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

• URL: http://arxiv.org/abs/2603.14125v1
• Date: Sat, 14 Mar 2026 21:25:46 GMT
• Title: Low-Field Magnetic Resonance Image Enhancement using Undersampled k-Space
• Abstract summary: 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.<n>Our results show that undersampled k-space reconstructions achieve comparable quality to full k-space acquisitions.
• Score: 1.2599533416395765
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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