DWI LMMSE denoising using multiple magnitude directions.
- Author:
Xi WU
;
Jin HE
;
Ming ZHU
- Publication Type:Journal Article
- MeSH:
Algorithms;
Brain;
anatomy & histology;
Computer Simulation;
Diffusion Magnetic Resonance Imaging;
Diffusion Tensor Imaging;
Humans;
Image Enhancement;
methods;
Image Interpretation, Computer-Assisted;
methods;
Least-Squares Analysis;
Reproducibility of Results
- From:
Journal of Biomedical Engineering
2014;31(1):7-12
- CountryChina
- Language:Chinese
-
Abstract:
Because of the long acquisition time and spin-echo planar imaging sequence, diffusion weight magnetic resonance image (DWI) should be denoised effectively to ensure the follow-up applications. The commonly used denoising methods which induced from gray level image lack the use of the specific information from multiple magnitude directions. This paper, therefore, proposes a modified linear minimum mean square error (LMMSE) denosing method used for DWI. The proposed method uses the local information to estimate the parameter of the Rician noise and modifies the LMMSE using the information of multiple magnitude directions synthetically. The simulation and experiment of the synthetic DWI and real human brain DWI dataset demonstrate that the proposed method can more effectively remove the Rician noise compared to the commonly used denoising method and improve the robustness and validity of the diffusion tensor magnetic resonance image (DTI).
