Evidence map›Paper›PMID 42837090›Full record

ArticleMolecular imaging and biology2026

Deep Learning-based Synthesis of Amyloid PET Images from Structural MRI in Alzheimer's Disease.

Zongpai Zhang, Jingpu Wu, Puyang Wang, Keyi Chai, Shanshan Jiang, Chiadi U Onyike, Gwenn S Smith, Jinyuan Zhou, Alzheimer’s Disease Neuroimaging Initiative

Abstract read
PubMed Publisher
In one paragraph

Article in Molecular imaging and biology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
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

9 authors.

Zongpai ZhangRussel Morgan Department of Radiology and Radiological Sciences, School of Medicine, Johns Hopkins University, Baltimore, MD, USA.
Jingpu WuRussel Morgan Department of Radiology and Radiological Sciences, School of Medicine, Johns Hopkins University, Baltimore, MD, USA.
Puyang WangRussel Morgan Department of Radiology and Radiological Sciences, School of Medicine, Johns Hopkins University, Baltimore, MD, USA.
Keyi ChaiRussel Morgan Department of Radiology and Radiological Sciences, School of Medicine, Johns Hopkins University, Baltimore, MD, USA.
Shanshan JiangRussel Morgan Department of Radiology and Radiological Sciences, School of Medicine, Johns Hopkins University, Baltimore, MD, USA.
Chiadi U OnyikeDepartment of Psychiatry and Behavioral Sciences, School of Medicine, Johns Hopkins University, Baltimore, MD, USA.
Gwenn S SmithRussel Morgan Department of Radiology and Radiological Sciences, School of Medicine, Johns Hopkins University, Baltimore, MD, USA.
Jinyuan ZhouRussel Morgan Department of Radiology and Radiological Sciences, School of Medicine, Johns Hopkins University, Baltimore, MD, USA. jzhou2@jhmi.edu.ORCID http://orcid.org/0000-0002-6684-7920
Alzheimer’s Disease Neuroimaging Initiative

Funding

Project 1U19AG024904 · NIA · NORTHERN CALIFORNIA INSTITUTE/RES/EDU · PI Duygu Tosun-Turgut · 2016 to 2026
$226.7M
Longitudinal Molecular Imaging of Neuropathology and Serotonin in Mild Cognitive ImpairmentR01AG059390 · NIA · JOHNS HOPKINS UNIVERSITY · PI SMITH, GWENN S · 2018 to 2022
$4.8M
Developing Protein-based MRI Biomarkers for Alzheimer's DiseaseR01AG069179 · NIA · JOHNS HOPKINS UNIVERSITY · PI ZHOU, JINYUAN · 2020 to 2024
$3.3M
NIA NIH HHS R01AG059390NIA NIH HHS R01AG069179NIA NIH HHS U19AG024904
6 · The paper itself

Abstract

rationaleAmyloid-beta (Aβ) plaques in Alzheimer's disease (AD) are commonly imaged with specific PET radiotracers. To overcome the cost and availability limitations of PET, developing a more accessible diagnostic tool is essential. This study aimed to develop a deep-learning model that can synthesize Aβ-PET images from widely available MRI scans.

methodsThe study utilized 431 subjects with both structural MRI and Aβ-PET scans from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database, including 213 cognitively normal (CN), 180 mild cognitive impairment (MCI), and 38 AD dementia cases. A specialized Vector Quantized Generative Adversarial Network (VQGAN) framework has been trained to generate Aβ-PET images from MRI features, utilizing datasets from patients across various stages of AD spectrum. A paired t-test was used to compare synthetic and real Aβ-PET scans, while an ANOVA was performed to evaluate standardized uptake value ratio differences among the CN, MCI, and AD dementia groups.

resultsThe synthesized Aβ-PET images closely resemble real Aβ-PET scans in terms of regional Aβ distribution, accurately capturing disease-stage characteristics across the AD spectrum. In three key cortical brain regions (frontal cortex, lateral temporal lobe, and posterior cingulate cortex and precuneus), the synthetic images have successfully replicated disease-related Aβ trends, showing statistically significant group differences (p < 0.05). Quantitative evaluation has confirmed the superiority of VQGAN over other models (lower MAE, higher PSNR/SSIM), demonstrating minimized errors, reduced noise, and better structural fidelity in synthesized images.

conclusionThis technology potentially offers a cost-effective, non-invasive alternative method for AD diagnosis and staging.

Indexed as

Alzheimer's diseaseAmyloid-PET synthesisDeep-learningMRIVQGAN

Identifiers

PMID42837090

What OpenQuestion 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 OpenQuestion graph.