RESEARCH ARTICLE


Patient-Specific Volume Conductor Modeling for Non-Invasive Imaging of Cardiac Electrophysiology



B Pfeifer*, §, 1, F Hanser§, 2, M Seger1, G Fischer1, R Modre-Osprian3, B Tilg1
1 Institute of Biomedical Signal Processing and Imaging, University for Health Sciences, Medical Informatics and Technology (UMIT), Hall i.T., Austria
2 Research Division for Pervasive Health, University for Health Sciences, Medical Informatics and Technology (UMIT), Hall i.T. Austria
3 ARC Seibersdorf Research GmbH, Innsbruck, Austria


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© Pfeifer et al.; Licensee Bentham Open.

open-access license: This is an open access article licensed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/3.0/) which permits unrestricted, non-commercial use, distribution and reproduction in any medium, provided the work is properly cited.

* Address correspondence to this author at the Institute of Biomedical Signal Processing and Imaging, University for Health Sciences, Medical Informatics and Technology (UMIT), Eduard-Wallnöfer-Zentrum 6060, Hall in Triol, Austria; Email: bernhard.pfeifer@umit.at
§ These authors contributed equally.


Abstract

We propose a general workflow to numerically estimate the spread of electrical excitation in the patients’ hearts. To this end, a semi-automatic segmentation pipeline for extracting the volume conductor model of structurally normal hearts is presented. The cardiac electrical source imaging technique aims to provide information about the spread of electrical excitation in order to assist the cardiologist in developing strategies for the treatment of cardiac arrhythmias. The volume conductor models of eight patients were extracted from cine-gated short-axis magnetic resonance imaging (MRI) data. The non-invasive estimation of electrical excitation was compared with the CARTO™ maps. The development of a volume conductor modeling pipeline for constructing a patient-specific volume conductor model in a fast and accurate way is one essential step to make the technique clinically applicable.

Keywords: Image processing, segmentation, model building, inverse problem, cardiac imaging.