## 4 Discussion  
This study investigated the effect of SUVr, calculated by PMOD and FreeSurfer with different calculation algorithms, on the classification performance of amyloid-negative and amyloid-positive groups. Furthermore, the effect of the difference between the voxel-based algorithm analysis of PMOD and the FEM-based algorithm analysis of FreeSurfer on the classification results was investigated. The FEM-based algorithm analysis of the FreeSurfer algorithm made it possible to precisely parcellate the brain cortex region on MRI; however, the effect did not significantly affect the conclusion. The parcellation region of the FEM-based algorithm is smaller than the PET resolution. The advantage of a sophisticated structure resolution may disappear in the procedure of performing co-registration with SPM after the conversion to a voxel image using mri_convert. In conclusion, though performing a classification using PMOD saves time, it is considered to show similar results. The matrix size of the re-sliced image can be calculated based on the brain PET image and T1-weighted MRI, and a slight difference may occur in the extracted SUV. The SUV extraction calculations in this study were performed using high-resolution T1-weighted MRI. Because PMOD uses the AAL atlas and FreeSurfer uses the Desikan-Killiany atlas to extract the SUV, there may be some differences in the comparative analysis. Regions with a high probability of amyloid accumulation were set as the regions of interest in the brain cortex [9], and composite regions were established by synthesis. Depending on the setting range of the composite region, the classification performance may differ and may vary for each radiopharmaceutical, and new research may be required to further analyze these differences. The classification of the normal and patient groups was most effective when compared using the distribution of SUVr extracted from the FMM brain PET, as shown in Figure 5. As shown in Figure 6, the brain PET SUVr distributions of FBB and FMM, calculated using PMOD in the composite region, were more concentrated in the amyloid-negative group than in the FPN group, and more widely distributed in the positive group. The classification of amyloid-negative and amyloid-positive groups using the SUVr distribution calculated using PMOD in the composite region was observed to have a good classification ability in the order of FBB, FMM, and FPN. The sensitivity and specificity of the FreeSurfer analysis using FBB, FMM, and FPN brain PET SUVr were slightly better; however, there was no significant difference in the classification performance. The distribution of SUVr calculated by the FreeSurfer algorithm was not significantly different from the distribution of SUVr calculated by the PMOD algorithm, as shown in Figures 4-10. The correlation coefficient of the cortex region for the PMOD and FreeSurfer algorithms using the FBB brain PET SUVr was 0.95 or more, indicating a very strong correlation; however, a relatively weak correlation was observed in the central region of the brain, as shown in Figures 8-10. The weak correlation coefficient of the central brain region may be because the volume is small and the boundary between the gray matter and white matter is relatively indistinct compared to the cortex. The correlation coefficients between PMOD and FreeSurfer in the FBB and FMM brain PET SUVr of the cortex region were very strong for the two radio-pharmaceuticals, and the difference between them was not significant. Because the correlation coefficients in the cortex region of SUVr by FBB and FMM have similar tendencies, they are highly compatible with each other. The correlation coefficient of the cortex region for the PMOD and FreeSurfer algorithms using FPN brain PET SUVr was very strong, as with the other two radiopharmaceuticals (FBB and FMM), but the range of the scatterplot was slightly different. If the range of SUVr is wider than that of the normal group, it can be concluded that the classification is performed relatively well.