SH-CASA: A Novel Algorithm for Denoising Diffusion MRI Data using Spherical Harmonics

Motivation: The diffusion MRI (dMRI) signal exhibits a low signal-to-noise ratio. Goal(s): This study endeavors to enrich the quality of dMRI data by employing a pioneering denoising technique, which combines Component Analysis with Standard-deviation Attenuation (CASA) and Spherical Harmonics (SH). Approach: Comparative analysis is conducted between the denoising capabilities of SH-based decomposition and the original PCA-based CASA technique using synthetic and in-vivo data. Results: The findings demonstrate that both denoising methods notably enhance image and tractography quality. In the case of synthetic data, SH-CASA displayed the most substantial improvement. Impact: Our novel denoising technique, SH-CASA, significantly enhances both the quality of raw diffusion MRI data and the resulting tractography. This holds particular significance in clinical settings where rescanning the patient is not always feasible.