目的·探讨生成式对抗网络（generative adversarial network，GAN）去除冠状动脉（冠脉）CT血管成像（CT angiography，CTA）运动伪影的作用。方法·纳入行单心动周期扫描多时相重建的冠脉CTA受检者，随机选取约80%作为训练组，其余作为验证组用于验证模型的准确性。研究运动伪影最明显的右冠状动脉（right coronary artery，RCA）中段，将截取图像分为配对的有伪影图像和无伪影清晰参考图像。根据训练组建立GAN模型；在验证组中，使用图像分割软件ITK-SNAP把血管影像从周围组织中分割出来，包括有伪影的、GAN生成的和参考图像。计算有伪影-参考图像（dice1）和GAN生成-参考图像（dice2）的Dice系数。通过比较dice1和dice2的差异，评估GAN去除运动伪影的效果。结果·纳入90例受检者，随机选取71例（11 000张图像）为训练组，其余19例（3 006张图像）为验证组。基于受检者，RCA中段dice1和dice2分别为0.38±0.19和0.50±0.23，差异有统计学意义（P=0.006）；基于图像，RCA中段dice1和dice2分别为0.38±0.20和0.51±0.26，差异有统计学意义（P=0.000）。结论·GAN能够显著减少RCA中段的CTA运动伪影，有望成为去除冠脉CTA图像运动伪影的新方法。

Objective · To investigate the ability of generative adversarial network (GAN) to remove motion artifacts in coronary CT angiography (CTA) images. Methods · Subjects who underwent single-cardiac-cycle multi-phase CTA were included and divided into training and test group. The middle segment of the right coronary artery (RCA) was investigated because its motion artifact is the most prominent among all coronary branches. The patch image of the vessel with motion artifacts was extracted, and paired images without artifacts were considered as reference. The GAN model was established according to the training group. In the test group, vessel images were segmented out of the surrounding tissues by using ITK-SNAP software, including the vessel with artifacts, GAN-generated images and reference images. The Dice coefficients of the vessel with artifacts vs reference image (dice1) and GAN-generated images vs reference image (dice2) were calculated. By comparing the difference between dice1 and dice2, GAN’s ability in removing motion artifacts was evaluated. Results · Ninety subjects were included. Seventy-one (11 000 images) were randomly selected as the training group, and the other 19 (3 006 images) were as the test group. Based on subjects, dice1 and dice2 of the middle segment of RCA were 0.38±0.19 and 0.50±0.23, respectively (P=0.006). Based on images, the values of the middle segment of RCA were 0.38±0.20 and 0.51±0.26, respectively (P=0.000). Conclusion · GAN can significantly reduce the motion artifacts of CTA in the middle segment of RCA and has the potential to act as a new method to remove motion artifacts of coronary CTA images.