The impact of regularization methods for ECGI reconstructions during regular rhythms in an animal torso-tank model

Electrocardiographic imaging (ECGI) is a promising non-invasive technique that reconstructs epicardial potentials by combining high-density body-surface recordings with patient-specific 3D geometries. To systematically compare the performance of the main ECGI regularization methods, an experimental setup was developed using isolated Langendorff-perfused rabbit hearts. Panoramic optical mapping, epicardial electrograms, and torso-tank signals were acquired simultaneously during atria sinus rhythm and ventricular tachycardia. A tailored preprocessing pipeline was applied prior to inverse reconstruction using multiple methods, including Tikhonov (orders 0–2), truncated singular value decomposition (TSVD), damped singular value decomposition (DSVD), generalized minimal residual (GMRES), and Bayesian approaches. Results showed that no single method was universally optimal, with performance strongly dependent on cardiac region, rhythm, and evaluation metric. Tikhonov regularizations achieved the highest waveform similarity, reaching mean cross-correlation (CC) values up to 0.84 in the right atrium during sinus and 0.83 during ventricular tachycardia. In contrast, TSVD- and DSVD-based approaches yielded lower correlations (typically 0.62–0.78). GMRES provided improved spatial localization, achieving the lowest localization error (7.04 ± 2.36 mm) during tachycardia, while Bayes showed the highest CC variability across electrodes. Despite these differences, all methods consistently preserved dominant activation frequencies found in the measured signals (≈ 1.7 Hz in sinus rhythm and ≈ 4.8 Hz in tachycardia).