Multi-window back-projection residual networks for reconstructing COVID-19 CT super-resolution images. (March 2021)

• Record Type: Journal Article
• Authors: Qiu, Defu; Cheng, Yuhu; Wang, Xuesong; Zhang, Xiaoqiang
• Is Part Of: Computer methods and programs in biomedicine. Volume 200(2021)
• Publication Date: 2021-03
• Subjects: Coronavirus disease -- Super-resolution -- Multi-window -- Dilated convolution -- Back-projection -- Residual networks; Medicine -- Computer programs -- Periodicals; Biology -- Computer programs -- Periodicals; Computers -- Periodicals; Medicine -- Periodicals; Médecine -- Logiciels -- Périodiques; Biologie -- Logiciels -- Périodiques; Biology -- Computer programs; Medicine -- Computer programs; Periodicals; Electronic journals; 610.28
• DOI: 10.1016/j.cmpb.2021.105934
• Languages: English
• ISSNs: 0169-2607

Abstract

Highlights: Developed an effective multi-window back-projection residual network to reconstruct COVID-19 CT super-resolution images. Designed a multi-window back-projection residual network structure Combined with the advantages of deep super-resolution reconstruction network, residual blocks are used to deepen the network and effectively improve the image quality. To solve the problem of lack of relevance between COVID-19 CT images feature information. The proposed super-resolution method has good performance than the state-of-the-art methods. Abstract: Background and objective: With the increasing problem of coronavirus disease 2019 (COVID-19) in the world, improving the image resolution of COVID-19 computed tomography (CT) becomes a very important task. At present, single-image super-resolution (SISR) models based on convolutional neural networks (CNN) generally have problems such as the loss of high-frequency information and the large size of the model due to the deep network structure. Methods: In this work, we propose an optimization model based on multi-window back-projection residual network (MWSR), which outperforms most of the state-of-the-art methods. Firstly, we use multi-window to refine the same feature map at the same time to obtain richer high/low frequency information, and fuse and filter out the features needed by the deep network. Then, we develop a back-projection network based on the dilated convolution, using up-projection and down-projection modules to extract image features. Finally, we merge several repeated and continuous residual modules with global features, merge the information flow through the network, and input them to the reconstruction module. Results: The proposed method shows the superiority over the state-of-the-art methods on the benchmark dataset, and generates clear COVID-19 CT super-resolution images. Conclusion: Both subjective visual effects and objective evaluation indicators are improved, and the model specifications are optimized. Therefore, the MWSR method can improve the clarity of CT images of COVID-19 and effectively assist the diagnosis and quantitative assessment of COVID-19.