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Trayanova NA, Lyon A, Shade J, Heijman J. Computational modeling of cardiac electrophysiology and arrhythmogenesis: toward clinical translation. Physiol Rev 2024; 104:1265-1333. [PMID: 38153307 DOI: 10.1152/physrev.00017.2023] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2023] [Revised: 12/19/2023] [Accepted: 12/21/2023] [Indexed: 12/29/2023] Open
Abstract
The complexity of cardiac electrophysiology, involving dynamic changes in numerous components across multiple spatial (from ion channel to organ) and temporal (from milliseconds to days) scales, makes an intuitive or empirical analysis of cardiac arrhythmogenesis challenging. Multiscale mechanistic computational models of cardiac electrophysiology provide precise control over individual parameters, and their reproducibility enables a thorough assessment of arrhythmia mechanisms. This review provides a comprehensive analysis of models of cardiac electrophysiology and arrhythmias, from the single cell to the organ level, and how they can be leveraged to better understand rhythm disorders in cardiac disease and to improve heart patient care. Key issues related to model development based on experimental data are discussed, and major families of human cardiomyocyte models and their applications are highlighted. An overview of organ-level computational modeling of cardiac electrophysiology and its clinical applications in personalized arrhythmia risk assessment and patient-specific therapy of atrial and ventricular arrhythmias is provided. The advancements presented here highlight how patient-specific computational models of the heart reconstructed from patient data have achieved success in predicting risk of sudden cardiac death and guiding optimal treatments of heart rhythm disorders. Finally, an outlook toward potential future advances, including the combination of mechanistic modeling and machine learning/artificial intelligence, is provided. As the field of cardiology is embarking on a journey toward precision medicine, personalized modeling of the heart is expected to become a key technology to guide pharmaceutical therapy, deployment of devices, and surgical interventions.
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Affiliation(s)
- Natalia A Trayanova
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, Maryland, United States
- Alliance for Cardiovascular Diagnostic and Treatment Innovation, Johns Hopkins University, Baltimore, Maryland, United States
| | - Aurore Lyon
- Department of Biomedical Engineering, CARIM School for Cardiovascular Diseases, Maastricht University, Maastricht, The Netherlands
- Division of Heart and Lungs, Department of Medical Physiology, University Medical Centre Utrecht, Utrecht, The Netherlands
| | - Julie Shade
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, Maryland, United States
- Alliance for Cardiovascular Diagnostic and Treatment Innovation, Johns Hopkins University, Baltimore, Maryland, United States
| | - Jordi Heijman
- Department of Cardiology, CARIM School for Cardiovascular Diseases, Maastricht University, Maastricht, The Netherlands
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Cedilnik N, Pop M, Duchateau J, Sacher F, Jaïs P, Cochet H, Sermesant M. Efficient Patient-Specific Simulations of Ventricular Tachycardia Based on Computed Tomography-Defined Wall Thickness Heterogeneity. JACC Clin Electrophysiol 2023; 9:2507-2519. [PMID: 37804259 DOI: 10.1016/j.jacep.2023.08.008] [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: 10/31/2022] [Revised: 07/20/2023] [Accepted: 08/02/2023] [Indexed: 10/09/2023]
Abstract
BACKGROUND Electrophysiological mapping of ventricular tachycardia (VT) is tedious and poorly reproducible. Substrate analysis on imaging cannot explicitly display VT circuits. OBJECTIVES This study sought to introduce a computed tomography-based model personalization approach, allowing for the simulation of postinfarction VT in a clinically compatible time frame. METHODS In 10 patients (age 65 ± 11 years, 9 male) referred for post-VT ablation, computed tomography-derived wall thickness maps were registered to 25 electroanatomical maps (sinus rhythm, paced, and VT). The relationship between wall thickness and electrophysiological characteristics (activation-recovery interval) was analyzed. Wall thickness was then employed to parameterize a fast and tractable organ-scale wave propagation model. Pacing protocols were simulated from multiple sites to test VT induction in silico. In silico VTs were compared to VT circuits mapped clinically. RESULTS Clinically, 6 different VTs could be induced with detailed maps in 9 patients. The proposed model allowed for fast simulation (median: 6 min/pacing site). Simulations of steady pacing (600 milliseconds) from 100 different sites/patient never triggered any arrhythmia. Applying S1-S2 or S1-S2-S3 induction schemes allowed for the induction of in silico VTs in the 9 of 10 patients who were clinically inducible. The patient who was not inducible clinically was also noninducible in silico. A total of 42 different VTs were simulated (4.2 ± 2 per patient). Six in silico VTs matched a VT circuit mapped clinically. CONCLUSIONS The proposed framework allows for personalized simulations in a matter of hours. In 6 of 9 patients, simulations show re-entrant patterns matching intracardiac recordings.
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Affiliation(s)
- Nicolas Cedilnik
- Université Côte d'Azur, Epione, Inria, Sophia-Antipolis, France; Institut Hospitalo-Universitaire Liryc, Bordeaux, France.
| | - Mihaela Pop
- Université Côte d'Azur, Epione, Inria, Sophia-Antipolis, France
| | - Josselin Duchateau
- Institut Hospitalo-Universitaire Liryc, Bordeaux, France; Cardiac Pacing and Electrophysiology Department, Bordeaux University Hospital, Bordeaux, France
| | - Frédéric Sacher
- Institut Hospitalo-Universitaire Liryc, Bordeaux, France; Cardiac Pacing and Electrophysiology Department, Bordeaux University Hospital, Bordeaux, France
| | - Pierre Jaïs
- Institut Hospitalo-Universitaire Liryc, Bordeaux, France; Cardiac Pacing and Electrophysiology Department, Bordeaux University Hospital, Bordeaux, France
| | - Hubert Cochet
- Institut Hospitalo-Universitaire Liryc, Bordeaux, France; Radiology Department, Bordeaux University Hospital, Bordeaux, France
| | - Maxime Sermesant
- Université Côte d'Azur, Epione, Inria, Sophia-Antipolis, France; Institut Hospitalo-Universitaire Liryc, Bordeaux, France
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Yadan Z, Jian L, Jian W, Yifu L, Haiying L, Hairui L. An expert review of the inverse problem in electrocardiographic imaging for the non-invasive identification of atrial fibrillation drivers. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2023; 240:107676. [PMID: 37343376 DOI: 10.1016/j.cmpb.2023.107676] [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: 02/08/2023] [Revised: 06/06/2023] [Accepted: 06/09/2023] [Indexed: 06/23/2023]
Abstract
BACKGROUND AND OBJECTIVE Electrocardiographic imaging (ECGI) has emerged as a non-invasive approach to identify atrial fibrillation (AF) driver sources. This paper aims to collect and review the current research literature on the ECGI inverse problem, summarize the research progress, and propose potential research directions for the future. METHODS AND RESULTS The effectiveness and feasibility of using ECGI to map AF driver sources may be influenced by several factors, such as inaccuracies in the atrial model due to heart movement or deformation, noise interference in high-density body surface potential (BSP), inconvenient and time-consuming BSP acquisition, errors in solving the inverse problem, and incomplete interpretation of the AF driving source information derived from the reconstructed epicardial potential. We review the current research progress on these factors and discuss possible improvement directions. Additionally, we highlight the limitations of ECGI itself, including the lack of a gold standard to validate the accuracy of ECGI technology in locating AF drivers and the challenges associated with guiding AF ablation based on post-processed epicardial potentials due to the intrinsic difference between epicardial and endocardial potentials. CONCLUSIONS Before performing ablation, ECGI can provide operators with predictive information about the underlying locations of AF driver by non-invasively and globally mapping the biatrial electrical activity. In the future, endocardial catheter mapping technology may benefit from the use of ECGI to enhance the diagnosis and ablation of AF.
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Affiliation(s)
- Zhang Yadan
- Institute of Biomedical Engineering, Shenzhen International Graduate School, Tsinghua University, Shenzhen, Guangdong, China
| | - Liang Jian
- Fuwai Hospital Chinese Academy of Medical Sciences, Shenzhen, Guangdong, China
| | - Wu Jian
- Institute of Biomedical Engineering, Shenzhen International Graduate School, Tsinghua University, Shenzhen, Guangdong, China.
| | - Li Yifu
- Fuwai Hospital Chinese Academy of Medical Sciences, Shenzhen, Guangdong, China
| | - Li Haiying
- The University of Hong Kong-Shenzhen Hospital, Shenzhen, Guangdong, China
| | - Li Hairui
- The University of Hong Kong-Shenzhen Hospital, Shenzhen, Guangdong, China
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Marashly Q, Najjar SN, Hahn J, Rector GJ, Khawaja M, Chelu MG. Innovations in ventricular tachycardia ablation. J Interv Card Electrophysiol 2023; 66:1499-1518. [PMID: 35879516 DOI: 10.1007/s10840-022-01311-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/21/2022] [Accepted: 07/18/2022] [Indexed: 11/30/2022]
Abstract
Catheter ablation of ventricular arrhythmias (VAs) has evolved significantly over the past decade and is currently a well-established therapeutic option. Technological advances and improved understanding of VA mechanisms have led to tremendous innovations in VA ablation. The purpose of this review article is to provide an overview of current innovations in VA ablation. Mapping techniques, such as ultra-high density mapping, isochronal late activation mapping, and ripple mapping, have provided improved arrhythmogenic substrate delineation and potential procedural success while limiting duration of ablation procedure and potential hemodynamic compromise. Besides, more advanced mapping and ablation techniques such as epicardial and intramyocardial ablation approaches have allowed operators to more precisely target arrhythmogenic substrate. Moreover, advances in alternate energy sources, such as electroporation, as well as stereotactic radiation therapy have been proposed to be effective and safe. New catheters, such as the lattice and the saline-enhanced radiofrequency catheters, have been designed to provide deeper and more durable tissue ablation lesions compared to conventional catheters. Contact force optimization and baseline impedance modulation are important tools to optimize VT radiofrequency ablation and improve procedural success. Furthermore, advances in cardiac imaging, specifically cardiac MRI, have great potential in identifying arrhythmogenic substrate and evaluating ablation success. Overall, VA ablation has undergone significant advances over the past years. Innovations in VA mapping techniques, alternate energy source, new catheters, and utilization of cardiac imaging have great potential to improve overall procedural safety, hemodynamic stability, and procedural success.
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Affiliation(s)
- Qussay Marashly
- Division of Cardiology, Department of Medicine, Montefiore Medical Center, Albert Einstein College of Medicine, Bronx, New York, USA
| | - Salim N Najjar
- Division of Cardiology, Baylor College of Medicine, 7200 Cambridge Suite A6.137, MS: BCM621, Houston, TX, 77030, USA
| | - Joshua Hahn
- Division of Cardiology, Baylor College of Medicine, 7200 Cambridge Suite A6.137, MS: BCM621, Houston, TX, 77030, USA
| | - Graham J Rector
- Division of Cardiology, Baylor College of Medicine, 7200 Cambridge Suite A6.137, MS: BCM621, Houston, TX, 77030, USA
| | - Muzamil Khawaja
- Division of Cardiology, Baylor College of Medicine, 7200 Cambridge Suite A6.137, MS: BCM621, Houston, TX, 77030, USA
| | - Mihail G Chelu
- Division of Cardiology, Baylor College of Medicine, 7200 Cambridge Suite A6.137, MS: BCM621, Houston, TX, 77030, USA.
- Baylor St. Luke's Medical Center, Houston, USA.
- Texas Heart Institute, Houston, USA.
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5
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Bhagirath P, Campos FO, Postema PG, Kemme MJB, Wilde AAM, Prassl AJ, Neic A, Rinaldi CA, Götte MJW, Plank G, Bishop MJ. Arrhythmogenic vulnerability of re-entrant pathways in post-infarct ventricular tachycardia assessed by advanced computational modelling. Europace 2023; 25:euad198. [PMID: 37421339 PMCID: PMC10481251 DOI: 10.1093/europace/euad198] [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: 03/10/2023] [Revised: 04/26/2023] [Accepted: 06/21/2023] [Indexed: 07/10/2023] Open
Abstract
AIMS Substrate assessment of scar-mediated ventricular tachycardia (VT) is frequently performed using late gadolinium enhancement (LGE) images. Although this provides structural information about critical pathways through the scar, assessing the vulnerability of these pathways for sustaining VT is not possible with imaging alone.This study evaluated the performance of a novel automated re-entrant pathway finding algorithm to non-invasively predict VT circuit and inducibility. METHODS Twenty post-infarct VT-ablation patients were included for retrospective analysis. Commercially available software (ADAS3D left ventricular) was used to generate scar maps from 2D-LGE images using the default 40-60 pixel-signal-intensity (PSI) threshold. In addition, algorithm sensitivity for altered thresholds was explored using PSI 45-55, 35-65, and 30-70. Simulations were performed on the Virtual Induction and Treatment of Arrhythmias (VITA) framework to identify potential sites of block and assess their vulnerability depending on the automatically computed round-trip-time (RTT). Metrics, indicative of substrate complexity, were correlated with VT-recurrence during follow-up. RESULTS Total VTs (85 ± 43 vs. 42 ± 27) and unique VTs (9 ± 4 vs. 5 ± 4) were significantly higher in patients with- compared to patients without recurrence, and were predictive of recurrence with area under the curve of 0.820 and 0.770, respectively. VITA was robust to scar threshold variations with no significant impact on total and unique VTs, and mean RTT between the four models. Simulation metrics derived from PSI 45-55 model had the highest number of parameters predictive for post-ablation VT-recurrence. CONCLUSION Advanced computational metrics can non-invasively and robustly assess VT substrate complexity, which may aid personalized clinical planning and decision-making in the treatment of post-infarction VT.
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Affiliation(s)
- Pranav Bhagirath
- School of Biomedical Engineering and Imaging Sciences, King's College London, 4th Floor, Lambeth Wing, St. Thomas' Hospital, London SE1 7EH, UK
- Department of Cardiology, St Thomas' Hospital, London SE1 7EH, UK
| | - Fernando O Campos
- School of Biomedical Engineering and Imaging Sciences, King's College London, 4th Floor, Lambeth Wing, St. Thomas' Hospital, London SE1 7EH, UK
| | - Pieter G Postema
- Department of Cardiology, Amsterdam University Medical Center, Amsterdam, the Netherlands
| | - Michiel J B Kemme
- Department of Cardiology, Amsterdam University Medical Center, Amsterdam, the Netherlands
| | - Arthur A M Wilde
- Department of Cardiology, Amsterdam University Medical Center, Amsterdam, the Netherlands
| | - Anton J Prassl
- Gottfried Schatz Research Center, Division of Biophysics, Medical University of Graz, Graz, Austria
| | - Aurel Neic
- Gottfried Schatz Research Center, Division of Biophysics, Medical University of Graz, Graz, Austria
| | | | - Marco J W Götte
- Department of Cardiology, Amsterdam University Medical Center, Amsterdam, the Netherlands
| | - Gernot Plank
- Gottfried Schatz Research Center, Division of Biophysics, Medical University of Graz, Graz, Austria
| | - Martin J Bishop
- School of Biomedical Engineering and Imaging Sciences, King's College London, 4th Floor, Lambeth Wing, St. Thomas' Hospital, London SE1 7EH, UK
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Kowalewski C, Ascione C, Nuñez-Garcia M, Ly B, Sermesant M, Bustin A, Sridi S, Bouteiller X, Yokoyama M, Vlachos K, Monaco C, Bouyer B, Buliard S, Arnaud M, Tixier R, Chauvel R, Derval N, Pambrun T, Duchateau J, Bordachar P, Hocini M, Hindricks G, Haïssaguerre M, Sacher F, Jais P, Cochet H. Advanced Imaging Integration for Catheter Ablation of Ventricular Tachycardia. Curr Cardiol Rep 2023; 25:535-542. [PMID: 37115434 DOI: 10.1007/s11886-023-01872-z] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 03/23/2023] [Indexed: 04/29/2023]
Abstract
PURPOSE OF REVIEW Imaging plays a crucial role in the therapy of ventricular tachycardia (VT). We offer an overview of the different methods and provide information on their use in a clinical setting. RECENT FINDINGS The use of imaging in VT has progressed recently. Intracardiac echography facilitates catheter navigation and the targeting of moving intracardiac structures. Integration of pre-procedural CT or MRI allows for targeting the VT substrate, with major expected impact on VT ablation efficacy and efficiency. Advances in computational modeling may further enhance the performance of imaging, giving access to pre-operative simulation of VT. These advances in non-invasive diagnosis are increasingly being coupled with non-invasive approaches for therapy delivery. This review highlights the latest research on the use of imaging in VT procedures. Image-based strategies are progressively shifting from using images as an adjunct tool to electrophysiological techniques, to an integration of imaging as a central element of the treatment strategy.
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Affiliation(s)
- Christopher Kowalewski
- Hôpital Cardiologique du Haut-Lévêque, CHU Bordeaux, L'Institut de RYthmologie et modélisation Cardiaque (LIRYC), Université Bordeaux, Bordeaux, France.
| | - Ciro Ascione
- Hôpital Cardiologique du Haut-Lévêque, CHU Bordeaux, L'Institut de RYthmologie et modélisation Cardiaque (LIRYC), Université Bordeaux, Bordeaux, France
| | - Marta Nuñez-Garcia
- Hôpital Cardiologique du Haut-Lévêque, CHU Bordeaux, L'Institut de RYthmologie et modélisation Cardiaque (LIRYC), Université Bordeaux, Bordeaux, France
| | - Buntheng Ly
- Hôpital Cardiologique du Haut-Lévêque, CHU Bordeaux, L'Institut de RYthmologie et modélisation Cardiaque (LIRYC), Université Bordeaux, Bordeaux, France
| | - Maxime Sermesant
- Hôpital Cardiologique du Haut-Lévêque, CHU Bordeaux, L'Institut de RYthmologie et modélisation Cardiaque (LIRYC), Université Bordeaux, Bordeaux, France
| | - Aurélien Bustin
- Hôpital Cardiologique du Haut-Lévêque, CHU Bordeaux, L'Institut de RYthmologie et modélisation Cardiaque (LIRYC), Université Bordeaux, Bordeaux, France
| | - Soumaya Sridi
- Hôpital Cardiologique du Haut-Lévêque, CHU Bordeaux, L'Institut de RYthmologie et modélisation Cardiaque (LIRYC), Université Bordeaux, Bordeaux, France
| | - Xavier Bouteiller
- Hôpital Cardiologique du Haut-Lévêque, CHU Bordeaux, L'Institut de RYthmologie et modélisation Cardiaque (LIRYC), Université Bordeaux, Bordeaux, France
| | - Masaaki Yokoyama
- Hôpital Cardiologique du Haut-Lévêque, CHU Bordeaux, L'Institut de RYthmologie et modélisation Cardiaque (LIRYC), Université Bordeaux, Bordeaux, France
| | - Konstantinos Vlachos
- Hôpital Cardiologique du Haut-Lévêque, CHU Bordeaux, L'Institut de RYthmologie et modélisation Cardiaque (LIRYC), Université Bordeaux, Bordeaux, France
| | - Cinzia Monaco
- Hôpital Cardiologique du Haut-Lévêque, CHU Bordeaux, L'Institut de RYthmologie et modélisation Cardiaque (LIRYC), Université Bordeaux, Bordeaux, France
| | - Benjamin Bouyer
- Hôpital Cardiologique du Haut-Lévêque, CHU Bordeaux, L'Institut de RYthmologie et modélisation Cardiaque (LIRYC), Université Bordeaux, Bordeaux, France
| | - Samuel Buliard
- Hôpital Cardiologique du Haut-Lévêque, CHU Bordeaux, L'Institut de RYthmologie et modélisation Cardiaque (LIRYC), Université Bordeaux, Bordeaux, France
| | - Marine Arnaud
- Hôpital Cardiologique du Haut-Lévêque, CHU Bordeaux, L'Institut de RYthmologie et modélisation Cardiaque (LIRYC), Université Bordeaux, Bordeaux, France
| | - Romain Tixier
- Hôpital Cardiologique du Haut-Lévêque, CHU Bordeaux, L'Institut de RYthmologie et modélisation Cardiaque (LIRYC), Université Bordeaux, Bordeaux, France
| | - Remi Chauvel
- Hôpital Cardiologique du Haut-Lévêque, CHU Bordeaux, L'Institut de RYthmologie et modélisation Cardiaque (LIRYC), Université Bordeaux, Bordeaux, France
| | - Nicolas Derval
- Hôpital Cardiologique du Haut-Lévêque, CHU Bordeaux, L'Institut de RYthmologie et modélisation Cardiaque (LIRYC), Université Bordeaux, Bordeaux, France
| | - Thomas Pambrun
- Hôpital Cardiologique du Haut-Lévêque, CHU Bordeaux, L'Institut de RYthmologie et modélisation Cardiaque (LIRYC), Université Bordeaux, Bordeaux, France
| | - Josselin Duchateau
- Hôpital Cardiologique du Haut-Lévêque, CHU Bordeaux, L'Institut de RYthmologie et modélisation Cardiaque (LIRYC), Université Bordeaux, Bordeaux, France
| | - Pierre Bordachar
- Hôpital Cardiologique du Haut-Lévêque, CHU Bordeaux, L'Institut de RYthmologie et modélisation Cardiaque (LIRYC), Université Bordeaux, Bordeaux, France
| | - Mélèze Hocini
- Hôpital Cardiologique du Haut-Lévêque, CHU Bordeaux, L'Institut de RYthmologie et modélisation Cardiaque (LIRYC), Université Bordeaux, Bordeaux, France
| | - Gerhard Hindricks
- Department of Cardiology, Charité-Universitätsmedizin Berlin, Berlin, Germany
| | - Michel Haïssaguerre
- Hôpital Cardiologique du Haut-Lévêque, CHU Bordeaux, L'Institut de RYthmologie et modélisation Cardiaque (LIRYC), Université Bordeaux, Bordeaux, France
| | - Frédéric Sacher
- Hôpital Cardiologique du Haut-Lévêque, CHU Bordeaux, L'Institut de RYthmologie et modélisation Cardiaque (LIRYC), Université Bordeaux, Bordeaux, France
| | - Pierre Jais
- Hôpital Cardiologique du Haut-Lévêque, CHU Bordeaux, L'Institut de RYthmologie et modélisation Cardiaque (LIRYC), Université Bordeaux, Bordeaux, France
| | - Hubert Cochet
- Hôpital Cardiologique du Haut-Lévêque, CHU Bordeaux, L'Institut de RYthmologie et modélisation Cardiaque (LIRYC), Université Bordeaux, Bordeaux, France
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O'Brien H, Whitaker J, O'Neill MD, Grigoryan K, Gill H, Mehta V, Elliot MK, Rinaldi CA, Morgan H, Perera D, Taylor J, Rajani R, Rhode K, Niederer S. Regional left ventricle scar detection from routine cardiac computed tomography angiograms using latent space classification. Comput Biol Med 2022; 150:106191. [PMID: 37859285 DOI: 10.1016/j.compbiomed.2022.106191] [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: 08/12/2022] [Revised: 10/01/2022] [Accepted: 10/08/2022] [Indexed: 11/29/2022]
Abstract
OBJECTIVES The aim of this study is to develop an automated method of regional scar detection on clinically standard computed tomography angiography (CTA) using encoder-decoder networks with latent space classification. BACKGROUND Localising scar in cardiac patients can assist in diagnosis and guide interventions. Magnetic resonance imaging (MRI) with late gadolinium enhancement (LGE) is the clinical gold standard for scar imaging; however, it is commonly contraindicated. CTA is an alternative imaging modality that has fewer contraindications and is widely used as a first-line imaging modality of cardiac applications. METHODS A dataset of 79 patients with both clinically indicated MRI LGE and subsequent CTA scans was used to train and validate networks to classify septal and lateral scar presence within short axis left ventricle slices. Two designs of encoder-decoder networks were compared, with one encoding anatomical shape in the latent space. Ground truth was established by segmenting scar in MRI LGE and registering this to the CTA images. Short axis slices were taken from the CTA, which served as the input to the networks. An independent external set of 22 cases (27% the size of the cross-validation set) was used to test the best network. RESULTS A network classifying lateral scar only achieved an area under ROC curve of 0.75, with a sensitivity of 0.79 and specificity of 0.62 on the independent test set. The results of septal scar classification were poor (AUC < 0.6) for all networks. This was likely due to a high class imbalance. The highest AUC network encoded anatomical shape information in the network latent space, indicating it was important for the successful classification of lateral scar. CONCLUSIONS Automatic lateral wall scar detection can be performed from a routine cardiac CTA with reasonable accuracy, without any scar specific imaging. This requires only a single acquisition in the cardiac cycle. In a clinical setting, this could be useful for pre-procedure planning, especially where MRI is contraindicated. Further work with more septal scar present is warranted to improve the usefulness of this approach.
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Affiliation(s)
- Hugh O'Brien
- 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; Cardiology Department, Guy's and St Thomas' NHS Foundation Trust, London, United Kingdom
| | - Mark D O'Neill
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom; Cardiology Department, Guy's and St Thomas' NHS Foundation Trust, London, United Kingdom
| | - Karine Grigoryan
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom; Cardiology Department, Guy's and St Thomas' NHS Foundation Trust, London, United Kingdom
| | - Harminder Gill
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom; Cardiology Department, Guy's and St Thomas' NHS Foundation Trust, London, United Kingdom
| | - Vishal Mehta
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom; Cardiology Department, Guy's and St Thomas' NHS Foundation Trust, London, United Kingdom
| | - Mark K Elliot
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom; Cardiology Department, Guy's and St Thomas' NHS Foundation Trust, London, United Kingdom
| | - Christopher Aldo Rinaldi
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom; Cardiology Department, Guy's and St Thomas' NHS Foundation Trust, London, United Kingdom
| | - Holly Morgan
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom; Cardiology Department, Guy's and St Thomas' NHS Foundation Trust, London, United Kingdom
| | - Divaka Perera
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom; Cardiology Department, Guy's and St Thomas' NHS Foundation Trust, London, United Kingdom
| | - Jonathan Taylor
- 3DLab, Sheffield Teaching Hospitals NHS Foundation Trust, Sheffield, United Kingdom
| | - Ronak Rajani
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom; Cardiology Department, Guy's and St Thomas' NHS Foundation Trust, London, United Kingdom
| | - Kawal Rhode
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
| | - Steven Niederer
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
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8
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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: 0] [Impact Index Per Article: 0] [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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9
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An Automata-Based Cardiac Electrophysiology Simulator to Assess Arrhythmia Inducibility. MATHEMATICS 2022. [DOI: 10.3390/math10081293] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
Personalized cardiac electrophysiology simulations have demonstrated great potential to study cardiac arrhythmias and help in therapy planning of radio-frequency ablation. Its application to analyze vulnerability to ventricular tachycardia and sudden cardiac death in infarcted patients has been recently explored. However, the detailed multi-scale biophysical simulations used in these studies are very demanding in terms of memory and computational resources, which prevents their clinical translation. In this work, we present a fast phenomenological system based on cellular automata (CA) to simulate personalized cardiac electrophysiology. The system is trained on biophysical simulations to reproduce cellular and tissue dynamics in healthy and pathological conditions, including action potential restitution, conduction velocity restitution and cell safety factor. We show that a full ventricular simulation can be performed in the order of seconds, emulate the results of a biophysical simulation and reproduce a patient’s ventricular tachycardia in a model that includes a heterogeneous scar region. The system could be used to study the risk of arrhythmia in infarcted patients for a large number of scenarios.
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10
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Rodriguez Padilla J, Petras A, Magat J, Bayer J, Bihan-Poudec Y, El-Hamrani D, Ramlugun G, Neic A, Augustin C, Vaillant F, Constantin M, Benoist D, Pourtau L, Dubes V, Rogier J, Labrousse L, Bernus O, Quesson B, Haissaguerre M, Gsell M, Plank G, Ozenne V, Vigmond E. Impact of Intraventricular Septal Fiber Orientation on Cardiac Electromechanical Function. Am J Physiol Heart Circ Physiol 2022; 322:H936-H952. [PMID: 35302879 PMCID: PMC9109800 DOI: 10.1152/ajpheart.00050.2022] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/22/2022]
Abstract
Cardiac fiber direction is an important factor determining the propagation of electrical activity, as well as the development of mechanical force. In this article, we imaged the ventricles of several species with special attention to the intraventricular septum to determine the functional consequences of septal fiber organization. First, we identified a dual-layer organization of the fiber orientation in the intraventricular septum of ex vivo sheep hearts using diffusion tensor imaging at high field MRI. To expand the scope of the results, we investigated the presence of a similar fiber organization in five mammalian species (rat, canine, pig, sheep, and human) and highlighted the continuity of the layer with the moderator band in large mammalian species. We implemented the measured septal fiber fields in three-dimensional electromechanical computer models to assess the impact of the fiber orientation. The downward fibers produced a diamond activation pattern superficially in the right ventricle. Electromechanically, there was very little change in pressure volume loops although the stress distribution was altered. In conclusion, we clarified that the right ventricular septum has a downwardly directed superficial layer in larger mammalian species, which can have modest effects on stress distribution. NEW & NOTEWORTHY A dual-layer organization of the fiber orientation in the intraventricular septum was identified in ex vivo hearts of large mammals. The RV septum has a downwardly directed superficial layer that is continuous with the moderator band. Electrically, it produced a diamond activation pattern. Electromechanically, little change in pressure volume loops were noticed but stress distribution was altered. Fiber distribution derived from diffusion tensor imaging should be considered for an accurate strain and stress analysis.
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Affiliation(s)
| | - Argyrios Petras
- Johann Radon Institute for Computational and Applied Mathematics (RICAM), Austrian Academy of Sciences, Linz, Austria
| | - Julie Magat
- Liryc, Electrophysiology and Heart Modeling Institute, Fondation Bordeaux Université, Pessac-Bordeaux, France.,Univ. Bordeaux, Centre de recherche Cardio-Thoracique de Bordeaux, Bordeaux, France.,INSERM, Centre de recherche Cardio-Thoracique de Bordeaux, Bordeaux, France
| | - Jason Bayer
- Liryc, Electrophysiology and Heart Modeling Institute, Fondation Bordeaux Université, Pessac-Bordeaux, France.,Univ. Bordeaux, IMB, UMR 5251, Talence, France
| | - Yann Bihan-Poudec
- Centre de Neuroscience Cognitive, CNRS UMR 5229, Université Claude Bernard Lyon I, France
| | - Dounia El-Hamrani
- Liryc, Electrophysiology and Heart Modeling Institute, Fondation Bordeaux Université, Pessac-Bordeaux, France.,Univ. Bordeaux, Centre de recherche Cardio-Thoracique de Bordeaux, Bordeaux, France.,INSERM, Centre de recherche Cardio-Thoracique de Bordeaux, Bordeaux, France
| | - Girish Ramlugun
- Liryc, Electrophysiology and Heart Modeling Institute, Fondation Bordeaux Université, Pessac-Bordeaux, France.,Univ. Bordeaux, Centre de recherche Cardio-Thoracique de Bordeaux, Bordeaux, France.,INSERM, Centre de recherche Cardio-Thoracique de Bordeaux, Bordeaux, France
| | - Aurel Neic
- Gottfried Schatz Research Center, Division of Biophysics, Medical University of Graz, Graz, Austria
| | - Christoph Augustin
- Gottfried Schatz Research Center, Division of Biophysics, Medical University of Graz, Graz, Austria.,BioTechMed-Graz, Graz, Austria
| | - Fanny Vaillant
- Liryc, Electrophysiology and Heart Modeling Institute, Fondation Bordeaux Université, Pessac-Bordeaux, France.,Univ. Bordeaux, Centre de recherche Cardio-Thoracique de Bordeaux, Bordeaux, France.,INSERM, Centre de recherche Cardio-Thoracique de Bordeaux, Bordeaux, France
| | - Marion Constantin
- Liryc, Electrophysiology and Heart Modeling Institute, Fondation Bordeaux Université, Pessac-Bordeaux, France.,Univ. Bordeaux, Centre de recherche Cardio-Thoracique de Bordeaux, Bordeaux, France.,INSERM, Centre de recherche Cardio-Thoracique de Bordeaux, Bordeaux, France
| | - David Benoist
- Liryc, Electrophysiology and Heart Modeling Institute, Fondation Bordeaux Université, Pessac-Bordeaux, France.,Univ. Bordeaux, Centre de recherche Cardio-Thoracique de Bordeaux, Bordeaux, France.,INSERM, Centre de recherche Cardio-Thoracique de Bordeaux, Bordeaux, France
| | - Line Pourtau
- Liryc, Electrophysiology and Heart Modeling Institute, Fondation Bordeaux Université, Pessac-Bordeaux, France.,Univ. Bordeaux, Centre de recherche Cardio-Thoracique de Bordeaux, Bordeaux, France.,INSERM, Centre de recherche Cardio-Thoracique de Bordeaux, Bordeaux, France
| | - Virginie Dubes
- Liryc, Electrophysiology and Heart Modeling Institute, Fondation Bordeaux Université, Pessac-Bordeaux, France.,Univ. Bordeaux, Centre de recherche Cardio-Thoracique de Bordeaux, Bordeaux, France.,INSERM, Centre de recherche Cardio-Thoracique de Bordeaux, Bordeaux, France
| | | | | | - Olivier Bernus
- Liryc, Electrophysiology and Heart Modeling Institute, Fondation Bordeaux Université, Pessac-Bordeaux, France.,Univ. Bordeaux, Centre de recherche Cardio-Thoracique de Bordeaux, Bordeaux, France.,INSERM, Centre de recherche Cardio-Thoracique de Bordeaux, Bordeaux, France
| | - Bruno Quesson
- Liryc, Electrophysiology and Heart Modeling Institute, Fondation Bordeaux Université, Pessac-Bordeaux, France.,Univ. Bordeaux, Centre de recherche Cardio-Thoracique de Bordeaux, Bordeaux, France.,INSERM, Centre de recherche Cardio-Thoracique de Bordeaux, Bordeaux, France
| | | | - Matthias Gsell
- 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.,BioTechMed-Graz, Graz, Austria
| | - Valéry Ozenne
- Centre de Résonance Magnétique des Systèmes Biologiques, UMR 5536, CNRS/Université de Bordeaux, Bordeaux, France
| | - Edward Vigmond
- Liryc, Electrophysiology and Heart Modeling Institute, Fondation Bordeaux Université, Pessac-Bordeaux, France.,Univ. Bordeaux, IMB, UMR 5251, Talence, France
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11
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The role of mechano-electric feedbacks and hemodynamic coupling in scar-related ventricular tachycardia. Comput Biol Med 2022; 142:105203. [DOI: 10.1016/j.compbiomed.2021.105203] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/29/2021] [Revised: 12/29/2021] [Accepted: 12/29/2021] [Indexed: 12/17/2022]
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12
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Mendonca Costa C, Gemmell P, Elliott MK, Whitaker J, Campos FO, Strocchi M, Neic A, Gillette K, Vigmond E, Plank G, Razavi R, O'Neill M, Rinaldi CA, Bishop MJ. Determining anatomical and electrophysiological detail requirements for computational ventricular models of porcine myocardial infarction. Comput Biol Med 2022; 141:105061. [PMID: 34915331 PMCID: PMC8819160 DOI: 10.1016/j.compbiomed.2021.105061] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2021] [Revised: 11/04/2021] [Accepted: 11/20/2021] [Indexed: 12/01/2022]
Abstract
BACKGROUND Computational models of the heart built from cardiac MRI and electrophysiology (EP) data have shown promise for predicting the risk of and ablation targets for myocardial infarction (MI) related ventricular tachycardia (VT), as well as to predict paced activation sequences in heart failure patients. However, most recent studies have relied on low resolution imaging data and little or no EP personalisation, which may affect the accuracy of model-based predictions. OBJECTIVE To investigate the impact of model anatomy, MI scar morphology, and EP personalisation strategies on paced activation sequences and VT inducibility to determine the level of detail required to make accurate model-based predictions. METHODS Imaging and EP data were acquired from a cohort of six pigs with experimentally induced MI. Computational models of ventricular anatomy, incorporating MI scar, were constructed including bi-ventricular or left ventricular (LV) only anatomy, and MI scar morphology with varying detail. Tissue conductivities and action potential duration (APD) were fitted to 12-lead ECG data using the QRS duration and the QT interval, respectively, in addition to corresponding literature parameters. Paced activation sequences and VT induction were simulated. Simulated paced activation and VT inducibility were compared between models and against experimental data. RESULTS Simulations predict that the level of model anatomical detail has little effect on simulated paced activation, with all model predictions comparing closely with invasive EP measurements. However, detailed scar morphology from high-resolution images, bi-ventricular anatomy, and personalized tissue conductivities are required to predict experimental VT outcome. CONCLUSION This study provides clear guidance for model generation based on clinical data. While a representing high level of anatomical and scar detail will require high-resolution image acquisition, EP personalisation based on 12-lead ECG can be readily incorporated into modelling pipelines, as such data is widely available.
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Affiliation(s)
- Caroline Mendonca Costa
- Department of Biomedical Engineering, School of Biomedical Engineering & Imaging Sciences, King's College London, UK.
| | - Philip Gemmell
- Department of Biomedical Engineering, School of Biomedical Engineering & Imaging Sciences, King's College London, UK
| | - Mark K Elliott
- Department of Biomedical Engineering, School of Biomedical Engineering & Imaging Sciences, King's College London, UK
| | - John Whitaker
- Department of Biomedical Engineering, School of Biomedical Engineering & Imaging Sciences, King's College London, UK
| | - Fernando O Campos
- Department of Biomedical Engineering, School of Biomedical Engineering & Imaging Sciences, King's College London, UK
| | - Marina Strocchi
- Department of Biomedical Engineering, School of Biomedical Engineering & Imaging Sciences, King's College London, UK
| | | | - Karli Gillette
- Gottfried Schatz Research Center, Biophysics, Medical University of Graz, Austria; Medical University of Graz, Austria and BioTechMed, Graz, Austria
| | - Edward Vigmond
- Institut de Rythmologie et de modélisation cardiaque (LIRYC), University of Bordeaux, France
| | - Gernot Plank
- Medical University of Graz, Austria and BioTechMed, Graz, Austria
| | - Reza Razavi
- Department of Biomedical Engineering, School of Biomedical Engineering & Imaging Sciences, King's College London, UK
| | - Mark O'Neill
- Department of Cardiology, Guy's and St Thomas' Hospital, London, UK
| | - Christopher A Rinaldi
- Department of Biomedical Engineering, School of Biomedical Engineering & Imaging Sciences, King's College London, UK; Department of Cardiology, Guy's and St Thomas' Hospital, London, UK
| | - Martin J Bishop
- Department of Biomedical Engineering, School of Biomedical Engineering & Imaging Sciences, King's College London, UK
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13
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González-Suárez A, Pérez JJ, Irastorza RM, D'Avila A, Berjano E. Computer modeling of radiofrequency cardiac ablation: 30 years of bioengineering research. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2022; 214:106546. [PMID: 34844766 DOI: 10.1016/j.cmpb.2021.106546] [Citation(s) in RCA: 16] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/16/2021] [Revised: 11/08/2021] [Accepted: 11/15/2021] [Indexed: 06/13/2023]
Abstract
This review begins with a rationale of the importance of theoretical, mathematical and computational models for radiofrequency (RF) catheter ablation (RFCA). We then describe the historical context in which each model was developed, its contribution to the knowledge of the physics of RFCA and its implications for clinical practice. Next, we review the computer modeling studies intended to improve our knowledge of the biophysics of RFCA and those intended to explore new technologies. We describe the most important technical details of the implementation of mathematical models, including governing equations, tissue properties, boundary conditions, etc. We discuss the utility of lumped element models, which despite their simplicity are widely used by clinical researchers to provide a physical explanation of how RF power is absorbed in different tissues. Computer model verification and validation are also discussed in the context of RFCA. The article ends with a section on the current limitations, i.e. aspects not yet included in state-of-the-art RFCA computer modeling and on future work aimed at covering the current gaps.
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Affiliation(s)
- Ana González-Suárez
- Electrical and Electronic Engineering, National University of Ireland Galway, Ireland; Translational Medical Device Lab, National University of Ireland Galway, Ireland
| | - Juan J Pérez
- Department of Electronic Engineering, BioMIT, Universitat Politècnica de València, Valencia, Spain
| | - Ramiro M Irastorza
- Instituto de Física de Líquidos y Sistemas Biológicos (CONICET), La Plata, Argentina; Instituto de Ingeniería y Agronomía, Universidad Nacional Arturo Jauretche, Florencio Varela, Argentina
| | - Andre D'Avila
- Division of Cardiovascular Medicine, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, United States
| | - Enrique Berjano
- Department of Electronic Engineering, BioMIT, Universitat Politècnica de València, Valencia, Spain.
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14
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Meister F, Passerini T, Audigier C, Lluch È, Mihalef V, Ashikaga H, Maier A, Halperin H, Mansi T. Extrapolation of Ventricular Activation Times From Sparse Electroanatomical Data Using Graph Convolutional Neural Networks. Front Physiol 2021; 12:694869. [PMID: 34733172 PMCID: PMC8558498 DOI: 10.3389/fphys.2021.694869] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2021] [Accepted: 09/15/2021] [Indexed: 11/25/2022] Open
Abstract
Electroanatomic mapping is the gold standard for the assessment of ventricular tachycardia. Acquiring high resolution electroanatomic maps is technically challenging and may require interpolation methods to obtain dense measurements. These methods, however, cannot recover activation times in the entire biventricular domain. This work investigates the use of graph convolutional neural networks to estimate biventricular activation times from sparse measurements. Our method is trained on more than 15,000 synthetic examples of realistic ventricular depolarization patterns generated by a computational electrophysiology model. Using geometries sampled from a statistical shape model of biventricular anatomy, diverse wave dynamics are induced by randomly sampling scar and border zone distributions, locations of initial activation, and tissue conduction velocities. Once trained, the method accurately reconstructs biventricular activation times in left-out synthetic simulations with a mean absolute error of 3.9 ms ± 4.2 ms at a sampling density of one measurement sample per cm2. The total activation time is matched with a mean error of 1.4 ms ± 1.4 ms. A significant decrease in errors is observed in all heart zones with an increased number of samples. Without re-training, the network is further evaluated on two datasets: (1) an in-house dataset comprising four ischemic porcine hearts with dense endocardial activation maps; (2) the CRT-EPIGGY19 challenge data comprising endo- and epicardial measurements of 5 infarcted and 6 non-infarcted swines. In both setups the neural network recovers biventricular activation times with a mean absolute error of less than 10 ms even when providing only a subset of endocardial measurements as input. Furthermore, we present a simple approach to suggest new measurement locations in real-time based on the estimated uncertainty of the graph network predictions. The model-guided selection of measurement locations allows to reduce by 40% the number of measurements required in a random sampling strategy, while achieving the same prediction error. In all the tested scenarios, the proposed approach estimates biventricular activation times with comparable or better performance than a personalized computational model and significant runtime advantages.
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Affiliation(s)
- Felix Meister
- Pattern Recognition Lab, Friedrich-Alexander University, Erlangen, Germany
- Digital Technology and Innovation, Siemens Healthineers, Erlangen, Germany
| | - Tiziano Passerini
- Digital Technology and Innovation, Siemens Healthineers, Princeton, NJ, United States
| | - Chloé Audigier
- Digital Technology and Innovation, Siemens Healthineers, Erlangen, Germany
| | - Èric Lluch
- Digital Technology and Innovation, Siemens Healthineers, Erlangen, Germany
| | - Viorel Mihalef
- Digital Technology and Innovation, Siemens Healthineers, Princeton, NJ, United States
| | - Hiroshi Ashikaga
- Cardiac Arrhythmia Service, Johns Hopkins University School of Medicine, Baltimore, MD, United States
| | - Andreas Maier
- Pattern Recognition Lab, Friedrich-Alexander University, Erlangen, Germany
| | - Henry Halperin
- Cardiac Arrhythmia Service, Johns Hopkins University School of Medicine, Baltimore, MD, United States
| | - Tommaso Mansi
- Digital Technology and Innovation, Siemens Healthineers, Princeton, NJ, United States
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15
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Schuler S, Pilia N, Potyagaylo D, Loewe A. Cobiveco: Consistent biventricular coordinates for precise and intuitive description of position in the heart - with MATLAB implementation. Med Image Anal 2021; 74:102247. [PMID: 34592711 DOI: 10.1016/j.media.2021.102247] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2021] [Revised: 09/13/2021] [Accepted: 09/14/2021] [Indexed: 11/18/2022]
Abstract
Ventricular coordinates are widely used as a versatile tool for various applications that benefit from a description of local position within the heart. However, the practical usefulness of ventricular coordinates is determined by their ability to meet application-specific requirements. For regression-based estimation of biventricular position, for example, a symmetric definition of coordinate directions in both ventricles is important. For the transfer of data between different hearts as another use case, the consistency of coordinate values across different geometries is particularly relevant. To meet these requirements, we compare different approaches to compute coordinates and present Cobiveco, a symmetric, consistent and intuitive biventricular coordinate system that builds upon existing coordinate systems, but overcomes some of their limitations. A novel one-way transfer error is introduced to assess the consistency of the coordinates. Normalized distances along bijective trajectories between two boundaries were found to be superior to solutions of Laplace's equation for defining coordinate values, as they show better linearity in space. Evaluation of transfer and linearity errors on 36 patient geometries revealed a more than 4-fold improvement compared to a state-of-the-art method. Finally, we show two application examples underlining the relevance for cardiac data processing. Cobiveco MATLAB code is available under a permissive open-source license.
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Affiliation(s)
- Steffen Schuler
- Institute of Biomedical Engineering, Karlsruhe Institute of Technology (KIT), Fritz-Haber-Weg 1, Karlsruhe 76131, Germany.
| | - Nicolas Pilia
- Institute of Biomedical Engineering, Karlsruhe Institute of Technology (KIT), Fritz-Haber-Weg 1, Karlsruhe 76131, Germany
| | | | - Axel Loewe
- Institute of Biomedical Engineering, Karlsruhe Institute of Technology (KIT), Fritz-Haber-Weg 1, Karlsruhe 76131, Germany
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16
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Sung E, Etoz S, Zhang Y, Trayanova NA. Whole-heart ventricular arrhythmia modeling moving forward: Mechanistic insights and translational applications. BIOPHYSICS REVIEWS 2021; 2:031304. [PMID: 36281224 PMCID: PMC9588428 DOI: 10.1063/5.0058050] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/19/2023]
Abstract
Ventricular arrhythmias are the primary cause of sudden cardiac death and one of the leading causes of mortality worldwide. Whole-heart computational modeling offers a unique approach for studying ventricular arrhythmias, offering vast potential for developing both a mechanistic understanding of ventricular arrhythmias and clinical applications for treatment. In this review, the fundamentals of whole-heart ventricular modeling and current methods of personalizing models using clinical data are presented. From this foundation, the authors summarize recent advances in whole-heart ventricular arrhythmia modeling. Efforts in gaining mechanistic insights into ventricular arrhythmias are discussed, in addition to other applications of models such as the assessment of novel therapeutics. The review emphasizes the unique benefits of computational modeling that allow for insights that are not obtainable by contemporary experimental or clinical means. Additionally, the clinical impact of modeling is explored, demonstrating how patient care is influenced by the information gained from ventricular arrhythmia models. The authors conclude with future perspectives about the direction of whole-heart ventricular arrhythmia modeling, outlining how advances in neural network methodologies hold the potential to reduce computational expense and permit for efficient whole-heart modeling.
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Affiliation(s)
- Eric Sung
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, Maryland 21218, USA
- Alliance for Cardiovascular Diagnostic and Treatment Innovation, Johns Hopkins University, Baltimore, Maryland 21218, USA
| | - Sevde Etoz
- Alliance for Cardiovascular Diagnostic and Treatment Innovation, Johns Hopkins University, Baltimore, Maryland 21218, USA
| | - Yingnan Zhang
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, Maryland 21218, USA
- Alliance for Cardiovascular Diagnostic and Treatment Innovation, Johns Hopkins University, Baltimore, Maryland 21218, USA
| | - Natalia A. Trayanova
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, Maryland 21218, USA
- Alliance for Cardiovascular Diagnostic and Treatment Innovation, Johns Hopkins University, Baltimore, Maryland 21218, USA
- Author to whom correspondence should be addressed: . Tel.: 410-516-4375
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17
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Bacoyannis T, Ly B, Cedilnik N, Cochet H, Sermesant M. Deep learning formulation of electrocardiographic imaging integrating image and signal information with data-driven regularization. Europace 2021; 23:i55-i62. [PMID: 33751073 DOI: 10.1093/europace/euaa391] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/20/2020] [Accepted: 12/07/2020] [Indexed: 12/22/2022] Open
Abstract
AIMS Electrocardiographic imaging (ECGI) is a promising tool to map the electrical activity of the heart non-invasively using body surface potentials (BSP). However, it is still challenging due to the mathematically ill-posed nature of the inverse problem to solve. Novel approaches leveraging progress in artificial intelligence could alleviate these difficulties. METHODS AND RESULTS We propose a deep learning (DL) formulation of ECGI in order to learn the statistical relation between BSP and cardiac activation. The presented method is based on Conditional Variational AutoEncoders using deep generative neural networks. To quantify the accuracy of this method, we simulated activation maps and BSP data on six cardiac anatomies.We evaluated our model by training it on five different cardiac anatomies (5000 activation maps) and by testing it on a new patient anatomy over 200 activation maps. Due to the probabilistic property of our method, we predicted 10 distinct activation maps for each BSP data. The proposed method is able to generate volumetric activation maps with a good accuracy on the simulated data: the mean absolute error is 9.40 ms with 2.16 ms standard deviation on this testing set. CONCLUSION The proposed formulation of ECGI enables to naturally include imaging information in the estimation of cardiac electrical activity from BSP. It naturally takes into account all the spatio-temporal correlations present in the data. We believe these features can help improve ECGI results.
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Affiliation(s)
- Tania Bacoyannis
- Inria, Université Côte d'Azur, Epione team, Sophia Antipolis, France
| | - Buntheng Ly
- Inria, Université Côte d'Azur, Epione team, Sophia Antipolis, France
| | - Nicolas Cedilnik
- Inria, Université Côte d'Azur, Epione team, Sophia Antipolis, France.,IHU Liryc, University of Bordeaux, Bordeaux, France
| | | | - Maxime Sermesant
- Inria, Université Côte d'Azur, Epione team, Sophia Antipolis, France.,IHU Liryc, University of Bordeaux, Bordeaux, France
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18
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O'Brien H, Whitaker J, Singh Sidhu B, Gould J, Kurzendorfer T, O'Neill MD, Rajani R, Grigoryan K, Rinaldi CA, Taylor J, Rhode K, Mountney P, Niederer S. Automated Left Ventricle Ischemic Scar Detection in CT Using Deep Neural Networks. Front Cardiovasc Med 2021; 8:655252. [PMID: 34277724 PMCID: PMC8283258 DOI: 10.3389/fcvm.2021.655252] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2021] [Accepted: 05/24/2021] [Indexed: 12/28/2022] Open
Abstract
Objectives: The aim of this study is to develop a scar detection method for routine computed tomography angiography (CTA) imaging using deep convolutional neural networks (CNN), which relies solely on anatomical information as input and is compatible with existing clinical workflows. Background: Identifying cardiac patients with scar tissue is important for assisting diagnosis and guiding interventions. Late gadolinium enhancement (LGE) magnetic resonance imaging (MRI) is the gold standard for scar imaging; however, there are common instances where it is contraindicated. CTA is an alternative imaging modality that has fewer contraindications and is faster than Cardiovascular magnetic resonance imaging but is unable to reliably image scar. Methods: A dataset of LGE MRI (200 patients, 83 with scar) was used to train and validate a CNN to detect ischemic scar slices using segmentation masks as input to the network. MRIs were segmented to produce 3D left ventricle meshes, which were sampled at points along the short axis to extract anatomical masks, with scar labels from LGE as ground truth. The trained CNN was tested with an independent CTA dataset (25 patients, with ground truth established with paired LGE MRI). Automated segmentation was performed to provide the same input format of anatomical masks for the network. The CNN was compared against manual reading of the CTA dataset by 3 experts. Results: Note that 84.7% cross-validated accuracy (AUC: 0.896) for detecting scar slices in the left ventricle on the MRI data was achieved. The trained network was tested against the CTA-derived data, with no further training, where it achieved an 88.3% accuracy (AUC: 0.901). The automated pipeline outperformed the manual reading by clinicians. Conclusion: Automatic ischemic scar detection can be performed from a routine cardiac CTA, without any scar-specific imaging or contrast agents. This requires only a single acquisition in the cardiac cycle. In a clinical setting, with near zero additional cost, scar presence could be detected to triage images, reduce reading times, and guide clinical decision-making.
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Affiliation(s)
- Hugh O'Brien
- 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.,Department of Cardiology, Guy's and St Thomas NHS Foundation Trust, London, United Kingdom
| | - Baldeep Singh Sidhu
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom.,Department of Cardiology, Guy's and St Thomas NHS Foundation Trust, London, United Kingdom
| | - Justin Gould
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom.,Department of Cardiology, Guy's and St Thomas NHS Foundation Trust, London, United Kingdom
| | | | - Mark D O'Neill
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom.,Department of Cardiology, Guy's and St Thomas NHS Foundation Trust, London, United Kingdom
| | - Ronak Rajani
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom.,Department of Cardiology, Guy's and St Thomas NHS Foundation Trust, London, United Kingdom
| | - Karine Grigoryan
- Department of Cardiology, Guy's and St Thomas NHS Foundation Trust, London, United Kingdom
| | - Christopher Aldo Rinaldi
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom.,Department of Cardiology, Guy's and St Thomas NHS Foundation Trust, London, United Kingdom
| | - Jonathan Taylor
- 3DLab, Sheffield Teaching Hospitals NHS Foundation Trust, Sheffield, United Kingdom
| | - Kawal Rhode
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
| | - Peter Mountney
- Siemens Healthineers, Medical Imaging Technologies, Princeton, NJ, United States
| | - Steven Niederer
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
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Monaci S, Gillette K, Puyol-Antón E, Rajani R, Plank G, King A, Bishop M. Automated Localization of Focal Ventricular Tachycardia From Simulated Implanted Device Electrograms: A Combined Physics-AI Approach. Front Physiol 2021; 12:682446. [PMID: 34276403 PMCID: PMC8281305 DOI: 10.3389/fphys.2021.682446] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2021] [Accepted: 05/31/2021] [Indexed: 11/13/2022] Open
Abstract
Background: Focal ventricular tachycardia (VT) is a life-threating arrhythmia, responsible for high morbidity rates and sudden cardiac death (SCD). Radiofrequency ablation is the only curative therapy against incessant VT; however, its success is dependent on accurate localization of its source, which is highly invasive and time-consuming. Objective: The goal of our study is, as a proof of concept, to demonstrate the possibility of utilizing electrogram (EGM) recordings from cardiac implantable electronic devices (CIEDs). To achieve this, we utilize fast and accurate whole torso electrophysiological (EP) simulations in conjunction with convolutional neural networks (CNNs) to automate the localization of focal VTs using simulated EGMs. Materials and Methods: A highly detailed 3D torso model was used to simulate ∼4000 focal VTs, evenly distributed across the left ventricle (LV), utilizing a rapid reaction-eikonal environment. Solutions were subsequently combined with lead field computations on the torso to derive accurate electrocardiograms (ECGs) and EGM traces, which were used as inputs to CNNs to localize focal sources. We compared the localization performance of a previously developed CNN architecture (Cartesian probability-based) with our novel CNN algorithm utilizing universal ventricular coordinates (UVCs). Results: Implanted device EGMs successfully localized VT sources with localization error (8.74 mm) comparable to ECG-based localization (6.69 mm). Our novel UVC CNN architecture outperformed the existing Cartesian probability-based algorithm (errors = 4.06 mm and 8.07 mm for ECGs and EGMs, respectively). Overall, localization was relatively insensitive to noise and changes in body compositions; however, displacements in ECG electrodes and CIED leads caused performance to decrease (errors 16-25 mm). Conclusion: EGM recordings from implanted devices may be used to successfully, and robustly, localize focal VT sources, and aid ablation planning.
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Affiliation(s)
| | - Karli Gillette
- Division of Biophysics, Medical University of Graz, Graz, Austria
| | | | | | - Gernot Plank
- Division of Biophysics, Medical University of Graz, Graz, Austria
| | - Andrew King
- King’s College London, London, United Kingdom
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20
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Gimelli A, Ernst S, Liga R. Multi-Modality Imaging for the Identification of Arrhythmogenic Substrates Prior to Electrophysiology Studies. Front Cardiovasc Med 2021; 8:640087. [PMID: 33996938 PMCID: PMC8113383 DOI: 10.3389/fcvm.2021.640087] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2020] [Accepted: 03/08/2021] [Indexed: 11/13/2022] Open
Abstract
Noninvasive cardiac imaging is crucial for the characterization of patients who are candidates for cardiac ablations, for both procedure planning and long-term management. Multimodality cardiac imaging can provide not only anatomical parameters but even more importantly functional information that may allow a better risk stratification of cardiac patients. Moreover, fusion of anatomical and functional data derived from noninvasive cardiac imaging with the results of endocavitary mapping may possibly allow a better identification of the ablation substrate and also avoid peri-procedural complications. As a result, imaging-guided electrophysiological procedures are associated with an improved outcome than traditional ablation procedures, with a consistently lower recurrence rate.
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Affiliation(s)
| | - Sabine Ernst
- NIHR Cardiovascular Biomedical Research Unit, Royal Brompton and Harefield NHS Foundation Trust, National Heart and Lung Institute, Imperial College, London, United Kingdom
| | - Riccardo Liga
- Cardiothoracic and Vascular Department, Università di Pisa, Pisa, Italy
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21
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3D MRI of explanted sheep hearts with submillimeter isotropic spatial resolution: comparison between diffusion tensor and structure tensor imaging. MAGNETIC RESONANCE MATERIALS IN PHYSICS BIOLOGY AND MEDICINE 2021; 34:741-755. [PMID: 33638739 PMCID: PMC8421292 DOI: 10.1007/s10334-021-00913-4] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/14/2020] [Revised: 01/29/2021] [Accepted: 02/02/2021] [Indexed: 11/04/2022]
Abstract
Objective The aim of the study is to compare structure tensor imaging (STI) with diffusion tensor imaging (DTI) of the sheep heart (approximately the same size as the human heart). Materials and methods MRI acquisition on three sheep ex vivo hearts was performed at 9.4 T/30 cm with a seven-element RF coil. 3D FLASH with an isotropic resolution of 150 µm and 3D spin-echo DTI at 600 µm were performed. Tensor analysis, angles extraction and segments divisions were performed on both volumes. Results A 3D FLASH allows for visualization of the detailed structure of the left and right ventricles. The helix angle determined using DTI and STI exhibited a smooth transmural change from the endocardium to the epicardium. Both the helix and transverse angles were similar between techniques. Sheetlet organization exhibited the same pattern in both acquisitions, but local angle differences were seen and identified in 17 segments representation. Discussion This study demonstrated the feasibility of high-resolution MRI for studying the myocyte and myolaminar architecture of sheep hearts. We presented the results of STI on three whole sheep ex vivo hearts and demonstrated a good correspondence between DTI and STI. Supplementary Information The online version contains supplementary material available at 10.1007/s10334-021-00913-4.
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22
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Building Models of Patient-Specific Anatomy and Scar Morphology from Clinical MRI Data. SYSTEMS MEDICINE 2021. [DOI: 10.1016/b978-0-12-801238-3.11663-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022] Open
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23
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Corral-Acero J, Margara F, Marciniak M, Rodero C, Loncaric F, Feng Y, Gilbert A, Fernandes JF, Bukhari HA, Wajdan A, Martinez MV, Santos MS, Shamohammdi M, Luo H, Westphal P, Leeson P, DiAchille P, Gurev V, Mayr M, Geris L, Pathmanathan P, Morrison T, Cornelussen R, Prinzen F, Delhaas T, Doltra A, Sitges M, Vigmond EJ, Zacur E, Grau V, Rodriguez B, Remme EW, Niederer S, Mortier P, McLeod K, Potse M, Pueyo E, Bueno-Orovio A, Lamata P. The 'Digital Twin' to enable the vision of precision cardiology. Eur Heart J 2020; 41:4556-4564. [PMID: 32128588 PMCID: PMC7774470 DOI: 10.1093/eurheartj/ehaa159] [Citation(s) in RCA: 193] [Impact Index Per Article: 48.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/06/2019] [Revised: 11/29/2019] [Accepted: 02/24/2020] [Indexed: 12/26/2022] Open
Abstract
Providing therapies tailored to each patient is the vision of precision medicine, enabled by the increasing ability to capture extensive data about individual patients. In this position paper, we argue that the second enabling pillar towards this vision is the increasing power of computers and algorithms to learn, reason, and build the 'digital twin' of a patient. Computational models are boosting the capacity to draw diagnosis and prognosis, and future treatments will be tailored not only to current health status and data, but also to an accurate projection of the pathways to restore health by model predictions. The early steps of the digital twin in the area of cardiovascular medicine are reviewed in this article, together with a discussion of the challenges and opportunities ahead. We emphasize the synergies between mechanistic and statistical models in accelerating cardiovascular research and enabling the vision of precision medicine.
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Affiliation(s)
| | - Francesca Margara
- Department of Computer Science, British Heart Foundation Centre of Research Excellence, University of Oxford, Oxford, UK
| | - Maciej Marciniak
- Department of Biomedical Engineering, Division of Imaging Sciences and Biomedical Engineering, King’s College London, London, UK
| | - Cristobal Rodero
- Department of Biomedical Engineering, Division of Imaging Sciences and Biomedical Engineering, King’s College London, London, UK
| | - Filip Loncaric
- Institut Clínic Cardiovascular, Hospital Clínic, Universitat de Barcelona, Institut d’Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Barcelona, Spain
| | - Yingjing Feng
- IHU Liryc, Electrophysiology and Heart Modeling Institute, fondation Bordeaux Université, Pessac-Bordeaux F-33600, France
- IMB, UMR 5251, University of Bordeaux, Talence F-33400, France
| | | | - Joao F Fernandes
- Department of Biomedical Engineering, Division of Imaging Sciences and Biomedical Engineering, King’s College London, London, UK
| | - Hassaan A Bukhari
- IMB, UMR 5251, University of Bordeaux, Talence F-33400, France
- Aragón Institute of Engineering Research, Universidad de Zaragoza, IIS Aragón, Zaragoza, Spain
| | - Ali Wajdan
- The Intervention Centre, Oslo University Hospital, Rikshospitalet, Oslo, Norway
| | | | | | - Mehrdad Shamohammdi
- CARIM School for Cardiovascular Diseases, Maastricht University, Maastricht, The Netherlands
| | - Hongxing Luo
- CARIM School for Cardiovascular Diseases, Maastricht University, Maastricht, The Netherlands
| | - Philip Westphal
- Medtronic PLC, Bakken Research Center, Maastricht, the Netherlands
| | - Paul Leeson
- Radcliffe Department of Medicine, Division of Cardiovascular Medicine, Oxford Cardiovascular Clinical Research Facility, John Radcliffe Hospital, University of Oxford, Oxford, UK
| | - Paolo DiAchille
- Healthcare and Life Sciences Research, IBM T.J. Watson Research Center, Yorktown Heights, NY, USA
| | - Viatcheslav Gurev
- Healthcare and Life Sciences Research, IBM T.J. Watson Research Center, Yorktown Heights, NY, USA
| | - Manuel Mayr
- King’s British Heart Foundation Centre, King’s College London, London, UK
| | - Liesbet Geris
- Virtual Physiological Human Institute, Leuven, Belgium
| | - Pras Pathmanathan
- Center for Devices and Radiological Health, U.S. Food and Drug Administration, Silver Spring, MD, USA
| | - Tina Morrison
- Center for Devices and Radiological Health, U.S. Food and Drug Administration, Silver Spring, MD, USA
| | | | - Frits Prinzen
- CARIM School for Cardiovascular Diseases, Maastricht University, Maastricht, The Netherlands
| | - Tammo Delhaas
- CARIM School for Cardiovascular Diseases, Maastricht University, Maastricht, The Netherlands
| | - Ada Doltra
- Institut Clínic Cardiovascular, Hospital Clínic, Universitat de Barcelona, Institut d’Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Barcelona, Spain
| | - Marta Sitges
- Institut Clínic Cardiovascular, Hospital Clínic, Universitat de Barcelona, Institut d’Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Barcelona, Spain
- CIBERCV, Instituto de Salud Carlos III, (CB16/11/00354), CERCA Programme/Generalitat de, Catalunya, Spain
| | - Edward J Vigmond
- IHU Liryc, Electrophysiology and Heart Modeling Institute, fondation Bordeaux Université, Pessac-Bordeaux F-33600, France
- IMB, UMR 5251, University of Bordeaux, Talence F-33400, France
| | - Ernesto Zacur
- Department of Engineering Science, University of Oxford, Oxford, UK
| | - Vicente Grau
- Department of Engineering Science, University of Oxford, Oxford, UK
| | - Blanca Rodriguez
- Department of Computer Science, British Heart Foundation Centre of Research Excellence, University of Oxford, Oxford, UK
| | - Espen W Remme
- The Intervention Centre, Oslo University Hospital, Rikshospitalet, Oslo, Norway
| | - Steven Niederer
- Department of Biomedical Engineering, Division of Imaging Sciences and Biomedical Engineering, King’s College London, London, UK
| | | | | | - Mark Potse
- IHU Liryc, Electrophysiology and Heart Modeling Institute, fondation Bordeaux Université, Pessac-Bordeaux F-33600, France
- IMB, UMR 5251, University of Bordeaux, Talence F-33400, France
- Inria Bordeaux Sud-Ouest, CARMEN team, Talence F-33400, France
| | - Esther Pueyo
- Aragón Institute of Engineering Research, Universidad de Zaragoza, IIS Aragón, Zaragoza, Spain
- CIBER in Bioengineering, Biomaterials and Nanomedicine (CIBER‐BBN), Madrid, Spain
| | - Alfonso Bueno-Orovio
- Department of Computer Science, British Heart Foundation Centre of Research Excellence, University of Oxford, Oxford, UK
| | - Pablo Lamata
- Department of Biomedical Engineering, Division of Imaging Sciences and Biomedical Engineering, King’s College London, London, UK
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Corrado C, Avezzù A, Lee AWC, Mendoca Costa C, Roney CH, Strocchi M, Bishop M, Niederer SA. Using cardiac ionic cell models to interpret clinical data. WIREs Mech Dis 2020; 13:e1508. [PMID: 33027553 DOI: 10.1002/wsbm.1508] [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: 05/31/2020] [Revised: 08/27/2020] [Accepted: 09/04/2020] [Indexed: 01/24/2023]
Abstract
For over 100 years cardiac electrophysiology has been measured in the clinic. The electrical signals that can be measured span from noninvasive ECG and body surface potentials measurements through to detailed invasive measurements of local tissue electrophysiology. These electrophysiological measurements form a crucial component of patient diagnosis and monitoring; however, it remains challenging to quantitatively link changes in clinical electrophysiology measurements to biophysical cellular function. Multi-scale biophysical computational models represent one solution to this problem. These models provide a formal framework for linking cellular function through to emergent whole organ function and routine clinical diagnostic signals. In this review, we describe recent work on the use of computational models to interpret clinical electrophysiology signals. We review the simulation of human cardiac myocyte electrophysiology in the atria and the ventricles and how these models are being used to link organ scale function to patient disease mechanisms and therapy response in patients receiving implanted defibrillators, \cardiac resynchronisation therapy or suffering from atrial fibrillation and ventricular tachycardia. There is a growing use of multi-scale biophysical models to interpret clinical data. This allows cardiologists to link clinical observations with cellular mechanisms to better understand cardiopathophysiology and identify novel treatment strategies. This article is categorized under: Cardiovascular Diseases > Computational Models Cardiovascular Diseases > Biomedical Engineering Cardiovascular Diseases > Molecular and Cellular Physiology.
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25
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Niederer SA, Aboelkassem Y, Cantwell CD, Corrado C, Coveney S, Cherry EM, Delhaas T, Fenton FH, Panfilov AV, Pathmanathan P, Plank G, Riabiz M, Roney CH, dos Santos RW, Wang L. Creation and application of virtual patient cohorts of heart models. PHILOSOPHICAL TRANSACTIONS. SERIES A, MATHEMATICAL, PHYSICAL, AND ENGINEERING SCIENCES 2020; 378:20190558. [PMID: 32448064 PMCID: PMC7287335 DOI: 10.1098/rsta.2019.0558] [Citation(s) in RCA: 42] [Impact Index Per Article: 10.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Accepted: 03/06/2020] [Indexed: 05/21/2023]
Abstract
Patient-specific cardiac models are now being used to guide therapies. The increased use of patient-specific cardiac simulations in clinical care will give rise to the development of virtual cohorts of cardiac models. These cohorts will allow cardiac simulations to capture and quantify inter-patient variability. However, the development of virtual cohorts of cardiac models will require the transformation of cardiac modelling from small numbers of bespoke models to robust and rapid workflows that can create large numbers of models. In this review, we describe the state of the art in virtual cohorts of cardiac models, the process of creating virtual cohorts of cardiac models, and how to generate the individual cohort member models, followed by a discussion of the potential and future applications of virtual cohorts of cardiac models. This article is part of the theme issue 'Uncertainty quantification in cardiac and cardiovascular modelling and simulation'.
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Affiliation(s)
| | | | | | | | | | - E. M. Cherry
- Georgia Institute of Technology, Atlanta, GA, USA
| | - T. Delhaas
- Maastricht University, Maastricht, the Netherlands
| | - F. H. Fenton
- Georgia Institute of Technology, Atlanta, GA, USA
| | - A. V. Panfilov
- Ghent University, Gent, Belgium
- Laboratory of Computational Biology and Medicine, Ural Federal University, Ekaterinburg, Russia
| | - P. Pathmanathan
- Center for Devices and Radiological Health, U.S. Food and Administration, Rockville, MD, USA
| | - G. Plank
- Medical University of Graz, Graz, Austria
| | | | | | | | - L. Wang
- Rochester Institute of Technology, La JollaRochester, NY, USA
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26
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Kuo L, Liang JJ, Nazarian S, Marchlinski FE. Multimodality Imaging to Guide Ventricular Tachycardia Ablation in Patients with Non-ischaemic Cardiomyopathy. Arrhythm Electrophysiol Rev 2020; 8:255-264. [PMID: 32685156 PMCID: PMC7358957 DOI: 10.15420/aer.2019.37.3] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/23/2022] Open
Abstract
Catheter ablation is an effective treatment option for ventricular tachycardia (VT) in patients with non-ischaemic cardiomyopathy (NICM). The heterogeneous nature of NICM aetiologies and VT substrate in patients with NICM play a role in long-term ablation outcomes in this population. Over the past decades, more precise identification of NICM aetiologies and better characterisation of various substrates have been made. Application of multimodal imaging has greatly contributed to the accurate diagnosis of NICM subtypes and improved VT ablation strategies. This article summarises the current knowledge of multimodal imaging used in the characterisation of non-ischaemic NICM substrates, procedural planning and image integration for the optimisation of VT ablation.
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Affiliation(s)
- Ling Kuo
- Heart Rhythm Center, Division of Cardiology, Department of Medicine, Taipei Veterans General Hospital, Taipei, Taiwan
| | - Jackson J Liang
- Electrophysiology Section, Cardiovascular Division, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, US
| | - Saman Nazarian
- Electrophysiology Section, Cardiovascular Division, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, US
| | - Francis E Marchlinski
- Electrophysiology Section, Cardiovascular Division, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, US
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28
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Deep Learning Formulation of ECGI for Data-Driven Integration of Spatiotemporal Correlations and Imaging Information. FUNCTIONAL IMAGING AND MODELING OF THE HEART 2019. [DOI: 10.1007/978-3-030-21949-9_3] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
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29
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Virag N, Jacquemet V, Kappenberger L, Krause R, Prinzen F, Auricchio A. 9th Theo Rossi di Montelera forum on computer simulation and experimental assessment of cardiac function: from model to clinical outcome. Europace 2018; 20:iii1-iii2. [PMID: 30476064 DOI: 10.1093/europace/euy256] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Affiliation(s)
- Nathalie Virag
- TRM Foundation, Lausanne Switzerland and Medtronic Europe, Tolochenaz, Switzerland
| | - Vincent Jacquemet
- Université de Montréal and Hôpital du Sacré-Cœur de Montréal, Montréal, QC, Canada
| | | | - Rolf Krause
- Center for Computational Medicine in Cardiology, Università della Svizzera Italiana, Lugano, Switzerland
| | - Frits Prinzen
- Department of Physiology, Maastricht University, Maastricht, the Netherlands
| | - Angelo Auricchio
- Center for Computational Medicine in Cardiology, Università della Svizzera Italiana, Lugano, Switzerland
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