Automatic Generation of Bi-Ventricular Models of Cardiac Electrophysiology for Patient Specific Personalization Using Non-Invasive Recordings

Introduction: Personalized in silico models of cardiac electrophysiology based on non-invasive recordings, such as body surface potential maps, are considered of pivotal importance in clinical modeling applications. Efficient, automated workflows are desired to construct patient-specific models for clinical use. Objective: We aimed to develop an automated workflow for the generation of a parameterizable cardiac EP model capable of simulating body surface potential maps independent of user interaction. Methods: A cardiac bi-ventricular model with torso was segmented and meshed from clinical MRI scans. Universal ventricular coordinates were computed for user-independent definition of fibers, a fast conducting endocardial layer, and earliest activation on the endocardium. The extracellular epicardial potential distribution was simulated and projected to the torso surface to acquire a body surface potential map. Results: Total model generation from segmentation required approximately 2 hours. Automatized simulation of a single depolarization sequence required approximately 30 minutes using a forward element method implementation. Discussion: The proposed workflow integrated recently-developed technologies to generate a parameterizable cardiac EP model within clinical time scales.