## 3 Results
### Quantitative Measurements  
Figure 7 shows the similarity between the real and generated set \(\rho(\Phi(\mathbb{P}_{\textit{r\_test}})\), \(\Phi(\mathbb{P}_{\textit{g}}))\) and between the training and test set \(\rho(\Phi(\mathbb{P}_{\textit{r\_train}})\), \(\Phi(\mathbb{P}_{\textit{r\_test}}))\) measured over the entire axial level. Instead of changing the scale to [0, 1], the values on the graphs in Figure 7 are the values directly calculated from a metric. \(\rho(\Phi(\mathbb{P}_{\textit{r\_test}})\), \(\Phi(\mathbb{P}_{\textit{g}}))\) and \(\rho(\Phi(\mathbb{P}_{\textit{r\_test}})\), \(\Phi(\mathbb{P}_{\textit{r\_test}}))\) were compared for both the A\(\beta\) and axial level classes. Contrary to the results of visual evaluation, the similarity of GAN-based synthetic images differed over axial level classes regardless of the metric used, and generally the lower and higher the axial level, the lower the similarity. Ideally, the similarity between real distributions should not vary with A\(\beta\) or axial level class, but diverse variance existed according to the metrics used. In MMD and FID, the change within each range for \(\rho(\Phi(\mathbb{P}_{\textit{r\_test}})\), \(\Phi(\mathbb{P}_{\textit{g}}))\) was greater than that of \(\rho(\Phi(\mathbb{P}_{\textit{r\_train}})\), \(\Phi(\mathbb{P}_{\textit{r\_test}}))\). Meanwhile, when the 1-NN LOO accuracy was evaluated, the difference in the ranges of \(\rho(\Phi(\mathbb{P}_{\textit{r\_train}})\), \(\Phi(\mathbb{P}_{\textit{r\_test}}))\) and \(\rho(\Phi(\mathbb{P}_{\textit{r\_test}})\), \(\Phi(\mathbb{P}_{\textit{g}}))\) appeared relatively small. The MMD and 1-NN LOO accuracies were the apparent similarities between \(\rho(\[PERSON](\mathbb{P}_{\textit{r\_train}})\), \(\[PERSON](\mathbb{P}_{\textit{r\_test}}))\) and \(\rho(\[PERSON](\mathbb{P}_{\textit{r\_test}})\), \(\[PERSON](\mathbb{P}_{\textit{g}}))\) at the axial level; however, similar FID measurements were obtained at or near the 22-th axial level.  
Figure 6: Feature visualization comparing the distribution between real and generated images according to brain amyloid distribution using t-SNE. Each scatter plot represents feature visualization according to a representative axial level including (**A**) temporal lobe (TL), (**B**) frontal lobe (FL), (**C**) posterior cingulate/precuneus 1 (PP1), (**D**) posterior cingulate/precuneus 2 (PP2), (**E**) parietal lobe 1 (PL1) and (**F**) parietal lobe 2 (PL2).  
Table 2 compares the similarity values between \(\rho(\Phi(\mathbb{P}_{r\_train})\), \(\Phi(\mathbb{P}_{r\_test}))\) and \(\rho(\Phi(\mathbb{P}_{r\_test})\), \(\Phi(\mathbb{P}_{g}))\) measured in the representative axial plane using MMD, FID, and 1-NN LOO. When evaluating A\(\beta\)-negative images according to quantitative metrics, the representative axial levels that appeared the most similar to real images were PL1 (MMD: 0.2284), PP2 (FID: 6.8253), and PP1 (1-NN LOO accuracy: 0.8562), and each of the metrics identified different levels as the most similar. A set of generated A\(\beta\)-positive images, meanwhile, were similar to real images in PP2, regardless of the selection of quantitative metrics (MMD: 0.1865, FID: 5.8919, 1-NN LOO accuracy: 0.7391). For all 3 metrics, the GAN model used in the experiment produced more realistic synthetic images for A\(\beta\)-positive images than the A\(\beta\)-negative images.