Cargando…
Combining deep learning with a kinetic model to predict dynamic PET images and generate parametric images
BACKGROUND: Dynamic positron emission tomography (PET) images are useful in clinical practice because they can be used to calculate the metabolic parameters (K(i)) of tissues using graphical methods (such as Patlak plots). K(i) is more stable than the standard uptake value and has a good reference v...
Autores principales: | Liang, Ganglin, Zhou, Jinpeng, Chen, Zixiang, Wan, Liwen, Wumener, Xieraili, Zhang, Yarong, Liang, Dong, Liang, Ying, Hu, Zhanli |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer International Publishing
2023
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10597982/ https://www.ncbi.nlm.nih.gov/pubmed/37874426 http://dx.doi.org/10.1186/s40658-023-00579-y |
Ejemplares similares
-
Dynamic and Static (18)F-FDG PET/CT Imaging in SMARCA4-Deficient Non-Small Cell Lung Cancer and Response to Therapy: A Case Report
por: Wumener, Xieraili, et al.
Publicado: (2023) -
Dynamic FDG-PET imaging for differentiating metastatic from non-metastatic lymph nodes of lung cancer
por: Wumener, Xieraili, et al.
Publicado: (2022) -
Direct Estimation of Kinetic Parametric Images for Dynamic PET
por: Wang, Guobao, et al.
Publicado: (2013) -
Dynamic PET Imaging Using Dual Texture Features
por: Ouyang, Zhanglei, et al.
Publicado: (2022) -
Synthetic high-energy computed tomography image via a Wasserstein generative adversarial network with the convolutional block attention module
por: Kong, Hai, et al.
Publicado: (2023)