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

2025-11-12

AI Summary

Ultra-low-field MRI (ULF-MRI) suffers from inherently low signal-to-noise ratio, poor spatial resolution, and contrast distortion. Existing image-to-image translation methods are hindered by the scarcity of paired high-field/low-field 3D volumetric data. To address this, we propose a task-adaptive data augmentation framework that, using only 50 paired 3D volumes, integrates strong geometric and intensity augmentations alongside auxiliary supervision derived from high-field images—specifically structural consistency reconstruction—to enhance model generalizability. Our method significantly improves ULF-MRI image quality, achieving third place in brain mask SSIM and fourth place in overall test score on the ULF-EnC Challenge public leaderboard. The source code is publicly released, establishing a reproducible paradigm for few-shot medical image enhancement.

Technology Category

• Machine Learning: Transfer, Domain Adaptation, Multi-Task Learning
• Computer Vision: Medical and Biological Imaging
• Natural Language Processing: Machine Translation, Multilinguality, Cross-Lingual NLP

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Responsible Web: Machine-in-the-loop, human agency and autonomy

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.

Problem

• Enhancing ultra-low-field MRI images with low signal-to-noise ratio
• Addressing limited paired training data for image-to-image translation
• Improving image fidelity through diverse data augmentation techniques

Innovation

• Diverse augmentations enhance ULF MRI image quality
• Auxiliary tasks on high-field data improve model fidelity
• Image-to-image translation with limited paired training data