Generating synthesized computed tomography from CBCT using a conditional generative adversarial network for head and neck cancer patients

Purpose: To overcome the imaging artifacts and Hounsfield unit inaccuracy limitations of cone-beam computed tomography, a conditional generative adversarial network is proposed to synthesize high-quality computed tomography-like images from cone-beam computed tomography images. Methods: A total of 1...

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Detalles Bibliográficos
Autores principales: Zhang, Yun, Ding, Sheng-gou, Gong, Xiao-chang, Yuan, Xing-xing, Lin, Jia-fan, Chen, Qi, Li, Jin-gao
Formato: Online Artículo Texto
Lenguaje:English
Publicado: SAGE Publications 2022
Materias:
Original Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8918752/
https://www.ncbi.nlm.nih.gov/pubmed/35262422
http://dx.doi.org/10.1177/15330338221085358

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8918752/
https://www.ncbi.nlm.nih.gov/pubmed/35262422
http://dx.doi.org/10.1177/15330338221085358

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