Subject-specific data-driven parameterization of preclinical 3D MRI-based heart models using an efficient algorithm to predict infarct-related tachycardia

The computational efficiency of contemporary multi-scale digital twins used to investigate cardiac arrhythmia is often hindered by the equation system complexity, while the accuracy of predictions depends on the calibration of model parameters and their intricate non-linear relation. Here we employed a robust pipeline for fast simulations of scar-related ventricular tachycardia (VT) inducibility using an efficient Lattice-Boltzmann Method with GPU-based accelerated code. We tested the pipeline on 3D digital twins built from MR images acquired in n = 8 swine with chronic infarction, by performing a subject-specific personalization of each model per tissue type (i.e., scar, border zone, and healthy) and tuning the key parameters (e.g., action potential duration, excitability, and wave speed) from recorded endocardial bipolar voltage maps and intracardiac electrograms. We first validated the VT simulation outcome by precisely replicating the stimulation protocol and pacing site used in the animal studies. Our results demonstrated very good agreement between the experiment and simulated VT outcome for personalized model parameters using subject-specific values, compared to a poor outcome when parameters were tuned from average values from all cases. Second, we performed a comprehensive in silico study where the stimulation was delivered from 10 000 virtual endocardial locations and found a strong dependence of VT (non)/-inducibility per case on the stimulation site. Simulating 10 s of sustained VT induced on a 3D model comprising 1.2 million cubic voxels (0.7 mm edge size) took 30 min on a laptop using GPU, underlying the computational tractability of our pipeline and its potential clinical translation to predict infarct-related VT risk.