Evaluation of approaches to solving electrocardiographic imaging problem

Electrocardiographic imaging (ECGI) is a widely used method of computing potentials on the epicardium from measured or simulated potentials on the torso surface. The main challenge of the electrocardiographic imaging problem lies in its intrinsic ill-posedness, and many regularization techniques have been developed to smooth out the solution. It is still an ongoing research subject to choose proper regularization methods and to determine their proper amount for obtaining clinically acceptable solutions. This study systematically compares various regularization techniques for the ECGI problem under a unified simulation framework. The framework consists of an electrolytic human torso tank containing a live canine heart, with the cardiac source being modeled by potentials measured on a cylindrical cage placed around the heart. We tested 14 different regularization techniques to solve the inverse problem of recovering epicardial potentials, and found that non-quadratic methods (total variation algorithms) were the most robust and resulted in the lowest reconstruction errors.