Abstract #1040

Diffusion parameter EStImation with Gibbs and NoisE Removal (DESIGNER)

Benjamin Ades-Aron¹, Jelle Veraart^1,2, 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.

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