## 5 Conclusions  
Taking into account the anatomy from which the pseudo-CTs are synthesized is extremely important to choose a specific deep learning architecture, as it has been demonstrated in this work. The first conclusion that has been extracted from this work is the importance of bone structures in the input volumes. The 3D scheme works better if the bone presents complex structures across the slices. The loss of context information for the use of 3D patches is compensated by a smoother bone depiction in the result. Instead, if the bone does not vary across slices, such as in pelvic anatomies, it is better to use a slice-by-slice strategy with 2D filters.  
Moreover, the current results indicate that the 3D U-net gives better results than other strategies, such as residuals or dilated convolutions. Therefore, a U-net would be the best option to build a 3D architecture due to the progressive down-sampling and up-sampling of the feature maps. In case a 2D input is used, the best option would be a network to extract complex features, such as the residual network that is presented in this paper.  
Therefore, according to these results, architectures that perform an aggressive sub-sampling, using strided convolutions or pooling operations, are quite successful if the input has a large enough field of view. Besides, the residuals work better with 2D inputs due to the limitations in the GPU memory, which do not allow to have big enough networks to extract highly processed features in 3D schemes.  
Finally, the importance of the MRI sequence that is used as input in pelvis reconstructions must be remarked. The Dixon-Vibe MRI is the usual sequence acquired in the clinic. However, it probably does not contain enough information for pseudo-CT generation, given the similar results between the evaluated networks and schemes. Hence, in future works it would be interesting to compare results with Dixon-VIBE, T1 or even the recent zero-echo-time acquisitions using a DCNN.  
Conceptualization, J.V.-O., A.T.-C., D.I.-G. and N.M.; Data curation, A.T.-C. and C.P.-d.-l.-L.; Formal Analysis, J.V.-O. and A.T.-C.; Funding acquisition, A.T.-C., Y.R. and N.M.; Investigation, J.V.-O., A.T.-C. and C.P.-d.-l.-L.; Methodology, J.V.-O., A.T.-C., C.P.-d.-l.-L., D.I.-G. and N.M.; Resources, A.T.-C., O.A.C., Y.R., F.M., A.S., M.S., D.I.-G. and N.M.; Software, J.V.-O. and A.T.-C.; Supervision, A.T.-C., D.I.-G. and N.M.; Validation, J.V.-O., A.T.-C. and C.P.-d.-l.-L.; Visualization, J.V.-O., A.T.-C. and C.P.-d.-l.-L., Writing--original draft preparation, J.V.-O. and A.T.-C., C.P.-d.-l.-L., D.I.-G. and N.M.; writing--review and editing, J.V.-O., A.T.-C., C.P.-d.-l.-L., O.A.C., Y.R., F.M., A.S., M.S., D.I.-G. and N.M. All authors have read and agreed to the published version of the manuscript.  
This project was partially supported by Young Researchers R&D Project Ref M2166 (MIMC3-PET/MR) financed by Community of Madrid and Rey Juan Carlos University and by project DPI2015-68664-C4-2-R of the Spanish Ministry of Economy and by Banco de Santander and Universidad Rey Juan Carlos Funding Program for Excellence Research Groups ref. \"Computer Vision and Image Processing (CVIP)\".  
**Institutional Review Board Statement:** The study was conducted in accordance with the Declaration of Helsinki. The data analysis was approved by the Partners Healthcare Ethics Committee (SDN-Pascale IRB-CODE No.1/16-16-03-16).  
**Informed Consent Statement:** Patient consent was waived due to the retrospective nature of this study.  
**Conflicts of Interest:** The authors declare no conflict of interest.