ReviewEuropean journal of nuclear medicine and molecular imaging2022
Applications of Generative Adversarial Networks (GANs) in Positron Emission Tomography (PET) imaging: A review.
Review in European journal of nuclear medicine and molecular imaging, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
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Who cites it
17 citing papers in PubMed.
- Generative Adversarial Networks for Modeling Bio-Electric Fields in Medicine: A Review of EEG, ECG, EMG, and EOG Applications.Bioengineering (Basel, Switzerland) · 2026Review
- Local large arterial perivascular adipose tissue metabolic and anatomical features are associated with hypertensive clinical outcomes: a PET/CT-based study.Annals of medicine · 2025Article
- Cross-modality image-to-image translation from MR to syntheticNeuroradiology · 2025Article
- Artificial Intelligence in Alzheimer's Disease Diagnosis and Prognosis Using PET-MRI: A Narrative Review of High-Impact Literature Post-Tauvid Approval.Journal of clinical medicine · 2025Review
- The potential of generative AI with prostate-specific membrane antigen (PSMA) PET/CT: challenges and future directions.Medical review (2021) · 2025Review
- Noise-aware system generative model (NASGM): positron emission tomography (PET) image simulation framework with observer validation studies.Medical physics · 2025Article
- Deep learning in nuclear medicine: from imaging to therapy.Annals of nuclear medicine · 2025Review
- Significance of multi-task deep learning neural networks for diagnosing clinically significant prostate cancer in plain abdominal CT.Frontiers in oncology · 2025Article
- LowQuantitative imaging in medicine and surgery · 2024Article
- Enhancing Interoperability and Harmonisation of Nuclear Medicine Image Data and Associated Clinical Data.Nuklearmedizin. Nuclear medicine · 2023Review
- MRAβ: A multimodal MRI-derived amyloid-β biomarker for Alzheimer's disease.Human brain mapping · 2023Article
- Quantitation of dynamic total-body PET imaging: recent developments and future perspectives.European journal of nuclear medicine and molecular imaging · 2023Review
- Generative Adversarial Network (Generative Artificial Intelligence) in Pediatric Radiology: A Systematic Review.Children (Basel, Switzerland) · 2023Review
- Hybrid Deep Learning Approach for Accurate Tumor Detection in Medical Imaging Data.Diagnostics (Basel, Switzerland) · 2023Article
- Deep learning-enhanced nuclear medicine SPECT imaging applied to cardiac studies.EJNMMI physics · 2023Review
- New research progress on 18F-FDG PET/CT radiomics for EGFR mutation prediction in lung adenocarcinoma: a review.Frontiers in oncology · 2023Review
- Generative adversarial network-created brain SPECTs of cerebral ischemia are indistinguishable to scans from real patients.Scientific reports · 2022Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
purposeThis paper reviews recent applications of Generative Adversarial Networks (GANs) in Positron Emission Tomography (PET) imaging. Recent advances in Deep Learning (DL) and GANs catalysed the research of their applications in medical imaging modalities. As a result, several unique GAN topologies have emerged and been assessed in an experimental environment over the last two years.
methodsThe present work extensively describes GAN architectures and their applications in PET imaging. The identification of relevant publications was performed via approved publication indexing websites and repositories. Web of Science, Scopus, and Google Scholar were the major sources of information.
resultsThe research identified a hundred articles that address PET imaging applications such as attenuation correction, de-noising, scatter correction, removal of artefacts, image fusion, high-dose image estimation, super-resolution, segmentation, and cross-modality synthesis. These applications are presented and accompanied by the corresponding research works.
conclusionGANs are rapidly employed in PET imaging tasks. However, specific limitations must be eliminated to reach their full potential and gain the medical community's trust in everyday clinical practice.
Indexed as
Identifiers
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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.