Preliminary study on motion artifacts removal of coronary CT angiography using generative adversarial network
10.3969/j.issn.1674-8115.2020.09.011
- VernacularTitle: 基于生成式对抗网络的冠状动脉CT血管成像运动伪影去除的初步研究
- Author:
Lu ZHANG
1
Author Information
1. Department of Radiology, Shanghai General Hospital, Shanghai Jiao Tong University
- Publication Type:Journal Article
- Keywords:
Artificial intelligence;
Coronary CT angiography;
Generative adversarial network;
Motion artifacts
- From:
Journal of Shanghai Jiaotong University(Medical Science)
2020;40(9):1229-1235
- CountryChina
- Language:Chinese
-
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 cal-culated. 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, re-spectively (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.
