一种SENSE模型下信号重建的类-Dykstra近点有效算法
AN EFFICIENT DYKSTRA-LIKE PROXIMAL ALGORITHM FOR SENSE MODEL SIGNAL RECONSTRUCTION

本文研究了SENSE模型下从部分傅里叶数据中信号的重建问题.利用类Dykstra近点方法和Bregman迭代方法,我们获得了一种SENSE模型下信号重建的加速类-Dykstra近点有效算法,并证明了该算法的收敛性.实验仿真显示,该方法比经典的分裂Bregman方法有效.
We consider the problem of the efficient reconstruction from partial Fourier data in Magnetic Resonance (MR) images.Based on Dykstra-like proximal method and Bregman method,we propose an accelerated Dykstra-like proximal algorithm (ADPA-BI) for SENSE model signal reconstruction,and obtain the proof of its convergence property.Our numerical simulations on recovering MR images indicate that the proposed method is more efficient than the classic Split Bregman Iteration (SBI) method