Enhanced optimization-based method for the generation of patient-specific models of Purkinje networks
Year: 2023
Type: article
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.
Source: Scientific Reports
Language: English
Subfield: Cardiology and Cardiovascular Medicine
Field: Medicine
Domain: Health Sciences
Open Access status: gold
Estimated APC paid: $2,390
Funders: National Science Foundation, Wellcome Trust, European Commission, Coordenação de Aperfeiçoamento de Pessoal de Nível Superior, Generalitat Valenciana, Conselho Nacional de Desenvolvimento Científico e Tecnológico, Fundação de Amparo à Pesquisa do Estado de Minas Gerais, Universidade Federal de Juiz de Fora, Engineering and Physical Sciences Research Council, European Regional Development Fund
Awards: G1422035950, G2601397997, G3681454997, G4042783231, G410571670, G434337789, G451917667, A Centre of Excellence in Computational Biomedicine, Human in silico clinical trials in post myocardial infarction: mechanistic investigations into phenotypic electromechanical variability and response to treatment, G5198364236, G52337223, G5658957218, G5967599077, Computational Biomedicine at the Exascale, G6340490125, CPS: Frontier: Collaborative Research: Compositional, Approximate, and Quantitative Reasoning for Medical Cyber-Physical Systems, G8633157904
OpenAlex ID: W4385064158