Local late gadolinium enhancement features to identify the electrophysiological substrate of post-infarction ventricular tachycardia: a machine learning approach

Background Most ventricular tachycardias occur on structurally diseased hearts with fibrotic scar, where bundles of surviving tissue promote electrical circuit re-entry. These bundles can be identified on invasive electrophysiological (EP) mapping as local abnormal ventricular activities (LAVA) during sinus rhythm. Although the elimination of LAVAs by radiofrequency ablation was shown to be an efficient therapeutic option, their identification requires is a lengthy and invasive process. Late gadolinium enhancement (LGE) magnetic resonance imaging enables a non-invasive 3D assessment of scar topology and heterogeneity with millimetric spatial resolution. The aim of this work is to identify imaging features associated with LAVA, features that may subsequently be used to target ablation or to stratify the risk of arrhythmia.