Keywords: Image Reconstruction, Artifacts, Parallel MRI, Deep Learning, Attention MechanismDeep learning has made significant progress in recent years, however the local receptive field in CNN raises questions about signal synthesis and artefact compensation. This paper proposes a deep learning Spatial-Channel Attention U-Net (SCA-U-Net) to solve the folding problems arising in MR images because of undersampling. The main idea is to enhance local features in the images and restrain the irrelevant features at the spatial and channel levels. The output from SCA-U-Net is further refined by adding a small number of originally acquired low-frequency k-space data. Experimental results show a better performance of the SCA-U-Net model than classical U-Net model.
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