Image reconstruction with subspace-assisted deep learning
Yue Guan1, Yudu Li2,3, Ruihao Liu1, Ziyu Meng1, Yao Li1, Leslie Ying4,5, Yiping P. Du1, and Zhi-Pei Liang2,3
1Institute for Medical Imaging Technology, School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China, 2Department of Electrical and Computer Engineering, University of Illinois at Urbana-Champaign, Urbana, IL, United States, 3Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, Urbana, IL, United States, 4Department of Biomedical Engineering, The State University of New York at Buffalo, Buffalo, NY, United States, 5Department of Electrical Engineering, The State University of New York at Buffalo, Buffalo, NY, United States
Deep learning-based image reconstruction methods are often sensitive to changes in data acquisition settings (e.g., sampling pattern and number of encodings). This work proposes a novel subspace-assisted deep learning method to effectively address this problem. The proposed method uses a subspace model to capture the global dependence of image features in the training data and a deep network to learn the mapping from a linear vector space to a nonlinear manifold. Significant improvement in reconstruction accuracy and robustness by the proposed method has been demonstrated using the fastMRI dataset.
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