Enhanced optimization-based method for the generation of patient-specific models of Purkinje networks

Abstract

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 activation 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.

In SilicoEvaluation of Cell Therapy in Acute versus Chronic Infarction: Role of Automaticity, Heterogeneity and Purkinje in Human

“…Ventricular fibre orientation was incorporated using the rule-based method by (Doste et al, 2019) following anatomical observations from (Streeter et al, 1970). The anatomical description of the Purkinje network was incorporated using the algorithm by (Berg et al, 2023; Camps et al, 2023) to achieve consistency between simulated and clinical QRS complexes, as shown in Figure 4A. CV in fibre, sheet and normal directions were 65, (Taggart et al, 2000), 38 and 47 cm/s (see supplementary Figure S9).…”

“…A) Patient heart and torso geometries with electrode locations (Mincholé et al, 2019) were combined with a personalised Purkinje conduction network to reproduce the patient’s ECG as in (Berg et al, 2023; Camps et al, 2023). B) Small, medium and large scar created using (Cardone-Noott et al, 2014; Hill et al, 2016).…”

“…Electrophysiological heterogeneity across the ventricles was modelled including APD gradients of 35 ms transmurally based on (Boukens et al, 2015;Franz et al, 1987) with 70 % endocardial and 30 % epicardial cells and in the apicobasal direction of 10 ms by scaling GKs (supplementary Figures S9 and S10). (Mincholé et al, 2019) were combined with a personalised Purkinje conduction network to reproduce the patient's ECG as in (Berg et al, 2023;Camps et al, 2023). B) Small, medium and large scar created using (Cardone-Noott et al, 2014;Hill et al, 2016)…”