  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.
