MICCAI '02 Proceedings of the 5th International Conference on Medical Image Computing and Computer-Assisted Intervention-Part I
A hamilton-jacobi-bellman approach to high angular resolution diffusion tractography
MICCAI'05 Proceedings of the 8th international conference on Medical Image Computing and Computer-Assisted Intervention - Volume Part I
VISSYM'04 Proceedings of the Sixth Joint Eurographics - IEEE TCVG conference on Visualization
IEEE Transactions on Visualization and Computer Graphics
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
Belief Propagation Based Segmentation of White Matter Tracts in DTI
MICCAI '09 Proceedings of the 12th International Conference on Medical Image Computing and Computer-Assisted Intervention: Part I
Statistical analysis of structural brain connectivity
MICCAI'10 Proceedings of the 13th international conference on Medical image computing and computer-assisted intervention: Part II
Probabilistic anatomical connectivity using completion fields
MICCAI'10 Proceedings of the 13th international conference on Medical image computing and computer-assisted intervention: Part I
Adaptive Riemannian metrics for improved geodesic tracking of white matter
IPMI'11 Proceedings of the 22nd international conference on Information processing in medical imaging
Rotation invariant completion fields for mapping diffusion MRI connectivity
IPMI'11 Proceedings of the 22nd international conference on Information processing in medical imaging
Joint fractional segmentation and multi-tensor estimation in diffusion MRI
IPMI'13 Proceedings of the 23rd international conference on Information Processing in Medical Imaging
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In this paper we present a volumetric approach for quantitatively studying white matter connectivity from diffusion tensor magnetic resonance imaging (DT-MRI). The proposed method is based on a minimization of path cost between two regions, defined as the integral of local costs that are derived from the full tensor data along the path. We solve the minimal path problem using a Hamilton-Jacobi formulation of the problem and a new, fast iterative method that computes updates on the propagating front of the cost function at every point. The solutions for the fronts emanating from the two initial regions are combined, giving a voxel-wise connectivity measurement of the optimal paths between the regions that pass through those voxels. The resulting high-connectivity voxels provide a volumetric representation of the white matter pathway between the terminal regions. We quantify the tensor data along these pathways using nonparametric regression of the tensors and of derived measures as a function of path length. In this way we can obtain volumetric measures on white-matter tracts between regions without any explicit integration of tracts. We demonstrate the proposed method on several fiber tracts from DT-MRI data of the normal human brain.