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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