基于非局部全变差和部分支撑已知的CS-MR图像重建方法

Compressed Sensing MR Image Reconstruction Based on Nonlocal Total Variation and Partially Known Support

提出一种基于压缩感知(CS)的磁共振(MR)图像重建方法.利用参考图像和目标图像结构的相似性,提取参考图像在小波域中L个大系数的索引集作为目标图像的已知支撑集,约束已知支撑集补集中小波系数的l1范数.此外,采用非局部全变差(NLTV)作为规整化项构造目标函数,通过快速合成分离算法(FCSA)重建目标图像.仿真结果证明,该方法能有效保留图像的边缘和细节信息,抑制噪声干扰,在相同采样数据量下,重建性能优于经典CS-MRI和其他同类方法.

By exploiting the similarity of the structure between the reference and the target images,a novel compressed sensing（CS）-based reconstruction method was proposed for MR image.Indexes of the Llargest wavelet coefficients of the reference image were extracted and regarded as the known part of the desired target images support,and the l1 norm of the wavelet coefficients belonging to the complement to the known support was constrained.Furthermore,the nonlocal total variation（NLTV）was utilized as a regularization term to construct the objective function.Then the target image was reconstructed via a fast composite splitting algorithm（FCSA）.Experimental results demonstrate that the proposed method can preserve edges and details while suppressing noise efficiently.It outperforms conventional CS-MRI and other similar reconstruction methods under the same sampling rate.