ArticleBrain : a journal of neurology2024
Synthesizing images of tau pathology from cross-modal neuroimaging using deep learning.
Article in Brain : a journal of neurology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers, 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
20 citing papers in PubMed, 1 synthesis or guideline pooled it, 18 citations in OpenAlex.
- Deep learning for synthetic PET imaging: a systematic mapping review of techniques, metrics, and clinical relevance.European radiology experimental · 2026Pooled it
- [European journal of nuclear medicine and molecular imaging · 2026Article
- 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 · 2026Article
- Language-enhanced generative modeling for amyloid PET synthesis from MRI and blood biomarkers.iScience · 2026Article
- Advances in artificial intelligence for neuroimaging.Journal of cerebral blood flow and metabolism : official journal of the International Society of Cerebral Blood Flow and Metabolism · 2026Review
- Generating synthetic tau-PET scans in Alzheimer's disease from MRI, blood biomarkers and demographics with deep learning.medRxiv : the preprint server for health sciences · 2026Article
- Clinical impact of the Alzheimer's Disease Neuroimaging Initiative: A review of studies using ADNI data (2023 to June 2025).Alzheimer's & dementia : the journal of the Alzheimer's Association · 2026Review
- Cyclic 2.5D Perceptual Loss for Cross-Modal 3D Medical Image Synthesis: T1w MRI to Tau PET.Human brain mapping · 2026Article
- Spatially convergent functional MRI signatures of diabetes and male sex identify genetic vulnerabilities to accelerated brain ageing.Brain communications · 2026Article
- Neuropathologically validated MRI to tau PET synthesis via Covariate-modulated attention networks.bioRxiv : the preprint server for biology · 2025Article
- Correlation and Interchangeability of Amyloid, Tau, and Glucose Metabolism PET in Mild Cognitive Impairment and Alzheimer: A Review.Brain sciences · 2025Review
- Artificial Intelligence in PET Imaging for Alzheimer's Disease: A Narrative Review.Brain sciences · 2025Review
- Deep Learning MRI Models for the Differential Diagnosis of Tumefactive Demyelination versusAJNR. American journal of neuroradiology · 2025Article
- Cross-modality PET image synthesis for Parkinson's Disease diagnosis: a leap from [European journal of nuclear medicine and molecular imaging · 2025Article
- Image Synthesis in Nuclear Medicine Imaging with Deep Learning: A Review.Sensors (Basel, Switzerland) · 2024Review
- A machine learning-based prediction of tau load and distribution in Alzheimer's disease using plasma, MRI and clinical variables.medRxiv : the preprint server for health sciences · 2024Article
- The diagnostic and prognostic value of tau-PET in amnestic MCI with different FDG-PET subtypes.Annals of clinical and translational neurology · 2024Article
- Deciphering the clinico-radiological heterogeneity of dysexecutive Alzheimer's disease.Cerebral cortex (New York, N.Y. : 1991) · 2023Article
- Deep learning interpretability in neuroimaging: A comprehensive survey and methodological recommendations.Imaging neuroscience (Cambridge, Mass.)Article
- Multispectral 7 Tesla MRI as a potential predictor of dopamine transporter deficiency in Parkinson's disease.Imaging neuroscience (Cambridge, Mass.)Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
24 authors at 3 institutions in 2 countries.
Funding
Abstract
Given the prevalence of dementia and the development of pathology-specific disease-modifying therapies, high-value biomarker strategies to inform medical decision-making are critical. In vivo tau-PET is an ideal target as a biomarker for Alzheimer's disease diagnosis and treatment outcome measure. However, tau-PET is not currently widely accessible to patients compared to other neuroimaging methods. In this study, we present a convolutional neural network (CNN) model that imputes tau-PET images from more widely available cross-modality imaging inputs. Participants (n = 1192) with brain T1-weighted MRI (T1w), fluorodeoxyglucose (FDG)-PET, amyloid-PET and tau-PET were included. We found that a CNN model can impute tau-PET images with high accuracy, the highest being for the FDG-based model followed by amyloid-PET and T1w. In testing implications of artificial intelligence-imputed tau-PET, only the FDG-based model showed a significant improvement of performance in classifying tau positivity and diagnostic groups compared to the original input data, suggesting that application of the model could enhance the utility of the metabolic images. The interpretability experiment revealed that the FDG- and T1w-based models utilized the non-local input from physically remote regions of interest to estimate the tau-PET, but this was not the case for the Pittsburgh compound B-based model. This implies that the model can learn the distinct biological relationship between FDG-PET, T1w and tau-PET from the relationship between amyloid-PET and tau-PET. Our study suggests that extending neuroimaging's use with artificial intelligence to predict protein specific pathologies has great potential to inform emerging care models.
Indexed as
Identifiers
What OpenQuestion 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 OpenQuestion graph.