What question did this study set out to answer?

To improve the accuracy and consistency of amyloid PET quantification using a deep learning method called DeepSUVR.

February 12, 2026Open Access

AI‐enhanced Centiloid quantification of amyloid PET images

Key Points

To improve the accuracy and consistency of amyloid PET quantification using a deep learning method called DeepSUVR.
Introduced DeepSUVR as a deep learning method for correcting Centiloid quantification.
Trained the model on 2,129 participants with 7,149 Aβ PET scans and validated on 15,807 scans from 10,543 participants across multiple datasets.
Penalized implausible longitudinal trajectories during training to enhance model effectiveness.
Increased correlation between different tracers and reduced variability in Aβ-negative results.
Showed stronger associations with cognitive outcomes and visual reads compared to standard methods.
Enhanced ability to detect small treatment effects related to amyloid accumulation.

Abstract

Abstract INTRODUCTION The Centiloid scale is the standard for amyloid (Aβ) PET quantification in research and clinical settings. However, variability between tracers and scanners remains a challenge. METHODS This study introduces DeepSUVR, a deep learning method to correct Centiloid quantification, by penalizing implausible longitudinal trajectories during training. The model was trained using data from 2,129 participants (7,149 Aβ positron emission tomography PET scans) in the Australian Imaging, Biomarkers and Lifestyle Study of ageing (AIBL)/Alzheimer's Disease Neuroimaging Initiative (ADNI) and validated using 15,807 Aβ PET scans from 10,543 participants across 10 external datasets. RESULTS DeepSUVR increased correlation between tracers, and reduced variability in the Aß‐negatives. It showed significantly stronger association with cognition, visual reads, neuropathology, and increased longitudinal consistency between studies. DeepSUVR also increased the effect size for detecting small treatment related slowing of amyloid accumulation in the A4 study. DISCUSSION DeepSUVR substantially advances Aβ PET quantification, outperforming all standard approaches, which is particularly important for clinical decision making and to detect subtle or early changes in Aβ. Highlights Novel artificial intelligence (AI)‐method that penalizes biologically implausible longitudinal trajectories, enabling the model to learn standardized uptake value ratios (SUVR) correction factors without requiring longitudinal data at inference time. Improves Centiloid consistency across tracers and studies, significantly enhancing cross‐sectional and longitudinal amyloid positron emission tomography (PET) quantification. DeepSUVR‐derived Centiloids show stronger associations with cognition, visual reads, and neuropathology. Longitudinal variability is reduced three‐ to five‐fold, enabling more reliable tracking of amyloid accumulation and better detection of treatment effects. Novel reference and target masks derived from DeepSUVR replicate most of the model's performance, offering a practical alternative for integration into existing pipelines.

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Cite This Study

Bourgeat et al. (Sun,) studied this question.

synapsesocial.com/papers/698d6efe5be6419ac0d54fc0 https://doi.org/https://doi.org/10.1002/alz.71162

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