Progressive U-Net residual network for computed tomography images super-resolution in the screening of COVID-19. Issue 1 (1st January 2021)

• Record Type: Journal Article
• Authors: Qiu, Defu; Cheng, Yuhu; Wang, Xuesong
• Is Part Of: Journal of Radiation Research and Applied Sciences. Volume 14:Issue 1(2021)
• Page Start: 369
• Page End: 379
• Publication Date: 2021-01-01
• Subjects: Confirmed coronavirus disease 2019 -- super-resolution -- U-Net -- residual network -- skip connection; Ionizing radiation -- Periodicals; Nuclear physics -- Periodicals; Radiation; Radiation; Periodicals; 539.7
• DOI: 10.1080/16878507.2021.1973760
• Languages: English
• ISSNs: 1687-8507

Abstract

ABSTRACT: Thin-slice computed tomography (CT) examination plays an important role in the screening of suspected and confirmed coronavirus disease 2019 (COVID-19) outbreak patients. Therefore, improving the image resolution of COVID-19 CT has important clinical value for the diagnosis and condition assessment of COVID-19. However, the existing single-image super-resolution (SISR) methods mainly increase the receptive field of convolution kernels by deepening and widening the network structure, and adopt the equal processing methods in the airspace and channel domains with different importance, and a large number of computing resources will be wasted on the unimportant features. We propose a progressive U-Net residual network (PURN) for COVID-19 CT images super-resolution (SR) to solve the practicality of existing models, to better extract features, and reduce the number of parameters. First, we design a dual U-Net module (DUM), which can efficiently extract low-resolution (LR) COVID-19 CT images feature. Second, the DUM module first performs up-block three times, and then down-blocks three times in order to learn the interdependence between high-resolution (HR) and LR images more efficiently. Finally, the local skip connection structure is introduced in the DUM module, and the global long skip connection structure is introduced in the reconstruction layer to further enrich the flow of reconstructed HR image information. Experimental results show that our algorithm effectively improves the SR reconstruction effect of COVID-19 CT images, restores its detailed features more sharply, and greatly improves the practicability of the algorithm.