## 1 Introduction  
Previous research has shown an association between accumulated amyloid and cognitive changes in individuals at risk for Alzheimer's disease (AD) [1; 2; 3]. The amyloid uptake radiopharmaceuticaleticals F-18 florbetaben (FBB), F-18 fluttemamol (FMM), and F-18 florapronol (FPN, Alzavue(r), FutureChem, Seoul, Korea) show non-specific white matter and cortical uptake in patients with AD and mild cognitive impairment (MCI) compared to cognitively normal controls (NCs) [4; 5; 6; 7]. Depending on the stage of amyloid accumulation, the direction of diffusion and the amount of accumulated change play an important role in the diagnosis of AD or cognitive decline using positron emission tomography (PET) imaging [8]. The use of amyloid PET images has increased diagnostic accuracy through quantitative analyses [9; 10].  
Amyloid imaging using PET and cortical thickness measurement using MRI constitute suitable biomarkers for diagnosing early AD progression within the small brain atlas region [11]. For amyloid brain PET analysis, the accuracy is determined by several combinations of spatial normalization, reference tissue determination, and partial volume correction [12; 13; 14]. Spatial normalization is an important factor affecting the accuracy of the quantitative analysis of amyloid PET images; however, not all subjects have appropriate MRI matching. An adaptive template method is useful because the statistical power can be increased when quantitative analysis is performed using data with only amyloid PET images [15; 16; 17; 18]. The finite element method (FEM)-based algorithm brain structure calculation using FreeSurfer [19] is effective for the normalization of regions with small structures because precise calculations are possible in microregions. The resulting calculations are different from those obtained using the PMOD [20], which performs a voxel-based normalization algorithm. The quantitative evaluation of amyloid accumulation using FBB, FMM, and FPN brain PET images in subjects with NC, MCI, and AD is a key research topic for diagnosis [21; 22]. The correlation coefficient between radiotracers for amyloid PET images in the same brain atlas regions is very strong [23], especially in the cortex region; therefore, a study on their compatibility has been conducted [21]. PMOD, which implements a voxel-based analysis algorithm, and FreeSurfer, which is an algorithm based on FEM, are the most widely used software programs for calculating the SUV from PET images. The PMOD is relatively convenient to use, and the calculation process is intuitive with a shorter calculation time. Both the automated anatomical labeling (AAL) atlas of PMOD and the Desikan-Killiany Atlas of FreeSurfer showed similar accuracies in the resulting measures of quantification of amyloid brain PET data [24].  
The automated quantitative analysis has been studied using statistical comparison [25; 26; 27] and a method employing artificial intelligence [28; 29]. Brain PET analysis using PMOD and FreeSurfer provides the most reliable SUVr in the standard atlas regions of the brain in neurodegeneration studies [28; 29; 30; 31; 32; 33]. The classification of amyloid PET scans using SUVr thresholds showed good agreement with visual diagnosis [17; 22; 34; 35]. This study aimed to statistically compare amyloid brain PET images scanned with three types of radiopharmaceuticals by calculating the SUV using PMOD3.6.0 and FreeSurfer5.3.0 software. After the quantitative analysis of the tracers, the classification performance was compared, and the compatibility was evaluated by calculating the correlation coefficients. Furthermore, this study aimed to investigate the diagnostic performance of PMOD and FreeSurfer and determine its usefulness in clinical diagnosis.