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重复加权极小化MR图像模型的分裂Bregman方法重构 被引量：1

Minimization model for MR image reconstruction with split Bregman method
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摘要 核磁共振(magnetic resonance,MR)图像重构的任务是基于小量的频域采样恢复出可供医学诊断的灰度图像.文中研究了各类MR变分重构模型,利用重复加权极小化能增强稀疏性的特性,并结合MR图像重构最有效的小波变分模型,提出了重复加权极小化MR图像重构模型.并借助最新的正则化技术—分裂Bregman方法对模型进行了求解,得到了相应的迭代算法,分析了算法的收敛性.仿真数值实验验证了文中的模型及算法的有效性. Magnetic resonance（MR）image reconstruction is to get a practicable gray-scale image from few of frequency domain coefficients.In this paper,diffierent reweighted minimization models for MR image reconstruction are studied,and a novel model named reweighted wavelet＋TV minimization model is proposed.By using split Bregman method,an iteration minimization algorithm is obtained for solving this new model,and its convergence has been established.Numerical simulations show that the proposed model and its algorithm are feasible and efficient.
作者 张慧 成礼智 李建平
机构地区 国防科学技术大学理学院数学与系统科学系
出处 《中国科学：信息科学》 CSCD 2011年第4期440-449,共10页 Scientia Sinica(Informationis)
基金 国家自然科学基金(批准号:10601068 61072118) 全国优秀博士论文基金(批准号:2005043)资助项目
关键词 重复加权极小化 分裂Bregman方法 MR图像 图像重构 reweighted minimization split Bregman method MR image image reconstruction
分类号 TP391.41 [自动化与计算机技术—计算机应用技术]
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参考文献27

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共引文献13

同被引文献11

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中国科学：信息科学
2011年 第4期

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