title: Lp-norm regularization in volumetric imaging of cardiac current sources.
abstract: Advances in computer vision have substantially improved our ability to analyze the structure and mechanics of the heart. In comparison, our ability to observe and analyze cardiac electrical activities is much limited. The progress to computationally reconstruct cardiac current sources from noninvasive voltage data sensed on the body surface has been hindered by the ill-posedness and the lack of a unique solution of the reconstruction problem. Common L2- and L1-norm regularizations tend to produce a solution that is either too diffused or too scattered to reflect the complex spatial structure of current source distribution in the heart. In this work, we propose a general regularization with Lp-norm (1 < p < 2) constraint to bridge the gap and balance between an overly smeared and overly focal solution in cardiac source reconstruction. In a set of phantom experiments, we demonstrate the superiority of the proposed Lp-norm method over its L1 and L2 counterparts in imaging cardiac current sources with increasing extents. Through computer-simulated and real-data experiments, we further demonstrate the feasibility of the proposed method in imaging the complex structure of excitation wavefront, as well as current sources distributed along the postinfarction scar border. This ability to preserve the spatial structure of source distribution is important for revealing the potential disruption to the normal heart excitation.
authors.affiliation: Rochester Institute of Technology, Rochester, NY 14623, USA.
authors.forename: Azar
authors.initials: A
authors.lastname: Rahimi
authors.affiliation: Rochester Institute of Technology, Rochester, NY 14623, USA.
authors.forename: Jingjia
authors.initials: J
authors.lastname: Xu
authors.affiliation: Rochester Institute of Technology, Rochester, NY 14623, USA.
authors.forename: Linwei
authors.initials: L
authors.lastname: Wang
mesh_terms: D000465:Algorithms; D003198:Computer Simulation; D003952:Diagnostic Imaging; D004562:Electrocardiography; D006321:Heart / Q000502:physiology*; D006801:Humans; D007091:Image Processing, Computer-Assisted; D008955:Models, Cardiovascular; D009206:Myocardium / Q000473:pathology*; D010363:Pattern Recognition, Automated; D019047:Phantoms, Imaging; D016012:Poisson Distribution; D012984:Software