In this study, a post-processing filter to enhance diffusion sensitivity, resulting in larger intensity changes in regions with the abrupt transition of local diffusivity in raw diffusion weighted image (DWI) volumes. Weights computed using a nonlinear three-dimensional neighbourhood operation are assigned to each voxel within the neighbourhood, with the weighted average representative of the enhanced DWI. The processed images exhibit better distinction among regions with differing levels of physical diffusion. While the resulting improvements in diffusion sensitivity are highlighted with the help of colour maps, parametric maps, and tractography, implications of the filtering process to recover missing information is illustrated in terms of ability to restore portions of fibre tracts which are otherwise absent in the unprocessed diffusion tensor imaging. Quantitative evaluation of the filtering process is performed using a metric representative of the estimated b-value, which is the consolidation machine parameters used for DWI acquisition.
ASJC Scopus subject areas
- Computer Vision and Pattern Recognition