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Generating synthetic images from cone beam computed tomography using self-attention residual UNet for head and neck radiotherapy

Background and purpose: Accurate CT numbers in Cone Beam CT (CBCT) are crucial for precise dose calculations in adaptive radiotherapy (ART). This study aimed to generate synthetic CT (sCT) from CBCT using deep learning (DL) models in head and neck (HN) radiotherapy. Materials and methods: A novel DL...

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Autori principali: S.A. Yoganathan, Souha Aouadi, Sharib Ahmed, Satheesh Paloor, Tarraf Torfeh, Noora Al-Hammadi, Rabih Hammoud
Natura: Artigo
Lingua:Inglês
Pubblicazione: Elsevier 2023-10-01
Serie:Physics and Imaging in Radiation Oncology
Soggetti:
CBCT
Synthetic CT
Deep-learning
Attention
UNet
Adaptive Radiotherapy
Accesso online:http://www.sciencedirect.com/science/article/pii/S2405631623001033
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