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
Fiber tract-oriented statistics for quantitative diffusion tensor MRI analysis
MICCAI'05 Proceedings of the 8th international conference on Medical Image Computing and Computer-Assisted Intervention - Volume Part I
Detection of DTI White Matter Abnormalities in Multiple Sclerosis Patients
MICCAI '08 Proceedings of the 11th international conference on Medical Image Computing and Computer-Assisted Intervention - Part I
Group Statistics of DTI Fiber Bundles Using Spatial Functions of Tensor Measures
MICCAI '08 Proceedings of the 11th international conference on Medical Image Computing and Computer-Assisted Intervention - Part I
Brain Lesion Segmentation through Physical Model Estimation
ISVC '08 Proceedings of the 4th International Symposium on Advances in Visual Computing
Voxel-wise group analysis of DTI
ISBI'09 Proceedings of the Sixth IEEE international conference on Symposium on Biomedical Imaging: From Nano to Macro
Unbiased white matter atlas construction using diffusion tensor images
MICCAI'07 Proceedings of the 10th international conference on Medical image computing and computer-assisted intervention
Probabilistic white matter and fiber tract atlas construction
ISBI'10 Proceedings of the 2010 IEEE international conference on Biomedical imaging: from nano to Macro
Group-wise diffeomorphic diffusion tensor image registration
MICCAI'10 Proceedings of the 13th international conference on Medical image computing and computer-assisted intervention: Part I
Diffusion tensor image registration with combined tract and tensor features
MICCAI'11 Proceedings of the 14th international conference on Medical image computing and computer-assisted intervention - Volume Part II
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We present a method for automatically finding correspondence in Diffusion Tensor Imaging (DTI) from deformable registration to a common atlas. The registration jointly produces an average DTI atlas, which is unbiased with respect to the choice of a template image, along with diffeomorphic correspondence between each image. The registration image match metric uses a feature detector for thin fiber structures of white matter, and interpolation and averaging of diffusion tensors use the Riemannian symmetric space framework. The anatomically significant correspondence provides a basis for comparison of tensor features and fiber tract geometry in clinical studies and for building DTI population atlases.