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The problem of using surface data to reconstruct transmural electrophysiological (EP) signals is intrinsically ill-posed without a unique solution in its unconstrained form. Incorporating physiological spatiotemporal priors through probabilistic integration of dynamic EP models, we have previously developed a Bayesian approach to transmural(More)
Myocardial scar is the most common substrate for malignant arrhythmia and cardiac arrest. Radiofrequency ablation, as one of the emerging mainstream therapies, currently relies on electrophysiologic (EP) map acquired on endocardial and occasionally epicardial surfaces. As myocardial scar is often complex with shapes varying with the depth of the myocardium,(More)
Electrical dyssynchrony is postulated to be one of the main factors contributing to non-response of patients to cardiac resynchronization therapy (CRT). We applied inverse epicardial imaging computed from patient-specific geometry and body-surface potential recordings to assess global and regional electrical dyssynchrony. Patients were imaged pre- and(More)
Patient-specific modeling of the heart is limited by lack of technology to acquire myocardial fiber orientations in the clinic. To overcome this limitation, we recently developed an image-based methodology to estimate the fiber orienta-tions. In this study, we test the efficacy of that methodology in infarcted hearts. To this end, we implemented a(More)
Transmural electrophysiological imaging (TEPI) using surface measurements is an ill-posed problem that does not have a unique solution in its general unconstrained form. We previously developed and preliminarily validated a Bayesian approach to TEPI that incorporates physiological, spatiotemporal priors through probabilistic integration of dynamic(More)
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