Principal Warps: Thin-Plate Splines and the Decomposition of Deformations
IEEE Transactions on Pattern Analysis and Machine Intelligence
Curvature Estimation of Surfaces in 3D Grey-Value Images
ICPR '02 Proceedings of the 16 th International Conference on Pattern Recognition (ICPR'02) Volume 1 - Volume 1
Kernel partial least squares regression in reproducing kernel hilbert space
The Journal of Machine Learning Research
GIMIAS: An Open Source Framework for Efficient Development of Research Tools and Clinical Prototypes
FIMH '09 Proceedings of the 5th International Conference on Functional Imaging and Modeling of the Heart
Hyperspherical von Mises-Fisher mixture (HvMF) modelling of high angular resolution diffusion MRI
MICCAI'07 Proceedings of the 10th international conference on Medical image computing and computer-assisted intervention - Volume Part I
Towards a statistical atlas of cardiac fiber structure
MICCAI'06 Proceedings of the 9th international conference on Medical Image Computing and Computer-Assisted Intervention - Volume Part I
Cardiac microstructure estimation from multi-photon confocal microscopy images
FIMH'13 Proceedings of the 7th international conference on Functional Imaging and Modeling of the Heart
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The construction of realistic subject-specific models of the myocardial fiber architecture is relevant to the understanding and simulation of the electromechanical 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.