SRR-Net: A Super-Resolution-Involved Reconstruction Method for High Resolution MR Imaging

• URL: http://arxiv.org/abs/2104.05901v1
• Date: Tue, 13 Apr 2021 02:19:12 GMT
• Title: SRR-Net: A Super-Resolution-Involved Reconstruction Method for High Resolution MR Imaging
• Authors: Wenqi Huang, Sen Jia, Ziwen Ke, Zhuo-Xu Cui, Jing Cheng, Yanjie Zhu and Dong Liang
• Abstract summary: The proposed SRR-Net is capable of recovering high-resolution brain images with both good visual quality and perceptual quality. Experiment results using in-vivo HR multi-coil brain data indicate that the proposed SRR-Net is capable of recovering high-resolution brain images.
• Score: 7.42807471627113
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Improving the image resolution and acquisition speed of magnetic resonance imaging (MRI) is a challenging problem. There are mainly two strategies dealing with the speed-resolution trade-off: (1) $k$-space undersampling with high-resolution acquisition, and (2) a pipeline of lower resolution image reconstruction and image super-resolution. However, these approaches either have limited performance at certain high acceleration factor or suffer from the error accumulation of two-step structure. In this paper, we combine the idea of MR reconstruction and image super-resolution, and work on recovering HR images from low-resolution under-sampled $k$-space data directly. Particularly, the SR-involved reconstruction can be formulated as a variational problem, and a learnable network unrolled from its solution algorithm is proposed. A discriminator was introduced to enhance the detail refining performance. Experiment results using in-vivo HR multi-coil brain data indicate that the proposed SRR-Net is capable of recovering high-resolution brain images with both good visual quality and perceptual quality.

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