Diffusion parameter EStImation with Gibbs and NoisE Removal (DESIGNER)
Benjamin Ades-Aron1, Jelle Veraart1,2, Elias Kellner3, Yvonne W. Lui1, Dmitry S. Novikov1, and Els Fieremans1
1Center for Biomedical Imaging, New York University School of Medicine, New York, NY, United States, 2iMinds Vision Lab, University of Anterp, Antwerp, Belgium, 3Department of Radiology, University Medical Center Freiburg, Freiburg, Germany
We propose a new pipeline (DESIGNER) for diffusion
image processing that includes Marchenko Pastur denoising and Gibbs artifact removal,
and thereby improves the precision and accuracy of the diffusion tensor and
kurtosis tensor parameter estimation. In particular, our results show no notorious
black voxels on kurtosis maps, while the original resolution is maintained in
contrast to state-of-the-art processing methods that apply smoothing.
This abstract and the presentation materials are available to members only;
a login is required.