| Paper: | TH-PM-PS2.8 | ||
| Session: | Diffusion Tensor Imaging | ||
| Time: | Thursday, April 6, 15:20 - 16:40 | ||
| Presentation: | Poster | ||
| Title: | Evaluation of Anisotropic Filters for Diffusion Tensor Imaging | ||
| Authors: | Jee Eun Lee; University of Wisconsin - Madison | ||
| Moo K. Chung; University of Wisconsin - Madison | |||
| Andrew Alexander; University of Wisconsin - Madison | |||
| Abstract: | We hypothesized that the diffusion tensor would be an approximate anisotropic Gaussian filter function because the blur will tend to be oriented parallel to the white matter structures. Thus, we implemented and evaluated an anisotropic Gaussian kernel smoothing method based on the diffusion tensor for preserving diffusion tensor structural features while significantly reducing the noise. We compared the diffusion tensor anisotropic filter with isotropic Gaussian filter, and a Perona-Malik algorithm. Human brain DTI data with high SNR was used as a gold standard for evaluation. Overall, the anisotropic filters performed similarly, with slightly better performance using the DT anisotropic filter across the whole brain. | ||
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