Augment to Augment: Diverse Augmentations Enable Competitive Ultra-Low-Field MRI Enhancement
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arXiv
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866908648326823936 |
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| author | Zimmermann, Felix F |
| author_facet | Zimmermann, Felix F |
| contents | Ultra-low-field (ULF) MRI promises broader accessibility but suffers from low signal-to-noise ratio (SNR), reduced spatial resolution, and contrasts that deviate from high-field standards. Image-to-image translation can map ULF images to a high-field appearance, yet efficacy is limited by scarce paired training data. Working within the ULF-EnC challenge constraints (50 paired 3D volumes; no external data), we study how task-adapted data augmentations impact a standard deep model for ULF image enhancement. We show that strong, diverse augmentations, including auxiliary tasks on high-field data, substantially improve fidelity. Our submission ranked third by brain-masked SSIM on the public validation leaderboard and fourth by the official score on the final test leaderboard. Code is available at https://github.com/fzimmermann89/low-field-enhancement. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_09366 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Augment to Augment: Diverse Augmentations Enable Competitive Ultra-Low-Field MRI Enhancement Zimmermann, Felix F Image and Video Processing Computer Vision and Pattern Recognition Medical Physics Ultra-low-field (ULF) MRI promises broader accessibility but suffers from low signal-to-noise ratio (SNR), reduced spatial resolution, and contrasts that deviate from high-field standards. Image-to-image translation can map ULF images to a high-field appearance, yet efficacy is limited by scarce paired training data. Working within the ULF-EnC challenge constraints (50 paired 3D volumes; no external data), we study how task-adapted data augmentations impact a standard deep model for ULF image enhancement. We show that strong, diverse augmentations, including auxiliary tasks on high-field data, substantially improve fidelity. Our submission ranked third by brain-masked SSIM on the public validation leaderboard and fourth by the official score on the final test leaderboard. Code is available at https://github.com/fzimmermann89/low-field-enhancement. |
| title | Augment to Augment: Diverse Augmentations Enable Competitive Ultra-Low-Field MRI Enhancement |
| topic | Image and Video Processing Computer Vision and Pattern Recognition Medical Physics |
| url | https://arxiv.org/abs/2511.09366 |