Denoising high angular resolution diffusion imaging data by combining singular value decomposition and non-local means filter.

Background: High angular resolution diffusion imaging (HARDI) data is typically corrupted with Rician noise. Although larger b-values help to retrieve more accurate angular diffusivity information, they also lead to an increase in noise generation.

New Method: In order to sufficiently reduce noise in HARDI images and improve the construction of orientation distribution function (ODF) fields, a novel denoising method was developed in this study by combining the singular value decomposition (SVD) and non-local means (NLM) filter. Similar 3D patches were first recruited into a matrix from a search volume. HARDI signals in the matrix were then re-estimated using the SVD low rank approximation, and a NLM filter was employed to filter out any residual noise.

Results: The performance of the proposed method was evaluated against the state-of-the-art denoising methods based on both synthetic and real HARDI datasets. Results demonstrated the superior performance of the developed SVD-NLM method in denoising HARDI data through preserving fine angular structural details and estimating diffusion orientations from improved ODF fields.

Conclusion: The proposed SVD-NLM method can improve HARDI quantitative computations, such as MRI brain tissue segmentation and diffusion profile estimation, that rely on the quality of imaging data.