventricular arrhythmias are among the most severe complications of heart disease and can result in sudden cardiac death. Patients at risk currently receive implantable defibrillators that deliver electrical shocks to terminate arrhythmias on demand. However, strong electrical shocks can damage the heart and cause severe pain. Therefore, we have tested optogenetic defibrillation using expression of the light-sensitive channel channelrhodopsin-2 (ChR2) in cardiac tissue. Epicardial illumination effectively terminated ventricular arrhythmias in hearts from transgenic mice and from WT mice after adeno-associated virus-based gene transfer of ChR2. We also explored optogenetic defibrillation for human hearts, taking advantage of a recently developed, clinically validated in silico approach for simulating infarct-related ventricular tachycardia (VT). Our analysis revealed that illumination with red light effectively terminates VT in diseased, ChR2-expressing human hearts. Mechanistically, we determined that the observed VT termination is due to ChR2-mediated transmural depolarization of the myocardium, which causes a block of voltage-dependent Na+ channels throughout the myocardial wall and interrupts wavefront propagation into illuminated tissue. Thus, our results demonstrate that optogenetic defibrillation is highly effective in the mouse heart and could potentially be translated into humans to achieve nondamaging and pain-free termination of ventricular arrhythmia.
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37
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Sanchez-Alonso JL, Bhargava A, O'Hara T, Glukhov AV, Schobesberger S, Bhogal N, Sikkel MB, Mansfield C, Korchev YE, Lyon AR, Punjabi PP, Nikolaev VO, Trayanova NA, Gorelik J. Microdomain-Specific Modulation of L-Type Calcium Channels Leads to Triggered Ventricular Arrhythmia in Heart Failure. Circ Res 2016; 119:944-55. [PMID: 27572487 PMCID: PMC5045818 DOI: 10.1161/circresaha.116.308698] [Citation(s) in RCA: 80] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/10/2016] [Accepted: 08/24/2016] [Indexed: 11/22/2022]
Abstract
Supplemental Digital Content is available in the text. Rationale: Disruption in subcellular targeting of Ca2+ signaling complexes secondary to changes in cardiac myocyte structure may contribute to the pathophysiology of a variety of cardiac diseases, including heart failure (HF) and certain arrhythmias. Objective: To explore microdomain-targeted remodeling of ventricular L-type Ca2+ channels (LTCCs) in HF. Methods and Results: Super-resolution scanning patch-clamp, confocal and fluorescence microscopy were used to explore the distribution of single LTCCs in different membrane microdomains of nonfailing and failing human and rat ventricular myocytes. Disruption of membrane structure in both species led to the redistribution of functional LTCCs from their canonical location in transversal tubules (T-tubules) to the non-native crest of the sarcolemma, where their open probability was dramatically increased (0.034±0.011 versus 0.154±0.027, P<0.001). High open probability was linked to enhance calcium–calmodulin kinase II–mediated phosphorylation in non-native microdomains and resulted in an elevated ICa,L window current, which contributed to the development of early afterdepolarizations. A novel model of LTCC function in HF was developed; after its validation with experimental data, the model was used to ascertain how HF-induced T-tubule loss led to altered LTCC function and early afterdepolarizations. The HF myocyte model was then implemented in a 3-dimensional left ventricle model, demonstrating that such early afterdepolarizations can propagate and initiate reentrant arrhythmias. Conclusions: Microdomain-targeted remodeling of LTCC properties is an important event in pathways that may contribute to ventricular arrhythmogenesis in the settings of HF-associated remodeling. This extends beyond the classical concept of electric remodeling in HF and adds a new dimension to cardiovascular disease.
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Affiliation(s)
- Jose L Sanchez-Alonso
- From the Department of Cardiovascular Sciences, Imperial Centre for Translational and Experimental Medicine, National Heart and Lung Institute (J.L.S.-A., A.B., A.V.G., S.S., N.B., M.B.S., C.M., A.R.L., P.P.P., J.G.), Department of Medicine (Y.E.K.), and Department of Cardiothoracic Surgery, Hammersmith Hospital, National Heart and Lung Institute (P.P.P.), Imperial College London, United Kingdom; Department of Biomedical Engineering and Institute for Computational Medicine, Johns Hopkins University, Baltimore, MD (T.O., N.A.T.); NIHR Cardiovascular Biomedical Research Unit, Royal Brompton Hospital, London, United Kingdom (A.R.L.); Institute of Experimental Cardiovascular Research, University Medical Center Hamburg-Eppendorf, Hamburg, Germany (S.S., V.O.N.); and Department of Biotechnology, Indian Institute of Technology Hyderabad, Kandi, Telangana, India (A.B.)
| | - Anamika Bhargava
- From the Department of Cardiovascular Sciences, Imperial Centre for Translational and Experimental Medicine, National Heart and Lung Institute (J.L.S.-A., A.B., A.V.G., S.S., N.B., M.B.S., C.M., A.R.L., P.P.P., J.G.), Department of Medicine (Y.E.K.), and Department of Cardiothoracic Surgery, Hammersmith Hospital, National Heart and Lung Institute (P.P.P.), Imperial College London, United Kingdom; Department of Biomedical Engineering and Institute for Computational Medicine, Johns Hopkins University, Baltimore, MD (T.O., N.A.T.); NIHR Cardiovascular Biomedical Research Unit, Royal Brompton Hospital, London, United Kingdom (A.R.L.); Institute of Experimental Cardiovascular Research, University Medical Center Hamburg-Eppendorf, Hamburg, Germany (S.S., V.O.N.); and Department of Biotechnology, Indian Institute of Technology Hyderabad, Kandi, Telangana, India (A.B.)
| | - Thomas O'Hara
- From the Department of Cardiovascular Sciences, Imperial Centre for Translational and Experimental Medicine, National Heart and Lung Institute (J.L.S.-A., A.B., A.V.G., S.S., N.B., M.B.S., C.M., A.R.L., P.P.P., J.G.), Department of Medicine (Y.E.K.), and Department of Cardiothoracic Surgery, Hammersmith Hospital, National Heart and Lung Institute (P.P.P.), Imperial College London, United Kingdom; Department of Biomedical Engineering and Institute for Computational Medicine, Johns Hopkins University, Baltimore, MD (T.O., N.A.T.); NIHR Cardiovascular Biomedical Research Unit, Royal Brompton Hospital, London, United Kingdom (A.R.L.); Institute of Experimental Cardiovascular Research, University Medical Center Hamburg-Eppendorf, Hamburg, Germany (S.S., V.O.N.); and Department of Biotechnology, Indian Institute of Technology Hyderabad, Kandi, Telangana, India (A.B.)
| | - Alexey V Glukhov
- From the Department of Cardiovascular Sciences, Imperial Centre for Translational and Experimental Medicine, National Heart and Lung Institute (J.L.S.-A., A.B., A.V.G., S.S., N.B., M.B.S., C.M., A.R.L., P.P.P., J.G.), Department of Medicine (Y.E.K.), and Department of Cardiothoracic Surgery, Hammersmith Hospital, National Heart and Lung Institute (P.P.P.), Imperial College London, United Kingdom; Department of Biomedical Engineering and Institute for Computational Medicine, Johns Hopkins University, Baltimore, MD (T.O., N.A.T.); NIHR Cardiovascular Biomedical Research Unit, Royal Brompton Hospital, London, United Kingdom (A.R.L.); Institute of Experimental Cardiovascular Research, University Medical Center Hamburg-Eppendorf, Hamburg, Germany (S.S., V.O.N.); and Department of Biotechnology, Indian Institute of Technology Hyderabad, Kandi, Telangana, India (A.B.)
| | - Sophie Schobesberger
- From the Department of Cardiovascular Sciences, Imperial Centre for Translational and Experimental Medicine, National Heart and Lung Institute (J.L.S.-A., A.B., A.V.G., S.S., N.B., M.B.S., C.M., A.R.L., P.P.P., J.G.), Department of Medicine (Y.E.K.), and Department of Cardiothoracic Surgery, Hammersmith Hospital, National Heart and Lung Institute (P.P.P.), Imperial College London, United Kingdom; Department of Biomedical Engineering and Institute for Computational Medicine, Johns Hopkins University, Baltimore, MD (T.O., N.A.T.); NIHR Cardiovascular Biomedical Research Unit, Royal Brompton Hospital, London, United Kingdom (A.R.L.); Institute of Experimental Cardiovascular Research, University Medical Center Hamburg-Eppendorf, Hamburg, Germany (S.S., V.O.N.); and Department of Biotechnology, Indian Institute of Technology Hyderabad, Kandi, Telangana, India (A.B.)
| | - Navneet Bhogal
- From the Department of Cardiovascular Sciences, Imperial Centre for Translational and Experimental Medicine, National Heart and Lung Institute (J.L.S.-A., A.B., A.V.G., S.S., N.B., M.B.S., C.M., A.R.L., P.P.P., J.G.), Department of Medicine (Y.E.K.), and Department of Cardiothoracic Surgery, Hammersmith Hospital, National Heart and Lung Institute (P.P.P.), Imperial College London, United Kingdom; Department of Biomedical Engineering and Institute for Computational Medicine, Johns Hopkins University, Baltimore, MD (T.O., N.A.T.); NIHR Cardiovascular Biomedical Research Unit, Royal Brompton Hospital, London, United Kingdom (A.R.L.); Institute of Experimental Cardiovascular Research, University Medical Center Hamburg-Eppendorf, Hamburg, Germany (S.S., V.O.N.); and Department of Biotechnology, Indian Institute of Technology Hyderabad, Kandi, Telangana, India (A.B.)
| | - Markus B Sikkel
- From the Department of Cardiovascular Sciences, Imperial Centre for Translational and Experimental Medicine, National Heart and Lung Institute (J.L.S.-A., A.B., A.V.G., S.S., N.B., M.B.S., C.M., A.R.L., P.P.P., J.G.), Department of Medicine (Y.E.K.), and Department of Cardiothoracic Surgery, Hammersmith Hospital, National Heart and Lung Institute (P.P.P.), Imperial College London, United Kingdom; Department of Biomedical Engineering and Institute for Computational Medicine, Johns Hopkins University, Baltimore, MD (T.O., N.A.T.); NIHR Cardiovascular Biomedical Research Unit, Royal Brompton Hospital, London, United Kingdom (A.R.L.); Institute of Experimental Cardiovascular Research, University Medical Center Hamburg-Eppendorf, Hamburg, Germany (S.S., V.O.N.); and Department of Biotechnology, Indian Institute of Technology Hyderabad, Kandi, Telangana, India (A.B.)
| | - Catherine Mansfield
- From the Department of Cardiovascular Sciences, Imperial Centre for Translational and Experimental Medicine, National Heart and Lung Institute (J.L.S.-A., A.B., A.V.G., S.S., N.B., M.B.S., C.M., A.R.L., P.P.P., J.G.), Department of Medicine (Y.E.K.), and Department of Cardiothoracic Surgery, Hammersmith Hospital, National Heart and Lung Institute (P.P.P.), Imperial College London, United Kingdom; Department of Biomedical Engineering and Institute for Computational Medicine, Johns Hopkins University, Baltimore, MD (T.O., N.A.T.); NIHR Cardiovascular Biomedical Research Unit, Royal Brompton Hospital, London, United Kingdom (A.R.L.); Institute of Experimental Cardiovascular Research, University Medical Center Hamburg-Eppendorf, Hamburg, Germany (S.S., V.O.N.); and Department of Biotechnology, Indian Institute of Technology Hyderabad, Kandi, Telangana, India (A.B.)
| | - Yuri E Korchev
- From the Department of Cardiovascular Sciences, Imperial Centre for Translational and Experimental Medicine, National Heart and Lung Institute (J.L.S.-A., A.B., A.V.G., S.S., N.B., M.B.S., C.M., A.R.L., P.P.P., J.G.), Department of Medicine (Y.E.K.), and Department of Cardiothoracic Surgery, Hammersmith Hospital, National Heart and Lung Institute (P.P.P.), Imperial College London, United Kingdom; Department of Biomedical Engineering and Institute for Computational Medicine, Johns Hopkins University, Baltimore, MD (T.O., N.A.T.); NIHR Cardiovascular Biomedical Research Unit, Royal Brompton Hospital, London, United Kingdom (A.R.L.); Institute of Experimental Cardiovascular Research, University Medical Center Hamburg-Eppendorf, Hamburg, Germany (S.S., V.O.N.); and Department of Biotechnology, Indian Institute of Technology Hyderabad, Kandi, Telangana, India (A.B.)
| | - Alexander R Lyon
- From the Department of Cardiovascular Sciences, Imperial Centre for Translational and Experimental Medicine, National Heart and Lung Institute (J.L.S.-A., A.B., A.V.G., S.S., N.B., M.B.S., C.M., A.R.L., P.P.P., J.G.), Department of Medicine (Y.E.K.), and Department of Cardiothoracic Surgery, Hammersmith Hospital, National Heart and Lung Institute (P.P.P.), Imperial College London, United Kingdom; Department of Biomedical Engineering and Institute for Computational Medicine, Johns Hopkins University, Baltimore, MD (T.O., N.A.T.); NIHR Cardiovascular Biomedical Research Unit, Royal Brompton Hospital, London, United Kingdom (A.R.L.); Institute of Experimental Cardiovascular Research, University Medical Center Hamburg-Eppendorf, Hamburg, Germany (S.S., V.O.N.); and Department of Biotechnology, Indian Institute of Technology Hyderabad, Kandi, Telangana, India (A.B.)
| | - Prakash P Punjabi
- From the Department of Cardiovascular Sciences, Imperial Centre for Translational and Experimental Medicine, National Heart and Lung Institute (J.L.S.-A., A.B., A.V.G., S.S., N.B., M.B.S., C.M., A.R.L., P.P.P., J.G.), Department of Medicine (Y.E.K.), and Department of Cardiothoracic Surgery, Hammersmith Hospital, National Heart and Lung Institute (P.P.P.), Imperial College London, United Kingdom; Department of Biomedical Engineering and Institute for Computational Medicine, Johns Hopkins University, Baltimore, MD (T.O., N.A.T.); NIHR Cardiovascular Biomedical Research Unit, Royal Brompton Hospital, London, United Kingdom (A.R.L.); Institute of Experimental Cardiovascular Research, University Medical Center Hamburg-Eppendorf, Hamburg, Germany (S.S., V.O.N.); and Department of Biotechnology, Indian Institute of Technology Hyderabad, Kandi, Telangana, India (A.B.)
| | - Viacheslav O Nikolaev
- From the Department of Cardiovascular Sciences, Imperial Centre for Translational and Experimental Medicine, National Heart and Lung Institute (J.L.S.-A., A.B., A.V.G., S.S., N.B., M.B.S., C.M., A.R.L., P.P.P., J.G.), Department of Medicine (Y.E.K.), and Department of Cardiothoracic Surgery, Hammersmith Hospital, National Heart and Lung Institute (P.P.P.), Imperial College London, United Kingdom; Department of Biomedical Engineering and Institute for Computational Medicine, Johns Hopkins University, Baltimore, MD (T.O., N.A.T.); NIHR Cardiovascular Biomedical Research Unit, Royal Brompton Hospital, London, United Kingdom (A.R.L.); Institute of Experimental Cardiovascular Research, University Medical Center Hamburg-Eppendorf, Hamburg, Germany (S.S., V.O.N.); and Department of Biotechnology, Indian Institute of Technology Hyderabad, Kandi, Telangana, India (A.B.)
| | - Natalia A Trayanova
- From the Department of Cardiovascular Sciences, Imperial Centre for Translational and Experimental Medicine, National Heart and Lung Institute (J.L.S.-A., A.B., A.V.G., S.S., N.B., M.B.S., C.M., A.R.L., P.P.P., J.G.), Department of Medicine (Y.E.K.), and Department of Cardiothoracic Surgery, Hammersmith Hospital, National Heart and Lung Institute (P.P.P.), Imperial College London, United Kingdom; Department of Biomedical Engineering and Institute for Computational Medicine, Johns Hopkins University, Baltimore, MD (T.O., N.A.T.); NIHR Cardiovascular Biomedical Research Unit, Royal Brompton Hospital, London, United Kingdom (A.R.L.); Institute of Experimental Cardiovascular Research, University Medical Center Hamburg-Eppendorf, Hamburg, Germany (S.S., V.O.N.); and Department of Biotechnology, Indian Institute of Technology Hyderabad, Kandi, Telangana, India (A.B.)
| | - Julia Gorelik
- From the Department of Cardiovascular Sciences, Imperial Centre for Translational and Experimental Medicine, National Heart and Lung Institute (J.L.S.-A., A.B., A.V.G., S.S., N.B., M.B.S., C.M., A.R.L., P.P.P., J.G.), Department of Medicine (Y.E.K.), and Department of Cardiothoracic Surgery, Hammersmith Hospital, National Heart and Lung Institute (P.P.P.), Imperial College London, United Kingdom; Department of Biomedical Engineering and Institute for Computational Medicine, Johns Hopkins University, Baltimore, MD (T.O., N.A.T.); NIHR Cardiovascular Biomedical Research Unit, Royal Brompton Hospital, London, United Kingdom (A.R.L.); Institute of Experimental Cardiovascular Research, University Medical Center Hamburg-Eppendorf, Hamburg, Germany (S.S., V.O.N.); and Department of Biotechnology, Indian Institute of Technology Hyderabad, Kandi, Telangana, India (A.B.).
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Ukwatta E, Arevalo H, Li K, Yuan J, Qiu W, Malamas P, Wu KC, Trayanova NA, Vadakkumpadan F. Myocardial Infarct Segmentation From Magnetic Resonance Images for Personalized Modeling of Cardiac Electrophysiology. IEEE TRANSACTIONS ON MEDICAL IMAGING 2016; 35:1408-1419. [PMID: 26731693 PMCID: PMC4891256 DOI: 10.1109/tmi.2015.2512711] [Citation(s) in RCA: 21] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/10/2023]
Abstract
Accurate representation of myocardial infarct geometry is crucial to patient-specific computational modeling of the heart in ischemic cardiomyopathy. We have developed a methodology for segmentation of left ventricular (LV) infarct from clinically acquired, two-dimensional (2D), late-gadolinium enhanced cardiac magnetic resonance (LGE-CMR) images, for personalized modeling of ventricular electrophysiology. The infarct segmentation was expressed as a continuous min-cut optimization problem, which was solved using its dual formulation, the continuous max-flow (CMF). The optimization objective comprised of a smoothness term, and a data term that quantified the similarity between image intensity histograms of segmented regions and those of a set of training images. A manual segmentation of the LV myocardium was used to initialize and constrain the developed method. The three-dimensional geometry of infarct was reconstructed from its segmentation using an implicit, shape-based interpolation method. The proposed methodology was extensively evaluated using metrics based on geometry, and outcomes of individualized electrophysiological simulations of cardiac dys(function). Several existing LV infarct segmentation approaches were implemented, and compared with the proposed method. Our results demonstrated that the CMF method was more accurate than the existing approaches in reproducing expert manual LV infarct segmentations, and in electrophysiological simulations. The infarct segmentation method we have developed and comprehensively evaluated in this study constitutes an important step in advancing clinical applications of personalized simulations of cardiac electrophysiology.
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Affiliation(s)
- Eranga Ukwatta
- Department of Biomedical Engineering, Institute for Computational Medicine, Johns Hopkins University, Baltimore, MD, USA
- Correspondent author:
| | - Hermenegild Arevalo
- Department of Biomedical Engineering, Institute for Computational Medicine, Johns Hopkins University, Baltimore, MD, USA
| | - Kristina Li
- Department of Biomedical Engineering, Institute for Computational Medicine, Johns Hopkins University, Baltimore, MD, USA
| | - Jing Yuan
- Robarts Research Institute, Western University, London, ON, Canada
| | - Wu Qiu
- Robarts Research Institute, Western University, London, ON, Canada
| | - Peter Malamas
- Department of Biomedical Engineering, Institute for Computational Medicine, Johns Hopkins University, Baltimore, MD, USA
| | - Katherine C. Wu
- Division of Cardiology, Department of Medicine, Johns Hopkins Medical Institutions, Baltimore, MD, USA
| | - Natalia A. Trayanova
- Department of Biomedical Engineering, Institute for Computational Medicine, Johns Hopkins University, Baltimore, MD, USA
| | - Fijoy Vadakkumpadan
- Department of Biomedical Engineering, Institute for Computational Medicine, Johns Hopkins University, Baltimore, MD, USA
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Arrhythmia risk stratification of patients after myocardial infarction using personalized heart models. Nat Commun 2016; 7:11437. [PMID: 27164184 PMCID: PMC4866040 DOI: 10.1038/ncomms11437] [Citation(s) in RCA: 225] [Impact Index Per Article: 28.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/26/2016] [Accepted: 03/24/2016] [Indexed: 12/13/2022] Open
Abstract
Sudden cardiac death (SCD) from arrhythmias is a leading cause of mortality. For patients at high SCD risk, prophylactic insertion of implantable cardioverter defibrillators (ICDs) reduces mortality. Current approaches to identify patients at risk for arrhythmia are, however, of low sensitivity and specificity, which results in a low rate of appropriate ICD therapy. Here, we develop a personalized approach to assess SCD risk in post-infarction patients based on cardiac imaging and computational modelling. We construct personalized three-dimensional computer models of post-infarction hearts from patients' clinical magnetic resonance imaging data and assess the propensity of each model to develop arrhythmia. In a proof-of-concept retrospective study, the virtual heart test significantly outperformed several existing clinical metrics in predicting future arrhythmic events. The robust and non-invasive personalized virtual heart risk assessment may have the potential to prevent SCD and avoid unnecessary ICD implantations.
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40
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Arevalo HJ, Vadakkumpadan F, Guallar E, Jebb A, Malamas P, Wu KC, Trayanova NA. Arrhythmia risk stratification of patients after myocardial infarction using personalized heart models. Nat Commun 2016. [PMID: 27164184 DOI: 10.1038/ncommsll437] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/16/2023] Open
Abstract
Sudden cardiac death (SCD) from arrhythmias is a leading cause of mortality. For patients at high SCD risk, prophylactic insertion of implantable cardioverter defibrillators (ICDs) reduces mortality. Current approaches to identify patients at risk for arrhythmia are, however, of low sensitivity and specificity, which results in a low rate of appropriate ICD therapy. Here, we develop a personalized approach to assess SCD risk in post-infarction patients based on cardiac imaging and computational modelling. We construct personalized three-dimensional computer models of post-infarction hearts from patients' clinical magnetic resonance imaging data and assess the propensity of each model to develop arrhythmia. In a proof-of-concept retrospective study, the virtual heart test significantly outperformed several existing clinical metrics in predicting future arrhythmic events. The robust and non-invasive personalized virtual heart risk assessment may have the potential to prevent SCD and avoid unnecessary ICD implantations.
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Affiliation(s)
- Hermenegild J Arevalo
- Institute for Computational Medicine and Department of Biomedical Engineering, Johns Hopkins University, Baltimore, Maryland 21218, USA
| | - Fijoy Vadakkumpadan
- Institute for Computational Medicine and Department of Biomedical Engineering, Johns Hopkins University, Baltimore, Maryland 21218, USA
| | - Eliseo Guallar
- Welch Center for Prevention, Epidemiology, and Clinical Research, Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland 21287, USA
| | - Alexander Jebb
- Institute for Computational Medicine and Department of Biomedical Engineering, Johns Hopkins University, Baltimore, Maryland 21218, USA
| | - Peter Malamas
- Institute for Computational Medicine and Department of Biomedical Engineering, Johns Hopkins University, Baltimore, Maryland 21218, USA
| | - Katherine C Wu
- Division of Cardiology, Department of Medicine, Johns Hopkins Medical Institutions, Baltimore, Maryland 21287, USA
| | - Natalia A Trayanova
- Institute for Computational Medicine and Department of Biomedical Engineering, Johns Hopkins University, Baltimore, Maryland 21218, USA
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41
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Ukwatta E, Arevalo H, Rajchl M, White J, Pashakhanloo F, Prakosa A, Herzka DA, McVeigh E, Lardo AC, Trayanova NA, Vadakkumpadan F. Image-based reconstruction of three-dimensional myocardial infarct geometry for patient-specific modeling of cardiac electrophysiology. Med Phys 2016; 42:4579-90. [PMID: 26233186 DOI: 10.1118/1.4926428] [Citation(s) in RCA: 29] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022] Open
Abstract
PURPOSE Accurate three-dimensional (3D) reconstruction of myocardial infarct geometry is crucial to patient-specific modeling of the heart aimed at providing therapeutic guidance in ischemic cardiomyopathy. However, myocardial infarct imaging is clinically performed using two-dimensional (2D) late-gadolinium enhanced cardiac magnetic resonance (LGE-CMR) techniques, and a method to build accurate 3D infarct reconstructions from the 2D LGE-CMR images has been lacking. The purpose of this study was to address this need. METHODS The authors developed a novel methodology to reconstruct 3D infarct geometry from segmented low-resolution (Lo-res) clinical LGE-CMR images. Their methodology employed the so-called logarithm of odds (LogOdds) function to implicitly represent the shape of the infarct in segmented image slices as LogOdds maps. These 2D maps were then interpolated into a 3D image, and the result transformed via the inverse of LogOdds to a binary image representing the 3D infarct geometry. To assess the efficacy of this method, the authors utilized 39 high-resolution (Hi-res) LGE-CMR images, including 36 in vivo acquisitions of human subjects with prior myocardial infarction and 3 ex vivo scans of canine hearts following coronary ligation to induce infarction. The infarct was manually segmented by trained experts in each slice of the Hi-res images, and the segmented data were downsampled to typical clinical resolution. The proposed method was then used to reconstruct 3D infarct geometry from the downsampled images, and the resulting reconstructions were compared with the manually segmented data. The method was extensively evaluated using metrics based on geometry as well as results of electrophysiological simulations of cardiac sinus rhythm and ventricular tachycardia in individual hearts. Several alternative reconstruction techniques were also implemented and compared with the proposed method. RESULTS The accuracy of the LogOdds method in reconstructing 3D infarct geometry, as measured by the Dice similarity coefficient, was 82.10% ± 6.58%, a significantly higher value than those of the alternative reconstruction methods. Among outcomes of electrophysiological simulations with infarct reconstructions generated by various methods, the simulation results corresponding to the LogOdds method showed the smallest deviation from those corresponding to the manual reconstructions, as measured by metrics based on both activation maps and pseudo-ECGs. CONCLUSIONS The authors have developed a novel method for reconstructing 3D infarct geometry from segmented slices of Lo-res clinical 2D LGE-CMR images. This method outperformed alternative approaches in reproducing expert manual 3D reconstructions and in electrophysiological simulations.
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Affiliation(s)
- Eranga Ukwatta
- Institute for Computational Medicine, Johns Hopkins University, Baltimore, Maryland 21205 and Department of Biomedical Engineering, Johns Hopkins University, Baltimore, Maryland 21205
| | - Hermenegild Arevalo
- Institute for Computational Medicine, Johns Hopkins University, Baltimore, Maryland 21205 and Department of Biomedical Engineering, Johns Hopkins University, Baltimore, Maryland 21205
| | - Martin Rajchl
- Department of Computing, Imperial College London, London SW7 2AZ, United Kingdom
| | - James White
- Stephenson Cardiovascular MR Centre, University of Calgary, Calgary, Alberta T2N 2T9, Canada
| | - Farhad Pashakhanloo
- Institute for Computational Medicine, Johns Hopkins University, Baltimore, Maryland 21205 and Department of Biomedical Engineering, Johns Hopkins University, Baltimore, Maryland 21205
| | - Adityo Prakosa
- Institute for Computational Medicine, Johns Hopkins University, Baltimore, Maryland 21205 and Department of Biomedical Engineering, Johns Hopkins University, Baltimore, Maryland 21205
| | - Daniel A Herzka
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, Maryland 21205
| | - Elliot McVeigh
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, Maryland 21205
| | - Albert C Lardo
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, Maryland 21205 and Division of Cardiology, Johns Hopkins Institute of Medicine, Baltimore, Maryland 21224
| | - Natalia A Trayanova
- Institute for Computational Medicine, Johns Hopkins University, Baltimore, Maryland 21205; Department of Biomedical Engineering, Johns Hopkins University, Baltimore, Maryland 21205; and Department of Biomedical Engineering, Johns Hopkins Institute of Medicine, Baltimore, Maryland 21205
| | - Fijoy Vadakkumpadan
- Institute for Computational Medicine, Johns Hopkins University, Baltimore, Maryland 21205 and Department of Biomedical Engineering, Johns Hopkins University, Baltimore, Maryland 21205
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Zahid S, Cochet H, Boyle PM, Schwarz EL, Whyte KN, Vigmond EJ, Dubois R, Hocini M, Haïssaguerre M, Jaïs P, Trayanova NA. Patient-derived models link re-entrant driver localization in atrial fibrillation to fibrosis spatial pattern. Cardiovasc Res 2016; 110:443-54. [PMID: 27056895 DOI: 10.1093/cvr/cvw073] [Citation(s) in RCA: 200] [Impact Index Per Article: 25.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/19/2015] [Accepted: 03/31/2016] [Indexed: 12/19/2022] Open
Abstract
AIMS The mechanisms underlying persistent atrial fibrillation (AF) in patients with atrial fibrosis are poorly understood. The goal of this study was to use patient-derived atrial models to test the hypothesis that AF re-entrant drivers (RDs) persist only in regions with specific fibrosis patterns. METHODS AND RESULTS Twenty patients with persistent AF (PsAF) underwent late gadolinium-enhanced MRI to detect the presence of atrial fibrosis. Segmented images were used to construct personalized 3D models of the fibrotic atria with biophysically realistic atrial electrophysiology. In each model, rapid pacing was applied to induce AF. AF dynamics were analysed and RDs were identified using phase mapping. Fibrosis patterns in RD regions were characterized by computing maps of fibrosis density (FD) and entropy (FE). AF was inducible in 13/20 models and perpetuated by few RDs (2.7 ± 1.5) that were spatially confined (trajectory of phase singularities: 7.6 ± 2.3 mm). Compared with the remaining atrial tissue, regions where RDs persisted had higher FE (IQR: 0.42-0.60 vs. 0.00-0.40, P < 0.05) and FD (IQR: 0.59-0.77 vs. 0.00-0.33, P < 0.05). Machine learning classified RD and non-RD regions based on FD and FE and identified a subset of fibrotic boundary zones present in 13.8 ± 4.9% of atrial tissue where 83.5 ± 2.4% of all RD phase singularities were located. CONCLUSION Patient-derived models demonstrate that AF in fibrotic substrates is perpetuated by RDs persisting in fibrosis boundary zones characterized by specific regional fibrosis metrics (high FE and FD). These results provide new insights into the mechanisms that sustain PsAF and could pave the way for personalized, MRI-based management of PsAF.
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Affiliation(s)
- Sohail Zahid
- Institute for Computational Medicine, Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA
| | - Hubert Cochet
- IHU LIRYC, Electrophysiology and Heart Modeling Institute, INSERM U1045, Bordeaux, France Hôpital Cardiologique du Haut-Lévêque, CHU Bordeaux, Université de Bordeaux, Bordeaux, France
| | - Patrick M Boyle
- Institute for Computational Medicine, Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA
| | - Erica L Schwarz
- Institute for Computational Medicine, Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA
| | - Kaitlyn N Whyte
- Institute for Computational Medicine, Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA
| | - Edward J Vigmond
- IHU LIRYC, Electrophysiology and Heart Modeling Institute, INSERM U1045, Bordeaux, France
| | - Rémi Dubois
- IHU LIRYC, Electrophysiology and Heart Modeling Institute, INSERM U1045, Bordeaux, France
| | - Mélèze Hocini
- IHU LIRYC, Electrophysiology and Heart Modeling Institute, INSERM U1045, Bordeaux, France Hôpital Cardiologique du Haut-Lévêque, CHU Bordeaux, Université de Bordeaux, Bordeaux, France
| | - Michel Haïssaguerre
- IHU LIRYC, Electrophysiology and Heart Modeling Institute, INSERM U1045, Bordeaux, France Hôpital Cardiologique du Haut-Lévêque, CHU Bordeaux, Université de Bordeaux, Bordeaux, France
| | - Pierre Jaïs
- IHU LIRYC, Electrophysiology and Heart Modeling Institute, INSERM U1045, Bordeaux, France Hôpital Cardiologique du Haut-Lévêque, CHU Bordeaux, Université de Bordeaux, Bordeaux, France
| | - Natalia A Trayanova
- Institute for Computational Medicine, Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, USA
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Augustin CM, Neic A, Liebmann M, Prassl AJ, Niederer SA, Haase G, Plank G. Anatomically accurate high resolution modeling of human whole heart electromechanics: A strongly scalable algebraic multigrid solver method for nonlinear deformation. JOURNAL OF COMPUTATIONAL PHYSICS 2016; 305:622-646. [PMID: 26819483 PMCID: PMC4724941 DOI: 10.1016/j.jcp.2015.10.045] [Citation(s) in RCA: 76] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/18/2023]
Abstract
Electromechanical (EM) models of the heart have been used successfully to study fundamental mechanisms underlying a heart beat in health and disease. However, in all modeling studies reported so far numerous simplifications were made in terms of representing biophysical details of cellular function and its heterogeneity, gross anatomy and tissue microstructure, as well as the bidirectional coupling between electrophysiology (EP) and tissue distension. One limiting factor is the employed spatial discretization methods which are not sufficiently flexible to accommodate complex geometries or resolve heterogeneities, but, even more importantly, the limited efficiency of the prevailing solver techniques which are not sufficiently scalable to deal with the incurring increase in degrees of freedom (DOF) when modeling cardiac electromechanics at high spatio-temporal resolution. This study reports on the development of a novel methodology for solving the nonlinear equation of finite elasticity using human whole organ models of cardiac electromechanics, discretized at a high para-cellular resolution. Three patient-specific, anatomically accurate, whole heart EM models were reconstructed from magnetic resonance (MR) scans at resolutions of 220 μm, 440 μm and 880 μm, yielding meshes of approximately 184.6, 24.4 and 3.7 million tetrahedral elements and 95.9, 13.2 and 2.1 million displacement DOF, respectively. The same mesh was used for discretizing the governing equations of both electrophysiology (EP) and nonlinear elasticity. A novel algebraic multigrid (AMG) preconditioner for an iterative Krylov solver was developed to deal with the resulting computational load. The AMG preconditioner was designed under the primary objective of achieving favorable strong scaling characteristics for both setup and solution runtimes, as this is key for exploiting current high performance computing hardware. Benchmark results using the 220 μm, 440 μm and 880 μm meshes demonstrate efficient scaling up to 1024, 4096 and 8192 compute cores which allowed the simulation of a single heart beat in 44.3, 87.8 and 235.3 minutes, respectively. The efficiency of the method allows fast simulation cycles without compromising anatomical or biophysical detail.
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Affiliation(s)
| | - Aurel Neic
- Institute of Biophysics, Medical University of Graz, Graz, Austria
| | - Manfred Liebmann
- Institute for Mathematics and Scientific Computing, Karl-Franzens-University Graz, Graz, Austria
| | - Anton J. Prassl
- Institute of Biophysics, Medical University of Graz, Graz, Austria
| | - Steven A. Niederer
- Dept. Biomedical Engineering, Division of Imaging Sciences and Biomedical Engineering, King’s College of London, London, United Kingdom
| | - Gundolf Haase
- Institute for Mathematics and Scientific Computing, Karl-Franzens-University Graz, Graz, Austria
| | - Gernot Plank
- Institute of Biophysics, Medical University of Graz, Graz, Austria
- Corresponding author (Gernot Plank)
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44
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Crozier A, Augustin CM, Neic A, Prassl AJ, Holler M, Fastl TE, Hennemuth A, Bredies K, Kuehne T, Bishop MJ, Niederer SA, Plank G. Image-Based Personalization of Cardiac Anatomy for Coupled Electromechanical Modeling. Ann Biomed Eng 2016. [PMID: 26424476 DOI: 10.1007/sl0439-015-1474-5] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/16/2023]
Abstract
Computational models of cardiac electromechanics (EM) are increasingly being applied to clinical problems, with patient-specific models being generated from high fidelity imaging and used to simulate patient physiology, pathophysiology and response to treatment. Current structured meshes are limited in their ability to fully represent the detailed anatomical data available from clinical images and capture complex and varied anatomy with limited geometric accuracy. In this paper, we review the state of the art in image-based personalization of cardiac anatomy for biophysically detailed, strongly coupled EM modeling, and present our own tools for the automatic building of anatomically and structurally accurate patient-specific models. Our method relies on using high resolution unstructured meshes for discretizing both physics, electrophysiology and mechanics, in combination with efficient, strongly scalable solvers necessary to deal with the computational load imposed by the large number of degrees of freedom of these meshes. These tools permit automated anatomical model generation and strongly coupled EM simulations at an unprecedented level of anatomical and biophysical detail.
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Affiliation(s)
- A Crozier
- Institute of Biophysics, Medical University of Graz, Harrachgasse 21/IV, 8010, Graz, Austria
| | - C M Augustin
- Institute of Biophysics, Medical University of Graz, Harrachgasse 21/IV, 8010, Graz, Austria
| | - A Neic
- Institute of Biophysics, Medical University of Graz, Harrachgasse 21/IV, 8010, Graz, Austria
| | - A J Prassl
- Institute of Biophysics, Medical University of Graz, Harrachgasse 21/IV, 8010, Graz, Austria
| | - M Holler
- Institute for Mathematics and Scientific Computing, University of Graz, Graz, Austria
| | - T E Fastl
- Department of Biomedical Engineering, King's College London, London, United Kingdom
| | - A Hennemuth
- Modeling and Simulation Group, Fraunhofer MEVIS, Bremen, Germany
| | - K Bredies
- Institute for Mathematics and Scientific Computing, University of Graz, Graz, Austria
| | - T Kuehne
- Non-Invasive Cardiac Imaging in Congenital Heart Disease Unit, Charité-Universitätsmedizin, Berlin, Germany
- German Heart Institute, Berlin, Germany
| | - M J Bishop
- Department of Biomedical Engineering, King's College London, London, United Kingdom
| | - S A Niederer
- Department of Biomedical Engineering, King's College London, London, United Kingdom
| | - G Plank
- Institute of Biophysics, Medical University of Graz, Harrachgasse 21/IV, 8010, Graz, Austria.
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Deng D, Arevalo H, Pashakhanloo F, Prakosa A, Ashikaga H, McVeigh E, Halperin H, Trayanova N. Accuracy of prediction of infarct-related arrhythmic circuits from image-based models reconstructed from low and high resolution MRI. Front Physiol 2015; 6:282. [PMID: 26528188 PMCID: PMC4602125 DOI: 10.3389/fphys.2015.00282] [Citation(s) in RCA: 43] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/15/2015] [Accepted: 09/22/2015] [Indexed: 11/13/2022] Open
Abstract
Identification of optimal ablation sites in hearts with infarct-related ventricular tachycardia (VT) remains difficult to achieve with the current catheter-based mapping techniques. Limitations arise from the ambiguities in determining the reentrant pathways location(s). The goal of this study was to develop experimentally validated, individualized computer models of infarcted swine hearts, reconstructed from high-resolution ex-vivo MRI and to examine the accuracy of the reentrant circuit location prediction when models of the same hearts are instead reconstructed from low clinical-resolution MRI scans. To achieve this goal, we utilized retrospective data obtained from four pigs ~10 weeks post infarction that underwent VT induction via programmed stimulation and epicardial activation mapping via a multielectrode epicardial sock. After the experiment, high-resolution ex-vivo MRI with late gadolinium enhancement was acquired. The Hi-res images were downsampled into two lower resolutions (Med-res and Low-res) in order to replicate image quality obtainable in the clinic. The images were segmented and models were reconstructed from the three image stacks for each pig heart. VT induction similar to what was performed in the experiment was simulated. Results of the reconstructions showed that the geometry of the ventricles including the infarct could be accurately obtained from Med-res and Low-res images. Simulation results demonstrated that induced VTs in the Med-res and Low-res models were located close to those in Hi-res models. Importantly, all models, regardless of image resolution, accurately predicted the VT morphology and circuit location induced in the experiment. These results demonstrate that MRI-based computer models of hearts with ischemic cardiomyopathy could provide a unique opportunity to predict and analyze VT resulting for from specific infarct architecture, and thus may assist in clinical decisions to identify and ablate the reentrant circuit(s).
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Affiliation(s)
- Dongdong Deng
- Department of Biomedical Engineering, Institute for Computational Medicine, Johns Hopkins University Baltimore, MD, USA
| | - Hermenegild Arevalo
- Department of Biomedical Engineering, Institute for Computational Medicine, Johns Hopkins University Baltimore, MD, USA
| | - Farhad Pashakhanloo
- Department of Biomedical Engineering, Institute for Computational Medicine, Johns Hopkins University Baltimore, MD, USA
| | - Adityo Prakosa
- Department of Biomedical Engineering, Institute for Computational Medicine, Johns Hopkins University Baltimore, MD, USA
| | - Hiroshi Ashikaga
- Division of Cardiology, Department of Medicine, Johns Hopkins Medical Institute Baltimore, MD, USA ; Department of Biomedical Engineering, Johns Hopkins University Baltimore, MD, USA
| | - Elliot McVeigh
- Department of Biomedical Engineering, Johns Hopkins University Baltimore, MD, USA
| | - Henry Halperin
- Division of Cardiology, Department of Medicine, Johns Hopkins Medical Institute Baltimore, MD, USA
| | - Natalia Trayanova
- Department of Biomedical Engineering, Institute for Computational Medicine, Johns Hopkins University Baltimore, MD, USA
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46
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Crozier A, Augustin CM, Neic A, Prassl AJ, Holler M, Fastl TE, Hennemuth A, Bredies K, Kuehne T, Bishop MJ, Niederer SA, Plank G. Image-Based Personalization of Cardiac Anatomy for Coupled Electromechanical Modeling. Ann Biomed Eng 2015; 44:58-70. [PMID: 26424476 PMCID: PMC4690840 DOI: 10.1007/s10439-015-1474-5] [Citation(s) in RCA: 42] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2015] [Accepted: 09/24/2015] [Indexed: 11/26/2022]
Abstract
Computational models of cardiac electromechanics (EM) are increasingly being applied to clinical problems, with patient-specific models being generated from high fidelity imaging and used to simulate patient physiology, pathophysiology and response to treatment. Current structured meshes are limited in their ability to fully represent the detailed anatomical data available from clinical images and capture complex and varied anatomy with limited geometric accuracy. In this paper, we review the state of the art in image-based personalization of cardiac anatomy for biophysically detailed, strongly coupled EM modeling, and present our own tools for the automatic building of anatomically and structurally accurate patient-specific models. Our method relies on using high resolution unstructured meshes for discretizing both physics, electrophysiology and mechanics, in combination with efficient, strongly scalable solvers necessary to deal with the computational load imposed by the large number of degrees of freedom of these meshes. These tools permit automated anatomical model generation and strongly coupled EM simulations at an unprecedented level of anatomical and biophysical detail.
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Affiliation(s)
- A Crozier
- Institute of Biophysics, Medical University of Graz, Harrachgasse 21/IV, 8010, Graz, Austria
| | - C M Augustin
- Institute of Biophysics, Medical University of Graz, Harrachgasse 21/IV, 8010, Graz, Austria
| | - A Neic
- Institute of Biophysics, Medical University of Graz, Harrachgasse 21/IV, 8010, Graz, Austria
| | - A J Prassl
- Institute of Biophysics, Medical University of Graz, Harrachgasse 21/IV, 8010, Graz, Austria
| | - M Holler
- Institute for Mathematics and Scientific Computing, University of Graz, Graz, Austria
| | - T E Fastl
- Department of Biomedical Engineering, King's College London, London, United Kingdom
| | - A Hennemuth
- Modeling and Simulation Group, Fraunhofer MEVIS, Bremen, Germany
| | - K Bredies
- Institute for Mathematics and Scientific Computing, University of Graz, Graz, Austria
| | - T Kuehne
- Non-Invasive Cardiac Imaging in Congenital Heart Disease Unit, Charité-Universitätsmedizin, Berlin, Germany
- German Heart Institute, Berlin, Germany
| | - M J Bishop
- Department of Biomedical Engineering, King's College London, London, United Kingdom
| | - S A Niederer
- Department of Biomedical Engineering, King's College London, London, United Kingdom
| | - G Plank
- Institute of Biophysics, Medical University of Graz, Harrachgasse 21/IV, 8010, Graz, Austria.
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Lopez-Perez A, Sebastian R, Ferrero JM. Three-dimensional cardiac computational modelling: methods, features and applications. Biomed Eng Online 2015; 14:35. [PMID: 25928297 PMCID: PMC4424572 DOI: 10.1186/s12938-015-0033-5] [Citation(s) in RCA: 78] [Impact Index Per Article: 8.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2014] [Accepted: 04/02/2015] [Indexed: 01/19/2023] Open
Abstract
The combination of computational models and biophysical simulations can help to interpret an array of experimental data and contribute to the understanding, diagnosis and treatment of complex diseases such as cardiac arrhythmias. For this reason, three-dimensional (3D) cardiac computational modelling is currently a rising field of research. The advance of medical imaging technology over the last decades has allowed the evolution from generic to patient-specific 3D cardiac models that faithfully represent the anatomy and different cardiac features of a given alive subject. Here we analyse sixty representative 3D cardiac computational models developed and published during the last fifty years, describing their information sources, features, development methods and online availability. This paper also reviews the necessary components to build a 3D computational model of the heart aimed at biophysical simulation, paying especial attention to cardiac electrophysiology (EP), and the existing approaches to incorporate those components. We assess the challenges associated to the different steps of the building process, from the processing of raw clinical or biological data to the final application, including image segmentation, inclusion of substructures and meshing among others. We briefly outline the personalisation approaches that are currently available in 3D cardiac computational modelling. Finally, we present examples of several specific applications, mainly related to cardiac EP simulation and model-based image analysis, showing the potential usefulness of 3D cardiac computational modelling into clinical environments as a tool to aid in the prevention, diagnosis and treatment of cardiac diseases.
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Affiliation(s)
- Alejandro Lopez-Perez
- Centre for Research and Innovation in Bioengineering (Ci2B), Universitat Politècnica de València, València, Spain.
| | - Rafael Sebastian
- Computational Multiscale Physiology Lab (CoMMLab), Universitat de València, València, Spain.
| | - Jose M Ferrero
- Centre for Research and Innovation in Bioengineering (Ci2B), Universitat Politècnica de València, València, Spain.
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McDowell KS, Zahid S, Vadakkumpadan F, Blauer J, MacLeod RS, Trayanova NA. Virtual electrophysiological study of atrial fibrillation in fibrotic remodeling. PLoS One 2015; 10:e0117110. [PMID: 25692857 PMCID: PMC4333565 DOI: 10.1371/journal.pone.0117110] [Citation(s) in RCA: 95] [Impact Index Per Article: 10.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/16/2014] [Accepted: 12/18/2014] [Indexed: 12/19/2022] Open
Abstract
Research has indicated that atrial fibrillation (AF) ablation failure is related to the presence of atrial fibrosis. However it remains unclear whether this information can be successfully used in predicting the optimal ablation targets for AF termination. We aimed to provide a proof-of-concept that patient-specific virtual electrophysiological study that combines i) atrial structure and fibrosis distribution from clinical MRI and ii) modeling of atrial electrophysiology, could be used to predict: (1) how fibrosis distribution determines the locations from which paced beats degrade into AF; (2) the dynamic behavior of persistent AF rotors; and (3) the optimal ablation targets in each patient. Four MRI-based patient-specific models of fibrotic left atria were generated, ranging in fibrosis amount. Virtual electrophysiological studies were performed in these models, and where AF was inducible, the dynamics of AF were used to determine the ablation locations that render AF non-inducible. In 2 of the 4 models patient-specific models AF was induced; in these models the distance between a given pacing location and the closest fibrotic region determined whether AF was inducible from that particular location, with only the mid-range distances resulting in arrhythmia. Phase singularities of persistent rotors were found to move within restricted regions of tissue, which were independent of the pacing location from which AF was induced. Electrophysiological sensitivity analysis demonstrated that these regions changed little with variations in electrophysiological parameters. Patient-specific distribution of fibrosis was thus found to be a critical component of AF initiation and maintenance. When the restricted regions encompassing the meander of the persistent phase singularities were modeled as ablation lesions, AF could no longer be induced. The study demonstrates that a patient-specific modeling approach to identify non-invasively AF ablation targets prior to the clinical procedure is feasible.
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Affiliation(s)
- Kathleen S. McDowell
- The Johns Hopkins University, Department of Biomedical Engineering, Baltimore, Maryland, United States of America
| | - Sohail Zahid
- The Johns Hopkins University, Department of Biomedical Engineering, Baltimore, Maryland, United States of America
| | - Fijoy Vadakkumpadan
- The Johns Hopkins University, Department of Biomedical Engineering, Baltimore, Maryland, United States of America
| | - Joshua Blauer
- University of Utah, Comprehensive Arrhythmia Research and Management Center, School of Medicine, Salt Lake City, Utah, United States of America
| | - Rob S. MacLeod
- University of Utah, Comprehensive Arrhythmia Research and Management Center, School of Medicine, Salt Lake City, Utah, United States of America
| | - Natalia A. Trayanova
- The Johns Hopkins University, Department of Biomedical Engineering, Baltimore, Maryland, United States of America
- * E-mail:
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Gurev V, Pathmanathan P, Fattebert JL, Wen HF, Magerlein J, Gray RA, Richards DF, Rice JJ. A high-resolution computational model of the deforming human heart. Biomech Model Mechanobiol 2015; 14:829-49. [PMID: 25567753 DOI: 10.1007/s10237-014-0639-8] [Citation(s) in RCA: 35] [Impact Index Per Article: 3.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2014] [Accepted: 12/04/2014] [Indexed: 10/24/2022]
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Methodology for image-based reconstruction of ventricular geometry for patient-specific modeling of cardiac electrophysiology. PROGRESS IN BIOPHYSICS AND MOLECULAR BIOLOGY 2014; 115:226-34. [PMID: 25148771 DOI: 10.1016/j.pbiomolbio.2014.08.009] [Citation(s) in RCA: 41] [Impact Index Per Article: 4.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/07/2014] [Revised: 07/31/2014] [Accepted: 08/10/2014] [Indexed: 01/29/2023]
Abstract
Patient-specific modeling of ventricular electrophysiology requires an interpolated reconstruction of the 3-dimensional (3D) geometry of the patient ventricles from the low-resolution (Lo-res) clinical images. The goal of this study was to implement a processing pipeline for obtaining the interpolated reconstruction, and thoroughly evaluate the efficacy of this pipeline in comparison with alternative methods. The pipeline implemented here involves contouring the epi- and endocardial boundaries in Lo-res images, interpolating the contours using the variational implicit functions method, and merging the interpolation results to obtain the ventricular reconstruction. Five alternative interpolation methods, namely linear, cubic spline, spherical harmonics, cylindrical harmonics, and shape-based interpolation were implemented for comparison. In the thorough evaluation of the processing pipeline, Hi-res magnetic resonance (MR), computed tomography (CT), and diffusion tensor (DT) MR images from numerous hearts were used. Reconstructions obtained from the Hi-res images were compared with the reconstructions computed by each of the interpolation methods from a sparse sample of the Hi-res contours, which mimicked Lo-res clinical images. Qualitative and quantitative comparison of these ventricular geometry reconstructions showed that the variational implicit functions approach performed better than others. Additionally, the outcomes of electrophysiological simulations (sinus rhythm activation maps and pseudo-ECGs) conducted using models based on the various reconstructions were compared. These electrophysiological simulations demonstrated that our implementation of the variational implicit functions-based method had the best accuracy.
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