Ziwen Ke1, Zhuo-Xu Cui1, 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
Our work tries to develop a deep optimization model on a nonlinear manifold directly. The comparisons with DC-CNN and CRNN show that our work can achieve improved results. To our knowledge, this work represents the first study applying a deep manifold optimization to dynamic MR images.
Figure 2. The proposed Manifold-Net for dynamic MR imaging.
Figure 1. Gradient descent on the low-rank tensor manifold.
