Diffusion-Assisted Frequency Attention Model for Whole-body Low-field MRI Reconstruction
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arXiv
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| Hauptverfasser: | , , , , |
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| Format: | Preprint |
| Veröffentlicht: |
2025
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| _version_ | 1866909703089422336 |
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| author | Xie, Xin Guan, Yu Cui, Zhuoxu Liang, Dong Liu, Qiegen |
| author_facet | Xie, Xin Guan, Yu Cui, Zhuoxu Liang, Dong Liu, Qiegen |
| contents | By integrating the generative strengths of diffusion models with the representation capabilities of frequency-domain attention, DFAM effectively enhances reconstruction performance under low-SNR condi-tions. Experimental results demonstrate that DFAM consistently outperforms both conventional reconstruction algorithms and recent learning-based approaches. These findings highlight the potential of DFAM as a promising solution to advance low-field MRI reconstruction, particularly in resource-constrained or underdeveloped clinical settings. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_17764 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Diffusion-Assisted Frequency Attention Model for Whole-body Low-field MRI Reconstruction Xie, Xin Guan, Yu Cui, Zhuoxu Liang, Dong Liu, Qiegen Medical Physics Computer Vision and Pattern Recognition By integrating the generative strengths of diffusion models with the representation capabilities of frequency-domain attention, DFAM effectively enhances reconstruction performance under low-SNR condi-tions. Experimental results demonstrate that DFAM consistently outperforms both conventional reconstruction algorithms and recent learning-based approaches. These findings highlight the potential of DFAM as a promising solution to advance low-field MRI reconstruction, particularly in resource-constrained or underdeveloped clinical settings. |
| title | Diffusion-Assisted Frequency Attention Model for Whole-body Low-field MRI Reconstruction |
| topic | Medical Physics Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2507.17764 |