A survey of image registration techniques
ACM Computing Surveys (CSUR)
Elastic matching of diffusion tensor images
Computer Vision and Image Understanding - Special issue on analysis of volumetric image
Nonrigid Registration of 3D Scalar, Vector and Tensor Medical Data
MICCAI '00 Proceedings of the Third International Conference on Medical Image Computing and Computer-Assisted Intervention
Automatic Deformable Diffusion Tensor Registration for Fiber Population Analysis
MICCAI '08 Proceedings of the 11th International Conference on Medical Image Computing and Computer-Assisted Intervention, Part II
Characterization of the similarity between diffusion tensors for image registration
Computers in Biology and Medicine
Nonlinear registration of diffusion MR images based on fiber bundles
MICCAI'07 Proceedings of the 10th international conference on Medical image computing and computer-assisted intervention - Volume Part I
Data driven groupwise registration of diffusion weighted images
ISBI'10 Proceedings of the 2010 IEEE international conference on Biomedical imaging: from nano to Macro
Affine registration of diffusion tensor MR images
MICCAI'06 Proceedings of the 9th international conference on Medical Image Computing and Computer-Assisted Intervention - Volume Part II
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In this paper, we present an affine image coregistration technique for Diffusion Tensor Magnetic Resonance Imaging (DT-MRI) data sets based on mutual information. The technique is based on a multi-channel approach where the diffusion weighted images are aligned according to the corresponding acquisition gradient directions. Also, in addition to the coregistration of the DT-MRI data sets, an appropriate reorientation of the diffusion tensor is worked out in order to remain consistent with the corresponding underlying anatomical structures. This reorientation strategy is determined from the spatial transformation while preserving the diffusion tensor shape. The method is fully automatic and has the advantage to be independent of the applied diffusion framework.