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

arXiv 2511.09366 ↗First posted 12 Nov 2025eess.IV cs.CV physics.med-phcited by 0

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Abstract (arXiv)

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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References · 16

  1. 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,604
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  11. 2018An Intriguing Failing of Convolutional Neural Networks and the CoordConv Solutioncited by 302
  12. 2020End-to-End Variational Networks for Accelerated MRI Reconstructioncited by 201
4 more
  1. 2023Fill the K-Space and Refine the Image: Prompting for Dynamic and Multi-Contrast MRI Reconstructioncited by 17
  2. 2024McCaD: Multi-Contrast MRI Conditioned, Adaptive Adversarial Diffusion Model for High-Fidelity MRI Synthesiscited by 3
  3. 2025MRpro: open framework for model-based, learned, and quantitative MR imagingcited by 2
  4. 2023NoSENSE: Learned unrolled cardiac MRI reconstruction without explicit sensitivity mapscited by 1

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