Semiautomatic volume conductor modeling pipeline for imaging the cardiac electrophysiology noninvasively.
In this paper we present an approach for extracting patient individual volume conductor models (VCM) using volume data acquired from Magnetic Resonance Imaging (MRI) for computational biology of electrical excitation in the patient's heart. The VCM consists of the compartments chest surface, lung surfaces, the atrial and ventricular myocardium, and the blood masses. For each compartment a segmentation approach with no or little necessity of user interaction was implemented and integrated into a VCM segmentation pipeline to enable the inverse problem of electrocardiography to become clinical applicable. The segmentation pipeline was tested using volume data from ten patients with structurally normal hearts.
Pfeifer
Bernhard
B
Institute of Biomedical Engineering, University for Health sciences, medical informatics and technology, Austria. bernhard.pfeifer@umit.at
Seger
Michael
M
Hintermüller
Christoph
C
Fischer
Gerald
G
Hanser
Friedrich
F
Modre
Robert
R
Mühlthaler
Hannes
H
Tilg
Bernhard
B
D000465:Algorithms; D001185:Artificial Intelligence; D018780:Body Surface Potential Mapping / Q000379:methods*; D003198:Computer Simulation; D022062:Electrophysiologic Techniques, Cardiac / Q000379:methods; D005260:Female; D006329:Heart Conduction System / Q000502:physiology*; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D021621:Imaging, Three-Dimensional / Q000379:methods*; D008279:Magnetic Resonance Imaging / Q000379:methods*; D008297:Male; D008955:Models, Cardiovascular; D009431:Neural Conduction / Q000502:physiology; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity