ArticleScientific reports2021
Restoration of amyloid PET images obtained with short-time data using a generative adversarial networks framework.
Article in Scientific reports, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers, 1 of them a synthesis that pooled it.
What it found
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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
15 citing papers in PubMed, 1 synthesis or guideline pooled it.
- From Single-Modal to Multi-Modal Artificial Intelligence in Alzheimer's Disease: A Systematic Review of Databases, Modalities, Diagnostic Performance, and Clinical Translation Challenges.Sensors (Basel, Switzerland) · 2026Pooled it
- Language-enhanced generative modeling for amyloid PET synthesis from MRI and blood biomarkers.iScience · 2026Article
- Technical Optimization Strategies for Amyloid PET Under Challenging Acquisition Conditions: A Comprehensive Narrative Review.Diagnostics (Basel, Switzerland) · 2026Review
- Ultra-fast [EJNMMI physics · 2024Article
- Impact of shortening time on diagnosis ofEJNMMI research · 2024Article
- Deep Learning-Driven Estimation of Centiloid Scales from Amyloid PET Images withBrain sciences · 2024Article
- A Review on Low-Dose Emission Tomography Post-Reconstruction Denoising with Neural Network Approaches.IEEE transactions on radiation and plasma medical sciences · 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
- Applications of Generative Adversarial Networks (GANs) in Positron Emission Tomography (PET) imaging: A review.European journal of nuclear medicine and molecular imaging · 2022Review
- Virtual high-count PET image generation using a deep learning method.Medical physics · 2022Article
- Deep learning-based image reconstruction and post-processing methods in positron emission tomography for low-dose imaging and resolution enhancement.European journal of nuclear medicine and molecular imaging · 2022Review
- The use of deep learning technology in dance movement generation.Frontiers in neurorobotics · 2022Article
- A Brief History of Nuclear Medicine Physics, Instrumentation, and Data Sciences in Korea.Nuclear medicine and molecular imaging · 2021Review
- Artificial Intelligence-Based Image Enhancement in PET Imaging: Noise Reduction and Resolution Enhancement.PET clinics · 2021Review
Corrections and comments
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Authors and funding
7 authors.
Funding
Abstract
Our purpose in this study is to evaluate the clinical feasibility of deep-learning techniques for F-18 florbetaben (FBB) positron emission tomography (PET) image reconstruction using data acquired in a short time. We reconstructed raw FBB PET data of 294 patients acquired for 20 and 2 min into standard-time scanning PET (PET
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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.