DeepTOFSino: A deep learning model for synthesizing full-dose time-of-flight bin sinograms from their corresponding low-dose sinograms
Purpose: Reducing the injected activity and/or the scanning time is a desirable goal to minimize radiation exposure and maximize patients’ comfort. To achieve this goal, we developed a deep neural network (DNN) model for synthesizing full-dose (FD) time-of-flight (TOF) bin sinograms from their corre...
Guardado en:
Autores principales: | Amirhossein Sanaat, Hossein Shooli, Sohrab Ferdowsi, Isaac Shiri, Hossein Arabi, Habib Zaidi |
---|---|
Formato: | article |
Lenguaje: | EN |
Publicado: |
Elsevier
2021
|
Materias: | |
Acceso en línea: | https://doaj.org/article/6e6aa4f1cd8c46f89f61150479de2fdc |
Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
Ejemplares similares
-
Low-dose high-pitch CT angiography of the supraaortic arteries using sinogram-affirmed iterative reconstruction.
por: Dietrich Beitzke, et al.
Publicado: (2014) -
Deep learning-based fully automated Z-axis coverage range definition from scout scans to eliminate overscanning in chest CT imaging
por: Yazdan Salimi, et al.
Publicado: (2021) -
Statistical Analysis of Weather Parameters for Sustainable Flight Operation in Nigeria
por: Olabode Abiodun Daniel
Publicado: (2021) -
Flight schedule adjustment for hub airports using multi-objective optimization
por: Tao Mei, et al.
Publicado: (2021) -
Flight Stability of Rigid Wing Airborne Wind Energy Systems
por: Filippo Trevisi, et al.
Publicado: (2021)