Parallelized Patch2Self & other denoising methods on 7T diffusion weighted imaging: comparisons of quality and effects on tractometry

Motivation: Performance of denoising methods and effects on downstream analyses for diffusion-weighted imaging at 7 Tesla are understudied. Goal(s): Determine which denoising methods provide better performance and whether any skew tractometry outcomes. Approach: Preliminary data acquired using two different diffusion sequences - 30 or 64 directions/shell multi-shell - were separately denoised using either Patch2Self, oversampled local-PCA, non-local means, or Marchenko-Pastur PCA before QSIPrep pre-processing and DSI Studio AutoTrack GQI. Results: Contrast-to-noise ratios were best for Patch2Self and oversampled local-PCA, agreeing with visual assessment. Fractional anisotropy distributions were higher and mean diffusivity lower in several major bundles for non-local means than Patch2Self, especially with lower angular resolution. Impact: The computationally-efficient parallel Patch2Self improves 7T diffusion data quality and produces lower fractional anisotropy values in tractometry of common bundles for biomarker searches than non-local means. Denoising methods should be considered in literature comparisons and image preprocessing in clinical trials.