## 3 Experiment and Results
### Experiment
#### 3.1.3 Ablation Study  
In order to further validate the performance of the proposed joint deep learning framework, we conducted ablation experiments by comparing the use of only the UNet network for correcting projection data and the use of only the ResNet for correcting metal artifacts in CT images. Specifically, we utilized the UNet network alone to correct the projection data, and the resulting output was directly input into the CT image reconstruction module to obtain the corrected CT images, omitting the ResNet network for image artifact correction. We also performed filtered back-projection reconstruction on the projection data in the dataset to obtain CT images contaminated with metal artifacts. These images were then input into the ResNet network for metal artifact correction, omitting the UNet network for projection data correction. The training details of UNet and ResNet are consistent with those of the joint network model. The Adam optimizer is used with an initial learning  
of different materials and rays, the complexity of the projection data of multi-material phantoms and CT image artifacts is significantly increased compared to single-material phantoms. The correction results further validate the generalization capability of our proposed joint deep learning framework.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{2}{c}{**Joint Network**} & \multicolumn{2}{c}{**UNet**} & \multicolumn{2}{c}{**ResNet**} \\ \hline Metrics & PSNR & SSIM & PSNR & SSIM & PSNR & SSIM \\ Image 1 & 39.07 & 0.960 & 24.50 & 0.816 & 36.74 & 0.956 \\ Image 2 & 36.97 & 0.971 & 28.05 & 0.829 & 34.6  
Figure 8: The CT images directly reconstructed from the projection data in the test set, as well as the corresponding output results of ResNet. Some output results show feature loss, as indicated by the yellow box in the figure.  
Figure 7: The CT images directly reconstructed from the projection data in the test set, as well as the corresponding output results of UNet.  
To verify the artifact correction effect of our proposed joint deep learning framework on real CT images, we obtained usable fan-beam projection data by controlling the gantry angle of the industrial cone-beam CT, and the corresponding image slices were the bronze  
Figure 10: After changing the CT scanning parameters, the calibration results of the ResNet network. As indicated by the yellow box in the figure, when the grayscale difference between metal artifacts and the phantom decreases, the CT image correction results show more severe feature loss.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{2}{c}{**Joint Network**} & \multicolumn{2}{c}{**ResNet**} \\ \hline Metrics & PSNR & SSIM & PSNR & SSIM \\ Image 1 & 33.75 & 0.956 & 31.83 & 0.944 \\ Image 2 & 34.59 & 0.960 & 26.90 & 0.885 \\ Image 3 & 35.25 & 0.951 & 32.81 & 0.933 \\ Image 4 & 34.32 & 0.967 & 25.32 & 0.852 \\ Average & 34.48 & 0.959 & 29.22 & 0.904 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Quantitative evaluation indicators for the calibration results of the two models after changing CT scanning parameters.  
Figure 9: The correction results of our proposed dual-domain joint deep learning architecture after changing the CT scanning parameters. From the graph, it can be intuitively seen that the correction result remains relatively ideal.  
sword and gear hull. The actual projection data were input into the trained network model, and the correction results are shown in Figure 12.