## 4 Experimental Results
### Image Reconstruction Experiment of Pelvic Image  
The first image reconstruction experiment is using a pelvic image to show the feasibility of our algorithm for SVCT reconstruction, as shown in Figure 1. We extracted 80, 64, and 48 views from a full scan and selected parameters empirically. The parameters are set as follows: \(a\) = 0.5, \(\gamma_{1}\) = 0.3, and \(\gamma_{2}\) = 0.08. Figures 2-4 show the ground truth and reconstruction images via FBP, OS-SART, TV, PICCS, TVPI-G, and our method NNPICCS. From these figures, it is evident that our algorithm outperforms the other methods in terms of recovering image structures and suppressing noise. Specifically, the FBP and OS-SART results (as shown in Figures 2b,c, 3b,c and 4b,c) contain high levels of noise and artifacts, while TV results (as shown in Figures 2d, 3d and 4d) are characterized by blurring and staircasing effects. The PICCS and TVPI-G methods provide better results than the other approaches because of the introduction of prior information, but image edges are missing as shown in Figures 2e,f, 3e,f and 4e,f. At the same time, it can be seen that our method is able to preserve image edges and suppress noise effectively, as shown in Figures 2g, 3g and 4g. To compare the recovery of reconstructed image details and edge information, we selected two regions of interest (ROIs) from the 48-view and 64-view reconstructed images and zoomed in to show. The ROIs are labelled with red box as shown in Figures 2a and 3a. The corresponding zoomed-in results are shown in Figure 5. As can be seen from the figure, our algorithm can reconstruct some small image structures shown by the arrows, which are difficult to see by other algorithms. Also, we find that our algorithm performs better in edge retention. To visualize the accuracy of our proposed method for local reconstruction of CT images, we plot the one-dimensional line intensity distribution of CT images reconstructed from 80 projections at two areas (shown by the blue and red lines in Figure 4a), as displayed in Figure 6. It can be visually observed that the line intensity distribution of the reconstructed images from our proposed method is closest to the original CT images at the two areas. In comparison, the proposed reconstruction algorithm has higher accuracy. Figure 1: Pelvic image for the experiments: (**a**) the target image; (**b**) the prior image; and (**c**) the difference image between the prior image and the target image. Next, to further show the accuracy of the global reconstruction results, Figure 7 shows the absolute difference image comparison for 48-views reconstruction. It is commonly known that the smaller the absolute difference value, the greater the accuracy of the reconstructed image. As can be seen from the Figure 7, we can see that our result achieves the smaller absolute difference, which means our image reconstruction accuracy are better than other methods. In addition, we use RMSE, SSIM, and PSNR to compare the algorithms quantitatively. Table 1 shows the final metrics calculation result. Numerically, it can be seen that our method performs better in all three metrics, further demonstrating the superiority of the proposed algorithm. Figure 3: 64 views reconstruction results of pelvic image: (**a**) ground truth, (**b**) FBP, (**c**) OS-SART, (**d**) TV, (**e**) PICCS, (**f**) TVPI-G, and (**g**) NPICCS. The display window is \([-150\,250]\) HU. Figure 2: 48 views reconstruction results of pelvic image: (**a**) ground truth, (**b**) FBP, (**c**) OS-SART, (**d**) TV, (**e**) PICCS, (**f**) TVPI-G, and (**g**) NPICCS. The display window is \([-150\,250]\) HU. It is well known that determining the optimal parameter is a difficult problem for iterative CT reconstruction algorithm. The parameters \(a\), \(\gamma_{1}\) and \(\gamma_{2}\) in this work are determined by extensive experiments. We use PSNR and SSIM as the basis for selecting parameters. In our experiments, optimal parameters are close at different angles, so we set parameters as the same values for different angles.