Open AccessDiffusion sensitivity enhancement filter for raw DWIs
Author(s) -
Mathew Joshin John,
James Alex,
Kesavadas Chandrasekhar,
Paul Joseph Suresh
Publication year - 2018
Publication title -
iet computer vision
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.38
H-Index - 37
eISSN - 1751-9640
pISSN - 1751-9632
DOI - 10.1049/iet-cvi.2018.5213
Subject(s) - diffusion mri , voxel , artificial intelligence , tractography , mathematics , parametric statistics , sensitivity (control systems) , anisotropic diffusion , filter (signal processing) , computer science , computer vision , pattern recognition (psychology) , algorithm , image (mathematics) , statistics , engineering , medicine , electronic engineering , magnetic resonance imaging , radiology
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 non‐linear 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.