An Innovative Low-dose CT Inpainting Algorithm based on Limited-angle Imaging Inpainting Model

Article type: Research Article

Authors: Zhang, Ziheng^{a; b} | Yang, Minghan^{a; *} | Li, Huijuan^{a; b} | Chen, Shuai^a | Wang, Jianye^a | Xu, Lei^c

Affiliations: [a] Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei, Anhui, China | [b] University of Science and Technology of China, Hefei, Anhui, China | [c] The First Affiliated Hospitalof University of Science and Technology of China, Hefei, Anhui, China

Correspondence: [*] Corresponding author: Minghan Yang, 1Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei, Anhui, 230031, China. E-mail: [email protected].

Abstract: Background:With the popularity of computed tomography (CT) technique, an increasing number of patients are receiving CT scans. Simultaneously, the public’s attention to CT radiation dose is also increasing. How to obtain CT images suitable for clinical diagnosis while reducing the radiation dose has become the focus of researchers. Objective:To demonstrate that limited-angle CT imaging technique can be used to acquire lower dose CT images, we propose a generative adversarial network-based image inpainting model—Low-dose imaging and Limited-angle imaging inpainting Model (LDLAIM), this method can effectively restore low-dose CT images with limited-angle imaging, which verifies that limited-angle CT imaging technique can be used to acquire low-dose CT images. Methods:In this work, we used three datasets, including chest and abdomen dataset, head dataset and phantom dataset. They are used to synthesize low-dose and limited-angle CT images for network training. During training stage, we divide each dataset into training set, validation set and testing set according to the ratio of 8:1:1, and use the validation set to validate after finishing an epoch training, and use the testing set to test after finishing all the training. The proposed method is based on generative adversarial networks(GANs), which consists of a generator and a discriminator. The generator consists of residual blocks and encoder-decoder, and uses skip connection. Results:We use SSIM, PSNR and RMSE to evaluate the performance of the proposed method. In the chest and abdomen dataset, the mean SSIM, PSNR and RMSE of the testing set are 0.984, 35.385 and 0.017, respectively. In the head dataset, the mean SSIM, PSNR and RMSE of the testing set are 0.981, 38.664 and 0.011, respectively. In the phantom dataset, the mean SSIM, PSNR and RMSE of the testing set are 0.977, 33.468 and 0.022, respectively. By comparing the experimental results of other algorithms in these three datasets, it can be found that the proposed method is superior to other algorithms in these indicators. Meanwhile, the proposed method also achieved the highest score in the subjective quality score. Conclusions:Experimental results show that the proposed method can effectively restore CT images when both low-dose CT imaging techniques and limited-angle CT imaging techniques are used simultaneously. This work proves that the limited-angle CT imaging technique can be used to reduce the CT radiation dose, and also provides a new idea for the research of low-dose CT imaging.

Keywords: Computed tomography, Low-dose imaging, Limited-angle imaging, Deep learning, Generative adversarial networks

DOI: 10.3233/XST-221260

Journal: Journal of X-Ray Science and Technology, vol. 31, no. 1, pp. 131-152, 2023