Image Quality Improvement for Chest Four-Dimensional Cone-Beam Computed Tomography by Cycle-Generative Adversarial Network

抄録

Four-dimensional cone-beam computed tomography (4D-CBCT) can visualize moving tumors, thus the 4D-CBCT-based adaptive radiation therapy (ART) may improve the quality of radiation therapy. The aim of this study is to improve the quality of 4D-CBCT images using cycle-generative adversarial network (Cycle-GAN) and evaluate these images by a quantitative index. In this study, unpaired thoracic 4D-CBCT images and four-dimensional multislice computed tomography (4D-MSCT) images in 20 patients were used for training, and synthesis of 4D-CBCT (sCT) images with improved quality was tested in another 10 patients. The mean error (ME) and mean absolute errors (MAE) were calculated to assess CT number deviation, and peak signal-to-noise ratio (PSNR) and structural similarity index (SSIM) were used to evaluate image similarity. The sCT image generated by our Cycle-GAN model effectively reduced artifacts on 4D-CBCT image. The ME and MAE were 46.5 and 61.9 in lung regions, whereas soft tissue and bone regions insufficiently restored CT number. Results of the SSIM and PSNR were significantly improved in the sCT image. The proposed Cycle-GAN method generates sCT images with a quality close to 4D-MSCT image, particularly in the lung region; however, anatomical regions with soft tissue and bone still require further improvement.