Denoising of diffusion MRI using random matrix theory

•Denoising enhances the image quality for improved visual, quantitative, and statistical interpretation.•Random matrix theory enables data-driven threshold for PCA denoising.•The Marchenko-Pastur distribution is a universal signature of noise.•The technique suppresses signal fluctuations that solely originate in thermal noise.•Precision of diffusion parameter estimators increases without lowering accuracy.

We introduce and evaluate a post-processing technique for fast denoising of diffusion-weighted MR images. By exploiting the intrinsic redundancy in diffusion MRI using universal properties of the eigenspectrum of random covariance matrices, we remove noise-only principal components, thereby enabling signal-to-noise ratio enhancements. This yields parameter maps of improved quality for visual, quantitative, and statistical interpretation. By studying statistics of residuals, we demonstrate that the technique suppresses local signal fluctuations that solely originate from thermal noise rather than from other sources such as anatomical detail. Furthermore, we achieve improved precision in the estimation of diffusion parameters and fiber orientations in the human brain without compromising the accuracy and spatial resolution.