# Sparse-View Artifact Correction of High-Pixel-Number Synchrotron Radiation CT
## 4 Conclusions  
This paper proposes a deep learning method for artifact correction in synchrotron sparse-view CT. In contrast to artifact correction for medical CT images, synchrotron CT imaging commonly uses a 2 k \(\times\) 2 k detector and the reconstructed slices with the same dimensions. Therefore, we propose a method for aliasing artifact correction with a high pixel number (1728 \(\times\) 1728 pixels) to fit practical applications better. Specifically, local details are addressed by cropping CT slices with aliasing artifacts into overlapping patches, and the artifacts are corrected through neural network optimization. Then, the local patches are reintegrated into the global CT slice, further constraining the spliced global slice and high-quality labels to be consistent in feature structure. In practical scenarios, obtaining a sufficient number of samples is challenging. Therefore, this study utilized only three sample slices as the training set, one sample as the validation set to verify the network training effect, and the reconstructed slices of another sample as the test set. The experiments demonstrate that our proposed method can successfully correct aliasing artifacts, indicating the feasibility of utilizing sparse-view CT imaging to achieve fast experiments and reduce radiation damage in practical CT imaging. We believe that the proposed method can also be applied to other fields. Meanwhile, we investigated the artifact correction effect of the proposed method at different sparsity levels. The results indicate that the quality of the neural network-processed images improves with an increased number of acquired projections, but the  
Figure 7: Qualitative and quantitative comparison of network artifact correction ability under different sparse views: (**a**) 100 views, (**b**) 200 views, (**c**) 300 views, (**d**) 400 views, (**e**) full-view CT slice. (**a1–e1**) Enlarged image of (**a–e**) in the red box area. (**f**) Quantitative evaluation of MS-SSIM. (**g**) Quantitative evaluation of PSNR. enhancement rate of the method gradually diminishes. Moreover, the rise in the number of projections implies longer experimental data and higher radiation damage, leading to diminishing returns, which need to be weighed. Based on this, we recommend using a sparse ratio of 1/12 or 1/6 if the sample's complexity is similar to that of the mouse brain and the researcher wishes to observe significant features. If high-quality CT images are needed, a higher sparse ratio needs to be considered. The deployment of the proposed method on the TiW dataset led to the elimination of aliasing artifacts and an enhancement in image quality, thereby further substantiating the effectiveness of the proposed technique. The neural network model utilized in this study is Attention U-Net. In subsequent research, we plan to explore other neural network architectures to achieve superior artifact correction results. Furthermore, our future endeavors will also focus on developing artifact correction methods capable of simultaneously addressing multiple types of samples. Conceptualization, M.H., G.L. and J.Z. ; methodology, M.H. ; validation, M.H. ; investigation, M.H. ; resources, R.S., Z.W. and Y.W. ; software, T.D. and R.S. ; writing--original draft preparation, M.H. ; writing--review and editing, M.H. and G.L. ; visualization, M.H., J.Z. and B.Y. All authors have read and agreed to the published version of the manuscript. This research was supported by the National Key Research and Development Program of China (2023 YFA1609200), National Natural Science Foundation of China \"Ye Qisun\" Science Fund project (No. U2241283), the Equipment Development Project of the Chinese Academy of Sciences (YJKYYQ20190060), and National Natural Science Foundation of China (11627901). Not applicable. The data that support the findings of this study are available from the corresponding author upon reasonable request.