Low-Field Magnetic Resonance Image Enhancement using Undersampled k-Space
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
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2026
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| _version_ | 1866918388218986496 |
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| author | Anyimadu, Daniel Tweneboah Abdalla, Mohammed Abdelsamea, Mohammed M. Eldaly, Ahmed Karam |
| author_facet | Anyimadu, Daniel Tweneboah Abdalla, Mohammed Abdelsamea, Mohammed M. Eldaly, Ahmed Karam |
| contents | Low-field magnetic resonance imaging (MRI) offers a cost-effective alternative for medical imaging in resource-limited settings. However, its widespread adoption is hindered by two key challenges: prolonged scan times and reduced image quality. Accelerated acquisition can be achieved using k-space undersampling, while image enhancement traditionally relies on spatial-domain postprocessing. In this work, we propose a novel deep learning framework based on a U-Net variant that operates directly in k-space to super-resolve low-field MR images directly using undersampled data while quantifying the impact of reduced k-space sampling. Unlike conventional approaches that treat image super-resolution as a postprocessing step following image reconstruction from undersampled k-space, our unified model integrates both processes, leveraging k-space information to achieve superior image fidelity. Extensive experiments on synthetic and real low-field brain MRI datasets demonstrate that k-space-driven image super-resolution outperforms conventional spatial-domain counterparts. Furthermore, our results show that undersampled k-space reconstructions achieve comparable quality to full k-space acquisitions, enabling substantial scan-time acceleration without compromising diagnostic utility. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_14125 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Low-Field Magnetic Resonance Image Enhancement using Undersampled k-Space Anyimadu, Daniel Tweneboah Abdalla, Mohammed Abdelsamea, Mohammed M. Eldaly, Ahmed Karam Computer Vision and Pattern Recognition Low-field magnetic resonance imaging (MRI) offers a cost-effective alternative for medical imaging in resource-limited settings. However, its widespread adoption is hindered by two key challenges: prolonged scan times and reduced image quality. Accelerated acquisition can be achieved using k-space undersampling, while image enhancement traditionally relies on spatial-domain postprocessing. In this work, we propose a novel deep learning framework based on a U-Net variant that operates directly in k-space to super-resolve low-field MR images directly using undersampled data while quantifying the impact of reduced k-space sampling. Unlike conventional approaches that treat image super-resolution as a postprocessing step following image reconstruction from undersampled k-space, our unified model integrates both processes, leveraging k-space information to achieve superior image fidelity. Extensive experiments on synthetic and real low-field brain MRI datasets demonstrate that k-space-driven image super-resolution outperforms conventional spatial-domain counterparts. Furthermore, our results show that undersampled k-space reconstructions achieve comparable quality to full k-space acquisitions, enabling substantial scan-time acceleration without compromising diagnostic utility. |
| title | Low-Field Magnetic Resonance Image Enhancement using Undersampled k-Space |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2603.14125 |