Contrastive Learning for Local and Global Learning MRI Reconstruction

• URL: http://arxiv.org/abs/2111.15200v1
• Date: Tue, 30 Nov 2021 08:23:53 GMT
• Title: Contrastive Learning for Local and Global Learning MRI Reconstruction
• Authors: Qiaosi Yi, Jinhao Liu, Le Hu, Faming Fang, and Guixu Zhang
• Abstract summary: 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. Compared with self-attention and transformer, the SFL has a stronger learning ability and can achieve better performance in less time.
• Score: 25.279021256319467
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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