Li Xiuhan, Feng Rui, Xiao Funan, Yin Yue, Cao Da, Wu Xiaoling, Zhu Songsheng, Wang Wei
Key Laboratory of Clinical Engineering, School of Biomedical Engineering and Informatics, Nanjing Medical University, Nanjing 211166, China.
Jiangsu Province Hospital of Chinese Medicine, Affiliated Hospital of Nanjing University of Chinese Medicine, Nanjing 210029, China.
Math Biosci Eng. 2022 Sep 9;19(12):13214-13226. doi: 10.3934/mbe.2022618.
As an advanced technique, compressed sensing has been used for rapid magnetic resonance imaging in recent years, Two-step Iterative Shrinkage Thresholding Algorithm (TwIST) is a popular algorithm based on Iterative Thresholding Shrinkage Algorithm (ISTA) for fast MR image reconstruction. However TwIST algorithms cannot dynamically adjust shrinkage factor according to the degree of convergence. So it is difficult to balance speed and efficiency. In this paper, we proposed an algorithm which can dynamically adjust the shrinkage factor to rebalance the fidelity item and regular item during TwIST iterative process. The shrinkage factor adjusting is judged by the previous reconstructed results throughout the iteration cycle. It can greatly accelerate the iterative convergence while ensuring convergence accuracy. We used MR images with 2 body parts and different sampling rates to simulate, the results proved that the proposed algorithm have a faster convergence rate and better reconstruction performance. We also used 60 MR images of different body parts for further simulation, and the results proved the universal superiority of the proposed algorithm.
作为一种先进技术，压缩感知近年来已被用于快速磁共振成像。两步迭代收缩阈值算法（TwIST）是一种基于迭代阈值收缩算法（ISTA）的流行算法，用于快速磁共振图像重建。然而，TwIST算法不能根据收敛程度动态调整收缩因子。因此，难以平衡速度和效率。在本文中，我们提出了一种算法，该算法可以在TwIST迭代过程中动态调整收缩因子，以重新平衡保真项和正则项。在整个迭代周期中，收缩因子的调整是根据先前的重建结果来判断的。它可以在确保收敛精度的同时大大加速迭代收敛。我们使用了具有2个身体部位和不同采样率的磁共振图像进行模拟，结果证明所提出的算法具有更快的收敛速度和更好的重建性能。我们还使用了60幅不同身体部位的磁共振图像进行进一步模拟，结果证明了所提出算法的普遍优越性。
