Synthetic CT generation from CBCT and MRI using StarGAN in the Pelvic Region

Synthetic CT generation from CBCT and MRI using StarGAN in the Pelvic Region

Abstract Rationale and objectives This study evaluated StarGAN, a deep learning model designed to generate synthetic computed tomography (sCT) images from magnetic resonance imaging (MRI) and cone-beam computed tomography (CBCT) data using a single model. The goal was to provide accurate Hounsfield...

Full description

Saved in:
Bibliographic Details
Main Authors: Paritt Wongtrakool, Chanon Puttanawarut, Pimolpun Changkaew, Supakiet Piasanthia, Pareena Earwong, Nauljun Stansook, Suphalak Khachonkham
Format: Article
Language:English
Published: BMC 2025-02-01
Series:Radiation Oncology
Subjects:
Radiotherapy
Synthetic CT
Deep learning
StarGAN
CycleGAN
CBCT
Online Access:https://doi.org/10.1186/s13014-025-02590-2
Tags: Add Tag
No Tags, Be the first to tag this record!

Internet

https://doi.org/10.1186/s13014-025-02590-2

Similar Items