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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 toreconstruct images with a clear structure for two main reasons. Firstly,similar patches widely exist in MR images, while most previous deeplearning-based methods ignore this property and only adopt CNN to learn localinformation. Secondly, the existing methods only use clear images to constrainthe upper bound of the solution space, while the lower bound is notconstrained, so that a better parameter of the network cannot be obtained. Toaddress these problems, we propose a Contrastive Learning for Local and GlobalLearning MRI Reconstruction Network (CLGNet). Specifically, according to theFourier theory, each value in the Fourier domain is calculated from all thevalues in Spatial domain. Therefore, we propose a Spatial and Fourier Layer(SFL) to simultaneously learn the local and global information in Spatial andFourier domains. Moreover, compared with self-attention and transformer, theSFL has a stronger learning ability and can achieve better performance in lesstime. Based on the SFL, we design a Spatial and Fourier Residual block as themain component of our model. Meanwhile, to constrain the lower bound and upperbound of the solution space, we introduce contrastive learning, which can pullthe result closer to the clear image and push the result further away from theundersampled image. Extensive experimental results on different datasets andacceleration rates demonstrate that the proposed CLGNet achieves newstate-of-the-art results.

Contrastive Learning for Local and Global Learning MRI Reconstruction