基于变量分裂法的稀疏约束并行磁共振图像重建

Sparse Constrained Reconstruction for Parallel Magnetic Resonance Image Based on Variable Splitting Method

针对并行磁共振成像技术中,数据欠采样造成重建图像存在的混迭伪影和噪声问题,提出一种稀疏约束下并行磁共振的图像重建算法.该算法将一阶差分作为稀疏投影算子,构建在各向异性全变分最小化约束下并行磁共振的图像重建问题.同时,提出基于变量分裂法的求解方法,并在不同实验环境下分析该算法的有效性和鲁棒性.结果表明该算法可显著提高加速因子最大时并行磁共振重建图像的质量.

In order to reduce the aliasing artifacts and noise in the reconstructed images due to under-sampling data, a sparse constrained image reconstruction algorithm is proposed for parallel magnetic resonance imaging. In this paper, first-order difference is viewed as the sparse project Operator, and a parallel mag- netic resonance image reconstruction algorithm restrained by anisotropic total variation minimization is re- searched. Meanwhile, a solution based on variable splitting method is proposed, and the effectiveness and robustness of the proposed algorithm are analyzing in some specified experimental environments. The results show that the quality of reconstructed images is evidently improved for parallel magnetic resonance imaging by the proposed method at a maximum acceleration factor.