  Purpose: CBCT-based adaptive radiotherapy requires daily images for accurate
dose calculations. This study investigates the feasibility of applying a single
convolutional network to facilitate CBCT-to-CT synthesis for head-and-neck,
lung, and breast cancer patients. Methods: Ninety-nine patients diagnosed with
head-and-neck, lung or breast cancer undergoing radiotherapy with CBCT-based
position verification were included in this study. CBCTs were registered to
planning CTs according to clinical procedures. Three cycle-consistent
generative adversarial networks (cycle-GANs) were trained in an unpaired manner
on 15 patients per anatomical site generating synthetic-CTs (sCTs). Another
network was trained with all the anatomical sites together. Performances of all
four networks were compared and evaluated for image similarity against rescan
CT (rCT). Clinical plans were recalculated on CT and sCT and analysed through
voxel-based dose differences and {\gamma}-analysis. Results: A sCT was
generated in 10 seconds. Image similarity was comparable between models trained
on different anatomical sites and a single model for all sites. Mean dose
differences < 0.5% were obtained in high-dose regions. Mean gamma (2%,2mm)
pass-rates > 95% were achieved for all sites. Conclusions: Cycle-GAN reduced
CBCT artefacts and increased HU similarity to CT, enabling sCT-based dose
calculations. The speed of the network can facilitate on-line adaptive
radiotherapy using a single network for head-and-neck, lung and breast cancer
patients.
