## 4 Experimental Results
### Image Reconstruction Experiment of Abdomen Image  
The second image reconstruction experiment is conducted on an abdomen image with more image details to show the feasibility of our algorithm for SVCT reconstruction, as shown in Figure 9. We extracted 80, 64, and 48 views from a full scan and set the parameters as follows: \(a=0.5\), \(\gamma_{1}=0.1\), and \(\gamma_{2}=0.06\). The reconstructed results using different algorithms are shown in Figures 10-12. As can be seen from the figures, our algorithm performs better than other algorithms in preserving small structures and reducing noise. Specifically, FBP reconstruction results contain severe artifacts as displayed in Figures 10b, 11b and 12b. OS-SART reconstruction results also have obvious noise as displayed in Figures 10c, 11c and 12c. TV has a better performance than FBP and OS-SART, but the result has blocky artifacts, as displayed in Figures 10d, 11d and 12d. The PICCS and TVPI-G algorithms have an improvement in image quality, but fine image structures are losing detail and some edge information is blurring, as displayed in Figures 10e,f, 11e,f and 12e,f. As can be observed from Figures 10g, 11g and 12g, the proposed algorithm performs better not only in preserving edge and image details, but also in reducing noise. Subsequently, we select two ROIs, marked by a red box as shown in Figures 10a and 11a, and zoom in on them to show the results. The enlarged ROIs are shown in Figure 13. It could be observed that our algorithm can reconstruct some of the important tissues shown by the arrows, which are blurred or difficult to see in other ROIs. To show the accuracy of the different methods for local reconstruction of CT images, we plot the 1D line intensity distribution in CT images reconstructed from 80 projections in two regions (shown as blue and red lines in Figure 12a); the curves are as shown in Figure 14. Visually, it can be seen that the line intensity distribution of the image reconstructed by the proposed method is the closest to the original CT image in these two regions. In comparison, the proposed reconstruction algorithm in this paper is more accurate. At the same time, the red enlarged part of the figure indicates the edge area, from which it can be seen that the proposed algorithm has better performance in edge protection. \begin{table}
\begin{tabular}{l c c c c c c c c c} \hline \hline \multirow{2}{*}{**AlgorithmViews**} & \multicolumn{2}{c}{**48**} & \multicolumn{2}{c}{**64**} & \multicolumn{2}{c}{**80**} \\ \cline{2-10}  & **RMSE** & **PSNR** & **SSIM** & **RMSE** & **PSNR** & **SSIM** & **RMSE** & **PSNR** & **SSIM** \\ \hline FBP & 0.0702 & 23.0708 & 0.4545 & 0.0667 & 23.5219 & 0.5329 & 0.0642 & 23.8551 & 0.5920 \\ OS-SART & 0.0275 & 31.2206 & 0.7862 & 0.0233 & 31.9316 & 0.8141 & 0.0196 & 32.3883 & 0.8859 \\ TV & 0.0211 & 32.2309 & 0.8333 & 0.0187 & 32.5749 & 0.8917 & 0.0154 & 35.2963 & 0.9185 \\ PICCS & 0.0181 & 34.3983 & 0.9016 & 0.0164 & 35.2711 & 0.9132 & 0.0126 & 37.0012 & 0.9329 \\ TVPI-G & 0.0143 & 35.5365 & 0.9259 & 0.0118 & 37.1833 & 0.9406 & 0.0096 & 38.1127 & 0.9560 \\ NPICCS & **0.0103** & **37.8746** & **0.9486** & **0.0084** & **39.1328** & **0.9601** & **0.0068** & **40.1574** & **0.9705** \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison of metrics under different views by different algorithms. Figure 8: Parameters selection of pelvic image: (**a**) parameter \(a\); (**b**) parameter \(\gamma_{1}\); and (**c**) parameter \(\gamma_{2}\). To show the global reconstruction accuracy, the absolute difference images are utilized to compare and are displayed in Figure 15. It could be seen that our results obtain the smallest absolute difference. Next, we utilize PSNR, RMSE, and SSIM to evaluate the algorithms from numerical indicators, as displayed in Table 2. As can be seen from the table, our algorithm performs better in all three metric values, which illustrates the advantage of our algorithm.