## 3 The Experimental Results
### The Result of Removing Metal Artifacts  
The CT slices need to undergo Radon transformation. Figure 9a shows the original CT slice after scanning, and it can be seen that there are very heavy metal artifacts. Figure 9b shows the sinogram of the original CT section after the Radon transformation.  
A single NVIDIA GeForce RTX 3090 (24 GB) GPU and a 13 th Gen Intel Core i9-13900K were used to train all models. With a total epoch of 100 and default approaches for other parameters, the training was based on the deep learning framework PyTorch 2.0.1. Figure 10 shows the result after the training of EDSR-Tiny. The model tends to stabilize after 40 epochs.  
After removing the metal artifacts, the sinogram was reconstructed using the learned model. The reconstructed sinogram is displayed in Figure 11. The reconstructed sinogram is more lucid than the original sinogram (Figure 9b). The PSNR and SNR were computed using the inverse Radon transform.  
Table 2 shows that the original image's average signal-to-noise ratio (SNR) was 14.27 dB. Following optimization, the SNR result was 29.75 dB, with a one-time improvement in signal quality. The reconstructed slice has a PSNR of 40.39 dB, making it a high-quality image. Additionally, Table 2 shows the SNR, PSNR, and PSNR's variance values of the inverse Radon transform following the removal of metal artifacts from the sinogram using bicubic interpolation. The CT slice that was reconstructed using biometric interpolation had an SNR of 20.13 dB and a PSNR of 30.25 dB, according to the data. The results of CycleGAN and bicubic interpolation are not significantly different. The pix2 pix network  
Figure 8: Training dataset of EDSR-Tiny. The training set consists of high-resolution sinograms and low-resolution images sampled under the high-resolution sinograms. The contrast of the high-resolution sinogram is clear, while that of the low-resolution sinogram is generally brighter.  
Figure 9: The results of Radon transformation. (**a**) shows the original CT section containing metal artifacts, and (**b**) shows the sinogram obtained by its Radon transform.  
has seen significant improvement, and the optimization results of pix2 pix are the most stable, but the PSNR still falls short of the method proposed in this paper. Figure 12 shows the PSNR distribution of the pix2 pix, CycleGAN, and EDSR-Tiny test sets. The results of EDSR-Tiny are almost always superior to the other two methods, which is consistent with the conclusion in Table 2. A visual comparison chart between the original and reconstructed images can be found in Figure 13.  
Figure 13: Optimization comparison results of metal artifacts. The results of Radon transformation. The results show that the method proposed in this paper is better than pix2 pix and CycleGAN networks at removing metal artifacts. (**a**) Original CT slice 1. (**b**) Original CT slice 2. (**c**) Original CT slice 3. (**d**) CT slice 1 after removing artifacts by pix2 pix. (**e**) CT slice 2 after removing artifacts by pix2 pix. (**f**) CT slice 3 after removing artifacts by pix2 pix. (**g**) CT slice 1 after removing artifacts by CycleGAN. (**h**) CT slice 2 after removing artifacts by CycleGAN. (**i**) CT slice 3 after removing artifacts by CycleGAN. (**j**) CT slice 1 after removing artifacts by EDSR-Tiny. (**k**) CT slice 2 after removing artifacts by EDSR-Tiny. (**l**) CT slice 3 after removing artifacts by EDSR-Tiny.  
An ablation study was suggested in order to demonstrate the efficacy of the EDSR-Tiny improvement, and Table 3 displays the findings. Table 3 shows that the following are the outcomes of the reconstruction utilizing the original EDSR-Tiny: 22.13 dB is the average SNR, and 33.25 dB is the average PSNR. The CALayer was used to boost the average SNR of the reconstruction findings by 3.15 dB and the average PSNR by 2.12 dB. The results show that the CALayer was a successful introduction. The Radon loss was used to boost the average SNR of the reconstruction findings by 3.45 dB and the average PSNR by 3.56 dB. It was demonstrated that