Diffusion sensitivity enhancement filter for raw DWIs
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...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2018-10-01
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| Series: | IET Computer Vision |
| Subjects: | |
| Online Access: | https://doi.org/10.1049/iet-cvi.2018.5213 |
| _version_ | 1827817517284851712 |
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| author | Joshin John Mathew Alex James Chandrasekhar Kesavadas Joseph Suresh Paul |
| author_facet | Joshin John Mathew Alex James Chandrasekhar Kesavadas Joseph Suresh Paul |
| author_sort | Joshin John Mathew |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-12T00:35:18Z |
| format | Article |
| id | doaj.art-912620eb11ba4382b9a16535cb551ee7 |
| institution | Directory Open Access Journal |
| issn | 1751-9632 1751-9640 |
| language | English |
| last_indexed | 2024-03-12T00:35:18Z |
| publishDate | 2018-10-01 |
| publisher | Wiley |
| record_format | Article |
| series | IET Computer Vision |
| spelling | doaj.art-912620eb11ba4382b9a16535cb551ee72023-09-15T09:52:03ZengWileyIET Computer Vision1751-96321751-96402018-10-0112795095610.1049/iet-cvi.2018.5213Diffusion sensitivity enhancement filter for raw DWIsJoshin John Mathew0Alex James1Chandrasekhar Kesavadas2Joseph Suresh Paul3Computer Vision TeamARS Traffic and Transport TechnologyTrivandrumIndiaDepartment of Electrical and Computer EngineeringNazarbayev UniversityAstanaKazakhstanDepartment of Imaging Sciences and Interventional RadiologySCTIMSTTrivandrumIndiaMedical Image Computing and Signal Processing GroupIIITM‐KTrivandrumIndiaIn 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.https://doi.org/10.1049/iet-cvi.2018.5213nonlinear three-dimensional neighbourhood operationweighted average representativeenhanced DWIprocessed imagesphysical diffusionresulting improvements |
| spellingShingle | Joshin John Mathew Alex James Chandrasekhar Kesavadas Joseph Suresh Paul Diffusion sensitivity enhancement filter for raw DWIs IET Computer Vision nonlinear three-dimensional neighbourhood operation weighted average representative enhanced DWI processed images physical diffusion resulting improvements |
| title | Diffusion sensitivity enhancement filter for raw DWIs |
| title_full | Diffusion sensitivity enhancement filter for raw DWIs |
| title_fullStr | Diffusion sensitivity enhancement filter for raw DWIs |
| title_full_unstemmed | Diffusion sensitivity enhancement filter for raw DWIs |
| title_short | Diffusion sensitivity enhancement filter for raw DWIs |
| title_sort | diffusion sensitivity enhancement filter for raw dwis |
| topic | nonlinear three-dimensional neighbourhood operation weighted average representative enhanced DWI processed images physical diffusion resulting improvements |
| url | https://doi.org/10.1049/iet-cvi.2018.5213 |
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