基于快速分裂Bregman迭代的全变差正则化SENSE磁共振图像重建

在并行磁共振成像中，由于敏感度编码(SENSE)重建过程的病态性，当加速因子增大时，其重建图像的信噪比将会明显降低.通过深入分析全变差(TV)正则化的SENSE重建模型，引入一种高效快速的分裂Bregman迭代算法来得到优化解，进而有效改善图像重建效果.分别对磁共振的体模数据和大脑数据进行仿真实验研究.结果表明，与传统TV正则化SENSE重建相比，此算法不但迭代次数少、收敛速度快，而且能够有效消除混叠伪影，提高图像信噪比并减小归一化均方误差.

Total Variation Regularized SENSE MRI Reconstruction Based on Fast Split Bregman Iteration

In parallel magnetic resonance imaging (MRI)， the signal to noise ratio (SNR) of reconstruction image would be obviously reduced under the high acceleration factors because of the ill posed problem in the process of sensitivity encoding (SENSE) reconstruction. Through in depth analysis of total variation (TV) regularized SENSE reconstruction model， an efficient and fast split Bregman iteration algorithm was introduced to obtain the optimal solution and effectively improve the image reconstruction results. The simulation experiments were carried on the phantom data and brain data of MRI， respectively. The experimental results demonstrated that compared with the traditional TV regularized SENSE reconstruction algorithm， the proposed algorithm not only has fewer iterations and faster convergence speed， but also can alleviate the aliasing artifacts， significantly improves the SNR and decreases the normalized mean squared error of reconstruction image.