## 4 Discussion  
From Figures 6-8, it can be observed that the dual-domain joint deep learning framework effectively removes metal artifacts from industrial CT images while preserving the characteristic image details. In contrast, employing only the UNet network for processing projection data does not completely eliminate the metal artifacts when reconstructed from the processed projection data. This is primarily attributed to the non-linear effects resulting  
Figure 11: The calibration results of the additional projection data of multi-material phantoms, with the corresponding materials being copper–iron combination, aluminum–titanium alloy combination, iron–titanium alloy combination, and iron–zinc alloy combination. From the images, it can be observed that the joint deep learning framework has achieved good calibration results. Figure 12: Artifact correction effect of our proposed network model on real industrial CT images. It can be seen that a relatively ideal artifact correction result is achieved, but it is limited by the interference between different layers in obtaining fan-beam projection data during the actual operation, so the projection data are not absolutely ideal fan-beam data. Thus, there is still room for improvement in the correction effect of some edges. from the distinct attenuation characteristics of different materials, which are not prominently manifested in the projection domain. Thus, relying solely on the UNet network for projection data correction does not adequately address the non-linear effects, leading to residual strong metal artifacts in the reconstructed CT images. It is precisely for this reason that in the comparison experiment of correction results after changing CT scanning parameters, we directly abandoned the operation of Unet correction projection data and only compared our proposed network architecture with single-domain ResNet. Utilizing only the ResNet for metal artifact correction in the image domain typically yields favorable correction results. However, in cases where the metal artifacts and the grayscale of the phantom are closely matched, ResNet fails to effectively preserve image details and features during artifact correction, as evident in the highlighted section in Figure 6. This deficiency arises from the exclusive correction in the image domain, which overlooks pertinent information from the original measurements. Particularly in the presence of severe artifacts, the image domain information undergoes substantial distortion. The quantitative comparison presented in Table 2 demonstrates that the CT image metal artifact correction outcomes achieved via the dual-domain joint deep learning framework surpass those obtained through single-domain correction methods, with average PSNR and SSIM values reaching 36.13 and 0.953, respectively. This quantitative analysis further substantiates the superior performance of the proposed dual-domain joint deep learning framework in CT image metal artifact correction. Furthermore, modifying the CT scan parameters results in conspicuous changes in the reconstructed CT image features. When inputting data acquired with altered parameters into our trained dual-domain joint deep learning architecture and the ResNet network model, the output results are depicted in Figures 9 and 10, respectively. It is evident that the dual-domain joint deep learning network maintains a consistently ideal output, achieving commendable metal artifact correction effects. Conversely, the ResNet network, which exhibited satisfactory performance before the CT scan parameter modifications, exhibits significant distortion in its output results. The quantitative evaluation presented in Table 3 further demonstrates the stability of the metal artifact correction performance of the joint deep learning network, with average PSNR and SSIM values remaining consistent, while a substantial decline is observed in the correction results produced by the ResNet network.