Background: Standard methods for deriving Centiloid scales from amyloid PET images are time-consuming and require considerable expert knowledge. We aimed to develop a deep learning method of automating Centiloid scale calculations from amyloid PET images with
11C-Pittsburgh Compound-B (PiB) tracer and assess
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Background: Standard methods for deriving Centiloid scales from amyloid PET images are time-consuming and require considerable expert knowledge. We aimed to develop a deep learning method of automating Centiloid scale calculations from amyloid PET images with
11C-Pittsburgh Compound-B (PiB) tracer and assess its applicability to
18F-labeled tracers without retraining. Methods: We trained models on 231
11C-PiB amyloid PET images using a 50-layer 3D ResNet architecture. The models predicted the Centiloid scale, and accuracy was assessed using mean absolute error (MAE), linear regression analysis, and Bland–Altman plots. Results: The MAEs for Alzheimer’s disease (AD) and young controls (YC) were 8.54 and 2.61, respectively, using
11C-PiB, and 8.66 and 3.56, respectively, using
18F-NAV4694. The MAEs for AD and YC were higher with
18F-florbetaben (39.8 and 7.13, respectively) and
18F-florbetapir (40.5 and 12.4, respectively), and the error rate was moderate for
18F-flutemetamol (21.3 and 4.03, respectively). Linear regression yielded a slope of 1.00, intercept of 1.26, and R
2 of 0.956, with a mean bias of −1.31 in the Centiloid scale prediction. Conclusions: We propose a deep learning means of directly predicting the Centiloid scale from amyloid PET images in a native space. Transferring the model trained on
11C-PiB directly to
18F-NAV4694 without retraining was feasible.
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