关键词: 磁共振成像重建, 深度学习, 自蒸馏, Transformer, UNet, 注意力机制

Abstract:

To remove Gibbs artifacts in Magnetic Resonance Imaging （MRI）， a Self-attention connection UNet based on Self-Distillation training （SD-SacUNet） algorithm was proposed. In order to reduce the semantic gap between the encoding and decoding features at both ends of the skip connection in the UNet framework and help to capture the location information of artifacts， the output features of each down-sampling layer at the UNet encoding end was input to the corresponding self-attention connection module for the calculation of the self-attention mechanism， then they were fused with the decoding features to participate in the reconstruction of the features. Self-distillation training was performed on the network decoding end， by establishing the loss function between the deep and shallow features， the feature information of the deep reconstruction network was used to guide the training of the shallow network， and at the same time， the entire network was optimized to improve the level of image reconstruction quality. The performance of SD-SacUNet algorithm was evaluated on the public MRI dataset CC359， with the Peak Signal-to-Noise Ratio （PSNR） of 30.261 dB and the Structure Similarity Index Measure （SSIM） of 0.917 9. Compared with GRACNN （Gibbs-Ringing Artifact reduction using Convolutional Neural Network）， the proposed algorithm had the PSNR increased by 0.77 dB and SSIM increased by 0.018 3； compared with SwinIR （Image Restoration using Swin Transformer）， the proposed algorithm had the PSNR increased by 0.14 dB and SSIM increased by 0.003 3. Experimental results show that SD-SacUNet algorithm improves the image reconstruction performance of MRI with Gibbs artifacts removal and has potential application values.

Key words: Magnetic Resonance Imaging (MRI) reconstruction, deep learning, self-distillation, Transformer, UNet, attention mechanism