Anisotropic wave propagation and apparent conductivity estimation in a fast electrophysiological model: Application to XMR interventional imaging

P. P. Chinchapatnam, K. S. Rhode, A. King, G. Gao, Y. Ma, T. Schaeffter, David J. Hawkes, R.S. Razavi, Derek L.G. Hill, S. Arridge, Maxime Sermesant

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

10 Citations (Scopus)

Abstract

Cardiac arrhythmias are increasingly being treated using ablation procedures. Development of fast electrophysiological models and estimation of parameters related to conduction pathologies can aid in the investigation of better treatment strategies during Radio-frequency ablations. We present a fast electrophysiological model incorporating anisotropy of the cardiac tissue. A global-local estimation procedure is also outlined to estimate a hidden parameter (apparent electrical conductivity) present in the model. The proposed model is tested on synthetic and real data derived using XMR imaging. We demonstrate a qualitative match between the estimated conductivity parameter and possible pathology locations. This approach opens up possibilities to directly integrate modelling in the intervention room.
Original languageEnglish
Title of host publicationMedical Image Computing and Computer-Assisted Intervention
Subtitle of host publicationMICCAI 2007
EditorsNicholas Ayache, Sébastien Ourselin, Anthony Maeder
PublisherSpringer
Pages575–583
Number of pages9
ISBN (Electronic)978-3-540-75757-3
ISBN (Print)978-3-540-75756-6
DOIs
Publication statusPublished - 2007

Publication series

NameLecture Notes in Computer Science
Volume4791

Cite this