## 4 Discussion
Recently, works on deep-learning pseudo-CT synthesis reported a Pearson correlation of up to 0.943 \(\pm\) 0.009 using a similar architecture to the one presented in this work [37]; again, this demonstrates: (i) the great potential of deep learning methods to extract features and information and, (ii) the increased performance of architectures including residual operations to avoid the degradation in the feature maps generated. Additionally, the correlation and the Bland-Altman plots (Figure 10) suggest this method leads to very accurate results, and in line with those previously reported in the literature, despite minorerrors mainly accumulated in voxels between 25 and 125 HU (banding artifacts in the correlation and Bland-Altman plots). We further investigated this issue and found that most of these mislabeled voxels are predominantly located in image boundaries/edges and ears (due to slight differences between MR and CT), air-filled cavities, and dental implants, as could be appreciated in Figure 9. Despite the weaknesses presented, as the synthesized image is not going to be used for diagnostic reading performed by radiologists, but just for PET attenuation correction, the level of detail of the pseudo-CT does not need to be equivalent to a real acquired CT. In this specific context, a minor loss in spatial resolution is acceptable, as the inherent PET resolution is lower than CT or MR spatial resolution. The approach presented in this work could be of great potential for tasks where the skull estimation and/or the pseudo-CT computation is needed, such as PET/MR attenuation correction and radiotherapy planning where MR-CT datasets are usually limited, leading to larger and highly flexible and generalizable atlases. This method could be adapted to real clinical scenarios as training these algorithms requires long times but their use and application for synthesis is very fast, synthesizing a complete pseudo-CT volume in a similar time to that needed to acquire an actual CT scan. Additionally, our technique could, in theory, be applied in other regions of the body, potentially allowing for whole-body pseudo-CT synthesis using atlases designed with the same hypothesis. Further research will be aimed in that direction. Our study presents several limitations. First, our training set was relatively small, which has a bigger effect when using the described Franken-CT approach compared to traditional methods. this is due to the fact that we only have a subset of subjects including information for specific anatomical areas, decreasing the effective N of the atlas. Additionally, our atlas contained subjects biased towards high ages. However, our approach proves that not discarding reduced FOV images allows producing accurate results; therefore, using as many datasets as available to increase the number of datasets and produce larger databases for DL training will allow to train more generalizable CNNs than those reported previously. Further improvement of the current model could be achieved by increasing the number of training volumes as well as its heterogeneity. Furthermore, in a real clinical scenario, the atlas should be designed to include the necessary anatomical heterogeneity to map any potential conditions related to anatomical and pathological variability among patients (i.e., patients scanned with contrast agents, patients with lesions, such as tumors or sclerotic lesions, or with implants). Again, this limitation leads to the need for larger datasets to generalize our results; nevertheless, our method proved its potential to use reduced FOV datasets and, consequently, facilitating the generation of such databases. Thus, the design of specific atlases and models that consider these conditions should be considered as a future line of exploration.