Deep neural networks have shown promise in image reconstruction tasks, although often on
the premise of large amounts of training data. In this paper, we present a new approach to …

Objective: Noninvasive electrocardiographic imaging (ECGI) is a promising tool for revealing
crucial cardiac electrical events with diagnostic potential. We propose a novel …

Traditional approaches to image reconstruction uses physics-based loss with data-efficient
inference, although the difficulty to properly model the inverse solution precludes learning …

Clinical adoption of personalized virtual heart simulations faces challenges in model
personalization and expensive computation. While an ideal solution is an efficient neural …

Personalized virtual heart models have demonstrated increasing potential for clinical use,
although the estimation of their parameters given patient-specific data remain a challenge …

Accurate reconstruction and imaging of cardiac transmembrane potential through body
surface ECG signals can provide great help for the diagnosis of heart disease. In this paper …

This study introduces an innovative approach combining deep-learning techniques with
classical physics-based electrocardiographic imaging (ECGI) methods. Our objective is to …

Objective. The primary aim of our study is to advance our understanding and diagnosis of
cardiac diseases. We focus on the reconstruction of myocardial transmembrane potential …

Despite substantial progress in deep learning approaches to time-series reconstruction, no
existing methods are designed to uncover local activities with minute signal strength due to …

Subject-specific geometry such as cardiac position and torso size plays an important role in
electrocardiographic imaging (ECGI). Previously, we introduced a graph-based neural …