# End-to-End Deep Learning CT Image Reconstruction for Metal Artifact Reduction
## 4 Discussion
In the comparison study, a sinogram pre-processing and an image post-processing approach were investigated. We have found that the image-based post-processing deep learning approach provides better results than the sinogram pre-processing approach. This is consistent with the findings of [PERSON] et al. [37]. Since the reference methods are all FBP-based, they are superior to the iCTU-Net in the absence of artifacts due to the aforementioned blurring. However, the artifacts introduced by the FBP cannot be completely mitigated by the reference methods. The iCTU-Net is the only method that removes all metal artifacts and yields the best results of all methods for severe metal artifacts. Since the iCTU-Net is trained end-to-end, the network can fully utilize the raw data and learn to reconstruct an artifact-free image. The U-Net Sino learns to mitigate disturbances in the sinogram with the raw data as input. However, small errors in the sinogram can lead to significant deviations in the reconstruction [28], which the U-Net Sino cannot correct. The U-Net Image only mitigates the artifacts in the image domain introduced by the FBP. In doing so, the network no longer has the original raw data to learn from. The usage of digital XCAT phantom data for metal data simulation instead of real patient data has several advantages. First of all, with the organ masks provided by the XCAT, metal objects can automatically be inserted in specific body regions. In this work, we inserted iron into the blood vessels. For future studies it would be better to insert attenuation coefficients of materials that are commonly used for contrast agents and catheters. Moreover, for the simulation of polychromatic projections, it is not necessary to segment the images into soft tissue, bone, and metal to assign the corresponding attenuation coefficients, as is done in several other works [14, 21, 37]. Instead, the organ masks of the XCAT allow for the insertion of energy-dependent attenuation coefficients for every organ. In the future, it will be desirable to test the iCTU-Net on experimental raw data instead of simulated data. However, this requires the iCTU-Net to be adapted to work with the raw data of multirow detector CT scanners. The two-dimensional projection data might lead to restrictions due to GPU memory limitations. Since dual-energy CT has been shown to help reduce metal artifacts [5, 10], the iCTU-Net should benefit from the additional spectral information. Photon-counting CT is another spectral technology that can be used to reduce metal artifacts [38]. The energy of individual photons can be measured by energy-resolving detectors [39]. The iCTU-Net is readily applicable to energy-resolved raw data by including the energy information in separate input channels. The additional spectral information in the raw data is expected to mitigate beam hardening artifacts. We will also investigate the ability of the iCTU-Net to simultaneously mitigate different kinds of artifacts. This is achievable by using a training dataset that contains a combination of artifacts. Promising results for the isolated mitigation of artifacts with the iCTU-Net in low-dose CT and sparse-angle CT have already been shown [28].