Rochester Institute of Technology, Rochester, NY 14623, USA.
Comput Math Methods Med. 2013;2013:276478. doi: 10.1155/2013/276478. Epub 2013 Nov 20.
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.
计算机视觉的进步极大地提高了我们分析心脏结构和力学的能力。相比之下，我们观察和分析心脏电活动的能力要受到很大限制。从体表非侵入性电压数据计算重建心脏电流源的进展受到重建问题的不适定性和缺乏唯一解的阻碍。常见的 L2 和 L1 范数正则化往往会产生一种过于弥散或过于分散的解，无法反映电流源在心脏中分布的复杂空间结构。在这项工作中，我们提出了一种具有 Lp 范数（1 < p < 2）约束的通用正则化方法，以弥合心脏源重建中过于弥散和过于聚焦解之间的差距。在一组幻影实验中，我们证明了所提出的 Lp 范数方法在成像具有不同程度的心脏电流源方面优于其 L1 和 L2 对应方法。通过计算机模拟和真实数据实验，我们进一步证明了该方法在成像兴奋波前的复杂结构以及沿梗塞后疤痕边界分布的电流源方面的可行性。这种保留源分布空间结构的能力对于揭示对正常心脏兴奋的潜在干扰非常重要。
