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Biophysical numerical model for pulsed field ablation
Simon Bihoreau Duchemin1, Michael Leguèbe1, Guido Caluori2
1Centre Inria de l'Université de Bordeaux, Talence, F-33400, France; Institut de Mathématiques de Bordeaux, Université de Bordeaux, Talence, F-33400, France; IHU Liryc, Fondation Bordeaux Université, Pessac, F-33600, France.
A new biophysics model for cardiac Pulsed-Field Ablation (PFA) accurately predicts lesion size and shape by considering tissue anisotropy. This transmembrane voltage-based approach improves predictions compared to standard methods.
Area of Science:
- Biophysics
- Computational Modeling
- Cardiac Electrophysiology
Background:
- Pulsed-Field Ablation (PFA) is a promising non-thermal treatment for cardiac arrhythmias.
- Current predictive models for PFA lesion characteristics are limited in accuracy.
- Existing models often rely on macroscopic electric field criteria, neglecting cellular-level details.
Purpose of the Study:
- To develop a novel, biophysics-grounded, anisotropic model for cardiac PFA.
- To incorporate cellular geometry and orientation via transmembrane voltage for lesion prediction.
- To improve the accuracy of predicting PFA lesion size and shape, particularly width-to-depth ratios.
Main Methods:
- Derivation of an anisotropic PFA model through homogenization of a microscopic electroporation model.
- Utilizing local transmembrane voltage as a biologically meaningful ablation criterion.
- Development of a static model for predicting outcomes of multiple consecutive pulses.
Main Results:
- The model demonstrates that myocardial fiber anisotropy significantly impacts lesion morphology.
- The transmembrane voltage criterion improves prediction of lesion width-to-depth ratios compared to standard methods.
- Preliminary numerical results align with experimental epicardial ventricular observations.
Conclusions:
- The proposed mechanistic modeling framework links cell-scale and tissue-scale phenomena in PFA.
- Local tissue anisotropy is a critical factor influencing PFA lesion characteristics.
- This model offers a more accurate approach for predicting PFA outcomes in cardiac arrhythmia treatment.
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