[1]裴颖,朱金秀,杨语晨,等.基于小波树和互补分解的CS-MRI重建算法[J].计算机技术与发展,2018,28(12):152-156.[doi:10.3969/j.issn.1673-629X.2018.12.032]
　PEI Yin9,ZHU Jinxiul,YANG Yuchenl,et al.A CS-MRI Algorithm Based on Complementary Dual Decomposition and Wavelet Tree[J].,2018,28(12):152-156.[doi:10.3969/j.issn.1673-629X.2018.12.032]
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基于小波树和互补分解的CS-MRI重建算法()
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《计算机技术与发展》[ISSN:1006-6977/CN:61-1281/TN]

卷:
28
期数:
2018年12期
页码:
152-156
栏目:
应用开发研究
出版日期:
2018-12-10

文章信息/Info

Title:
A CS-MRI Algorithm Based on Complementary Dual Decomposition and Wavelet Tree
文章编号:
1673—629X(2018)12—0152—05
作者:
裴颖1; 2; 朱金秀1; 2; 杨语晨1; 吴文霞1; 2
1．河海大学物联网工程学院，江苏常州213022；2．南通河海大学海洋与近海工程研究院，江苏南通226300
Author(s):
PEI Yin91”; ZHU Jin—xiul”; YANG Yu—chenl; WU Wen—xial’2
1．School of Internet of Things Engineering，Hohai University，Changzhou 21 3022，China；2．Research Institute of Ocean and Offshore Engineering，Hohai Ulfiversity，Nantong 226300，China
关键词:
核磁共振成像;  压缩感知;  互补分解;  小波树结构稀疏(小波树);  目标函数;  重建算
Keywords:
magnetic resonance imaging; compressed sensing; complementary dual decomposition; wavelet tree structure; objective function; reconstruction algorithm
分类号:
TP301.6
DOI:
10.3969/j.issn.1673-629X.2018.12.032
摘要:
针对压缩感知(CS)核磁共振成像(MRI)重建算法中全变分(TV)正则项会导致像细节丢失的问题,引入互补分解模型,结合小波树结构稀疏(简称小波树),提出一种基于小波树和互补分解的CS-MRI重建算法.利用互补分解将图像分成平滑分量和残差分量两个部分,并将平滑分量用于TV正则项,残差分量用于e1范数,可避免TV正则项在滤除噪声的同时滤除过多的细节信息;利用小波树结构稀疏可进一步补充小波稀疏等先验信息,减少测量值或提高信噪比.针对目标函数中存在平滑和残差两个未知分量,将目标函数分解为相应的两个子问题交替最小化进行求解.实验结果表明,与基于小波树的WaTMRI和基于TV的TVCMRI、FCSA等重建算法相比,其能在滤除噪声的同时有效改善MRI图像的细节信息.
Abstract:
Aiming at the problem that total variation(TV)regularities in compressed sensing(CS)nuclear magnetic resonance imaging (MRI)reconstruction algofithm Call lead to the loss of image details，we propose a CS—MRI reconstruction flgofithm based on wavelet tree and complementary decomposition by introducingthe complementary decomposition model and combining the sparse wavelet tree structure(referred to as wavelet tree)．By using complementary decomposition，the image is divided into smooth component and residual component，and the smooth component is used for TV regular term，and the residual component for fl norm，which Can avoid the TV reg·ular term from filtering out too much details while removing noise．By using the sparse wavelet tree structure，the prior information such as the sparse wavelet Can be further supplemented to reduce the measured value or improve the signal-to-noise ratio．The objective function is decomposed into the corresponding subproblems and minimized alternately to solve the unknown components of smooth and residual in it．Experiment demonstrates that compared with WaTMRI based on wavelet tree and TVCMRI and FCSA based on TV，the proposed algorithm Can effectively improve the details of MRJ images while filtering out noise．

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更新日期/Last Update: 2018-12-10