SynthPET: A 3D generative AI approach for FDG-PET image synthesis from T1-weighted MRI and ASL CBF in Alzheimer's disease.
Overview
- Department of Bioengineering, University of Pennsylvania, Philadelphia, Pennsylvania, USA
- Department of Neurology, University of Pennsylvania, Philadelphia, Pennsylvania, USA
- Department of Radiology, University of Pennsylvania, Philadelphia, Pennsylvania, USA
- Department of Digital Technology and Innovation, Siemens Healthineers, Princeton, New Jersey, USA
- Department of Radiology, Stanford University, Stanford, California, USA
Abstract
INTRODUCTION: Fluorodeoxyglucose (FDG) positron emission tomography (PET) is widely used for detecting metabolic changes associated with neurodegeneration in Alzheimer's disease (AD) and other dementias but is costly and involves ionizing radiation. Here we developed SynthPET, a multimodal deep learning framework that synthesizes FDG PET images from T1‐weighted (T1w) and arterial spin labeling (ASL) perfusion magnetic resonance imaging (MRI) data.
METHODS: Two conditional generative models were trained on paired MRI and FDG PET acquisitions: one using T1w alone (n = 1170) and one integrating T1w with ASL via cross‐modality transfer learning (n = 220). We evaluated synthetic FDG PET through voxel‐wise fidelity metrics, regional correlation and receiver operating characteristic analyses, blinded reader studies, and differential diagnosis across independent external cohorts.
RESULTS: Both models achieved high fidelity with real FDG PET (structural similarity > 93%) and strong diagnostic performance in AD‐specific regions, with ASL providing modest improvements. Results generalized to an independent AD external cohort.
DISCUSSION: SynthPET generates clinically plausible FDG PET from routinely acquired MRI, potentially expanding access to metabolic neuroimaging where PET is unavailable.
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Code
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adni.loni.usc.edu/data-samples/access-data
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Data
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Data availability statement
All requests for raw and analyzed data from the Penn ADRC and Penn FTD Center will be reviewed by the Penn Neurodegenerative Data Sharing Committee (PNDSC) and shared for appropriate uses through a data sharing agreement (https://
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Version 1, 27 September 2026: the first record
Recorded: type, language, journal, volume, issue, pages, dates, 16 authors, 11 keywords, 16 MeSH terms, 2 funders, 64 references.
Cite
This paper
Beltran‐Urbano, X., Jobson, K. R., Nasrallah, I. M., Kumar, A., Taso, M., Das, S. R., Brown, C. A., Li, S., Xie, L., McMillan, C. T., Yushkevich, P. A., Wolk, D. A., Zaharchuk, G., Dolui, S., Detre, J. A., & for the Alzheimer's Disease Neuroimaging Initiative. (2026). SynthPET: A 3D generative AI approach for FDG-PET image synthesis from T1-weighted MRI and ASL CBF in Alzheimer's disease. Alzheimer's & dementia : the journal of the Alzheimer's Association, 22(9), e71822. https://
BibTeX
@article{beltranurbano20
author = {Beltran‐Urbano, Xavier and Jobson, Katie R and Nasrallah, Ilya M and Kumar, Ajay and Taso, Manuel and Das, Sandhitsu R and Brown, Christopher A and Li, Sipei and Xie, Long and McMillan, Corey T and Yushkevich, Paul A and Wolk, Dave A and Zaharchuk, Greg and Dolui, Sudipto and Detre, John A and {for the Alzheimer's Disease Neuroimaging Initiative}},
title = {{SynthPET: A 3D generative AI approach for FDG-PET image synthesis from T1-weighted MRI and ASL CBF in Alzheimer's disease}},
journal = {Alzheimer's \& dementia : the journal of the Alzheimer's Association},
year = {2026},
month = sep,
volume = {22},
number = {9},
pages = {e71822},
publisher = {Wiley},
issn = {1552-5260},
doi = {10.1002/
url = {https://
pmid = {42728741},
pmcid = {PMC13569989}
}
RIS
TY - JOUR
AU - Beltran‐Urbano, Xavier
AU - Jobson, Katie R
AU - Nasrallah, Ilya M
AU - Kumar, Ajay
AU - Taso, Manuel
AU - Das, Sandhitsu R
AU - Brown, Christopher A
AU - Li, Sipei
AU - Xie, Long
AU - McMillan, Corey T
AU - Yushkevich, Paul A
AU - Wolk, Dave A
AU - Zaharchuk, Greg
AU - Dolui, Sudipto
AU - Detre, John A
AU - for the Alzheimer's Disease Neuroimaging Initiative
TI - SynthPET: A 3D generative AI approach for FDG-PET image synthesis from T1-weighted MRI and ASL CBF in Alzheimer's disease
T2 - Alzheimer's & dementia : the journal of the Alzheimer's Association
J2 - Alzheimers Dement
PY - 2026
DA - 2026/
VL - 22
IS - 9
SP - e71822
SN - 1552-5260
PB - Wiley
DO - 10.1002/
UR - https://
LA - en
ER -
CSL-JSON
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