DWI Rician Noise Restoration Using Modified LMMSE

WU Xi; ZHOU Ji-liu; XIE Ming-yuan

doi:10.3969/j.issn.0372-2112.2013.04.015

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DWI Rician Noise Restoration Using Modified LMMSE

更新时间：2025-07-16

- DWI Rician Noise Restoration Using Modified LMMSE
- Acta Electronica Sinica Vol. 41, Issue 4, Pages: 717-721(2013)
- 作者机构：
  
  1. 成都信息工程学院电子工程系,四川,成都,610225
  2. 四川大学计算机学院,四川,成都,610065
  3. 成都信息工程学院电子工程系四川成都,610225
  4. 四川大学计算机学院四川成都,610065
- 作者简介：
- 基金信息：
  
  National Natural Science Foundation of China (No.81201158, No.61271330)
- DOI：10.3969/j.issn.0372-2112.2013.04.015
  CLC： TP391
- Published：2013
- 稿件说明：
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WU Xi, ZHOU Ji-liu, XIE Ming-yuan. DWI Rician Noise Restoration Using Modified LMMSE[J]. Acta Electronica Sinica, 2013, 41(4): 717-721.
DOI：

WU Xi, ZHOU Ji-liu, XIE Ming-yuan. DWI Rician Noise Restoration Using Modified LMMSE[J]. Acta Electronica Sinica, 2013, 41(4): 717-721. DOI： 10.3969/j.issn.0372-2112.2013.04.015.

摘要

弥散加权磁共振图像(DWI)由于其本身扫描成像和应用特点

易被噪声干扰

且其噪声一般呈Rician分布

需要有效去噪以保证后续应用.目前使用较多的局部去噪方法缺乏对噪声统计信息的综合应用

缺乏针对DWI图像特殊Rician噪声分布的针对性应用.本文提出一种DWI图像Rician噪声的线性最小均方误差(LMMSE)复原方法

使用局部信息的统计特征

对DWI图像的Rician噪声进行有效估计

并引用各向异性滤波的原理改进使用LMMSE进行递归复原.在合成模拟DWI数据和真实人体脑部DWI数据上进行的仿真和实验表明

本文方法较之现有常用局部性去噪方法能够更好地去除DWI图像中Rician噪声

改善计算获得的DTI图像标量和方向信息的有效性和准确性.

Abstract

Diffusion weighted magnetic resonance image (DWI) should be denoised effectively for the corresponding procedure due to its property of imaging and application.Different from the normal gray level image

the noise in DWI is distributed under the Rician distribution.The commonly used local denoising method is lack of the synthetic implementation of the statistical information of noise

especially the Rician noise in the DWI.This paper proposed a modified LMMSE restoration method used for DWI.The proposed method used the local information to estimate the parameter of the Rician noise and modified the LMMSE using the principle of the anisotropic filter.The simulation and experiment of the synthetic DWI and real human brain DWI dataset demonstrated that the proposed method can effectively remove the Rician noise compared to the commonly used local denoising method and improve the robustness and validity of the DTI.

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