  Diffusion-weighted magnetic resonance imaging (DWI) is the only noninvasive
method for quantifying microstructure and reconstructing white-matter pathways
in the living human brain. Fluctuations from multiple sources create
significant additive noise in DWI data which must be suppressed before
subsequent microstructure analysis. We introduce a self-supervised learning
method for denoising DWI data, Patch2Self, which uses the entire volume to
learn a full-rank locally linear denoiser for that volume. By taking advantage
of the oversampled q-space of DWI data, Patch2Self can separate structure from
noise without requiring an explicit model for either. We demonstrate the
effectiveness of Patch2Self via quantitative and qualitative improvements in
microstructure modeling, tracking (via fiber bundle coherency) and model
estimation relative to other unsupervised methods on real and simulated data.
