## 3 Results
### 3D Reconstruction Results  
Figures 10 and 11 show the results of the three merging strategies that have been tested: stride 32, stride 16 with averaging of overlapping voxels and stride 16 considering the inner cube. The average time to synthesize a volume is shown in Tables 15 and 30. The first method--referred as \"stride 32\" in Figures 10 and 11--generated artifacts in the boundaries of the cube and misalignment in the bone and air structures. Moreover, this approach showed a greater error than the other two in the quantitative results. Nevertheless, this method was quite fast, generating a volume in 8-19 s. The other two methods provided, in average, a similar quantitative result, being the use of the inner cube slightly better. However, the  
a faster computation time when the network is deployed, the lack of the need for a registration to a common space once the model is trained, and a lower error in the generated pseudo-CT. However, the lack of a common database among research groups makes hard to assess which architecture or strategy would be the best to apply in the future. In this work, different architectures using 2D and 3D approaches were tested on two different datasets. This way, it is possible to extract conclusions when different networks are compared with the same datasets. The datasets utilized in this study were composed of 3D T1-weighted MR images of the head and Dixon-VIBE MR images of the pelvis. According to the results of the current work, there is no preferred network for every problem. Thus, the results depend on the specific anatomy defining the problem and the MRI sequence that is used. As shown in Section 4. Results, if the anatomy is similar to a head with complex bone structures and geometries, 2D schemes generate aliasing and artifacts across bone structures. Instead, 3D schemes and reconstructions with stride 16 provide bones with smooth boundaries, which is translated into a significant reduction of the error. Moreover, the best architecture to achieve the best detail in a head pseudo-CT is the 3D U-net (\(89.54\pm 7.79\)). When using 3D architectures, the input of the network is usually a 3D patch due to GPU memory limitations. 3D patches only depict a part of the anatomy. Therefore, they provide limited contextual information. In this case, the progressive spatial reduction and up-sampling of the feature maps are probably the best option, as it occurs in the U-net. In case the anatomy does not have complex structures across slices, 3D schemes are not the best option. Moreover, if the input image has very similar areas -as in pelvis acquisitions-, 3D patches will not satisfactory synthesize the pseudo-CT due to the lack of contextual information. In this scenario, according to our results, it would be better to use a 2D scheme. Specifically, the residual network obtained the best results in 2D (\(51.41\pm 6.81\)), which is consistent with the results in general computer vision, where 2D approaches are used [26; 39]. The dilated network did not stand out in any dataset but in the 2D scheme it performed similarly to the residual network. Nevertheless, dilated convolutions have been reported to give interesting results in segmentation in other areas of computer vision. Thus, their accommodation in an architecture in the future could improve the quality of the synthesized image. In the pelvis dataset, the results were quite similar between networks, having differences in a range of 5% in the bone. This could be due to the input data used in the experiment: the Dixon-VIBE MRI. The Dixon-VIBE, as shown in Figure 2, does not depict the bone well. Moreover, it is fairly probable that the information to generate the pseudo-CT that it is contained in the image is low or moderate compared to T1 acquisitions (see Figure 1). That is, the networks gave similar results because the information that can be extracted from the input images is limited.