  Cone beam computed tomography (CBCT) images can be used for dose calculation
in adaptive radiation therapy (ART). The main challenges are the large
artefacts and inaccurate Hounsfield unit (HU) values. Currently, deformed
planning CT images are often used for this purpose, although anatomical
accuracy might be a concern. Ideally, we would like to convert CBCT images to
CT images with artifacts removed or greatly reduced and HU values corrected
while keeping the anatomical accuracy. Recently, deep learning has achieved
great success in image-to-image translation tasks. It is very difficult to
acquire paired CT and CBCT images with exactly matching anatomy for supervised
training. To overcome this limitation, we developed and tested a cycle
generative adversarial network (CycleGAN) which is an unsupervised learning
method and does not require paired training datasets to synthesize CT images
from CBCT images. The synthesized CT (sCT) images have been compared with the
deformed planning CT (dpCT) showing visual and quantitative similarity with
artifacts being removed and HU value errors being reduced from 71.78 HU to
27.98 HU. Dose calculation accuracy using sCT images has been improved over the
original CBCT images, with the average Gamma Index passing rate increased from
95.4% to 97.4% for 1 mm/1% criteria. A deformable phantom study has been
conducted and demonstrated better anatomical accuracy for sCT over dpCT.
