Incorporating the synthetic CT image for improving the performance of deformable image registration between planning CT and cone-beam CT

OBJECTIVE: To develop a contrast learning-based generative (CLG) model for the generation of high-quality synthetic computed tomography (sCT) from low-quality cone-beam CT (CBCT). The CLG model improves the performance of deformable image registration (DIR). METHODS: This study included 100 post-bre...

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Detalles Bibliográficos
Autores principales: Li, Na, Zhou, Xuanru, Chen, Shupeng, Dai, Jingjing, Wang, Tangsheng, Zhang, Chulong, He, Wenfeng, Xie, Yaoqin, Liang, Xiaokun
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2023
Materias:
Oncology
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9993856/
https://www.ncbi.nlm.nih.gov/pubmed/36910636
http://dx.doi.org/10.3389/fonc.2023.1127866

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9993856/
https://www.ncbi.nlm.nih.gov/pubmed/36910636
http://dx.doi.org/10.3389/fonc.2023.1127866

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