TY - JOUR
T1 - Diffusion sensitivity enhancement filter for raw DWIs
AU - Mathew, Joshin John
AU - James Pappachen, Alex
AU - Kesavadas, Chandrasekhar
AU - Paul, Joseph Suresh
N1 - Publisher Copyright:
© The Institution of Engineering and Technology 2018.
Copyright:
Copyright 2018 Elsevier B.V., All rights reserved.
PY - 2018/10/1
Y1 - 2018/10/1
N2 - 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.
AB - 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.
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U2 - 10.1049/iet-cvi.2018.5213
DO - 10.1049/iet-cvi.2018.5213
M3 - Article
AN - SCOPUS:85053464170
VL - 12
SP - 950
EP - 956
JO - IET Computer Vision
JF - IET Computer Vision
SN - 1751-9632
IS - 7
ER -