ArticleEuropean radiology2026
MRI-to-PET synthesis via deep learning for amyloid-β quantification in Alzheimer's disease.
Article in European radiology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper, 1 of them a synthesis that pooled 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.
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.
Who cites it
1 citing paper in PubMed, 1 synthesis or guideline pooled it.
Corrections and comments
- Commented on by
Authors and funding
17 authors.
Funding
Abstract
objectivesAmyloid-β (Aβ) PET is crucial for diagnosing and monitoring Alzheimer's disease (AD), but its high cost and radiation exposure limit its use. Deep learning techniques make it possible to generate PET from structured MRI data. In this study, we built a deep learning model to generate 3D synthetic Aβ PET images from structural MRI. MATERIALS AND
methodsThe generative adversarial network with share parameters (ShareGAN) model was trained and tested with 1009 Aβ PET and paired MRI images from the Alzheimer's Disease Neuroimaging Initiative database and three tertiary hospitals in China. The 3D synthetic model operates on the whole volume rather than 2D image slices, realistically reproducing minor discrepancies between neighboring image planes. ShareGAN-based PET images were evaluated using quantitative metrics and visual assessment. Pearson correlation coefficient and Bland-Altman analyses were used to assess the correlation and concordance between synthetic and real PETs.
results3D Synthetic PET images showed high similarity and correlation with real Aβ PET in external testing sets 1 and 2 in terms of structural similarity index measure (0.898, 0.899), peak signal-to-noise ratio (34.690, 34.725), mean absolute error (0.031, 0.031), and standardized uptake value ratio (R = 0.758, 0.828). The diagnostic accuracy of PET positive or negative status in external testing sets 1 and 2 was 88.5% and 89.4%, respectively.
conclusionMRI-based 3D synthetic Aβ PET images can serve as a safe and cost-effective tool for Aβ status visualization, providing PET-eligible patients with Aβ PET-like imaging analysis to guide subsequent real Aβ PET scans. KEY POINTS: Question Amyloid-β (Aβ) PET limitations (high cost, radiation, limited access) hinder early Alzheimer's disease (AD) detection. Clinical practice urgently requires a suitable supplementary method for Aβ pathology assessment. Findings AI-synthesized 3D Synthetic Aβ PET from structural MRI demonstrated strong consistency with real PET and effectively triaged high-risk patients for confirmatory scans. Clinical relevance This non-invasive, cost-effective method holds the promise of enabling wider Aβ pathology screening, reduces unnecessary PET scans, and supports early intervention in resource-limited settings, while preserving diagnostic rigor for treatment decisions.
Indexed as
Identifiers
What Socratic holds
Registered trials
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.