## 5 Conclusions  
In this study, we employed an innovative dual-domain processing strategy to correct metal artifacts. By integrating information obtained from both the projection domain and the image domain, using a joint deep learning framework that combines the UNet and ResNet architectures, we aimed to better address complex artifact issues.  
Firstly, projection domain correction allows the direct manipulation of the measured raw data, enabling the early correction of physical artifacts generated during the data acquisition process, such as non-linear artifacts caused by X-ray spectrum hardening and scattering effects. This direct correction at the raw data level is crucial for the final image quality as it helps reduce artifacts that may be amplified during subsequent reconstruction steps. Furthermore, the image domain correction focuses on artifacts induced by data reconstruction, including limitations of the reconstruction algorithm (such as deficiencies in filtered back-projection) or residual artifacts caused by imperfect correction in the projection domain. Image domain correction not only provides a complementary mechanism to further enhance the quality of the reconstructed images but also refines the final output by leveraging spatial priors (e.g., image continuity and edge information). By combining these two correction methods, our framework capitalizes on the complementary advantages of different domains and adapts to variations in artifact types and intensities, robustly providing correction effects under various conditions. Additionally, dual-domain processing facilitates network self-calibration as systematic biases can be detected and corrected during domain transformations, significantly improving the robustness of the correction strategy.  
The results demonstrate that the joint deep learning framework effectively utilizes the rich information present in both domains, greatly improving the quality of the images. The average PSNR of the corrected CT images reached 36.13, with an average SSIM value of 0.953. In comparison to single-domain correction methods, the proposed approach demonstrates a notable improvement in correction effectiveness. Furthermore, it exhibits stable performance even after modifying the CT scanning parameters, highlighting the remarkable effectiveness of the proposed method. In summary, the success of our approach underscores the potential of the joint deep learning framework in addressing the complex challenges in industrial CT imaging, paving the way for future optimizations in leveraging multi-domain information to enhance image reconstruction and reduce artifacts.  
Methodology, S.J., Y.S., Z.Z. and Z.W.; Software, S.J. and Y.S.; Validation, S.J.; Formal analysis, S.X.; Investigation, S.X.; Data curation, Z.Z.; Writing--original draft, S.J.; Writing--review and editing, S.J.; Supervision, Z.W. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The data presented in this study are available on request from the corresponding author. The code and model are available online ([[https://github.com/jiang-16/gan_jiang](https://github.com/jiang-16/gan_jiang)]([https://github.com/jiang-16/gan_jiang](https://github.com/jiang-16/gan_jiang)), accessed on 16 September 2023).  
The authors declare no conflicts of interest.