Denoising diffusion MRI with an improved non-local principal component analysis approach

Motivation: Previously, we proposed an improved 2-step non-local principal component analysis (PCA) approach and demonstrated its utility for denoising diffusion MRI with many diffusion directions. Goal(s): Our goal here was to investigate how our approach would benefit diffusion tensor MRI (DTI) with a few diffusion directions. Approach: we evaluated our approach’s denoising performances using both simulation and human-data experiments, and compared the results to those obtained with existing local-PCA-based methods. Results: Our approach substantially enhanced image quality relative to the noisy counterpart, yielding improved performances for estimation of relevant DTI metrics. It also outperformed existing local-PCA-based methods in reducing noise while preserving anatomic details. Impact: Capable of improving image quality for DTI with reduced diffusion directions, our improved non-local PCA denoising approach is believed to have utility for many applications, especially those targeting quality DTI or parametric mapping or both within a clinically relevant timeframe.