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

• URL: http://arxiv.org/abs/2605.28016v1
• Date: Wed, 27 May 2026 06:11:34 GMT
• Title: Enhancing Ultra-low-field MRI with Segmentation-guided Adversarial Learning
• Abstract summary: We present our submission to the 2025 ULF Enhancement Challenge (ULF-EnC)<n>The goal is to synthesise high-field-like MRIs from 64 mT scans.<n>Our pipeline enhances ULF MRI through a combination of anatomical conditioning and model ensembling.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

Related papers

• Mamba-driven MRI-to-CT Synthesis for MRI-only Radiotherapy Planning [4.3531418255555545]
  Mamba is a state-space model for cross-modality translation.<n>We adapt both the U-Mamba and the SegMamba architecture, originally proposed for segmentation, to perform cross-modality image generation.<n>Our 3D Mamba architecture effectively captures complex features and long-range volumetric dependencies, thus allowing accurate CT synthesis.
  arXiv  Detail & Related papers  (2026-03-24T14:56:39Z)
• UniField: A Unified Field-Aware MRI Enhancement Framework [45.03230466428695]
  methodname is a unified framework integrating multiple modalities and enhancement tasks.<n>We exploit comprehensive 3D volumetric information by leveraging pre-trained 3D foundation models.<n>We organize and publicly release a comprehensive paired multi-field MRI dataset.
  arXiv  Detail & Related papers  (2026-03-10T05:45:12Z)
• Self-Supervised Weighted Image Guided Quantitative MRI Super-Resolution [0.4757311250629737]
  High-resolution (HR) quantitative MRI (qMRI) relaxometry provides objective tissue characterization but remains clinically underutilized due to lengthy acquisition times.<n>We propose a physics-informed, self-supervised framework for qMRI super-resolution that uses routinely acquired HR weighted MRI (wMRI) scans as guidance.
  arXiv  Detail & Related papers  (2025-12-19T14:15:31Z)
• A Physics-Driven Neural Network with Parameter Embedding for Generating Quantitative MR Maps from Weighted Images [25.060349614170953]
  We propose a deep learning-based approach that integrates MRI sequence parameters to improve the accuracy and generalizability of quantitative image synthesis from clinical weighted MRI.<n>Our physics-driven neural network embeds MRI sequence parameters directly into the model via parameter embedding.
  arXiv  Detail & Related papers  (2025-08-11T16:01:12Z)
• Enhancing Cross-Modality Synthesis: Subvolume Merging for MRI-to-CT Conversion [4.256879489558776]
  We employ the advanced SwinUNETR framework for synthesizing CT from MRI images. We introduce a three-dimensional subvolume merging technique in the prediction process.
  arXiv  Detail & Related papers  (2024-09-09T18:25:26Z)
• Phy-Diff: Physics-guided Hourglass Diffusion Model for Diffusion MRI Synthesis [45.074243735766]
  We propose a physics-guided diffusion model to generate high-quality dMRI. Our model introduces the physical principles of dMRI in the noise evolution in the diffusion process. Our experiment results show that our method outperforms other state-of-the-art methods.
  arXiv  Detail & Related papers  (2024-06-05T07:09:19Z)
• NeuroPictor: Refining fMRI-to-Image Reconstruction via Multi-individual Pretraining and Multi-level Modulation [55.51412454263856]
  This paper proposes to directly modulate the generation process of diffusion models using fMRI signals. By training with about 67,000 fMRI-image pairs from various individuals, our model enjoys superior fMRI-to-image decoding capacity.
  arXiv  Detail & Related papers  (2024-03-27T02:42:52Z)
• SDR-Former: A Siamese Dual-Resolution Transformer for Liver Lesion Classification Using 3D Multi-Phase Imaging [59.78761085714715]
  This study proposes a novel Siamese Dual-Resolution Transformer (SDR-Former) framework for liver lesion classification. The proposed framework has been validated through comprehensive experiments on two clinical datasets. To support the scientific community, we are releasing our extensive multi-phase MR dataset for liver lesion analysis to the public.
  arXiv  Detail & Related papers  (2024-02-27T06:32:56Z)
• Transferring Ultrahigh-Field Representations for Intensity-Guided Brain Segmentation of Low-Field Magnetic Resonance Imaging [51.92395928517429]
  The use of 7T MRI is limited by its high cost and lower accessibility compared to low-field (LF) MRI. This study proposes a deep-learning framework that fuses the input LF magnetic resonance feature representations with the inferred 7T-like feature representations for brain image segmentation tasks.
  arXiv  Detail & Related papers  (2024-02-13T12:21:06Z)
• Accurate super-resolution low-field brain MRI [0.6501025489527174]
  We report on the extension of a machine learning algorithm to synthesize 1 mm isotropic MPRAGE-like scans from LFMRI T1-weighted sequences. These results lay the foundation for future work to enhance the detection of normal and abnormal image findings at LFMRI.
  arXiv  Detail & Related papers  (2022-02-07T23:57:28Z)
• ShuffleUNet: Super resolution of diffusion-weighted MRIs using deep learning [47.68307909984442]
  Single Image Super-Resolution (SISR) is a technique aimed to obtain high-resolution (HR) details from one single low-resolution input image. Deep learning extracts prior knowledge from big datasets and produces superior MRI images from the low-resolution counterparts.
  arXiv  Detail & Related papers  (2021-02-25T14:52:23Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.