A diffusion-matched principal component analysis (DM-PCA) based denoising procedure for high-resolution diffusion-weighted MRI

Abstract

A concern with high-resolution DWI and DTI is the limited SNR. Here we report a new denoising procedure, termed diffusion-matched principal component analysis (DM-PCA), which comprises 1) identifying a group of voxels with very similar signal variation patterns along the diffusion dimension, 2) performing PCA along the diffusion dimension for those voxels, and 3) suppressing noisy PCA components. The DM-PCA method performs reliably for input data with a range of SNR and different numbers of diffusion encoding scans, without compromising anatomic resolvability, and should prove highly valuable for imaging studies in research and clinical uses.