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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...

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Principais autores: Paritt Wongtrakool, Chanon Puttanawarut, Pimolpun Changkaew, Supakiet Piasanthia, Pareena Earwong, Nauljun Stansook, Suphalak Khachonkham
Format: Artigo
Sprog:Inglês
Udgivet: BMC 2025-02-01
Serier:Radiation Oncology
Fag:
Radiotherapy
Synthetic CT
Deep learning
StarGAN
CycleGAN
CBCT
Online adgang:https://doi.org/10.1186/s13014-025-02590-2
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