ArticleMedical physics2021
Generation of synthetic PET images of synaptic density and amyloid from
Article in Medical physics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 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
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Who cites it
16 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Deep learning for synthetic PET imaging: a systematic mapping review of techniques, metrics, and clinical relevance.European radiology experimental · 2026Pooled it
- Synthesis of Amyloid Images Using a Generative Adversarial Network from 2-DimensionalJournal of nuclear medicine technology · 2026Article
- Comparison of deep learning and particle smoother EM methods for estimation of Rb-82 myocardial perfusion PET kinetic parameters.Medical physics · 2026Article
- Instrumentation Digital Twins in PET and SPECT Imaging: Current Status, Challenges, and Future Directions.Computational and structural biotechnology journal · 2026Review
- Paired PET-MRI Deep Learning Model for Translating [Medical physics · 2025Article
- Correlation and Interchangeability of Amyloid, Tau, and Glucose Metabolism PET in Mild Cognitive Impairment and Alzheimer: A Review.Brain sciences · 2025Review
- The Role of Artificial Intelligence in Advancing Theranostics Dosimetry for Cancer Therapy: a Review.Nuclear medicine and molecular imaging · 2025Review
- Deep learning in nuclear medicine: from imaging to therapy.Annals of nuclear medicine · 2025Review
- Generating synthetic brain PET images of synaptic density based on MR T1 images using deep learning.EJNMMI physics · 2025Article
- A deep learning model for generating [European journal of nuclear medicine and molecular imaging · 2024Article
- TAI-GAN: A Temporally and Anatomically Informed Generative Adversarial Network for early-to-late frame conversion in dynamic cardiac PET inter-frame motion correction.Medical image analysis · 2024Article
- Machine learning methods for tracer kinetic modelling.Nuklearmedizin. Nuclear medicine · 2023Review
- Generation of Whole-Body FDG ParametricIEEE transactions on radiation and plasma medical sciences · 2023Article
- Artificial intelligence for nuclear medicine in oncology.Annals of nuclear medicine · 2022Review
- A review on AI in PET imaging.Annals of nuclear medicine · 2022Review
- Synthetic O-(2-Neuro-oncology advancesArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
16 authors.
Funding
Abstract
purposePositron emission tomography (PET) imaging with various tracers is increasingly used in Alzheimer's disease (AD) studies. However, access to PET scans using new or less-available tracers with sophisticated synthesis and short half-life isotopes may be very limited. Therefore, it is of great significance and interest in AD research to assess the feasibility of generating synthetic PET images of less-available tracers from the PET image of another common tracer, in particular
methodsWe implemented advanced deep learning methods using the U-Net model to predict
resultsThe results showed that all four network models obtained satisfactory
conclusionsIt is feasible to use 3D U-Net-based methods to generate synthetic
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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.