UEPS: Robust and Efficient MRI Reconstruction (Pre-trained Model and Demo Data)
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2026
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| _version_ | 1866902014120689664 |
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| author | Zhou, Xiang Shang, Hong Zhan, Zijian He, Tianyu Meng, Jintao Liang, Dong |
| author_facet | Zhou, Xiang Shang, Hong Zhan, Zijian He, Tianyu Meng, Jintao Liang, Dong |
| contents | <p>This record contains the pre-trained weights (ckpt_pick.pth), sample MRI data, and visual results for the UEPS framework.</p> <p>UEPS is a novel deep unrolled model (DUM) architecture designed for robust and efficient MRI reconstruction. It features three key innovations: (i) an Unrolled Expanded (UE) design that eliminates coil sensitivity maps (CSM) dependency by expanding multi-coil data to the batch dimension; (ii) progressive resolution, which leverages k-space-to-image mapping for efficient coarse-to-fine refinement; and (iii) sparse attention tailored to MRI's 1D undersampling nature.</p> <p>Files included:<br>* ckpt_pick.pth: Pre-trained model weights.<br>* demo_data.zip: Sample MRI data for quick testing and visualization.<br>* reconstruction_examples.zip: Qualitative visualization examples of reconstructed slices. The image filenames follow the format '{slice_index}_nmse_{value}_psnr_{value}_ssim_{value}.png' (e.g., 140_nmse_0.00663_psnr_42.3608_ssim_0.9784.png).</p> <p>For the official PyTorch implementation, please visit our GitHub repository: https://github.com/HongShangGroup/UEPS</p> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_19494819 |
| institution | Zenodo |
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| publishDate | 2026 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | UEPS: Robust and Efficient MRI Reconstruction (Pre-trained Model and Demo Data) Zhou, Xiang Shang, Hong Zhan, Zijian He, Tianyu Meng, Jintao Liang, Dong <p>This record contains the pre-trained weights (ckpt_pick.pth), sample MRI data, and visual results for the UEPS framework.</p> <p>UEPS is a novel deep unrolled model (DUM) architecture designed for robust and efficient MRI reconstruction. It features three key innovations: (i) an Unrolled Expanded (UE) design that eliminates coil sensitivity maps (CSM) dependency by expanding multi-coil data to the batch dimension; (ii) progressive resolution, which leverages k-space-to-image mapping for efficient coarse-to-fine refinement; and (iii) sparse attention tailored to MRI's 1D undersampling nature.</p> <p>Files included:<br>* ckpt_pick.pth: Pre-trained model weights.<br>* demo_data.zip: Sample MRI data for quick testing and visualization.<br>* reconstruction_examples.zip: Qualitative visualization examples of reconstructed slices. The image filenames follow the format '{slice_index}_nmse_{value}_psnr_{value}_ssim_{value}.png' (e.g., 140_nmse_0.00663_psnr_42.3608_ssim_0.9784.png).</p> <p>For the official PyTorch implementation, please visit our GitHub repository: https://github.com/HongShangGroup/UEPS</p> |
| title | UEPS: Robust and Efficient MRI Reconstruction (Pre-trained Model and Demo Data) |
| url | https://doi.org/10.5281/zenodo.19494819 |