Background: When the heart rate of a patient exceeds the physical limits of a scanning device, even retrospective electrocardiography (ECG) gating technology cannot correct motion artifacts. The purpose of this study was to use deep learning methods to correct motion artifacts in coronary computed tomography angiography (CCTA) images acquired with retrospective ECG gating. Methods: To correct motion artifacts in CCTA images, we used a cycle Wasserstein generative adversarial network with a gradient penalty (WGAN-GP) to synthesize CCTA images without motion artifacts, and applied objective image indicators and clinical quantitative scores to evaluate the images. The objective image indicators included peak signal-to-noise ratio (PSNR), structural similarity (SSIM), and normalized mean square error (NMSE). For clinical quantitative scoring, we randomly selected 50 sets of images from the test data set as the scoring data set. We invited 2 radiologists from Zhongnan Hospital of Wuhan University to score the composite images. Results: In the test images, the PSNR, SSIM, NMSE and clinical quantitative score were 24.96±1.54, 0.769±0.055, 0.031±0.023, and 4.12±0.61, respectively. The images synthesized by cycle WGAN-GP performed better on objective image indicators and clinical quantitative scores than those synthesized by cycle least squares generative adversarial network (LSGAN), UNet, WGAN, and cycle WGAN. Conclusions: Our proposed method can effectively correct the motion artifacts of coronary arteries in CCTA images and performs better than other methods. According to the performance of the clinical score, correction of images by this method does not affect the clinical diagnosis.