Computational reduction for noninvasive transmural electrophysiological imaging

• Published in: Computers in biology and medicine Vol. 43; no. 3; pp. 184 - 199
• Main Author: Wang, Linwei
• Format: Journal Article
• Language: English
• Published: United States Elsevier Ltd 01.03.2013; Elsevier Limited
• Subjects: Algorithms; Cardiac electrophysiological imaging; Computational reduction; Data assimilation; Diagnostic Imaging - methods; electrocardiography; Electrophysiologic Techniques, Cardiac - methods; Electrophysiological Phenomena; Heart; Humans; image analysis; Internal Medicine; Models, Cardiovascular; Myocardial infarction; myocardium; Other; uncertainty; Unscented Kalman filter; Myocardial infarction; Cardiac electrophysiological imaging; Unscented Kalman filter; Computational reduction; Data assimilation
• ISSN: 0010-4825; 1879-0534; 1879-0534
• DOI: 10.1016/j.compbiomed.2012.12.003

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.