Robust transmural electrophysiological imaging: Integrating sparse and dynamic physiological models into ECG-based inference

Jingjia Xu, John L. Sapp, Azar Rahimi Dehaghani, Fei Gao, Milan Horacek, Linwei Wang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

10 Citations (Scopus)

Abstract

Noninvasive inference of patient-specific intramural electrical activity from surface electrocardiograms (ECG) lacks a unique solution in the absence of prior assumptions. While 3D cardiac electrophysiological models emerged to be a viable vehicle for constraining this inference with knowledge about the spatiotemporal dynamics of cardiac excitation, it is important for the inference to be robust to errors in these highdimensional model predictions given the limited ECG data. We present an innovative solution to this problem by exploiting the low-dimensional structure of the solution space – a powerful regularizer in overcoming the lack of measurements – within the dynamic inference guided by physiological models. We present the first Bayesian inference framework that allows the exploration of both the spatial sparsity of cardiac excitation and its complex nonlinear spatiotemporal dynamics for an improved inference of patient-specific intramural electrical activity. The benefit of this integration is verified in both synthetic and real-data experiments, where we present one of the first detailed, point-by-point comparison of the reconstructed electrical activity to in-vivo catheter mapping data.

Original languageEnglish
Title of host publicationMedical Image Computing and Computer-Assisted Intervention - MICCAI 2015 - 18th International Conference, Proceedings
EditorsJoachim Hornegger, Alejandro F. Frangi, William M. Wells, Alejandro F. Frangi, Nassir Navab, Joachim Hornegger, Nassir Navab, William M. Wells, William M. Wells, Alejandro F. Frangi, Joachim Hornegger, Nassir Navab
PublisherSpringer Verlag
Pages519-527
Number of pages9
ISBN (Print)9783319245706, 9783319245706, 9783319245706
DOIs
Publication statusPublished - 2015
Event18th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2015 - Munich, Germany
Duration: Oct 5 2015Oct 9 2015

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume9350
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference18th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2015
Country/TerritoryGermany
CityMunich
Period10/5/1510/9/15

Bibliographical note

Publisher Copyright:
© Springer International Publishing Switzerland 2015.

ASJC Scopus Subject Areas

  • Theoretical Computer Science
  • General Computer Science

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Cite this

Xu, J., Sapp, J. L., Dehaghani, A. R., Gao, F., Horacek, M., & Wang, L. (2015). Robust transmural electrophysiological imaging: Integrating sparse and dynamic physiological models into ECG-based inference. In J. Hornegger, A. F. Frangi, W. M. Wells, A. F. Frangi, N. Navab, J. Hornegger, N. Navab, W. M. Wells, W. M. Wells, A. F. Frangi, J. Hornegger, & N. Navab (Eds.), Medical Image Computing and Computer-Assisted Intervention - MICCAI 2015 - 18th International Conference, Proceedings (pp. 519-527). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9350). Springer Verlag. https://doi.org/10.1007/978-3-319-24571-3_62