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Undersampled Magnetic Resonance Image Reconstruction based on Support Prior and Deep Image Prior without Pre-Training
2022
Wuli xuebao
Magnetic resonance imaging (MRI) method based on deep learning needs large, high-quality patient-based datasets for pre-training. However, this is a challenge in clinical applications because it is difficult to obtain sufficient amounts of patient-based MR datasets due to the equipment and patient privacy concerns. In this paper, we propose a novel undersampled MR image reconstruction method based on deep learning. This method does not require any pre-training procedures and does not depend on
doi:10.7498/aps.71.20211761
fatcat:r45lxemqs5c7rafwcg4iop4y74