Abstract
Introduction: inFAT is a pathological hallmark in ARVC that is associated with pro-arrhythmic remodeling that contributes to the substrate for VA. Identification of the inFAT distribution on contrast-enhanced computed tomography (CE-CT), which is highly accessible and has reliable image quality, may be useful in pre-procedural planning for catheter ablation treatment of VA. Objective: We aimed to develop and validate a novel non-invasive CT-based computational approach for VA ablation targeting in ARVC. Methods: As a retrospective pilot study, we constructed 3D personalized computational models of ARVC patient hearts with implanted cardioverter defibrillators from CE-CT scans representing the patient-specific inFAT distribution in the RV (Fig A, B). Three patients were included. P1 and P2 had clinical VA and electrophysiological study with ablation. P3 did not present with clinical VA and did not have ablation. We evaluated the inducibility of each model via rapid pacing and compared the results to electroanatomic voltage maps (EAM). In-silico ablation was performed and compared to clinical ablation locations (Fig C). Results: For all 3 patients, heart model predictions corresponded well with clinical observations.In both P1 and P2, reentrant circuits’ locations matched clinical records (Fig C). Both reentrant circuits manifested at the border of inFAT and fat-myocardium admixture, where tissue heterogeneity was prominent. Regions of inFAT co-localized well with low-voltage areas on EAM. Predicted ablation sites successfully terminated VA and coincided with the clinical ablation locations. In P2, the predicted ablation yielded a much smaller lesion than clinical ablation. For P3, the model correctly predicted that VA could not be induced in the heart. Conclusions: Our novel ARVC CT-based computational approach accurately predicts VA ablation targets and could potentially become a highly accessible tool in assisting ARVC ablation.
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