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Low-field magnetic resonance image enhancement via stochastic image quality transfer

arXiv 2304.13385 ↗First posted 26 Apr 2023eess.IV cs.CVcited by 19

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Abstract (arXiv)

Low-field (<1T) magnetic resonance imaging (MRI) scanners remain in widespread use in low- and middle-income countries (LMICs) and are commonly used for some applications in higher income countries e.g. for small child patients with obesity, claustrophobia, implants, or tattoos. However, low-field MR images commonly have lower resolution and poorer contrast than images from high field (1.5T, 3T, and above). Here, we present Image Quality Transfer (IQT) to enhance low-field structural MRI by estimating from a low-field image the image we would have obtained from the same subject at high field. Our approach uses (i) a stochastic low-field image simulator as the forward model to capture uncertainty and variation in the contrast of low-field images corresponding to a particular high-field image, and (ii) an anisotropic U-Net variant specifically designed for the IQT inverse problem. We evaluate the proposed algorithm both in simulation and using multi-contrast (T1-weighted, T2-weighted, and fluid attenuated inversion recovery (FLAIR)) clinical low-field MRI data from an LMIC hospital. We show the efficacy of IQT in improving contrast and resolution of low-field MR images. We demonstrate that IQT-enhanced images have potential for enhancing visualisation of anatomical structures and pathological lesions of clinical relevance from the perspective of radiologists. IQT is proved to have capability of boosting the diagnostic value of low-field MRI, especially in low-resource settings.

Key facts. Cited by 19 papers in the map (data through 14 Oct 2026). Builds on 38 of its 77 references that are in the map. First posted on arXiv 26 Apr 2023.

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38 of 77 references.

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2023: 1; 2024: 2; 2025: 7; 2026: 9.

19 papers.

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  1. 2023A 3D Conditional Diffusion Model for Image Quality Transfer -- An Application to Low-Field MRIcited by 13
  2. 2025Tackling Hallucination from Conditional Models for Medical Image Reconstruction with DynamicDPScited by 11
  3. 2025MRI Super-Resolution with Deep Learning: A Comprehensive Surveycited by 7
  4. 20247T MRI Synthesization from 3T Acquisitionscited by 6
  5. 2024Alternative Learning Paradigms for Image Quality Transfercited by 4
  6. 2025Deep learning of personalized priors from past MRI scans enables fast, quality-enhanced point-of-care MRI with low-cost systemscited by 3
  7. 2025Low-Field Magnetic Resonance Image Quality Enhancement using a Conditional Flow Matching Modelcited by 3
  8. 2026Direct low-field MRI super-resolution using undersampled k-spacecited by 2
  9. 2025Cyclic Self-Supervised Diffusion for Ultra Low-field to High-field MRI Synthesiscited by 1
  10. 2025HalluGen: Synthesizing Realistic and Controllable Hallucinations for Evaluating Image Restorationcited by 1
  11. 2025HULFSynth : An INR based Super-Resolution and Ultra Low-Field MRI Synthesis via Contrast factor estimationcited by 0
  12. 2026Resolving Blind Inverse Problems under Dynamic Range Compression via Structured Forward Operator Modelingcited by 0
7 more
  1. 2026Zero-shot Low-Field MRI Enhancement via Diffusion-Based Adaptive Contrast Transportcited by 0
  2. 2026MPFlow: Multi-modal Posterior-Guided Flow Matching for Zero-Shot MRI Reconstructioncited by 0
  3. 2026Low-Field Magnetic Resonance Image Quality Enhancement using Undersampled k-Space and Out-of-Distribution Generalisationcited by 0
  4. 2026Low-Field Magnetic Resonance Image Enhancement using Undersampled k-Spacecited by 0
  5. 2026Uncertainty-Aware 3D Residual Wavelet Diffusion for Ultra Low-Field MRI Super-Resolutioncited by 0
  6. 2026You Cannot Recover What Was Never Measured: Quantifying the Information Ceiling of Ultra-Low-Field MRI Super-Resolutioncited by 0
  7. 2026MRI Super-Resolution with RCDM/WaveMix and Task-Aware Segmentationcited by 0