## 4 Discussion  
The generation of precise pseudo-CT images, and thus the production of accurate AC maps, is a basic step for PET/MRI quantification. Several approaches have been proposed in the literature providing exciting results, but most of them focus on specific areas of the body or use specific acquisitions in order to try those methods. However, the reality in a clinical setting shows the trend to minimize FOVs in CT acquisitions, making it difficult to create high quality atlas to be used for these applications. Additionally, there is a tendency to extend FOVs to increase the amount of multimodal information and to move to whole-body applications in PET/MR. In this work, we proposed the use of DL to acquire knowledge from diverse anatomical areas, from an overlapping MR-CT atlas, and use that information to be able to synthesize a pseudo-CT volume corresponding to a bigger FOV image. Thus, we demonstrated how using different images including brain, paranasal sinuses, facial orbits, and neck studies can lead to the successful generation of continuous head and neck pseudo-CTs. For reproducing this achievement through a new dataset with the Franken-CT approach, all the specific preprocessing steps described previously in the Materials and Methods section need to be followed every time. Our results are in line with those recently described by other authors but using complete and/or dedicated atlases. The qualitative (Figure 9) and quantitative (Figure 10) image quality analyses performed showed that the CT and the pseudo-CT obtained with our Franken-CT method are very similar. On the one hand, the visual inspection shows a good correspondence between both images but a limited detail in the neck region, nasal sinus cavity, mastoid cells, and air cavity delineation. This fact is possibly due to the higher complexity of those regions and the limited amount of neck scans in the train atlas; increasing the number of such MR-CT pairs in the training dataset should improve the resulting pseudo-CTs, as previously demonstrated in state-of-the-art investigations about the effect of training dataset sizes [31]. However, despite these limitations, visual comparison of our synthesized pseudo-CT images with those previously reported in previous works shows our method provides better images than most classical and recently proposed deep-learning methods in the literature [14,32,33]. On the other hand, the high ZNCC values indicate that our method can accurately approximate the patient-specific CT volume, despite using an atlas composed of diverse anatomical overlapping MR-CT scans. Previously described patch-based pseudo-CT synthesis methods reported an experimental ZNCC of 0.9349 \(\pm\) 0.0049 for a whole head and neck atlas including 18 MR-CT datasets [11,29], which is very similar to the experimental ZNCC of 0.9220 \(\pm\) 0.0255 achieved in this work. Likewise, other patch-based methods of the state-of-the-art provide average ZNCC of 0.91 \(\pm\) 0.03 and mean MAE of 125.46 \(\pm\) 24.45 HU [34], in contrast with the results presented in this work of average ZNCC and the experimental MAE of 73.9149 \(\pm\) 9.2101 HU. Works based in CNN report average SSIM of 0.92 \(\pm\) 0.02 and mean MAE of 75.7 \(\pm\) 14.6 [35] in comparison with average SSIM of 0.9943 \(\pm\) 0.0009 and mean MAE achieved by our Franken-CT approach. In the evaluation of overlapping in bone tissues other authors reported a Dice coefficient of 0.73 \(\pm\) 0.08 [36] which is in line with the average Dice coefficient of 0.7051 \(\pm\) 0.1126 for bone overlap reported in this work, quite promising but probably impacted due to the neck region results discussed previously. These facts suggest that (i) the use of an atlas composed of diverse anatomical overlapping MR-CT scans can produce similar results to those reached by complete datasets and, (ii) deep learning methods enable extracting more features and information than classical methods.