<ul><li>Cone-beam computed tomography (CBCT) for image-guided radiotherapy lacks soft-tissue contrast and accuracy in dose calculation, leading to interest in Synthetic CT (sCT) generation using deep learning methods.</li><li>A privacy-preserving federated learning (FL) framework was proposed for CBCT-to-sCT translation in head and neck regions, showcasing cross-silo horizontal FL approach.</li><li>A generative adversarial network was collaboratively trained on data from three European medical centers, demonstrating good generalization across centers with promising results on an external validation dataset.</li><li>The federated model achieved comparable performance on the validation dataset without additional training, highlighting the potential of FL for CBCT-to-sCT synthesis across institutions while preserving data privacy.</li></ul>

A Privacy-Preserving Federated Learning Framework for Generalizable CBCT to Synthetic CT Translation in Head and Neck

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