ArticleEuropean journal of nuclear medicine and molecular imaging2021
Artificial intelligence enables whole-body positron emission tomography scans with minimal radiation exposure.
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 41 papers, 2 of them syntheses that pooled it.
What it found
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The trial behind it
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Who cites it
41 citing papers in PubMed, 2 syntheses or guidelines pooled it, 77 citations in OpenAlex.
- Deep learning for synthetic PET imaging: a systematic mapping review of techniques, metrics, and clinical relevance.European radiology experimental · 2026Pooled it
- Deep learning-based techniques for estimating high-quality full-dose positron emission tomography images from low-dose scans: a systematic review.BMC medical imaging · 2024Pooled it
- Image optimization for low-doseEJNMMI physics · 2026Article
- Diffused Multi-scale Generative Adversarial Network for low-dose PET images reconstruction.Biomedical engineering online · 2025Article
- Review
- CT-Free Attenuation Correction in Paediatric Long Axial Field-of-View Positron Emission Tomography Using Synthetic CT from Emission Data.Diagnostics (Basel, Switzerland) · 2024Article
- Deep learning-based low count whole-body positron emission tomography denoising incorporating computed tomography priors.Quantitative imaging in medicine and surgery · 2024Article
- AI-Based Noise-Reduction Filter for Whole-Body Planar Bone Scintigraphy Reliably Improves Low-Count Images.Diagnostics (Basel, Switzerland) · 2024Article
- Applications of Artificial Intelligence for Pediatric Cancer Imaging.AJR. American journal of roentgenology · 2024Review
- A Review on Low-Dose Emission Tomography Post-Reconstruction Denoising with Neural Network Approaches.IEEE transactions on radiation and plasma medical sciences · 2024Article
- Fully automated computational measurement of noise in positron emission tomography.European radiology · 2024Article
- Deep learning-based PET image denoising and reconstruction: a review.Radiological physics and technology · 2024Review
- A Review on Low-Dose Emission Tomography Post-Reconstruction Denoising with Neural Network Approaches.ArXiv · 2024Article
- Artificial Intelligence's Transformative Role in Illuminating Brain Function in Long COVID Patients Using PET/FDG.Brain sciences · 2024Article
- Short-axis PET image quality improvement based on a uEXPLORER total-body PET system through deep learning.European journal of nuclear medicine and molecular imaging · 2023Article
- Comparative study of the quantitative accuracy of oncological PET imaging based on deep learning methods.Quantitative imaging in medicine and surgery · 2023Article
- Algorithm for Reducing Overall Biological Detriment Caused by PET/CT: an Age-Based Study.Nuclear medicine and molecular imaging · 2023Article
- Acquisition time reduction in pediatricJournal of applied clinical medical physics · 2023Article
- AI Transformers for Radiation Dose Reduction in Serial Whole-Body PET Scans.Radiology. Artificial intelligence · 2023Article
- Low-count whole-body PET/MRI restoration: an evaluation of dose reduction spectrum and five state-of-the-art artificial intelligence models.European journal of nuclear medicine and molecular imaging · 2023Article
Corrections and comments
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Authors and funding
12 authors at 2 institutions in 1 country.
Funding
Abstract
purposeTo generate diagnostic
methodsWe used whole-body
resultsThe peak signal-to-noise ratio and structural similarity index were significantly higher, and the normalized root-mean-square error was significantly lower on the AI-reconstructed PET images compared to simulated 6.25% dose images (p < 0.001). Compared to the ground-truth standard-dose PET, SUV
conclusionsOur CNN model could generate simulated clinical standard
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.