# End-to-End Deep Learning CT Image Reconstruction for Metal Artifact Reduction
## 3 Results
### Comparison Study  
The results of the evaluation metrics for the comparison study are shown in Table 3, and reconstructed images are shown in Figure 6. The deep learning reference methods U-Net Sino and U-Net Image both perform better than NMAR in terms of SSIM, especially for severe artifacts. In terms of PSNR, they perform worse when artifacts are not present and similarly when artifacts are present. The U-Net Image achieves a slightly higher SSIM than the U-Net Sino, but the performance of both methods is very similar. In Figure 6, no substantial removal of metal artifacts can be observed for the U-Net Sino and U-Net Image, only a smoothing of the streak artifacts is observed for the U-Net Image.  
Figure 5: Results of the input study, where the ground truth and FBP are compared to iCTU-Nets trained with different input sinograms. All networks are trained with the U-Net post-processing layers and the SSIM loss, which yield the best results in the ablation study. No Metal Input, Metal Input, and iCTU-Net are, respectively, trained with metal-free, metal, and NMAR pre-corrected sinograms. A slice without metal artifacts, with moderate metal artifacts, and with severe metal artifacts is shown. The scans are windowed to [\(-300\) HU, \(300\) HU] to increase the visibility of the artifacts. The arrows in the zoomed images indicate an anatomy that the Metal Input network cannot resolve accurately.  
Figure 6: Results of the comparison study, where the ground truth and FBP are compared to NMAR, U-Net Sino, U-Net Image, and iCTU-Net. A slice without metal artifacts, with moderate metal artifacts, and with severe metal artifacts is shown. The scans are windowed to [\(-\)300 HU, 300 HU] to increase the visibility of the artifacts. The arrows and circles in the zoomed images indicate anatomies that could only be recovered by the iCTU-Net.  
Without artifacts, the iCTU-Net is outperformed by all methods in terms of PSNR and SSIM as they are all FBP-based and already outperformed the iCTU-Net in the ablation study. For moderate artifacts, the iCTU-Net achieves competitive SSIM values compared to the reference methods but performs worse in terms of PSNR. Nevertheless, the iCTU-Net is the only method capable of completely removing moderate metal artifacts, as shown in Figure 6. As indicated by the arrows in the zoomed images in Figure 6, the iCTU-net is also the only method that can restore a blood vessel into which a metal object has been inserted. For severe artifacts, the iCTU-Net performs better than all reference methods with SSIM = \(0.970\pm 0.009\) and PSNR = \(40.7\pm 1.6\). The second best method, the U-Net Image, only achieved SSIM = \(0.944\pm 0.024\) and PSNR = \(39.8\pm 1.9\). Averaged over all images, the SSIM of the iCTU-Net is competitive with the U-Net Image, but a worse PSNR is achieved. The iCTU-Net is able to remove severe metal artifacts completely, whereas for the other methods strong streak artifacts are still present over the whole image. The iCTU-Net can not only efficiently remove severe artifacts but also reliably restore the anatomy that is obstructed by these artifacts. This is especially evident inside the circles shown in the zoomed images in Figure 6. All other methods fail to restore the anatomy in this region.