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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