Properties of surface-normal transformations
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Computational Cardiology: Modeling Of Anatomy, Electrophysiology, And Mechanics (LECTURE NOTES IN COMPUTER SCIENCE)
Large Deformation Diffeomorphic Metric Mapping of Fiber Orientations
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision - Volume 2
A Riemannian Framework for Tensor Computing
International Journal of Computer Vision
Fast and simple calculus on tensors in the log-euclidean framework
MICCAI'05 Proceedings of the 8th international conference on Medical Image Computing and Computer-Assisted Intervention - Volume Part I
MICCAI'07 Proceedings of the 10th international conference on Medical image computing and computer-assisted intervention - Volume Part I
Predictive modeling of cardiac fiber orientation using the knutsson mapping
MICCAI'11 Proceedings of the 14th international conference on Medical image computing and computer-assisted intervention - Volume Part II
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We propose here a framework to build a statistical atlas of diffusion tensors of canine hearts. The anatomical images of seven hearts are first non-rigidly registered in the same reference frame and their associated diffusion tensors are then transformed with a method that preserves the cardiac laminar sheets. In this referential frame, the mean tensor and its covariance matrix are computed based on the Log-Euclidean framework. With this method, we can produce a smooth mean tensor field that is suited for fiber tracking algorithms or the electromechanical modeling of the heart. In addition, by examining the covariance matrix at each voxel it is possible to assess the variability of the cardiac fiber directions and of the orientations of laminar sheets. The results show a strong coherence of the diffusion tensors and the fiber orientations among a population of seven normal canine hearts.