# Generation of Synthetic Rat Brain MRI Scans with a 3D Enhanced Alpha Generative Adversarial Network
## 4 Discussion
Comparison of the scans generated after 100,000 and 200,000 training iterations confirmed that the learning process is not directly related to the representation of the loss functions. This can be seen in Figure 9 by the lack of detail in the generated scans after 100,000 training iterations--first row--and a significant improvement after 200,000 training iterations--second row. The results presented in Table 3 show that some experts had several difficulties distinguishing between real and fake scans, although some of them had no problems due to their experience with MRI acquisition and the visualisation of rat brains. Rater 1, who was very familiar with the MRIs of rat brains, said that the generated scans looked very grainy, without sufficient anatomical detail, and that the signal distribution outside the skull did not look right. He was the expert best able to distinguish between synthetic and real scans, so his input and comments were very important. Raters 3 and 4 reported some  
Figure 8: Discriminator (blue) and Generator (orange) loss function plots of the \(\alpha\)-WGANSigmaRat2 model training process. Figure 9: Axial slices of a generated scan with the \(\alpha\)-WGANSigmaRat2 model after 100,000 iterations (first row) and after 200,000 iterations (second row) of training. L and R refer to the left and right sides of the brain respectively. distortions, some strange blurs and low signal homogeneity. Rater 2 is used to analyse MRI scans from humans but not from rats. This could be the reason why he rejected many responses because they were too homogeneous and symmetrical. After generating the scans for the final test, a selection was made to ensure that all scans had correct anatomy, i.e., whether the SPM12 software could--or could not--generate the GM, WM, and CSF labels. About half of the randomly generated scans had structural defects, and the SPM software was not able to create the GM, WM and CSF labels correctly. This is the main weakness of this work and should be improved in a future work; however, it can easily be overcome by a quick visual inspection to discard the imperfect scans. It was also tested whether SPM could create the labels of the synthetic data generated by the remaining models. It was found that almost all the scans generated by \(\alpha\)-WGAN_ADNI had problems in creating the labels, so the proposed methods, i.e., the new loss functions and the normalisation layer were able to improve the quality of the generated scans, as can be seen in Figure 10.  
The segmentation results using synthetic data are shown in Table 4. The best results for global and white matter segmentation were obtained when a dataset combining \(\mathrm{D_{r}}\)[174] and \(\mathrm{D_{s}}\)[348] was used, with an improvement in the Dice score of 0.0172 and 0.0129, respectively. For grey matter and CSF segmentation, the use of the \(\mathrm{D_{r}}\)[174] and the \(\mathrm{D_{s}}\)[87] resulted in an improvement of 0.0038 and 0.0764, respectively. The introduction of only 87 synthetic scans--\(\mathrm{D_{s}}\)[87]--into the \(\mathrm{D_{r}}\)[174] dataset had a big impact, particularly on CSF segmentation, evidencing that the use of GANs can significantly improve results without the need for more scanning sessions. The use of the conventional data augmentation was also tested, and the results are shown in Table 5. These tests were conducted to assess the impact of this type of data augmentation in this situation. It was found that in this case, the use of conventional data augmentation is detrimental to the training process. This situation occurred because the data augmentation used affects the quality of the data, e.g., interpolations are used when rotating and zooming, which affected the quality of the scans. In Test 10, where 826 conventional augmented samples were used, it can be seen that results were worse due to the aggressive data augmentation used. In Tests 11 and 12, the results improved when fewer augmented samples with conventional techniques were used.