Contrastive Learning for Local and Global Learning MRI Reconstruction

TL;DR

This paper introduces CLGNet, a novel MRI reconstruction network that combines local and global learning through a Spatial and Fourier Layer, utilizing contrastive learning to improve image quality and achieve state-of-the-art results.

Contribution

The paper proposes a new Spatial and Fourier Layer for simultaneous local and global learning, and integrates contrastive learning to better constrain the solution space in MRI reconstruction.

Findings

CLGNet outperforms existing methods on various datasets.

The Spatial and Fourier Layer improves learning efficiency and accuracy.

Contrastive learning enhances image quality by constraining solution bounds.

Abstract

Magnetic Resonance Imaging (MRI) is an important medical imaging modality, while it requires a long acquisition time. To reduce the acquisition time, various methods have been proposed. However, these methods failed to reconstruct images with a clear structure for two main reasons. Firstly, similar patches widely exist in MR images, while most previous deep learning-based methods ignore this property and only adopt CNN to learn local information. Secondly, the existing methods only use clear images to constrain the upper bound of the solution space, while the lower bound is not constrained, so that a better parameter of the network cannot be obtained. To address these problems, we propose a Contrastive Learning for Local and Global Learning MRI Reconstruction Network (CLGNet). Specifically, according to the Fourier theory, each value in the Fourier domain is calculated from all the values in Spatial domain. Therefore, we propose a Spatial and Fourier Layer (SFL) to simultaneously learn the local and global information in Spatial and Fourier domains. Moreover, compared with self-attention and transformer, the SFL has a stronger learning ability and can achieve better performance in less time. Based on the SFL, we design a Spatial and Fourier Residual block as the main component of our model. Meanwhile, to constrain the lower bound and upper bound of the solution space, we introduce contrastive learning, which can pull the result closer to the clear image and push the result further away from the undersampled image. Extensive experimental results on different datasets and acceleration rates demonstrate that the proposed CLGNet achieves new state-of-the-art results.