A rule-based method to model myocardial fiber orientation in cardiac biventricular geometries with outflow tracts
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Abstract (arXiv)
Rule-based methods are often used for assigning fiber orientation to cardiac anatomical models. However, existing methods have been developed using data mostly from the left ventricle. As a consequence, fiber information obtained from rule-based methods often does not match histological data in other areas of the heart such as the right ventricle, having a negative impact in cardiac simulations beyond the left ventricle. In this work, we present a rule-based method where fiber orientation is separately modeled in each ventricle following observations from histology. This allows to create detailed fiber orientation in specific regions such as the endocardium of the right ventricle, the interventricular septum and the outflow tracts. We also carried out electrophysiological simulations involving these structures and with different fiber configurations. In particular, we built a modelling pipeline for creating patient-specific volumetric meshes of biventricular geometries, including the outflow tracts, and subsequently simulate the electrical wavefront propagation in outflow tract ventricular arrhythmias with different origins for the ectopic focus. The resulting simulations with the proposed rule-based method showed a very good agreement with clinical parameters such as the 10 ms isochrone ratio in a cohort of nine patients suffering from this type of arrhythmia. The developed modelling pipeline confirms its potential for an in silico identification of the site of origin in outflow tract ventricular arrhythmias before clinical intervention.
Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). First posted on arXiv 20 Sep 2018.
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Citing papers per year: 2024: 1; 2025: 2; 2026: 1.
4 papers.
- 2024DIMON: Learning Solution Operators of Partial Differential Equations on a Diffeomorphic Family of Domainscited by 14
- 2026Shape-informed cardiac mechanics surrogates in data-scarce regimes via geometric encoding and generative augmentationcited by 2
- 2025Visualizing definitional divergence in high-dimensional data by manifold alignment: Application to 3D right ventricular strain computationscited by 0
- 2025WarpPINN-fibers: improved cardiac strain estimation from cine-MR with physics-informed neural networkscited by 0
- 2026Shape-informed cardiac mechanics surrogates in data-scarce regimes via geometric encoding and generative augmentation23 Feb 2026
- 2025WarpPINN-fibers: improved cardiac strain estimation from cine-MR with physics-informed neural networks10 Sep 2025
- 2025Visualizing definitional divergence in high-dimensional data by manifold alignment: Application to 3D right ventricular strain computations21 Jan 2025
- 2024DIMON: Learning Solution Operators of Partial Differential Equations on a Diffeomorphic Family of Domains11 Feb 2024