Markov random field modeling in image analysis
Markov random field modeling in image analysis
Orthonormal Vector Sets Regularization with PDE's and Applications
International Journal of Computer Vision
Oriented tensor reconstruction: tracing neural pathways from diffusion tensor MRI
Proceedings of the conference on Visualization '02
MICCAI '02 Proceedings of the 5th International Conference on Medical Image Computing and Computer-Assisted Intervention-Part I
Image Processing for Diffusion Tensor Magnetic Resonance Imaging
MICCAI '99 Proceedings of the Second International Conference on Medical Image Computing and Computer-Assisted Intervention
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
Variational Frameworks for DT-MRI Estimation, Regularization and Visualization
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Basis Pursuit Based Algorithm for Intra-Voxel Recovering Information in DW-MRI.
ENC '05 Proceedings of the Sixth Mexican International Conference on Computer Science
Connectivity-based parcellation of the cortical mantle using q-ball diffusion imaging
Journal of Biomedical Imaging - Recent Advances in Neuroimaging Methodology
Massive particles for brain tractography
MICAI'10 Proceedings of the 9th Mexican international conference on Advances in artificial intelligence: Part I
Variational Multi-Valued Velocity Field Estimation for Transparent Sequences
Journal of Mathematical Imaging and Vision
Improved diffusion basis functions fitting and metric distance for brain axon fiber estimation
PSIVT'11 Proceedings of the 5th Pacific Rim conference on Advances in Image and Video Technology - Volume Part II
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We present a regularized method for solving an inverse problem in Diffusion Tensor Magnetic Resonance Imaging (DT-MRI) data. In the case of brain images, DT-MR imagery technique produces a tensor field that indicates the local orientation of nerve bundles. Now days, the spatial resolution of this technique is limited by the partial volume effect produced in voxels that contain fiber crossings or bifurcations. In this paper, we proposed a method for recovering the intra-voxel information and inferring the brain connectivity. We assume that the observed tensor is a linear combination of a given tensor basis, therefore, the aim of our approach is the computation of the unknown coefficients of this linear combination. By regularizing the problem, we introduce the needed prior information about the piecewise smoothness of nerve bundles orientation. As a result, we recover a multi-tensor field. Moreover, for estimating the nerve bundles trajectory, we propose a method based on stochastic walks of particles through the computed multi-tensor field. The performance of the method is demonstrated by experiments in both synthetic and real data.