Generation and Evaluation of Synthetic Computed Tomography (CT) from Cone-Beam CT (CBCT) by Incorporating Feature-Driven Loss into Intensity-Based Loss Functions in Deep Convolutional Neural Network

SIMPLE SUMMARY: Despite numerous benefits of cone-beam computed tomography (CBCT), its applications to radiotherapy were limited mainly due to degraded image quality. Recently, enhancing the CBCT image quality by generating synthetic CT image by deep convolutional neural network (CNN) has become fre...

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Detalles Bibliográficos
Autores principales: Yoo, Sang Kyun, Kim, Hojin, Choi, Byoung Su, Park, Inkyung, Kim, Jin Sung
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9497126/
https://www.ncbi.nlm.nih.gov/pubmed/36139692
http://dx.doi.org/10.3390/cancers14184534

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9497126/
https://www.ncbi.nlm.nih.gov/pubmed/36139692
http://dx.doi.org/10.3390/cancers14184534

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