Cycle-Consistent Generative Adversarial Network: Effect on Radiation Dose Reduction and Image Quality Improvement in Ultralow-Dose CT for Evaluation of Pulmonary Tuberculosis

OBJECTIVE: To investigate the image quality of ultralow-dose CT (ULDCT) of the chest reconstructed using a cycle-consistent generative adversarial network (CycleGAN)-based deep learning method in the evaluation of pulmonary tuberculosis. MATERIALS AND METHODS: Between June 2019 and November 2019, 10...

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Detalles Bibliográficos
Autores principales: Yan, Chenggong, Lin, Jie, Li, Haixia, Xu, Jun, Zhang, Tianjing, Chen, Hao, Woodruff, Henry C., Wu, Guangyao, Zhang, Siqi, Xu, Yikai, Lambin, Philippe
Formato: Online Artículo Texto
Lenguaje:English
Publicado: The Korean Society of Radiology 2021
Materias:
Thoracic Imaging
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8154783/
https://www.ncbi.nlm.nih.gov/pubmed/33739634
http://dx.doi.org/10.3348/kjr.2020.0988

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8154783/
https://www.ncbi.nlm.nih.gov/pubmed/33739634
http://dx.doi.org/10.3348/kjr.2020.0988

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