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.
@article{arxiv.2104.05901,
title = {SRR-Net: A Super-Resolution-Involved Reconstruction Method for High Resolution MR Imaging},
author = {Wenqi Huang and Sen Jia and Ziwen Ke and Zhuo-Xu Cui and Jing Cheng and Yanjie Zhu and Dong Liang},
journal= {arXiv preprint arXiv:2104.05901},
year = {2021}
}