A multimodal optimisation framework for cardiac electrophysiology model personalisation using computed tomography and 12-lead electrocardiogram: Algorithm development and validation

Objectives Accurate parameterisation of cardiac patient-specific models is a major challenge. Here we aimed to test the feasibility of an optimised multimodal personalisation framework that combines structural information from computed tomography (CT) and 12-lead electrocardiograms (ECGs) features, for high-fidelity simulations of ventricular tachycardia (VT). Methods We studied a small cohort of six patients who had CT images and sinus-rhythm 12-lead ECGs available prior to VT ablation. The CT images were used to construct patient-specific biventricular simulation domains and to estimate ECG lead positions. We implemented a graphics processing unit-based computational pipeline and a two-stage optimisation framework consisting of: (i) an early activation onset estimation; and, (ii) the optimisation of conductivity parameter. For validation, the simulation outcomes were compared to clinical VT patterns and cycle lengths. Results Following early onset estimations, simulated 12-lead ECG signals achieved a 91.20 % mean Q wave, R wave, and S wave (QRS) peak accuracy. Using the optimised parameters, average QRS duration error was reduced to 3.94 ms, compared to 37.42 ms for baseline parameters. Notably, in four patients with available electro-anatomical VT maps, simulations reproduced the clinical VT patterns and cycle length closer to recordings than those obtained with baseline parameterisation. Conclusion Our pilot study demonstrates the feasibility of a robust electrophysiology modelling framework able to perform multimodal personalisation by integrating sinus-rhythm ECGs and cardiac CT images for VT simulations with tractable computational time, paving the way for its clinical translation and a less invasive scar-related VT risk assessment.