Ventricular tachycardia (VT), which can lead to sudden cardiac death, occurs frequently in patients with myocardial infarction. Catheter-based radiofrequency ablation of cardiac tissue has achieved only modest efficacy, owing to the inaccurate identification of ablation targets by current electrical mapping techniques, which can lead to extensive lesions and to a prolonged, poorly tolerated procedure. Here we show that personalized virtual-heart technology based on cardiac imaging and computational modelling can identify optimal infarct-related VT ablation targets in retrospective animal (5 swine) and human studies (21 patients) and in a prospective feasibility study (5 patients). We first assessed in retrospective studies (one of which included a proportion of clinical images with artifacts) the capability of the technology to determine the minimum-size ablation targets for eradicating all VTs. In the prospective study, VT sites predicted by the technology were targeted directly, without relying on prior electrical mapping. The approach could improve infarct-related VT ablation guidance, where accurate identification of patient-specific optimal targets could be achieved on a personalized virtual heart prior to the clinical procedure.

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Patients with myocardial infarction have an abundance of conduction channels (CC); however, only a small subset of these CCs sustain ventricular tachycardia (VT). Identifying these critical CCs (CCCs) in the clinic so that they can be targeted by ablation remains a significant challenge. The objective of this study is to use a personalized virtual-heart approach to conduct a three-dimensional (3D) assessment of CCCs sustaining VTs of different morphologies in these patients, to investigate their 3D structural features, and to determine the optimal ablation strategy for each VT. To achieve these goals, ventricular models were constructed from contrast enhanced magnetic resonance imagings of six postinfarction patients. Rapid pacing induced VTs in each model. CCCs that sustained different VT morphologies were identified. CCCs' 3D structure and type and the resulting rotational electrical activity were examined. Ablation was performed at the optimal part of each CCC, aiming to terminate each VT with a minimal lesion size. Predicted ablation locations were compared to clinical. Analyzing the simulation results, we found that the observed VTs in each patient model were sustained by a limited number (2.7 5 1.2) of CCCs. Further, we identified three types of CCCs sustaining VTs: I-type and T-type channels, with all channel branches bounded by scar, and functional reentry channels, which were fully or partially bounded by conduction block surfaces. The different types of CCCs accounted for 43.8, 18.8, and 37.4% of all CCCs, respectively. The mean narrowest width of CCCs or a branch of CCC was 9.7 5 3.6 mm. Ablation of the narrowest part of each CCC was sufficient to terminate VT. Our results demonstrate that a personalized virtual-heart approach can determine the possible VT morphologies in each patient and identify the CCCs that sustain reentry. The approach can aid clinicians in identifying accurately the optimal VT ablation targets in postinfarction patients.

Ventricular tachycardia (VT), which could lead to sudden cardiac death, occurs frequently in patients with myocardial infarction. Computational modeling has emerged as a powerful platform for the non-invasive investigation of lethal heart rhythm disorders in post-infarction patients and for guiding patient VT ablation. However, it remains unclear how VT dynamics and predicted ablation targets are influenced by inter-patient variability in action potential duration (APD) and conduction velocity (CV). The goal of this study was to systematically assess the effect of changes in the electrophysiological parameters on the induced VTs and predicted ablation targets in personalized models of post-infarction hearts. Simulations were conducted in 5 patient-specific left ventricular models reconstructed from late gadolinium-enhanced magnetic resonance imaging scans. We comprehensively characterized all possible pre-ablation and post-ablation VTs in simulations conducted with either an “average human VT”-based electrophysiological representation (i.e., EP avg ) or with ±10% APD or CV (i.e., EP var ); additional simulations were also executed in some models for an extended range of these parameters. The results showed that: (1) a subset of reentries (76.2–100%, depending on EP parameter set) conducted with ±10% APD/CV was observed in approximately the same locations as reentries observed in EP avg cases; (2) emergent VTs could be induced sometimes after ablation in EP avg models, and these emergent VTs often corresponded to the pre-ablation reentries in simulations with EP var parameter sets. These findings demonstrate that the VT ablation target uncertainty in patient-specific ventricular models with an average representation of VT-remodeled electrophysiology is relatively low and the ablation targets stable, as the localization of the induced VTs was primarily driven by the remodeled structural substrate. Thus, personalized ventricular modeling with an average representation of infarct-remodeled electrophysiology may uncover most targets for VT ablation.

The goal of this article is to review advances in computational modeling of the heart, with a focus on recent non-invasive clinical imaging- and simulation-based strategies aimed at improving the diagnosis and treatment of patients with arrhythmias and structural heart disease. Following a brief overview of the field of computational cardiology, we present recent applications of the personalized virtual-heart approach in predicting the optimal targets for infarct-related ventricular tachycardia and atrial fibrillation ablation, and in determining risk of sudden cardiac death in myocardial infarction patients. The hope is that with such models at the patient bedside, therapies could be improved, invasiveness of diagnostic procedures minimized, and health-care costs reduced.