Variational Methods for Multimodal Image Matching
International Journal of Computer Vision
Strategies for Data Reorientation during Non-rigid Warps of Diffusion Tensor Images
MICCAI '99 Proceedings of the Second International Conference on Medical Image Computing and Computer-Assisted Intervention
Incorporating Connected Region Labelling into Automatic Image Registration Using Mutual Information
MMBIA '96 Proceedings of the 1996 Workshop on Mathematical Methods in Biomedical Image Analysis (MMBIA '96)
Large Deformation Diffeomorphic Metric Mapping of Fiber Orientations
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision - Volume 2
Diffeomorphic Matching of Diffusion Tensor Images
CVPRW '06 Proceedings of the 2006 Conference on Computer Vision and Pattern Recognition Workshop
Optimization of mutual information for multiresolution image registration
IEEE Transactions on Image Processing
Multimodal Image Registration by Information Fusion at Feature Level
MICCAI '09 Proceedings of the 12th International Conference on Medical Image Computing and Computer-Assisted Intervention: Part I
ASM'10 Proceedings of the 4th international conference on Applied mathematics, simulation, modelling
Improving registration using multi-channel diffeomorphic demons combined with certainty maps
MBIA'11 Proceedings of the First international conference on Multimodal brain image analysis
Automated approaches for analysis of multimodal MRI acquisitions in a study of cognitive aging
Computer Methods and Programs in Biomedicine
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Deformation tensor morphometry provides a sensitive approach to detecting and mapping subtle volume changes in the brain from conventional high resolution T1W MRI data. However, it is limited in its ability to localize volume changes within sub-regions of uniform white matter in T1W MRI. In contrast, lower resolution DTI data provides valuable complementary microstructural information within white matter. An approach to incorporating information from DTI data into deformation tensor morphometry of conventional high resolution T1W imaging is described. A novel mutual information (MI) derived criteria is proposed, termed diffusion paired MI, using an approximation to collective many-channel MI between all images. This approximation avoids the evaluation of high dimensional joint probability distributions, but allows a combination of conventional and diffusion data in a single registration criteria. The local gradient of this measure is used to drive a viscous fluid registration between repeated DTI-MRI imaging studies. Results on example data from clinical studies of Alzheimer's disease illustrate the improved localization of tissue loss patterns within regions of white matter.