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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Hlavní autoři:	Paritt Wongtrakool, Chanon Puttanawarut, Pimolpun Changkaew, Supakiet Piasanthia, Pareena Earwong, Nauljun Stansook, Suphalak Khachonkham
Médium:	Artigo
Jazyk:	Inglês
Vydáno:	BMC 2025-02-01
Edice:	Radiation Oncology
Témata:	Radiotherapy Synthetic CT Deep learning StarGAN CycleGAN CBCT
On-line přístup:	https://doi.org/10.1186/s13014-025-02590-2
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