ArticleEuropean radiology2023
Deep learning-based dynamic PET parametric K
Article in European radiology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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Who cites it
16 citing papers in PubMed.
- Applications of artificial intelligence in nuclear medicine.Zeitschrift fur medizinische Physik · 2026Review
- Parametric imaging of dynamic long-axial-field-of-view PET scans: Technical challenges, statistical insights and clinical applications.Zeitschrift fur medizinische Physik · 2026Review
- The impact of scan time on dynamic [Formula: see text]-FAPI-04 total-body PET parametric imaging generated by deep learning models.EJNMMI physics · 2026Article
- Patlak-PBNT: a simple population-based Patlak model to generate [EJNMMI physics · 2026Article
- Unlocking drug development: the distinctive roles of PET and SPECT imaging from molecular kinetics to therapeutic response.Archives of pharmacal research · 2026Review
- Differences in the quantitative parameter of lung lesions in dynamic chest FDG PET/CT imaging: framing protocol, reconstruction algorithm, software processing.Frontiers in oncology · 2026Article
- Noise-aware system generative model (NASGM): positron emission tomography (PET) image simulation framework with observer validation studies.Medical physics · 2025Article
- Self-supervised neural network for Patlak-based parametric imaging in dynamic [European journal of nuclear medicine and molecular imaging · 2025Article
- Bidirectional dynamic frame prediction network for total-body [EJNMMI physics · 2024Article
- Clinical feasibility study of early 30-minute dynamic FDG-PET scanning protocol for patients with lung lesions.EJNMMI physics · 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
- Machine learning methods for tracer kinetic modelling.Nuklearmedizin. Nuclear medicine · 2023Review
- Quantitation of dynamic total-body PET imaging: recent developments and future perspectives.European journal of nuclear medicine and molecular imaging · 2023Review
- Automatic brain structure segmentation forQuantitative imaging in medicine and surgery · 2023Article
- Generation of Whole-Body FDG ParametricIEEE transactions on radiation and plasma medical sciences · 2023Article
- Computed tomography-based radial endobronchial ultrasound image simulation of peripheral pulmonary lesions using deep learning.Endoscopic ultrasoundArticle
Corrections and comments
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Authors and funding
16 authors.
Funding
Abstract
objectivesPET/CT is a first-line tool for the diagnosis of lung cancer. The accuracy of quantification may suffer from various factors throughout the acquisition process. The dynamic PET parametric K
methodsBased on the imaging data of 203 participants, an improved cycle generative adversarial network incorporated with squeeze-and-excitation attention block was introduced to learn the potential mapping relationship between static PET and K
resultsCompared with those of other networks, the images synthesized by our proposed network exhibited superior performance in both qualitative and quantitative evaluation, statistical analysis, and clinical scoring. Our synthesized K
conclusionsOur proposed deep learning method can be used to synthesize highly correlated and consistent dynamic parametric images obtained from static lung PET. KEY POINTS: • Compared with conventional static PET, dynamic PET parametric K
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Registered trials
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