## 4 Discussion and Conclusions  
It is well known that the amount of training data is a crucial factor that affects the performance of the neural network and the results of limited-angle reconstruction; however, it is often difficult to acquire a large number of sinogram reconstruction data pairs. Moreover, the degree of difficulty in producing reconstruction results of acceptable quality increases with a decreasing amount of angular data in practice. The SIAR-GAN proposed in this paper can be effectively trained without having paired sinogram reconstructed image data. Using an unsupervised method, we generated reconstruction results comparable to full-scan reconstruction with only \([0^{\circ},~{}90^{\circ}]\) of limited sinogram projection data on the basis of maintaining a large image size. Compared to other existing unsupervised methods, the proposed method produced images of higher quality according to the evaluation of image quality indicators. Although the proposed method performed better than other methods in comparison, there are still some limitations. For example, the data distribution of the sinogram dataset has a significant impact on the performance of SI-GAN. Therefore, in practical training and clinical applications, the collected data should come from a certain area of the body that is as small as possible. Moreover, in order to improve the centrality of the data distribution, the sinogram should be guaranteed to have the same scanning starting point. AR-GAN has a huge network structure, which will be limited by computer hardware. To make such a network work well, a GPU with at least 16G memory is required. In addition, the value of the batch size of AR-GAN is 1, which makes training AR-GAN somewhat difficult. For the network to achieve a good performance, it requires a certain amount of experience. An alternative approach, of course, is to increase the batch size, but this would result in an even larger number of GPUs being required. As the experimental results show, the lungs and heads reconstructed by our method have a satisfactory image quality, so our algorithm is expected to work well for a wide scope of clinical applications. However, it is worth noting that the reconstruction results of this method produce some reasonable organizations that do not exist in the reference image; these results are caused by the characteristics of the generative adversarial network. In the future, some structural and profile restrictions should be added to the network to constrain the output of the network to solve such problems. Therefore, manual image quality assessment should be performed prior to clinical deployment to ensure that the SIAR-GAN reconstructed images are of sufficient quality for a given clinical application. To summarize, our proposed framework model for a limited-angle CT reconstruction network does not rely on the actual sinogram reconstruction image pair. Compared with current unsupervised deep learning hybrid-domain processing methods, our proposed method further reduced the range of the limited angle and generated images of higher quality. In future work, we will impose some structural and profile restrictions on the output of the network to avoid the local feature errors mentioned above. Moreover, we will also conduct network performance tests on projection data that have a lower range than \([0^{\circ},~{}90^{\circ}]\) to further explore the limiting effects of the missing data in unsupervised reconstruction. Conceptualization, X.L. ; methodology, E.X. ; software, E.X., R.Z. ; validation, E.X., X.L. ; formal analysis, E.X., P.N., X.L. ; investigation, E.X., P.N., X.L. ; resources, X.L. ; data curation, X.L. ; writing--original draft preparation, E.X. ; writing--review and editing, R.Z., P.N., X.L. ; visualization, E.X., R.Z. ; supervision, X.L. ; project administration, E.X. All authors have read and agreed to the published version of the manuscript.