Abstract Background Catheter ablation is a cornerstone treatment for scar related post-infarction ventricular arrhythmias. Functional substrate driven ablation strategies guided by local activation time (LAT) mapping have been proven as efficient and effective approaches, especially when dealing with large scar areas. In-silico electrophysiological modeling is emerging as a potential tool for procedural planning and guidance. Purpose To clinically validate the feasibility, diagnostic performance, and accuracy of a novel late gadolinium enhancement cardiac magnetic resonance (LGE-CMR) based digital twin framework for simulated LAT map generation in post-infarction patients and their integration with CARTO3 EAM suite. Methods Five ischemic subjects who underwent CMR-aided scar-related ventricular tachycardia ablation at our referral center were retrospectively enrolled. Their CMR derived 3D pixel-signal-intensity (PSI) maps generated with ADAS3D were processed with novel in-house software to obtain patient-specific ventricular geometry, tissue characterization, and to generate a model of Purkinje system and myocyte fiber orientation. According to local normalized pixel intensity (NPI) values, myocardial regions were marked as healthy, scar, or border-zone. Electrophysiological properties of border-zone (BZ) were modulated according to local NPI. Continuous and 8-steps isochronal endo-epicardial LAT maps were generated and imported in CARTO3 system, aligned with electroanatomical mapping (EAM), and compared at an ASE 17-segments level, in terms of sensitivity, specificity, predictive values, and accuracy to actual high-density LAT maps obtained with a multipolar catheter. Deceleration Zones (DZ) were defined as previously described (>3 isochrones within 1 cm radius). Results A total of 85 ASE segments (34 epicardial) were analyzed. 24 EAM segments (28%) held DZs, 40 (47%) segments harbored bipolar scar or BZ. Thirty-seven (44%) CMR segments held scar core/BZ. Thirty (35%) digital model segments harbored DZs. Generated LAT maps were successfully imported and aligned with CARTO3 EAM suite for validation. In terms of DZ localization prediction, our LAT model showed 83% sensitivity (95% CI 63%-95%), 90% specificity (95% CI 80%-96%), 77% positive predictive value (PPV) (95% CI 56%-91%), 93% negative predictive value (NPV) (95% CI 83%-98%), 88% accuracy (95% CI 79%-94%). Conclusion Our newly developed framework for LGE-CMR derived functional substrate simulation in post-infarction patients holds high sensitivity, specificity, predictive values, and accuracy for DZ localization. Further validation, with a larger sample size, is required for evaluating potential implications in clinical practice.