Deep learning-based prediction of regional brain Tau PET burden from MRI
Related Experiment Video
Updated: Nov 1, 2026
Hybrid PET/MRI Imaging of Alzheimer's Disease Based on 18F-AV-1451
Published on: April 18, 2025
Donghoon Kim1, Brandon C Ho1, Henk van Voorst1
1Department of Radiology, Stanford University, Stanford, California, USA.
Introduction:
Regional tau is strongly associated with Alzheimer's disease (AD) symptoms, but tau positron emission tomography (PET) is not routinely available, limiting early risk stratification.
Methods:
We trained a deep learning model to predict regional tau PET standardized uptake value ratios (SUVRs) from magnetic resonance imaging (MRI) with and without basic demographics using public cohorts (Alzheimer's Disease Neuroimaging Initiative [ADNI], Open Access Series of Imaging Studies 3 [OASIS3-tau], Anti-Amyloid Treatment in Asymptomatic Alzheimer's Disease Study [A4], Harvard Aging Brain Study [HABS]). Mean absolute error (MAE), Pearson r, area-under-the-receiver-operating-curve (AUC), and comparison with cognitive testing were assessed in external test sets (SCAN, Stanford University).
Results:
The model achieved an MAE of 0.14 (95% confidence interval [CI]: 0.12-0.16) and r = 0.72 for an early uptake region (Braak I-IV), and an MAE of 0.14 (95% CI: 0.12-0.17) and r = 0.71 for a late uptake region (Braak V-VI). AUCs for binary tau positivity in these regions were 0.87 (95% CI: 0.83-0.90) and 0.88 (95% CI: 0.84-0.91), respectively.
Discussion:
Predicting regional tau burden from magnetic resonance imaging (MRI) is feasible and may enable scalable tau-informed clinical AD care.
