## 2 Materials and Methods
### Images and Statistical Analysis  
For the convenience of image data management, the MRI and PET image files in the.dcm file format were converted to.nii file format. The PET scan file was loaded as a function image in the PMOD tool and cropped as required. PMOD was used to load the PET image, and, after registration in the MRI, was normalized to the MRI template. After cropping by loading the T1-weighted MRI matched with PET as the T1 MR image input, the AAL atlas was selected, segmentation was performed, the SUV was extracted, and SUVr was calculated using the reference tissue. Using the PET and MR images cropped in PMOD as the input file for FreeSurfer, segmentation was performed using recon-all. After checking the brainmask.mgz result using Freeview, the SUV was extracted from the aparc.DKTatlas+aseg.mgz and wpmarc.mgz files using SPM, and the SUVr was then calculated using the reference tissue. A MATLAB script was used to calculate SUVr using the built-in parcellation number labeled in the brain region. Image analysis was performed using a MW Digitbox (Single Processor 4 GPU, Miruware) at the Neuroscience Translational Research Solution Center (Busan, Republic of Korea). In FBB, FMM, and FPN brain PET, amyloid negativity and positivity were visually classified by three nuclear medicine specialists. Three physicians independently classified the PET images as positive or negative scans according to the interpretation guidelines for each radio-pharmaceutical. In cases of disagreement in classification, the final decision was made by consensus, which was considered the gold standard. The volume and SUVr of the individual space gray matter (GM) regions were calculated using the AAL atlas. The SUVr values of eight regions of the frontal cortex (FC), lateral temporal cortex (LTC), mesial temporal cortex (MTC), parietal cortex (PC), occipital cortex (OC), anterior and posterior cingulate cortex (GCA, GCP), and composite were calculated. The major regions with high amyloid accumulation potentials are shown in Table 2[9]. After calculating the correlation of SUVr using the PMOD and FreeSurfer algorithms, and calculating the AUC, the classification ability was calculated, and the SVUr threshold was calculated using the Youden index by MedCalc Ver. 19.7.2[40; 41]. A comparison of the diagnostic ability between the three radiopharmaceuticals using the AUC comparison algorithm demonstrated that there was no significant difference (\(p>0.05\)). The negative and positive FBB brain PET images for each individual are shown in Figure 1. For quantitative analysis, cropped T1-weighted MRI and cropped PET scans in the MRI space were used. Cropped brain segment images using the AAL atlas in the MRI space were extracted using PMOD, and are presented in Figure 1. Using [PERSON]'s \begin{table}
\begin{tabular}{l l} \hline FC * & Frontal cortex (r/l) * \\ LTC * & Lateral temporal cortex (r/l) * \\ MTC & Mesial temporal cortex (r/l) \\ PC * & Parietal cortex (r/l) * \\ OC * & Occipital cortex (r/l) * \\ GCA * & Anterior cingulate cortex (r/l) * \\ GCP * & Posterior cingulate cortex (r/l) * \\ CN & caudate nucleus (r/l) \\ PUT & Putamen (r/l) \\ THA & Thalamus (r/l) \\ PQ & precuneus (r/l) \\ CBLCTX ** & cerebellum (r/l) ** \\ Composite & Sum of regions marked with * \\ \hline \end{tabular}  
* Reference tissue.  
\end{table} 
Table 2: List of the 11 regions evaluated for SUV calculations. A composite region is the sum of regions marked with *. region classification [9], SUVr was calculated for 11 regions and the cerebellar cortex was used as the reference tissue. In most cases, it was possible to visually confirm that the segment using MRI performed well without any error. The'mri_convert' is a general purpose utility for converting between different file formats. The'mgz' file was converted to a 'nii' file and SUVr were extracted using an in-house software.