Modeling dose uncertainty in cone-beam computed tomography: Predictive approach for deep learning-based synthetic computed tomography generation

Modeling dose uncertainty in cone-beam computed tomography: Predictive approach for deep learning-based synthetic computed tomography generation

Background and purpose:: Cone-beam computed tomography (CBCT) is essential in image-guided radiotherapy (RT) for patient positioning and daily dose calculation. However, CT numbers in CBCT fluctuate and differ from those in computed tomography (CT), requiring synthetic CT (sCT) generation to improve...

Full description

Saved in:
Bibliographic Details
Main Authors: Cédric Hémon, Lucía Cubero, Valentin Boussot, Romane-Alize Martin, Blanche Texier, Joël Castelli, Renaud de Crevoisier, Anaïs Barateau, Caroline Lafond, Jean-Claude Nunes
Format: Article
Language:English
Published: Elsevier 2025-01-01
Series:Physics and Imaging in Radiation Oncology
Subjects:
CBCT-to-CT generation
Uncertainty estimation
Dose uncertainty
Head and Neck
Online Access:http://www.sciencedirect.com/science/article/pii/S2405631625000090
Tags: Add Tag
No Tags, Be the first to tag this record!

Internet

http://www.sciencedirect.com/science/article/pii/S2405631625000090

Similar Items