ArticleAJNR. American journal of neuroradiology2023
Generative Adversarial Network-Enhanced Ultra-Low-Dose [
Article in AJNR. American journal of neuroradiology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed, 7 citations in OpenAlex.
- 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
- Scanner-integrated reconstruction versus post-processing deep learning for low-countEJNMMI physics · 2026Article
- 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
- Current Trends and Applications of PET/MRI Hybrid Imaging in Neurodegenerative Diseases and Normal Aging.Diagnostics (Basel, Switzerland) · 2024Review
- [18F]PI-2620 Tau PET signal across the aging and Alzheimer's disease clinical spectrum.Imaging neuroscience (Cambridge, Mass.) · 2024Article
Corrections and comments
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Authors and funding
11 authors at 3 institutions in 2 countries.
Funding
Abstract
background and purposeWith the utility of hybrid τ PET/MR imaging in the screening, diagnosis, and follow-up of individuals with neurodegenerative diseases, we investigated whether deep learning techniques can be used in enhancing ultra-low-dose [ MATERIALS AND
methodsForty-four healthy aging participants and patients with neurodegenerative diseases were recruited for this study, and [
resultsThe enhanced ultra-low-dose τ images showed apparent noise reduction compared with the ultra-low-dose images. The regional standard uptake value ratios showed that while, in general, there is an underestimation for both image types, especially in regions with higher uptake, when focusing on the healthy-but-amyloid-positive population (with relatively lower τ uptake), this bias was reduced in the enhanced ultra-low-dose images. The radiotracer uptake patterns in the enhanced images were read accurately compared with their full-dose counterparts.
conclusionsThe clinical readings of deep learning-enhanced ultra-low-dose τ PET images were consistent with those performed with full-dose imaging, suggesting the possibility of reducing the dose and enabling more frequent examinations for dementia monitoring.
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Registered trials
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