Evidence map›Paper›PMID 42728741›Full record

ArticleAlzheimer's & dementia : the journal of the Alzheimer's Association2026

SynthPET: A 3D generative AI approach for FDG-PET image synthesis from T1-weighted MRI and ASL CBF in Alzheimer's disease.

Xavier Beltran-Urbano, Katie R Jobson, Ilya M Nasrallah, Ajay Kumar, Manuel Taso, Sandhitsu R Das, Christopher A Brown, Sipei Li, Long Xie, Corey T McMillan and 6 more

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Article in Alzheimer's & dementia : the journal of the Alzheimer's Association, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed
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1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

16 authors.

Xavier Beltran-UrbanoDepartment of Bioengineering, University of Pennsylvania, Philadelphia, Pennsylvania, USA.ORCID https://orcid.org/0009-0008-7790-2850
Katie R JobsonDepartment of Neurology, University of Pennsylvania, Philadelphia, Pennsylvania, USA.
Ilya M NasrallahDepartment of Radiology, University of Pennsylvania, Philadelphia, Pennsylvania, USA.
Ajay KumarDepartment of Radiology, University of Pennsylvania, Philadelphia, Pennsylvania, USA.
Manuel TasoDepartment of Radiology, University of Pennsylvania, Philadelphia, Pennsylvania, USA.
Sandhitsu R DasDepartment of Neurology, University of Pennsylvania, Philadelphia, Pennsylvania, USA.
Christopher A BrownDepartment of Neurology, University of Pennsylvania, Philadelphia, Pennsylvania, USA.
Sipei LiDepartment of Bioengineering, University of Pennsylvania, Philadelphia, Pennsylvania, USA.
Long XieDepartment of Digital Technology and Innovation, Siemens Healthineers, Princeton, New Jersey, USA.
Corey T McMillanDepartment of Neurology, University of Pennsylvania, Philadelphia, Pennsylvania, USA.
Paul A YushkevichDepartment of Radiology, University of Pennsylvania, Philadelphia, Pennsylvania, USA.
Dave A WolkDepartment of Neurology, University of Pennsylvania, Philadelphia, Pennsylvania, USA.
Greg ZaharchukDepartment of Radiology, Stanford University, Stanford, California, USA.
Sudipto DoluiDepartment of Radiology, University of Pennsylvania, Philadelphia, Pennsylvania, USA.
John A DetreDepartment of Neurology, University of Pennsylvania, Philadelphia, Pennsylvania, USA.
Alzheimer's Disease Neuroimaging Initiative

Funding

NIH HHS
6 · The paper itself

Abstract

introductionFluorodeoxyglucose (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.

methodsTwo 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.

resultsBoth 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.

Indexed as

Alzheimer DiseaseDeep LearningMagnetic Resonance ImagingPositron-Emission TomographyAgedBrainCerebrovascular CirculationFemaleFluorodeoxyglucose F18Generative Artificial IntelligenceHumansImaging, Three-DimensionalMalePerfusion Magnetic Resonance ImagingRadiopharmaceuticalsSpin LabelsFluorodeoxyglucose F18RadiopharmaceuticalsSpin LabelsAlzheimer's diseasearterial spin labelingcerebral blood flowcross‐modality synthesisdeep learningfluorodeoxyglucose positron emission tomographygenerative adversarial networkneurodegenerationsynthetic positron emission tomographyT1‐weighted magnetic resonance imagingtransfer learning

Identifiers

PMID42728741
PMCPMC13569989

What Socratic holds

Textmetadata
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.