Key result
Novel optimization method successfully generates patient-specific Purkinje networks with controlled morphology and local activation times.
Why the study?
Patient-specific modeling of cardiac Purkinje networks remains challenging due to their high morphological complexity, despite their known role in initiating arrhythmias.
This novel optimization-based computational method enables the generation of patient-specific Purkinje networks with high morphological and electrical accuracy, which may improve the modeling of cardiac arrhythmias.
May refine computational models of ventricular activation; leaves open human validation for arrhythmia applications.
Cardiac Purkinje networks are a fundamental part of the conduction system and are known to initiate a variety of cardiac arrhythmias. However, patient-specific modeling of Purkinje networks remains a challenge due to their high morphological complexity. This work presents a novel method based on optimization principles for the generation of Purkinje networks that combines geometric and electrical accuracy in branch size, bifurcation angles, and Purkinje-ventricular-junction activation times. Three biventricular meshes with increasing levels of complexity are used to evaluate the performance of our approach. Purkinje-tissue coupled monodomain simulations are executed to evaluate the generated networks in a realistic scenario using the most recent Purkinje/ventricular human cellular models and physiological values for the Purkinje-ventricular-junction characteristic delay. The results demonstrate that the new method can generate patient-specific Purkinje networks with controlled morphological metrics and specified local activation times at the Purkinje-ventricular junctions.
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Berg et al. (2023) studied Cardiac arrhythmias (computational modeling). Optimization-based method for Purkinje network generation was evaluated on Geometric and electrical accuracy (branch size, bifurcation angles, and activation times). A novel optimization-based method successfully generated patient-specific Purkinje networks with controlled morphological metrics and specified local activation times at the Purkinje-ventricular junctions.
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