ArticleAnnals of biomedical engineering2012
A novel rule-based algorithm for assigning myocardial fiber orientation to computational heart models.
Article in Annals of biomedical engineering, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 236 papers.
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236 citing papers in PubMed, 543 citations in OpenAlex.
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- Accounting for uncertainty in computational models of ventricular tachycardia improves ablation guidance.Nature cardiovascular research · 2026Article
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- HeartSimSage: Attention-Enhanced Graph Neural Networks for Accelerating Cardiac Mechanics Modeling.Journal of computational physics · 2026Article
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- Computational Modelling of Selective Capture Mechanisms in Conduction System Pacing.Annals of biomedical engineering · 2026Article
- Body surface potential driven personalisation of electrophysiological digital twins in hypertrophic cardiomyopathy.PLoS computational biology · 2026Article
- Uncertainty quantification via polynomial chaos expansion of myocardial fibre orientation and cardiac activation patterns.The Journal of physiology · 2026Article
- Assessing the importance of sex and disease-specific anatomy in electrophysiology and mechanical simulations with a newly developed public virtual cohort of four-chamber heart models.PLoS computational biology · 2026Article
- Genotype-specific digital twins for arrhythmia ablation targeting in arrhythmogenic right ventricular cardiomyopathy.Communications medicine · 2026Article
- Uncertainty quantification of conduction velocity in models of cardiac spread of activation.Medical & biological engineering & computing · 2026Article
- Effects of Purkinje Fiber Conduction Block on Cardiac Pump Function: Computational Modeling Study.Bioengineering (Basel, Switzerland) · 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
- Rapid calibration of atrial electrophysiology models using Gaussian process emulators in the ensemble Kalman filter.Scientific reports · 2026Article
- Rapid prediction of cardiac activation in the left ventricle with geometric deep learning: a step towards cardiac resynchronization therapy planning.NPJ digital medicine · 2026Article
- Volumetric non-invasive cardiac mapping for accessible global arrhythmia characterization.Communications medicine · 2026Article
- Statistical Atlas-Based Surrogate Model of Biventricular Wall Mechanics.bioRxiv : the preprint server for biology · 2026Article
- Resynchronisation efficacy of pacing strategies depends on the assessment metric: a computational study.Frontiers in physiology · 2026Article
176 more citing papers are in PubMed but not listed here.
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Authors and funding
4 authors at 3 institutions in 2 countries.
Funding
Abstract
Electrical waves traveling throughout the myocardium elicit muscle contractions responsible for pumping blood throughout the body. The shape and direction of these waves depend on the spatial arrangement of ventricular myocytes, termed fiber orientation. In computational studies simulating electrical wave propagation or mechanical contraction in the heart, accurately representing fiber orientation is critical so that model predictions corroborate with experimental data. Typically, fiber orientation is assigned to heart models based on Diffusion Tensor Imaging (DTI) data, yet few alternative methodologies exist if DTI data is noisy or absent. Here we present a novel Laplace-Dirichlet Rule-Based (LDRB) algorithm to perform this task with speed, precision, and high usability. We demonstrate the application of the LDRB algorithm in an image-based computational model of the canine ventricles. Simulations of electrical activation in this model are compared to those in the same geometrical model but with DTI-derived fiber orientation. The results demonstrate that activation patterns from simulations with LDRB and DTI-derived fiber orientations are nearly indistinguishable, with relative differences ≤6%, absolute mean differences in activation times ≤3.15 ms, and positive correlations ≥0.99. These results convincingly show that the LDRB algorithm is a robust alternative to DTI for assigning fiber orientation to computational heart models.
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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.