Anatomical evaluation of deep-learning synthetic computed tomography images generated from male pelvis cone-beam computed tomography

BACKGROUND AND PURPOSE: To improve cone-beam computed tomography (CBCT), deep-learning (DL)-models are being explored to generate synthetic CTs (sCT). The sCT evaluation is mainly focused on image quality and CT number accuracy. However, correct representation of daily anatomy of the CBCT is also im...

Descripción completa

Detalles Bibliográficos
Autores principales: de Hond, Yvonne J.M., Kerckhaert, Camiel E.M., van Eijnatten, Maureen A.J.M., van Haaren, Paul M.A., Hurkmans, Coen W., Tijssen, Rob H.N.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2023
Materias:
Original Research Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10037090/
https://www.ncbi.nlm.nih.gov/pubmed/36969503
http://dx.doi.org/10.1016/j.phro.2023.100416

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10037090/
https://www.ncbi.nlm.nih.gov/pubmed/36969503
http://dx.doi.org/10.1016/j.phro.2023.100416

Ejemplares similares