Magnetic resonance image reconstruction based on graph convolutional Unet network

Qiaoyu Ma; Haotian Zhang; Yiran Qiu; Zongying Lai

doi:10.1117/12.2673934

6 April 2023 Magnetic resonance image reconstruction based on graph convolutional Unet network

Qiaoyu Ma, Haotian Zhang, Yiran Qiu, Zongying Lai

Proceedings Volume 12615, International Conference on Signal Processing and Communication Technology (SPCT 2022); 126150P (2023) https://doi.org/10.1117/12.2673934
Event: International Conference on Signal Processing and Communication Technology (SPCT 2022), 2022, Harbin, China

Abstract

Magnetic Resonance Imaging (MRI) acquisition is a long time process that leads to patient discomfort and motion artifacts. Convolution network has been implemented in MRI image reconstruction to speed up MRI. Convolution network utilizes spacial local convolution kernel successfully, but non-local information with self-similarity is ignored. In order to effectively explore non-local information in MRI reconstruction, we propose graph convolutional Unet (GCN-Unet) for MRI image reconstruction. In the GCN-Unet, non-local information is represented by a patch graph extracted from MRI images, in which nodes are consist of image patches, and edges are self-similarities between nodes, reflecting non-local structure similarity intra an MRI image. We utilize graph convolution with a Unet framework to effectively represent features of non-local information with self-similarities and aggregate the extracted non-local features to reconstruct MRI image. Experiments demonstrate the effectiveness of the GCN-Unet, the Peak Signal-to-Noise Ratio (PSNR) and Structure Similarty Index Measure (SSIM) of reconstructions are improved.

Citation Download Citation

Qiaoyu Ma, Haotian Zhang, Yiran Qiu, and Zongying Lai "Magnetic resonance image reconstruction based on graph convolutional Unet network", Proc. SPIE 12615, International Conference on Signal Processing and Communication Technology (SPCT 2022), 126150P (6 April 2023); https://doi.org/10.1117/12.2673934

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