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

TL;DR

This paper introduces a deep learning method that enhances low-field MRI images directly in k-space, enabling faster scans and improved image quality by integrating super-resolution with undersampled data processing.

Contribution

It presents a novel k-space-based deep learning framework that jointly performs super-resolution and image reconstruction, outperforming traditional spatial-domain methods.

Findings

k-space super-resolution outperforms spatial methods

undersampled k-space achieves comparable quality to full data

significant scan-time reduction without losing diagnostic utility

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.