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Low-Field Magnetic Resonance Image Enhancement using Undersampled k-Space

Daniel Tweneboah Anyimadu, Mohammed Abdalla, Mohammed M. Abdelsamea, Ahmed Karam Eldaly

Abstract

Low-field magnetic resonance imaging (MRI) offers a cost-effective alternative for medical imaging in resource-limited settings. However, its widespread adoption is hindered by two key challenges: prolonged scan times and reduced image quality. Accelerated acquisition can be achieved using k-space undersampling, while image enhancement traditionally relies on spatial-domain postprocessing. In this work, we propose a novel deep learning framework based on a U-Net variant that operates directly in k-space to super-resolve low-field MR images directly using undersampled data while quantifying the impact of reduced k-space sampling. Unlike conventional approaches that treat image super-resolution as a postprocessing step following image reconstruction from undersampled k-space, our unified model integrates both processes, leveraging k-space information to achieve superior image fidelity. Extensive experiments on synthetic and real low-field brain MRI datasets demonstrate that k-space-driven image super-resolution outperforms conventional spatial-domain counterparts. Furthermore, our results show that undersampled k-space reconstructions achieve comparable quality to full k-space acquisitions, enabling substantial scan-time acceleration without compromising diagnostic utility.

Low-Field Magnetic Resonance Image Enhancement using Undersampled k-Space

Abstract

Low-field magnetic resonance imaging (MRI) offers a cost-effective alternative for medical imaging in resource-limited settings. However, its widespread adoption is hindered by two key challenges: prolonged scan times and reduced image quality. Accelerated acquisition can be achieved using k-space undersampling, while image enhancement traditionally relies on spatial-domain postprocessing. In this work, we propose a novel deep learning framework based on a U-Net variant that operates directly in k-space to super-resolve low-field MR images directly using undersampled data while quantifying the impact of reduced k-space sampling. Unlike conventional approaches that treat image super-resolution as a postprocessing step following image reconstruction from undersampled k-space, our unified model integrates both processes, leveraging k-space information to achieve superior image fidelity. Extensive experiments on synthetic and real low-field brain MRI datasets demonstrate that k-space-driven image super-resolution outperforms conventional spatial-domain counterparts. Furthermore, our results show that undersampled k-space reconstructions achieve comparable quality to full k-space acquisitions, enabling substantial scan-time acceleration without compromising diagnostic utility.
Paper Structure (15 sections, 5 equations, 8 figures, 1 table)

This paper contains 15 sections, 5 equations, 8 figures, 1 table.

Table of Contents

  1. Introduction
  2. Methods
  3. Deep learning architecture configuration
  4. Experiments
  5. Datasets for model training
  6. Undersampling strategies
  7. Data preprocessing
  8. Implementation and Training
  9. Testing
  10. Results
  11. Quantitative Results using Synthetic LF-MRI Datasets
  12. Qualitative Results using Synthetic LF-MRI Datasets
  13. Comparison with existing work
  14. Results using real LF-MRI datasets and and clinical relevance
  15. Conclusion

Figures (8)

  • Figure 1: Low-field versus high-field MR scans of the same subject. First column shows contrast change on axial plane, and middle and last columns show resolution and contrast change on coronal and sagittal planes. High-field images correspond to the 3T MRI from Human Connectome Project sotiropoulos2013advances; and low-field images are simulated from the high-field images using a 0.3T MRI system.
  • Figure 2: kSURF: K-space dual channel U-Net for LF-MRI joint reconstruction, super-resolution/quality transfer and uncertainty quantification from undersampled k-space.
  • Figure 3: Images of sampling masks. (a) Cartesian sampling,(b) pseudo-radial sampling and (c) 2D variable density random sampling. Here, we show the 10% (top) and 50% (bottom) masks for Cartesian and Pseudo-radial sampling, and 5% (top) and 40% (bottom) masks of 2D-random sampling.
  • Figure 4: Ground-truth high-field MR images used as representative examples for the InD and OOD test datasets.
  • Figure 5: Results using InD data. (A) reconstructions using LF/zero-filling, sIQT and kSURF across increasing pseudo-radial undersampling rates. (B) Corresponding error maps. (C) Corresponding uncertainty maps.
  • ...and 3 more figures