Ruiyang Zhao1, Tao Wang2, Kang Yan1, Chengcheng Zhang3, Zhipei Liang4, Yiping Du1, Dianyou Li3, Bomin Sun3, and Yuan Feng1
1Institute for Medical Imaging Technology, Shanghai Jiao Tong University, Shanghai, China, 2Functional Neurosurgery,Ruijin Hospital affiliated to Shanghai Jiao Tong University, Shanghai, China, 3Shanghai Jiao Tong University Medical School Affiliated Ruijin Hospital, Shanghai, CHINA, Shanghai, China, 4Beckman Institute for Advanced Science & Technology, Department of Electrical & Computer Engineering，University of Illinois at Urbana-Champaign, Urbana-Champaign, IL, United States
1Institute for Medical Imaging Technology, Shanghai Jiao Tong University, Shanghai, China, 2Functional Neurosurgery,Ruijin Hospital affiliated to Shanghai Jiao Tong University, Shanghai, China, 3Shanghai Jiao Tong University Medical School Affiliated Ruijin Hospital, Shanghai, CHINA, Shanghai, China, 4Beckman Institute for Advanced Science & Technology, Department of Electrical & Computer Engineering，University of Illinois at Urbana-Champaign, Urbana-Champaign, IL, United States
In this study, using radial sampling scheme, we proposed a recurrent network to reconstruct Interventional MRI by utilizing the time coherence information
between consecutive frames. The proposed method provided great potentials for application in MR-guided interventional therapy.
Figure 2. Network architecture of the proposed
end-to-end recurrent neural network for I-MRI reconstruction.
Figure 3. Reconstruction results of 5
consecutive frames from (a) Ground truth, (b) NUFFT, (c) GRASP, (d) LSTM-masked
RNN, and (e) the proposed method.