ArticleEJNMMI physics2023
Combining deep learning with a kinetic model to predict dynamic PET images and generate parametric images.
Article in EJNMMI physics, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Realization of long axial field-of-view PET technology in the clinic and research environment.The British journal of radiology · 2026Review
- Patlak-Guided Self-Supervised Learning for Dynamic PET Denoising.IEEE transactions on radiation and plasma medical sciences · 2026Article
- Kinetic modeling with total body PET-current status and future applications.The British journal of radiology · 2026Review
- Applications of artificial intelligence in nuclear medicine.Zeitschrift fur medizinische Physik · 2026Review
- A deep learning method for total-body dynamic PET imaging with dual-time-window protocols.European journal of nuclear medicine and molecular imaging · 2025Article
- Bidirectional dynamic frame prediction network for total-body [EJNMMI physics · 2024Article
- Exact parameter identification in PET pharmacokinetic modeling using the irreversible two tissue compartment modelPhysics in medicine and biology · 2024Article
Corrections and comments
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Authors and funding
9 authors.
Funding
Abstract
backgroundDynamic positron emission tomography (PET) images are useful in clinical practice because they can be used to calculate the metabolic parameters (K
methodsIn this paper, we use U-Net as a feature extractor to obtain feature vectors with a priori knowledge about the image structure of interest and then utilize a parameter generator to obtain five parameters for a two-tissue, three-compartment model and generate a time activity curve (TAC), which will become close to the original 1-h TAC through training. The above-generated dynamic PET image finally obtains the K
resultsA quantitative analysis showed that the network-generated K
conclusionsThe proposed method is feasible, and satisfactory PET quantification accuracy can be achieved using the proposed deep learning method. Further clinical validation is needed before implementing this approach in routine clinical applications.
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Registered trials
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