Swin Transformer and the Unet Architecture to Correct Motion Artifacts in Magnetic Resonance Image Reconstruction
We present a deep learning-based method that corrects motion artifacts and thus accelerates data acquisition and reconstruction of magnetic resonance images. The novel model, the Motion Artifact Correction by Swin Network (MACS-Net), uses a Swin transformer layer as the fundamental block and the Une...
| Κύριοι συγγραφείς: | , , , , , , |
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| Μορφή: | Άρθρο |
| Γλώσσα: | English |
| Έκδοση: |
Hindawi Limited
2024-01-01
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| Σειρά: | International Journal of Biomedical Imaging |
| Διαθέσιμο Online: | http://dx.doi.org/10.1155/2024/8972980 |