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Updated: Jan 3, 2026
Quantitative 3D In Silico Modeling q3DISM of Cerebral Amyloid-beta Phagocytosis in Rodent Models of Alzheimer's Disease
Published on: December 26, 2016
Amyloid PET Quantification Via End-to-End Training of a Deep Learning
Ji-Young Kim1, Hoon Young Suh1, Hyun Gee Ryoo1
1Department of Nuclear Medicine, Seoul National University Hospital, 010 Daehak-Ro Jongno-Gu, Seoul, 03080 South Korea.
Purpose:
Although quantification of amyloid positron emission tomography (PET) is important for evaluating patients with cognitive impairment, its routine clinical use is hampered by complicated preprocessing steps and required MRI. Here, we suggested a one-step quantification based on deep learning using native-space amyloid PET images of different radiotracers acquired from multiple centers.
Methods:
Amyloid PET data of the Alzheimer Disease Neuroimaging Initiative (ADNI) were used for this study. A training/validation consists of 850 florbetapir PET images. Three hundred sixty-six florbetapir and 89 florbetaben PET images were used as test sets to evaluate the model. Native-space amyloid PET images were used as inputs, and the outputs were standardized uptake value ratios (SUVRs) calculated by the conventional MR-based method.
Results:
The mean absolute errors (MAEs) of the composite SUVR were 0.040, 0.060, and 0.050 of training/validation and test sets for florbetapir PET and a test set for florbetaben PET, respectively. The agreement of amyloid positivity measured by Cohen's kappa for test sets of florbetapir and florbetaben PET were 0.87 and 0.89, respectively.
Conclusion:
We suggest a one-step quantification method for amyloid PET via a deep learning model. The model is highly reliable to quantify the amyloid PET regardless of multicenter images and various radiotracers.
