## 3 Results
### Pelvis Dataset Results  
The results for all tissues using the pelvis dataset are depicted in Tables 16-18; the results using only the bone voxels are detailed in Tables 19-21; the results using only the fat voxels are detailed in Tables 22-24; and the results using only the soft-tissue voxels are detailed in Tables 25-27. In the pelvis dataset all networks performed very similar when all tissues were considered. However, 3D networks obtained slightly worse results when assessing bone alone and very similar results for all tissues. The best network in the bone dataset was the 2D Residual network that obtained a MAE of 201.56 HU, a PSNR of 23.20 and a Pearson Coefficient of 0.476 in the bone. Additionally, the error in bone with all networks increased when the 3D scheme was used. The ANOVA test for the 2D results reported a statistically significant effect of the networks in all tissues and bone MAE (all tissues: \(F_{2,56}=6.7\), \(p<0.005\); bone: \(F_{2,56}=8.5\), \(p<0.001\)) and PSNR (all tissues: \(F_{2,56}=8.5\), \(p<0.001\), bone: \(F_{2,56}=5.3\), \(p<0.01\)). According to 3D results, the ANOVA \begin{table}
\begin{tabular}{c c c c} \hline \hline
**PSNR\(\backslash\)MAE** & **Residual-Net** & **Atrous-Net** & **U-Net** \\ \hline
**Residual-Net** & - & \(<\)0.001 & \(<\)0.001 \\ \hline
**Atrous-Net** & 0.87 & - & \(<\) 0.001 \\ \hline
**U-Net** & \(<\) 0.01 & \(<\) 0.001 & - \\ \hline \hline \end{tabular}
\end{table} 
Table 15: Synthesis Times for Head. Average time in seconds to synthesize a whole volume from the Head data set using 2D networks and 3D networks reconstructing with stride 16 and 32. \begin{table}
\begin{tabular}{c c c c} \hline \hline  & **MAE** & **PSNR** & **Pearson** \\ \hline
**2D** & \(\mathbf{27.18\pm 3.47}\) & \(33.65\pm 1.093\) & 0.338 \(\pm\) 0.047 \\ \hline
**3D-32** & \(29.19\pm 4.72\) & \(33.30\pm 1.148\) & 0.328 \(\pm\) 0.037 \\ \hline
**3D-16 av** & \(27.61\pm 4.41\) & \(\mathbf{33.86\pm 1.21}\) & \(\mathbf{0.350\pm 0.042}\) \\ \hline
**3D-16** & \(27.69\pm 3.69\) & \(33.38\pm 1.063\) & 0.341 \(\pm\) 0.040 \\ \hline \hline \end{tabular}
\end{table} 
Table 12: Head dataset Soft-tissue with Residual-Net. Mean Absolute Error, PSNR and Pearson coefficient within the bone on the head data set obtained using Residual net with different convolutions and reconstructions. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**PSNR\(\backslash\)MAE** & **Residual-Net** & **Atrous-Net** & **U-Net** \\ \hline
**Residual-Net** & - & \(<\)0.001 & \(<\)0.001 \\ \hline
**Atrous-Net** & 0.07 & - & \(<\) 0.001 \\ \hline
**U-Net** & \(<\) 0.01 & \(<\) 0.001 & - \\ \hline \hline \end{tabular}
\end{table} 
Table 15: Synthesis Times for Head. Average time in seconds to synthesize a whole volume from the Head data set using 2D networks and 3D networks reconstructing with stride 16 and 32.  
test did not expose statistically significant differences when using different architectures on all tissue MAE (all tissues: \(F_{2,56}=2.3\), \(p=0.10\); bone: \(F_{2,56}=6.2\), \(p<0.005\)) and PSNR (all tissues: \(F_{2,56}=1.4\), \(p=0.25\); bone: \(F_{2,56}=4.3\), \(p<0.05\)). Post hoc Student's \(t\)-test is depicted in Tables 28 and 29. It reveals that the Residual-net and Atrous-net did not provide statistically significant differences. Visual result examples of pelvis pseudo-CTs are depicted in Figures 8 and 9. Table 30 shows the time needed to synthesize a whole pelvis volume using the different architectures. \begin{table}
\begin{tabular}{c c c c} \hline \hline  & **MAE** & **PSNR** & **Pearson** \\ \hline
**2D** & 52.46 \(\pm\) 6.42 & 31.31 \(\pm\) 1.169 & 0.683 \(\pm\) 0.045 \\ \hline
**3D-32** & 51.25 \(\pm\) 6.66 & 31.33 \(\pm\) 1.281 & 0.682 \(\pm\) 0.058 \\ \hline
**3D-16 av** & **50.76 \(\pm\) 6.45** & **31.56 \(\pm\) 1.220** & **0.700 \(\pm\) 0.050** \\ \hline
**3D-16** & 51.14 \(\pm\) 7.20 & 31.38 \(\pm\) 1.308 & 0.688 \(\pm\) 0.056 \\ \hline \hline \end{tabular}
\end{table} 
Table 16: Pelvis dataset with U-Net.