关键词: 快速磁共振成像, 压缩感知, 原始-对偶, 近似算子

Abstract: Compressed Sensing（CS） based Magnetic Resonance Imaging（MRI） is a fast imaging technology which exploits the sparsity of Magnetic Resonance（MR） images. In view of the linear composite regularization term in the canonic reconstruction model for CS-MRI, a primal-dual iterative reconstruction algorithm is proposed which solves augmented Lagrangian of the primal and dual problems iteratively. The Moreau envelope is utilized to cope with the non-smooth regularization terms followed by a gradient calculation step using the approximate operator. Experiment results of phantom and real MR images show that compared with Conjugate Gradient algorithm（CG）, operator splitting algorithm（TVCMRI）, variable splitting algorithm （RecPF） and fast composite splitting algorithm（FCSA）, the algorithm gives the best reconstruction effect. In addition, the algorithm complexity compares favorably with FCSA which is the fastest algorithm so far.

Key words: fast magnetic resonance imaging, compressed sensing, primal-dual, approximate operator