Spherical Ridgelets for Multi-Diffusion-Tensor Refinement - Concept and Evaluation

High Angular Resolution Diffusion Imaging (HARDI) improved many neurosurgical areas due to its ability to represent complex intra-voxel structures, but is limited for clinical use mainly due to long acquisition times, but also due to noise. To transcend these limits, our work addresses these problems by combining a state-of-the-art multi diffusion tensor model enhanced with spherical ridgelets. Spherical ridgelets are able to reconstruct a signal based on a limited number of measured directions by utilizing compressed sensing. This concept shows that combining spherical ridgelets with a multi diffusion tensor model can improve the accuracy in case of low signal-to-noise ratios and makes it possible to use less than 15 directional measurements per voxel.