Unsupervised Cone-Beam Artifact Removal using CycleGAN and Spectral Blending for Adaptive Radiotherapy

Cone-beam computed tomography (CBCT) used in radiotherapy (RT) has the advantage of being taken daily, but is difficult to use for purposes other than patient setup because of the poor image quality compared to fan-beam computed tomography (CT). Even though several methods have been proposed including the deformable image registration method to improve the quality of CBCT, the outcomes have not yet been satisfactory. Recently, deep learning has shown to produce high-quality results for various image-to-image translation tasks, suggesting the possibility of being an effective tool for converting CBCT into CT. In the field of RT, however, it may not always be possible to obtain paired datasets which consist of exactly matching CBCT and CT images. This study aimed to develop a novel, unsupervised deep-learning algorithm, which requires only unpaired CBCT and fan-beam CT images to remove the cone-beam artifact and thereby improve the quality of CBCT. Specifically, two cycle consistency generative adversarial networks (CycleGAN) were trained in the sagittal and coronal directions, and the generated results along those directions were then combined using spectral blending technique. To evaluate our methods, we applied it to American Association of Physicists in Medicine dataset. The experimental results show that our method outperforms the existing CylceGAN-based method both qualitatively and quantitatively.