摘要： 在核磁共振成像的应用中,一般采用联合方式求解L1范数算子和全变差分算子,而联合正则算子的求解模型比较复杂,为此,利用算子分裂技术求解联合正则算子,以降低求解模型的复杂度。在此基础上,提出一种迭代加权的压缩感知核磁共振重构算法,根据图像在离散傅里叶变换下系数的先验统计特性优化观测矩阵。仿真结果表明,该重构算法不仅提高了算法的重构精度而且减少了重构时间。

关键词: 压缩感知, 迭代加权, 核磁共振成像, 全变差分L1压缩, 算子分裂

Abstract: In the application of Magnetic Resonance Imaging(MRI),it is common to solve the problem by combining L1 norm with total variation operator.Because the model of solving compound regularizer is more complicated,the operator splitting technique is used to solve the problem of compound regularizer,which is in order to lower the complexity of the solving model,and puts forward a reconstruction method which is iterative weighted.The observation matrix is optimized,according to the priori statistical properties of imaging,which is under different transformations.Simulation results show that this image reconstruction algorithm not only enhances the reconstruction accuracy,but also decreases the time for the reconstruction.

Key words: Compressed Sensing(CS), iterative weighted, Magnetic Resonance Imaging(MRI), total variation L1 compression, operator splitting

中图分类号: 

• TP301.6