Ziwen Ke1, Wenqi Huang1, Jing Cheng1, Leslie Ying2, Xin Liu1, Hairong Zheng1, Yanjie Zhu1, and Dong Liang1
1Shenzhen Institutes of Advanced Technology, Shenzhen, China, 2University at Buffalo, The State University of New York, Buffalo, NY, United States
1Shenzhen Institutes of Advanced Technology, Shenzhen, China, 2University at Buffalo, The State University of New York, Buffalo, NY, United States
This paper explores a plug-and-play low-rank network module in dynamic MR imaging. It can be easily embedded into any other dynamic MR neural networks. Experimental results show that the proposed plug-and-play low-rank module can improve the reconstruction results.
Figure 1. The proposed plug-and-play LR network module. (a) The original ISTA-Net. (b) The original DC-CNN. (c) The original CRNN. (d) ISTA-LR-Net by embedding the LR network module into the original ISTA-Net. (e) DC-CNN-LR by embedding the LR network module into the original DC-CNN.(f) CRNN-LR by embedding the LR network module into the original CRNN. The numbers in the dotted box represent the locations where the LR module can be embedded.
Figure 2. The reconstruction results of the different methods (DC-CNN, DC-CNN-LR, CRNN, and CRNN-LR) at 8-fold acceleration. From left to right, the first row shows the ground truth, the reconstruction result of these methods. The second row shows the enlarged view of their respective heart regions framed by a yellow box. The third row shows the error map (display ranges [0, 0.07]). The y-t image (extraction of the 124th slice along the y and temporal dimensions) and the error of the y-t image are also given for each signal to show the reconstruction performance in the temporal dimension.
