Background Quantitative evaluation of amyloid positron emission tomography (PET) with standardized uptake value ratio (SUVR) plays a key role in clinical studies of Alzheimer’s disease (AD). We have proposed a PET-only (MR-free) amyloid quantification method, although some commercial software packages are required. The aim of this study was to develop an automated quantification tool for amyloid PET without using commercial software. Methods The quantification tool was created by combining four components: (1) anatomical standardization to positive and negative templates using NEUROSTAT stereo.exe; (2) similarity calculation between standardized images and respective templates based on normalized cross-correlation (selection of the image for SUVR measurement); (3) voxel value normalization by the mean value of reference regions (making an SUVR-scaled image); and (4) SUVR calculation based on pre-defined regions of interest (ROIs). We examined 166 subjects who underwent a [11C] Pittsburgh compound-B PET scan through the Japanese Alzheimer’s Disease Neuroimaging Initiative (J-ADNI) study. SUVRs in five ROIs (frontal lobe, temporal lobe, parietal lobe, occipital lobe, and posterior cingulate cortex and precuneus) were calculated with the cerebellar cortex as the reference region. The SUVRs obtained by our tool were compared with manual step-by-step processing and the conventional PMOD-based method (PMOD Technologies, Switzerland). Results Compared with manual step-by-step processing, our developed automated quantification tool reduced processing time by 85%. The SUVRs obtained by the developed quantification tool were consistent with those obtained by manual processing. Compared with the conventional PMOD-based method, the developed quantification tool provided 1.5% lower SUVR values, on average. We determined that this bias is likely due to the difference in anatomical standardization methods. Conclusions We developed an automated quantification tool for amyloid PET images. Using this tool, SUVR values can be quickly measured without individual MRI and without commercial software. This quantification tool may be useful for clinical studies of AD.

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Amyloid PET noninvasively visualizes amyloid-b accumulation in the brain. Visual binary reading is the standard method for interpreting amyloid PET, whereas objective quantitative evaluation is required in research and clinical trials. Anatomic standardization is important for quantitative analysis, and various standard templates are used for this purpose. To address the large differences in radioactivity distribution between amyloid-positive and amyloidnegative participants, an adaptive-template method has been proposed for the anatomic standardization of amyloid PET. In this study, we investigated the difference between the adaptivetemplate method and the single-template methods (use of a positive or a negative template) in amyloid PET quantitative evaluation, focusing on the accuracy in diagnosing Alzheimer's disease (AD). Methods: In total, 166 participants (58 healthy controls [HCs], 62 patients with mild cognitive impairment [MCI], and 46 patients with AD) who underwent 11 C-Pittsburgh compound B ( 11 C-PiB) PET through the Japanese Alzheimer's Disease Neuroimaging Initiative study were examined. For the anatomic standardization of 11 C-PiB PET images, we applied 3 methods: a positivetemplate-based method, a negative-template-based method, and an adaptive-template-based method. The positive template was created by averaging the PET images for 4 patients with AD and 7 patients with MCI. Conversely, the negative template was created by averaging the PET images for 8 HCs. In the adaptivetemplate-based method, either of the templates was used on the basis of the similarity (normalized cross-correlation [NCC]) between the individual standardized image and the corresponding template. An empiric PiB-prone region of interest was used to evaluate specific regions where amyloid-b accumulates. The reference region was the cerebellar cortex, and the evaluated regions were the posterior cingulate gyrus and precuneus and the frontal, lateral temporal, lateral parietal, and occipital lobes. The mean cortical SUV ratio (mcSUVR) was calculated for quantitative evaluation. Results: The NCCs of single-template-based methods (the positive template or negative template) showed a significant difference among the HC, MCI, and AD groups (P , 0.05), whereas the NCC of the adaptive-template-based method did not (P . 0.05). The mcSUVR exhibited significant differences among the HC, MCI, and AD groups with all methods (P , 0.05). The mcSUVR area under the curve by receiver operating characteristic analysis between the positive group (MCI and AD) and the HC group did not significantly differ among templates. With regard to diagnostic accuracy based on mcSUVR, the sensitivity of the negative-template-based and adaptive-template-based methods was superior to that of the positive-template-based method (P , 0.05); however, there was no significant difference in specificity between them. Conclusion: In quantitative evaluation of AD by amyloid PET, the adaptivetemplate-based anatomic standardization method had greater diagnostic accuracy tha...