Single-Beat Noninvasive Imaging of Cardiac Electrophysiology of Ventricular Pre-Excitation Original Research Article

Objectives The aim of this study was to determine whether noninvasive imaging of cardiac electrophysiology (NICE) is feasible in patients with Wolff-Parkinson-White (WPW) syndrome in the clinical setting of a catheter laboratory and to test the accuracy of the noninvasively obtained ventricular activation sequences as compared with that of standard invasive electroanatomic mapping. Background NICE of ventricular activation could serve as a useful tool in the treatment of cardiac arrhythmias and might help improve our understanding of arrhythmia mechanisms. Methods NICE works by fusing the data from high-resolution electrocardiographic mapping and a model of the patient’s cardiac anatomy obtained by magnetic resonance imaging. The ventricular activation sequence was computed with a bidomain theory-based heart model to solve this inverse problem. Noninvasive imaging of cardiac electrophysiology was performed in 7 patients with WPW syndrome undergoing catheter ablation of the accessory pathway. The position error of NICE was defined as the distance between the site of earliest activation computed by NICE and the successful ablation site identified by electroanatomic mapping (CARTO; Biosense Webster, Diamond Bar, California) for normal atrioventricular (AV) conduction as well as for adenosine-induced AV block. Results The error introduced by geometric coupling of the CARTO data and the NICE model was 5 ± 3 mm (model discretization 10 mm). All ventricular accessory pathway insertion sites were identified with an accuracy of 18.7 ± 5.8 mm (baseline) and 18.7 ± 6.4 mm (adenosine). Conclusions The individual cardiac anatomy model obtained for each patient enables accurate noninvasive electrocardiographic imaging of ventricular pre-excitation in patients with WPW syndrome. Noninvasive imaging of cardiac electrophysiology might be used as a complementary noninvasive approach to localize the origin and help identify and understand the underlying mechanisms of cardiac arrhythmias.