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

CBCT-to-CT synthesis with a single neural network for head-and-neck, lung and breast cancer adaptive radiotherapy