# Generation of Synthetic Rat Brain MRI Scans with a 3D Enhanced Alpha Generative Adversarial Network
## 3 Results  
As explained in the Section 2, eleven different models were trained, but only the baseline model for comparison and the two best models are examined and evaluated here, as the other models are not realistic enough. To select the best metrics to evaluate the different models, various MRI-related articles dealing with 3D scans and GANs were reviewed. The evaluation metrics were then divided into two categories: quantitative--Multi-Scale Structural Similarity Index Measure (MS-SSIM), Mean Absolute Error (MAE), Normalized Cross-Correlation (NCC), Maximum-Mean Discrepancy (MMD) and Dice score--and qualitative--Visual Turing Test performed by experts. The most commonly used metrics for evaluating GANs, e.g., Frechet Inception Distance, Inception Score, and Kernel Inception Distance, were not used due to the complexity of their adaptation to 3D volumes. The mean and standard deviation were calculated for each metric. For MMD, MAE, and NCC metrics, 21,000--the number of scans in dataset *100--comparisons were made between a random real scan and a generated scan; for MS-SSIM, only 2100 comparisons were made for both real and generated data, since the computational power required was much higher than for the other metrics. The MS-SSIM value was calculated under the same conditions as in [PERSON] et al. (2019) [26], i.e., with a batch size of 8. The MS-SSIM value should be similar to the MS-SSIM value of the real dataset to prove that the distribution is similar. There is no consensus on which metrics should be used to evaluate the best GAN models; however, the use of the quantitative metrics helped to identify the worst models in the first step [35; 36; 37]. From Table 2, it can be seen that the new models outperformed the baseline model in all quantitative metrics. The \(\alpha\)-WGANSigmaRat1 had the best overall quantitative  
Figure 5: \(\alpha\)-WGANSigmaRat1 architecture. Near each layer, there is a code that explains the result of each convolution, e.g., n256k3s1p1: n256, size of the output channels (in this case 256); k3, kernel size (cubic dimension \(3\times 3\times 3\)); s1, stride (\(1\times 1\times 1\)); p1, padding (\(1\times 1\times 1\)). Please use the virtual version to get a more detailed visualisation. Figure 6: \(\alpha\)-WGANSigmaRat2 architecture. Near each layer is displayed a code that explains the result of each convolution, e.g., n256k3s1p1: n256, size of the output channels (in this case 256); k3, kernel size (cubic dimension \(3\times 3\times 3\)); s1, stride (\(1\times 1\times 1\)); p1, padding (\(1\times 1\times 1\)). Please use the virtual version to get a more detailed visualisation. results, but after a quick analysis of several scans by some specialists and the authors, the \(\alpha\)-WGANSigmaRat2 model seems to produce more realistic scans with less blur and fewer structural anomalies, as shown in Figure 7. Therefore, these two models were used for the Turing test. The \(\alpha\)-WGAN_ADNI model was discarded for the Turing test because it was too easy to distinguish between real and generated scans. This architecture, adapted from [26], was unable to properly learn the distribution of the dataset, and the generated scans contain artefacts, as seen in Figure 7.  
The Turing test was performed by four MRI experts on 50 scans, of which 20 were real and 15 + 15 were generated by each model, \(\alpha\)-WGANSigmaRat1 and \(\alpha\)-WGANSigmaRat2. Only 50 scans were selected because each evaluation is very time consuming. Therefore, it was agreed with the evaluators that 50 scans were a reasonable number. In addition, the experts had to follow some rules, such as: do not open more than one scan at a time; do not change any answer; consider only the original slices--axial plane; do not look at the dataset \(\text{D}_{\text{r}}\)[210] while performing the test. Table 3 presents the results of the Turing test and the respective expertise of each expert. The level of expertise was categorised as a function of the amount of time each expert had spent working with MRI scans of rat brains.