A multimodal optimisation framework for cardiac electrophysiology model personalisation using computed tomography and 12-lead electrocardiogram: Algorithm development and validation
Buntheng Ly, Nicolas Cedilnik, Mihaela Pop, Josselin Duchateau, Frédéric Sacher, Pierre Jaïs, Hubert Cochet, Maxime SermesantObjectives
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
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.