中国组织工程研究2012，Vol.16Issue(26)：4817-4821,5.DOI:10.3969/j.issn.1673-8225.2012.26.014
基于加权压缩感知的MR图像重建方法
Magnetic resonance image reconstruction based on weighted compressed sensing
李红 1杨晓梅 1李青1
作者信息
- 1. 四川大学电气信息学院,四川省成都市,610065
- 折叠
摘要
Abstract
BACKGROUND: Compressed sensing theory is widely used in the fast reconstruction of magnetic resonance image (MRI). According to randomly undersampling the k-space data, MRI with sparsity in the transform domain can be reconstructed exactly and this can be done by solving the constrained minimization problems using non-linear optimization algorithm.OBJECTIVE: To enhance the sparsity of the image in transform domain and improve the quality of MRI econstruction, a new approach to weight the sparse presentation of the image is proposed in this paper.METHODS: The nonlinear conjugate-gradient descent algorithm was utilized to solve the weighted norm minimization. In each iteration, according to the acquired image's sparse presentation, weighted matrix was updated to enhance the sparsity of MRI.RESULTS AND CONCLUSION: Several experiments were carried out with and without reweighting the norm. Results demonstrate that the proposed algorithm with weighted matrix can obviously improve the ability of image recovery.关键词
压缩感知/加权迭代/稀疏采样/图像重建/数字化图像与影像分类
医药卫生引用本文复制引用
李红,杨晓梅,李青..基于加权压缩感知的MR图像重建方法[J].中国组织工程研究,2012,16(26):4817-4821,5.
