Modeling cardiac muscle fibers in ventricular and atrial electrophysiology simulations
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Abstract (arXiv)
Since myocardial fibers drive the electric signal propagation throughout the myocardium, accurately modeling their arrangement is essential for simulating heart electrophysiology (EP). Rule-Based-Methods (RBMs) represent a commonly used strategy to include cardiac fibers in computational models. A particular class of such methods is known as Laplace-Dirichlet-Rule-Based-Methods (LDRBMs) since they rely on the solution of Laplace problems. In this work we provide a unified framework, based on LDRBMs, for generating full heart muscle fibers. First, we review existing ventricular LDRBMs providing a communal mathematical description and introducing also some modeling improvements with respect to the existing literature. We then carry out a systematic comparison of LDRBMs based on meaningful biomarkers produced by numerical EP simulations. Next we propose, for the first time, a LDRBM to be used for generating atrial fibers. The new method, tested both on idealized and realistic atrial models, can be applied to any arbitrary geometries. Finally, we present numerical results obtained in a realistic whole heart where fibers are included for all the four chambers using the discussed LDRBMs.
Key facts. Cited by 6 papers in the map (data through 14 Oct 2026). First posted on arXiv 26 Jan 2021.
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Citing papers per year: 2022: 1; 2023: 1; 2024: 3; 2025: 1.
6 papers.
- 2022Physics-informed neural networks to learn cardiac fiber orientation from multiple electroanatomical mapscited by 14
- 2023Branched Latent Neural Mapscited by 3
- 2024Liquid Fourier Latent Dynamics Networks for fast GPU-based numerical simulations in computational cardiologycited by 3
- 2025Full-field surrogate modeling of cardiac function encoding geometric variabilitycited by 3
- 2024Ensemble learning of the atrial fiber orientation with physics-informed neural networkscited by 1
- 2024An Optimization Framework to Personalize Passive Cardiac Mechanicscited by 0
- 2025Full-field surrogate modeling of cardiac function encoding geometric variability29 Apr 2025
- 2024Ensemble learning of the atrial fiber orientation with physics-informed neural networks30 Oct 2024
- 2024Liquid Fourier Latent Dynamics Networks for fast GPU-based numerical simulations in computational cardiology19 Aug 2024
- 2024An Optimization Framework to Personalize Passive Cardiac Mechanics3 Apr 2024
- 2023Branched Latent Neural Maps4 Aug 2023
- 2022Physics-informed neural networks to learn cardiac fiber orientation from multiple electroanatomical maps28 Jan 2022