Abstract
Background
Diffusion-weighted imaging (DWI) is an important technique to study brain microstructure. However, diffusion-weighted (DW) images suffer from severe low signal-to-noise ratio (SNR) problem, affecting subsequent diffusion analysis.
Objective
The goal of this paper is to develop advanced DWI denoising technique to effectively reduce noise while improving the accuracy and reliability of subsequent diffusion model fitting and diffusion analysis, thereby facilitating the research and analysis of brain science.
Methods
We propose a new method for denoising DW images based on patch-matching with higher-order singular value decomposition (HOSVD) by combined with the variance-stabilizing transformation technique. It starts with introducing a novel non-local mean algorithm as a prefiltering stage, and then denoises the noisy data using a local HOSVD algorithm based on the HOSVD bases learned from prefiltered images.
Results
Experiments are performed on simulation, HCP and in vivo brain DWI datasets. Results show that the proposed method significantly reduces spatially invariant and variant noise, improving the most reliable diffusion analysis compared with the different denoising methods.
Conclusions
The proposed method achieves state-of-the-art performance which can improve image quality and enable accurate diffusion analysis.
Keywords
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References
Supplementary Material
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