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arXiv · 2511.09366

Augment to Augment: Diverse Augmentations Enable Competitive Ultra-Low-Field MRI Enhancement

Felix F Zimmermann
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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.

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Felix F Zimmermann. 2025-11-12. Augment to Augment: Diverse Augmentations Enable Competitive Ultra-Low-Field MRI Enhancement. https://arxiv.org/abs/2511.09366

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