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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| Autori principali: | , , , , , , |
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| Natura: | Artigo |
| Lingua: | Inglês |
| Pubblicazione: |
BMC
2025-02-01
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| Serie: | Radiation Oncology |
| Soggetti: | |
| Accesso online: | https://doi.org/10.1186/s13014-025-02590-2 |
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