ArticleEuropean journal of nuclear medicine and molecular imaging2021
Improved amyloid burden quantification with nonspecific estimates using deep learning.
Article in European journal of nuclear medicine and molecular imaging, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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
12 citing papers in PubMed, 24 citations in OpenAlex.
- AI-enabled multimodal neuroimaging for neurotransmitter mapping in normal aging and age-related disease.Frontiers in aging neuroscience · 2026Review
- Evaluation of ComBat Harmonization for Reducing Across-Tracer Differences in Regional Amyloid PET Analyses.Human brain mapping · 2024Article
- Deep learning-based amyloid PET harmonization to predict cognitive decline in non-demented elderly.Radiology advances · 2024Article
- International Nuclear Medicine Consensus on the Clinical Use of Amyloid Positron Emission Tomography in Alzheimer's Disease.Phenomics (Cham, Switzerland) · 2023Article
- β-amyloid PET harmonisation across longitudinal studies: Application to AIBL, ADNI and OASIS3.NeuroImage · 2022Article
- Applications of Generative Adversarial Networks (GANs) in Positron Emission Tomography (PET) imaging: A review.European journal of nuclear medicine and molecular imaging · 2022Review
- Prediction of post-stroke cognitive impairment using brain FDG PET: deep learning-based approach.European journal of nuclear medicine and molecular imaging · 2022Article
- Application of artificial intelligence in brain molecular imaging.Annals of nuclear medicine · 2022Review
- Identifying Mild Alzheimer's Disease With First 30-MinFrontiers in aging neuroscience · 2022Article
- Artificial intelligence with deep learning in nuclear medicine and radiology.EJNMMI physics · 2021Review
- Deep learning improves utility of tau PET in the study of Alzheimer's disease.Alzheimer's & dementia (Amsterdam, Netherlands) · 2021Article
- Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
10 authors at 5 institutions in 2 countries.
Funding
Abstract
purposeStandardized uptake value ratio (SUVr) used to quantify amyloid-β burden from amyloid-PET scans can be biased by variations in the tracer's nonspecific (NS) binding caused by the presence of cerebrovascular disease (CeVD). In this work, we propose a novel amyloid-PET quantification approach that harnesses the intermodal image translation capability of convolutional networks to remove this undesirable source of variability.
methodsPaired MR and PET images exhibiting very low specific uptake were selected from a Singaporean amyloid-PET study involving 172 participants with different severities of CeVD. Two convolutional neural networks (CNN), ScaleNet and HighRes3DNet, and one conditional generative adversarial network (cGAN) were trained to map structural MR to NS PET images. NS estimates generated for all subjects using the most promising network were then subtracted from SUVr images to determine specific amyloid load only (SAβ
resultsMultimodal ScaleNet outperformed other networks in predicting the NS content in cortical gray matter with a mean relative error below 2%. Compared to SUVr, SAβ
conclusionRemoving the undesirable NS uptake from the amyloid load measurement is possible using deep learning and substantially improves its accuracy. This novel analysis approach opens a new window of opportunity for improved data modeling in Alzheimer's disease and for other neurodegenerative diseases that utilize PET imaging.
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.