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...
I tiakina i:
| Ngā kaituhi matua: | , , , , , , |
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| Hōputu: | Artigo |
| Reo: | Inglês |
| I whakaputaina: |
BMC
2025-02-01
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| Rangatū: | Radiation Oncology |
| Ngā marau: | |
| Urunga tuihono: | https://doi.org/10.1186/s13014-025-02590-2 |
| Ngā Tūtohu: |
Kāore He Tūtohu, Me noho koe te mea tuatahi ki te tūtohu i tēnei pūkete!
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