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A Variational Approach to Sparse Model Error Estimation in Cardiac Electrophysiological Imaging

  • Sandesh GhimireEmail author
  • John L. Sapp
  • Milan Horacek
  • Linwei Wang
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10434)

Abstract

Noninvasive reconstruction of cardiac electrical activity from surface electrocardiograms (ECG) involves solving an ill-posed inverse problem. Cardiac electrophysiological (EP) models have been used as important a priori knowledge to constrain this inverse problem. However, the reconstruction suffer from inaccuracy and uncertainty of the prior model itself which could be mitigated by estimating a priori model error. Unfortunately, due to the need to handle an additional large number of unknowns in a problem that already suffers from ill-posedness, model error estimation remains an unresolved challenge. In this paper, we address this issue by modeling and estimating the a priori model error in a low dimensional space using a novel sparse prior based on the variational approximation of L0 norm. This prior is used in a posterior regularized Bayesian formulation to quantify the error in a priori EP model during the reconstruction of transmural action potential from ECG data. Through synthetic and real-data experiments, we demonstrate the ability of the presented method to timely capture a priori model error and thus to improve reconstruction accuracy compared to approaches without model error correction.

Keywords

Variational method Sparse error estimation Posterior regularized bayes Electrophysiological imaging 

Notes

Acknowledgement

This work is supported by the National Science Foundation under CAREER Award ACI-1350374 and the National Institute of Heart, Lung, and Blood of the National Institutes of Health under Award R21Hl125998.

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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Sandesh Ghimire
    • 1
    Email author
  • John L. Sapp
    • 2
  • Milan Horacek
    • 2
  • Linwei Wang
    • 1
  1. 1.Rochester Institute of TechnologyRochesterUSA
  2. 2.Dalhouse UniversityHalifaxCanada

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