Personalising cardiac electrophysiology models from CT and ECG for 3D activation imaging and tissue characterisation

Background: Electrocardiographic imaging maps cardiac electrical activity non-invasively but is restricted to the epicardium. Computational electrophysiology models can predict 3D activation and tissue properties but require extensive parameter calibration. Methods: We introduce an unbiased workflow combining sensitivity analysis with emulator-based Bayesian history matching to calibrate over 100 organ- and tissue-scale parameters. The framework incorporates CT-scan images and 12-lead ECGs with a multi-scale electrophysiology model to generate personalised ventricular simulations. Results: The framework was tested on seven subjects (four with synthetic and three with clinical ECGs), with validation performed using high-density body surface potentials from a 252-electrode vest for the clinical cases. Calibrated models reproduced individual ECG morphologies and showed strong agreement with independent measurements (Pearson's correlation coefficient: 0.80±0.04). Conclusions: The study links non-invasive data with high-fidelity simulations to estimate spatially-varying properties, supporting personalised cardiac modelling for clinical use.