mlsanityData to 7 Oct 2026

Enhancing Ultra-low-field MRI with Segmentation-guided Adversarial Learning

arXiv 2605.28016 ↗First posted 27 May 2026cs.CV physics.med-phcited by 0

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Abstract (arXiv)

Ultra-low-field (ULF) MRI offers portable and low-cost imaging but suffers from poor image quality. To address this, we present our submission to the 2025 ULF Enhancement Challenge (ULF-EnC), where the goal is to synthesise high-field-like MRIs from 64 mT scans. Our pipeline enhances ULF MRI through a combination of anatomical conditioning and model ensembling. We first generate tissue segmentation priors using a Swin UNETR trained solely on challenge-provided data. These priors condition two independent enhancement networks - a CycleGAN and a transformer-based residual enhancement model (T-REX) - each trained to synthesise 3 T-like MRIs. Outputs from both models are combined using a weighted average. Our approach produces enhanced MRIs that were comparable to high-field scans both quantitatively and qualitatively.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 14 of its 29 references that are in the map. First posted on arXiv 27 May 2026.

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References · 14

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  12. 2024How we won BraTS 2023 Adult Glioma challenge? Just faking it! Enhanced Synthetic Data Augmentation and Model Ensemble for brain tumour segmentationcited by 27
2 more
  1. 2024McCaD: Multi-Contrast MRI Conditioned, Adaptive Adversarial Diffusion Model for High-Fidelity MRI Synthesiscited by 3
  2. 2025From Low Field to High Value: Robust Cortical Mapping from Low-Field MRIcited by 2

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