{"ID":2842854,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2511.09366","arxiv_id":"2511.09366","title":"Augment to Augment: Diverse Augmentations Enable Competitive Ultra-Low-Field MRI Enhancement","abstract":"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.","short_abstract":"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. W...","url_abs":"https://arxiv.org/abs/2511.09366","url_pdf":"https://arxiv.org/pdf/2511.09366v1","authors":"[\"Felix F Zimmermann\"]","published":"2025-11-12T14:27:08Z","proceeding":"eess.IV","tasks":"[\"eess.IV\",\"cs.CV\",\"physics.med-ph\"]","methods":"[]","has_code":false,"code_links":[{"ID":607165,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_id":2842854,"paper_url":"https://arxiv.org/abs/2511.09366","paper_title":"Augment to Augment: Diverse Augmentations Enable Competitive Ultra-Low-Field MRI Enhancement","repo_url":"https://github.com/fzimmermann89/low-field-enhancement","is_official":false,"mentioned_in_paper":false,"mentioned_in_github":true,"github_stars":0}]}