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Abstract #0866

Optimized denoising and removal of partial-Fourier induced Gibbs ringing improves accuracy and robustness of DTI and DKI parameters

Jenny Chen1, Benjamin Ades-aron1, Hong-Hsi Lee2, Durga Kullakanda1, Saurabh Maithani1, Dmitry S. Novikov1, Jelle Veraart1, and Els Fieremans1
1Department of Radiology, NYU School of Medicine, New York, NY, United States, 2Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Boston, MA, United States


Diffusion MRI (dMRI) is affected by noise and by artifacts such as Gibbs ringing and distortions. Using software phantoms as ground-truth, this study compares diffusion tensor imaging (DTI) and diffusional kurtosis imaging (DKI) parameter estimates to assess accuracy of various denoising and Gibbs removal methods, two key components of dMRI pipelines. An optimized Diffusion parameter EStImation with Gibbs and NoisE Removal (DESIGNER) pipeline is proposed, with non-local patch MP-PCA denoising and Removal of Partial-fourier induced Gibbs Ringing (RPG), that yields more accurate metrics, fewer outlier voxels in phantoms and more robust DTI/DKI maps in patient data.

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