Direct low-field MRI super-resolution using undersampled k-space

TL;DR

This paper introduces a novel k-space-based super-resolution framework for low-field MRI that reconstructs high-quality images directly from undersampled data, outperforming traditional spatial-domain methods.

Contribution

It is the first to directly perform super-resolution and image quality transfer from undersampled k-space in low-field MRI, using a dual-channel U-Net architecture.

Findings

Outperforms spatial-domain methods in image enhancement.

Achieves image quality comparable to full k-space acquisitions.

Demonstrates effectiveness on low-field brain MRI data.

Abstract

Low-field magnetic resonance imaging (MRI) provides affordable access to diagnostic imaging but suffers from prolonged acquisition and limited image quality. Accelerated imaging can be achieved with k-space undersampling, while super-resolution (SR) and image quality transfer (IQT) methods typically rely on spatial-domain post-processing. In this work, we propose a novel framework for reconstructing high-field MR like images directly from undersampled low-field k-space. Our approach employs a k-space dual channel U-Net that processes the real and imaginary components of undersampled k-space to restore missing frequency content. Experiments on low-field brain MRI demonstrate that our k-space-driven image enhancement consistently outperforms the counterpart spatial-domain method. Furthermore, reconstructions from undersampled k-space achieve image quality comparable to full k-space acquisitions. To the best of our knowledge, this is the first work that investigates low-field MRI SR/IQT directly from undersampled k-space.