Abstract: 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.

Key words: artificial intelligence, generative adversarial network, coronary CT angiography, motion artifacts