Image synthesis is used to generate synthetic CTs (sCTs) from on-treatment cone-beam CTs (CBCTs) with a view to improving image quality and enabling accurate dose computation to facilitate a CBCT-based adaptive radiotherapy workflow. As this area of research gains momentum, developments in sCT generation methods are difficult to compare due to the lack of large public datasets and sizeable variation in training procedures. To compare and assess the latest advancements in sCT generation, the SynthRAD2023 challenge provides a public dataset and evaluation framework for both MR and CBCT to sCT synthesis. Our contribution focuses on the second task, CBCT-to-sCT synthesis. By leveraging a multi-channel input to emphasize specific image features, our approach effectively addresses some of the challenges inherent in CBCT imaging, whilst restoring the contrast necessary for accurate visualisation of patients' anatomy. Additionally, we introduce an auxiliary fusion network to further enhance the fidelity of generated sCT images.

用于改善图像质量并实现精确剂量计算的图像合成技术，通过多通道输入强调特定图像特征，有效应对CBCT成像中的挑战，并引入辅助融合网络以进一步提高生成sCT图像的保真度。

多通道循环生成对抗网络在CBCT到CT的合成中的应用