Computational Biomedicine Laboratory, Golisano College of Computing and Information Sciences, Rochester Institute of Technology, Rochester, NY, USA.
Comput Biol Med. 2013 Mar;43(3):184-99. doi: 10.1016/j.compbiomed.2012.12.003. Epub 2013 Jan 12.
Noninvasive transmural electrophysiological imaging (TEPI) combines body-surface electrocardiograms and image-derived anatomic data to compute subject-specific electrical activity and the relevant diseased substrates deep into the ventricular myocardium. Based on the Bayesian estimation where the priors come from probabilistic simulations of high dimensional EP models, TEPI engages intensive computation that hinders its clinical translation. We present a reduced-rank square-root (RRSR) algorithm for TEPI that reduces computational time by neglecting minor components of estimation uncertainty and improves numerical stability by the square-root structure. Phantom and real-data experiments demonstrate the ability of RRSR-TEPI to bring notable computational reduction without significant sacrifice of diagnostic efficacy, particularly in imaging and quantifying post-infarct substrates.
非侵入性透壁电生理成像（TEPI）结合体表心电图和图像衍生的解剖数据，计算特定于个体的电活动和相关的心室心肌深处的病变底物。基于贝叶斯估计，先验来自高维 EP 模型的概率模拟，TEPI 需要大量的计算，这阻碍了它的临床转化。我们提出了一种用于 TEPI 的降秩平方根（RRSR）算法，该算法通过忽略估计不确定性的次要分量来减少计算时间，并通过平方根结构提高数值稳定性。幻影和真实数据实验证明了 RRSR-TEPI 在不显著牺牲诊断效果的情况下显著减少计算量的能力，特别是在成像和量化梗死后底物方面。
