Electrocardiographic Imaging (ECGI) is an exceptional resource in cardiology practice and research, allowing for non-invasive assessment of local cardiac electrical activities, through the acquisition of ECGs signals acquired with multi-electrodes vests. This approach is largely based on solving an ill-posed inverse problem. However, to date, there is no method sufficiently convincing to solve the inverse problem, to establish ECGI as the clinical modality of choice. Previously, we proposed a deep learning (DL) based method for ECGI reconstruction by exploiting multimodal information and prior knowledge from previous cases. Tested with synthetic data, the method proved to be effective and convincing, but clinical validation is lacking.