PCA denoising using random matrix theory provides an optimal compromise between noise suppression and preservation of non-Gaussian diffusion.

Recent studies showed that PCA denoising algorithms using random matrix theory provide an optimal compromise between noise suppression and loss of anatomical information for standard diffusion measures and tractography approaches. In this study, we show that this algorithm seems also to optimally preserve the non-Gaussian diffusion properties. Several factors that influence the performance of the PCA denoising algorithm are also assessed, such as the spatial heterogeneity of diffusion parameters across neighbour voxels and different scanning protocols. Moreover, the compatibility of PCA denoising with Gibbs artefact suppression and noise bias correction is evaluated.