# End-to-End Deep Learning CT Image Reconstruction for Metal Artifact Reduction
## 3 Results
### Input Study  
In the input study, we investigate different sinogram inputs for the iCTU-Net. The results of the evaluation metrics for the input study are shown in Table 2, and reconstructed images are shown in Figure 5. The SSIM and PSNR in Table 2 show that the network performs similarly independent of the input. The network trained without metal in the input sinogram achieves the best PSNR, and the network trained with the pre-corrected NMAR sinograms achieved the best SSIM. However, these differences in SSIM and PSNR are not significant. For the metal input, some reconstruction inaccuracies close to metal objects can be observed, as indicated by the arrows in the zoomed images in Figure 5. Apart from this, the reconstructions in Figure 5 show no noticeable differences in image quality. Therefore, we continue to use the pre-corrected NMAR sinograms for the iCTU-Net. This allows for a fairer comparison with the deep learning reference methods, since they also use NMAR inputs.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & & **FBP** & **No Post-** & **3 Conv** & **iCTU-Net** & **MSE** \\  & & & **Processing** & **Layers** & & **iCTU-Net** \\ \hline
**No** & SSIM & **0.988 \(\pm\) 0.020** & 0.865 \(\pm\) 0.038 & 0.903 \(\pm\) 0.028 & 0.969 \(\pm\) 0.008 & 0.962 \(\pm\) 0.010 \\
**Artifacts** & PSNR & **50.3 \(\pm\) 4.9** & 31.0 \(\pm\) 2.9 & 30.5 \(\pm\) 4.3 & 37.7 \(\pm\) 1.8 & 37.3 \(\pm\) 1.3 \\ \hline
**Moderate** & SSIM & 0.869 \(\pm\) 0.087 & 0.859 \(\pm\) 0.036 & 0.897 \(\pm\) 0.038 & **0.976 \(\pm\) 0.007** & 0.967 \(\pm\) 0.009 \\
**Artifacts** & PSNR & 15.2 \(\pm\) 3.1 & 33.1 \(\pm\) 2.1 & 34.3 \(\pm\) 2.8 & **39.5 \(\pm\) 1.8** & 39.3 \(\pm\) 1.5 \\ \hline
**Severe** & SSIM & 0.625 \(\pm\) 0.056 & 0.715 \(\pm\) 0.06 & 0.832 \(\pm\) 0.024 & **0.970 \(\pm\) 0.009** & 0.946 \(\pm\) 0.013 \\
**Artifacts** & PSNR & 10.6 \(\pm\) 1.4 & 24.9 \(\pm\) 5.6 & 34.2 \(\pm\) 1.3 & **40.7 \(\pm\) 1.6** & 39.6 \(\pm\) 1.3 \\ \hline
**All** & SSIM & 0.848 \(\pm\) 0.125 & 0.840 \(\pm\) 0.06 & 0.889 \(\pm\) 0.04 & **0.975 \(\pm\) 0.008** & 0.964 \(\pm\) 0.012 \\
**Images** & PSNR & 18.3 \(\pm\) 11.6 & 31.7 \(\pm\) 4.0 & 33.9 \(\pm\) 3.1 & **39.5 \(\pm\) 1.9** & 39.1 \(\pm\) 1.6 \\ \hline \hline \end{tabular}
\end{table}
Table 1: SSIM and PSNR evaluation metrics for the ablation study. All networks are trained with pre-corrected NMAR sinograms and the SSIM loss, except for the MSE iCTU-Net, which is trained with the MSE loss. The best result for each metric is marked **bold**.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & & **FBP** & **No Metal Input** & **Metal Input** & **iCTU-Net** \\ \hline
**No** & SSIM & **0.988 \(\pm\) 0.020** & 0.967 \(\pm\) 0.006 & 0.968 \(\pm\) 0.011 & 0.969 \(\pm\) 0.008 \\
**Artifacts** & PSNR & **50.3 \(\pm\) 4.9** & 37.8 \(\pm\) 1.5 & 37.4 \(\pm\) 1.9 & 37.7 \(\pm\) 1.8 \\ \hline
**Moderate** & SSIM & 0.869 \(\pm\) 0.087 & 0.974 \(\pm\) 0.004 & 0.975 \(\pm\) 0.009 & **0.976 \(\pm\) 0.007** \\
**Artifacts** & PSNR & 15.2 \(\pm\) 3.1 & **40.2 \(\pm\) 1.5** & 39.6 \(\pm\) 1.9 & 39.5 \(\pm\) 1.8 \\ \hline
**Severe** & SSIM & 0.625 \(\pm\) 0.056 & 0.968 \(\pm\) 0.005 & 0.962 \(\pm\) 0.012 & **0.970 \(\pm\) 0.009** \\
**Artifacts** & PSNR & 10.6 \(\pm\) 1.4 & **40.7 \(\pm\) 0.8** & 40.4 \(\pm\) 1.7 & **40.7 \(\pm\) 1.6** \\ \hline
**All** & SSIM & 0.848 \(\pm\) 0.125 & 0.972 \(\pm\) 0.005 & 0.972 \(\pm\) 0.011 & **0.975 \(\pm\) 0.008** \\
**Images** & PSNR & 18.3 \(\pm\) 11.6 & **40.0 \(\pm\) 1.6** & 39.5 \(\pm\) 2.0 & 39.5 \(\pm\) 1.9 \\ \hline \hline \end{tabular}
\end{table}
Table 2: SSIM and PSNR evaluation metrics for the input study. The differentiation of artifact severity is not meaningful for No Metal Input because none of the test data contain metal. Since this network is not trained with any metal data, it is not suitable for artifact reduction. However, to allow a reasonable comparison to the other methods, we keep the categories, meaning the same slices are used for evaluation. The best result for each metric is marked **bold**.