# End-to-End Deep Learning CT Image Reconstruction for Metal Artifact Reduction
## 4 Discussion  
We trained the iCTU-Net with metal-affected data, to investigate its ability to mitigate metal artifacts. The iCTU-Net outperformed the reference methods for reconstructions with severe metal artifacts. Similar results were found for the application of the iCTU-Net to sparse-angle CT reconstruction, where the iCTU-Net showed good performance for a small number of projections [28]. However, the iCTU-Net was not able to resolve small structures of only a few millimeters in size. The reconstructions were slightly blurred, which is probably the reason why the iCTU-Net could not match the quality of the FBP when no metal was present. In the ablation study, it was found that the loss function and the post-processing layers have a major impact on the quality of the reconstruction. We had attempted to sharpen the reconstructed image by combining the SSIM loss with an additional gradient difference loss [36], but no substantial improvements were observed. In the future, we will investigate alternatives to the U-Net as post-processing layers to further optimize the network. The iCTU-Net was trained with a dataset of 3964 slices, of which only 310 contained no metal. Due to this small fraction of metal-free training data, the network might not be able to learn how to properly reconstruct metal-free sinograms. In the input study, we trained the reconstruction network exclusively with metal-free data to test this hypothesis. We found that the network trained with metal-free raw data did not perform better than the iCTU-Net for the no artifact category. Therefore, we can conclude that training the network with mainly metal-affected data does not degrade the quality of the reconstructions. Interestingly, the evaluation metrics for the moderate and severe artifact categories also did not differ substantially. Thus, the network trained with \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{2}{c}{**FBP**} & **NMAR** & **U-Net Sino** & **U-Net Image** & **iCTU-Net** \\ \hline
**No** & SSIM & \(0.988\pm 0.020\) & \(0.988\pm 0.020\) & \(0.990\pm 0.011\) & \(\mathbf{0.993\pm 0.010}\) & \(0.969\pm 0.008\) \\
**Artifacts** & PSNR & \(\mathbf{50.3\pm 4.9}\) & \(\mathbf{50.3\pm 4.9}\) & \(46.5\pm 2.1\) & \(45.3\pm 3.2\) & \(37.7\pm 1.8\) \\ \hline
**Moderate** & SSIM & \(0.869\pm 0.087\) & \(0.976\pm 0.016\) & \(0.979\pm 0.012\) & \(\mathbf{0.983\pm 0.011}\) & \(0.976\pm 0.007\) \\
**Artifacts** & PSNR & \(15.2\pm 3.1\) & \(\mathbf{44.3\pm 3.1}\) & \(43.9\pm 2.6\) & \(42.8\pm 2.1\) & \(39.5\pm 1.8\) \\ \hline
**Severe** & SSIM & \(0.625\pm 0.056\) & \(0.911\pm 0.042\) & \(0.936\pm 0.025\) & \(0.944\pm 0.024\) & \(\mathbf{0.970\pm 0.009}\) \\
**Artifacts** & PSNR & \(10.6\pm 1.4\) & \(38.2\pm 2.4\) & \(39.3\pm 1.9\) & \(39.8\pm 1.9\) & \(\mathbf{40.7\pm 1.6}\) \\ \hline
**All** & SSIM & \(0.848\pm 0.125\) & \(0.968\pm 0.032\) & \(0.974\pm 0.021\) & \(\mathbf{0.978\pm 0.019}\) & \(0.975\pm 0.008\) \\
**Images** & PSNR & \(18.3\pm 11.6\) & \(\mathbf{44.1\pm 4.4}\) & \(43.5\pm 3.1\) & \(42.7\pm 2.6\) & \(39.5\pm 1.9\) \\ \hline \hline \end{tabular}
\end{table} 
Table 3: SSIM and PSNR evaluation metrics for the comparison study. The best result for each metric is marked **bold**. metal-affected input data reconstructs images with metal-affected test data just as well as the network trained without metal reconstructing images that do not include metal. This shows that the iCTU-Net reliably reduces metal artifacts. This is confirmed by the fact that all networks in the input study performed very similarly for all severities of artifacts. The network seems to handle metal objects in the raw data very well. The input study showed that the iCTU-Net performs similarly regardless of the sinogram input data used. Training the network with uncorrected metal sinograms revealed similar performances compared to the network trained with pre-corrected NMAR sinograms. This means that reconstruction without pre-correction is feasible, which reduces the complexity of the algorithm.