TY - GEN
T1 - Personalized modeling of cardiac electrophysiology using shape-based prediction of fiber orientation
AU - Lekadir, Karim
AU - Pashaei, Ali
AU - Hoogendoorn, Corné
AU - Pereanez, Marco
AU - Albà, Xènia
AU - Frangi, Alejandro F.
PY - 2014
Y1 - 2014
N2 - Fibers play an important role in electrophysiological (EP) simulations as they determine the shape and directions of the electrical waves traveling throughout the myocardium. Due to the limited unavailability of in vivo images of the fiber structure, computational modeling of electrophysiology has been performed thus far mostly using the well-known rule-based Streeter model. The aim of this paper is to present an EP simulation study based on a statistics-based fiber model. With this approach, the missing subject-specific fiber model is predicted directly from the available shape information based on a predictive model constructed from a training sample of ex vivo DTI images. Experiments are carried out based on a database of canine datasets (including normal and abnormal cases), by considering the DTI-, the Streeter-, and the statistics-based fiber models. The results show that the shape-based predicted fiber models improve significantly the estimation accuracy of the electrical activation times and patterns, from average errors of about 10% to 1%.
AB - Fibers play an important role in electrophysiological (EP) simulations as they determine the shape and directions of the electrical waves traveling throughout the myocardium. Due to the limited unavailability of in vivo images of the fiber structure, computational modeling of electrophysiology has been performed thus far mostly using the well-known rule-based Streeter model. The aim of this paper is to present an EP simulation study based on a statistics-based fiber model. With this approach, the missing subject-specific fiber model is predicted directly from the available shape information based on a predictive model constructed from a training sample of ex vivo DTI images. Experiments are carried out based on a database of canine datasets (including normal and abnormal cases), by considering the DTI-, the Streeter-, and the statistics-based fiber models. The results show that the shape-based predicted fiber models improve significantly the estimation accuracy of the electrical activation times and patterns, from average errors of about 10% to 1%.
UR - http://www.scopus.com/inward/record.url?scp=84898849352&partnerID=8YFLogxK
U2 - 10.1007/978-3-642-54268-8_23
DO - 10.1007/978-3-642-54268-8_23
M3 - Conference contribution
AN - SCOPUS:84898849352
SN - 9783642542671
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 196
EP - 203
BT - Statistical Atlases and Computational Models of the Heart
PB - Springer-Verlag Italia
T2 - 4th International Workshop on Statistical Atlases and Computational Models of the Heart: Imaging and Modelling Challenges, STACOM 2013, Held in Conjunction with MICCAI 2013
Y2 - 26 September 2013 through 26 September 2013
ER -