ArticleComputers in biology and medicine2022
Determining anatomical and electrophysiological detail requirements for computational ventricular models of porcine myocardial infarction.
Article in Computers in biology and medicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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14 citing papers in PubMed, 23 citations in OpenAlex.
- Accounting for uncertainty in computational models of ventricular tachycardia improves ablation guidance.Nature cardiovascular research · 2026Article
- A physics-informed eikonal model for simulating arrhythmias in the human heart in real-time.Nature communications · 2026Article
- In silico predictions of action potential propagation in doxorubicin cardiotoxicity: A parametric study using preclinical 3D magnetic resonance imaging-based fibrotic left ventricle models.The Journal of physiology · 2026Article
- In silico assessment of arrhythmic risk following the implantation of engineered heart tissues in porcine hearts with varying infarct locations.PLoS computational biology · 2026Article
- Integrated multi-modal data analysis for computational modeling of healthy and location-dependent myocardial infarction conditions in porcine hearts.PLoS computational biology · 2026Article
- Learning cardiac activation and repolarization times with operator learning.PLoS computational biology · 2026Article
- Reconstructing ventricular activation sequences from epicardial data: Insights from Geodesic Back-Propagation optimization in porcine models.Computers in biology and medicine · 2025Article
- STAR Locally Prolongs Effective Refractory Period and Increases Ventricular Tachycardia Cycle Length Without Short-Term Scar Formation or Functional Decline: Insights From a Translational Porcine Model Study.Circulation. Arrhythmia and electrophysiology · 2025Article
- Stochastic virtual heart model predictions.Nature cardiovascular research · 2025Article
- Progress of Conductivity and Conduction Velocity Measured in Human and Animal Hearts.Reviews in cardiovascular medicine · 2024Review
- Combination of personalized computational modeling and machine learning for optimization of left ventricular pacing site in cardiac resynchronization therapy.Frontiers in physiology · 2023Article
- A smoothed boundary bidomain model for cardiac simulations in anatomically detailed geometries.PloS one · 2023Article
- Ultra-high field cardiac MRI in large animals and humans for translational cardiovascular research.Frontiers in cardiovascular medicine · 2023Article
- An automated near-real time computational method for induction and treatment of scar-related ventricular tachycardias.Medical image analysis · 2022Article
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Authors and funding
14 authors at 4 institutions in 3 countries.
Funding
Abstract
backgroundComputational 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.
objectiveTo 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.
methodsImaging 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.
resultsSimulations 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.
conclusionThis 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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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.