TY - GEN
T1 - Predictive modeling of cardiac fiber orientation using the knutsson mapping
AU - Lekadir, Karim
AU - Ghafaryasl, Babak
AU - Muñoz-Moreno, Emma
AU - Butakoff, Constantine
AU - Hoogendoorn, Corné
AU - Frangi, Alejandro F.
PY - 2011
Y1 - 2011
N2 - The construction of realistic subject-specific models of the myocardial fiber architecture is relevant to the understanding and simulation of the electro-mechanical behavior of the heart. This paper presents a statistical approach for the prediction of fiber orientation from myocardial morphology based on the Knutsson mapping. In this space, the orientation of each fiber is represented in a continuous and distance preserving manner, thus allowing for consistent statistical analysis of the data. Furthermore, the directions in the shape space which correlate most with the myocardial fiber orientations are extracted and used for subsequent prediction. With this approach and unlike existing models, all shape information is taken into account in the analysis and the obtained latent variables are statistically optimal to predict fiber orientation in new datasets. The proposed technique is validated based on a sample of canine Diffusion Tensor Imaging (DTI) datasets and the results demonstrate marked improvement in cardiac fiber orientation modeling and prediction.
AB - The construction of realistic subject-specific models of the myocardial fiber architecture is relevant to the understanding and simulation of the electro-mechanical behavior of the heart. This paper presents a statistical approach for the prediction of fiber orientation from myocardial morphology based on the Knutsson mapping. In this space, the orientation of each fiber is represented in a continuous and distance preserving manner, thus allowing for consistent statistical analysis of the data. Furthermore, the directions in the shape space which correlate most with the myocardial fiber orientations are extracted and used for subsequent prediction. With this approach and unlike existing models, all shape information is taken into account in the analysis and the obtained latent variables are statistically optimal to predict fiber orientation in new datasets. The proposed technique is validated based on a sample of canine Diffusion Tensor Imaging (DTI) datasets and the results demonstrate marked improvement in cardiac fiber orientation modeling and prediction.
UR - http://www.scopus.com/inward/record.url?scp=82255163721&partnerID=8YFLogxK
U2 - 10.1007/978-3-642-23629-7_7
DO - 10.1007/978-3-642-23629-7_7
M3 - Conference contribution
C2 - 21995012
AN - SCOPUS:82255163721
VL - 6892 LNCS
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 50
EP - 57
BT - MICCAI 2011
T2 - 14th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2011
Y2 - 18 September 2011 through 22 September 2011
ER -