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MedSRGAN: medical images super-resolution using generative adversarial networks

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Abstract

Super-resolution (SR) in medical imaging is an emerging application in medical imaging due to the needs of high quality images acquired with limited radiation dose, such as low dose Computer Tomography (CT), low field magnetic resonance imaging (MRI). However, because of its complexity and higher visual requirements of medical images, SR is still a challenging task in medical imaging. In this study, we developed a deep learning based method called Medical Images SR using Generative Adversarial Networks (MedSRGAN) for SR in medical imaging. A novel convolutional neural network, Residual Whole Map Attention Network (RWMAN) was developed as the generator network for our MedSRGAN in extracting the useful information through different channels, as well as paying more attention on meaningful regions. In addition, a weighted sum of content loss, adversarial loss, and adversarial feature loss were fused to form a multi-task loss function during the MedSRGAN training. 242 thoracic CT scans and 110 brain MRI scans were collected for training and evaluation of MedSRGAN. The results showed that MedSRGAN not only preserves more texture details but also generates more realistic patterns on reconstructed SR images. A mean opinion score (MOS) test on CT slices scored by five experienced radiologists demonstrates the efficiency of our methods.

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Acknowledgments

This work was supported in part by the National Key R&D Program of China under Grant 2018YFC1704206, Grant 2016YFB0200602, in part by the NSFC under Grant 81971691, Grant 81801809, Grant 81830052, Grant 81827802, Grant U1811461, and Grant 11401601, in part by the Science and Technology Program of Guangzhou under Grant 20180420053, in part by the Science and Technology Innovative Project of Guangdong Province under Grant 2016B030307003, Grant 2015B010110003, and Grant 2015B020233008, in part by the Science and Technology Planning Project of Guangdong Province under Key Grant 2017B020210001, in part by the Guangzhou Science and Technology Creative Project under Key Grant 201604020003, in part by the Guangdong Province Key Laboratory of Computational Science Open Grant 2018009, in part by the Construction Project of Shanghai Key Laboratory of Molecular Imaging 18DZ2260400, and in part by China postdoctoral science foundation No.2019M653185.

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Authors and Affiliations

  1. School of Data and Computer Science, Sun Yat-sen University, Guangzhou, China

    Yuchong Gu, Zitao Zeng, Haibin Chen, Zhuoren Jiang & Yao Lu

  2. Department of Radiology, University of Michigan, Ann Arbor, MI, USA

    Jun Wei

  3. Department of Radiology, The Fifth Affiliated Hospital of Sun Yat-sen University, Zhuhai, China

    Yaqin Zhang, Binghui Chen, Yingqin Li, Yujuan Qin & Qing Xie

  4. Guangdong Province Key Laboratory of Computational Science, Guangzhou, China

    Yao Lu

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  1. Yuchong Gu
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Gu, Y., Zeng, Z., Chen, H. et al. MedSRGAN: medical images super-resolution using generative adversarial networks. Multimed Tools Appl 79, 21815–21840 (2020). https://doi.org/10.1007/s11042-020-08980-w

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