Affiliations: [a] Lauterbur Research Center for Biomedical Imaging, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
| [b] Computer Department, North China Electric Power University, Baoding, China
| [c] Department of Radiology, Zhongnan Hospital of Wuhan University, Wuhan, China
| [d] Radiology Department, Guangzhou Women and Children’s Medical Center, Guangzhou Medical University, Guangzhou, China
Correspondence:
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Corresponding author: Zhanli Hu, Lauterbur Research Center for Biomedical Imaging, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China. E-mail: [email protected] and Xiaochun Zhang, Radiology Department, Guangzhou Women and Children’s Medical Center, Guangzhou Medical University, Guangzhou 510623, China. E-mail: [email protected].
Note: [1] These authors contributed equally.
Abstract: BACKGROUND:Coronary computed tomography angiography (CCTA) is a noninvasive imaging modality to detect and diagnose coronary artery disease. Due to the limitations of equipment and the patient’s physiological condition, some CCTA images collected by 64-slice spiral computed tomography (CT) have motion artifacts in the right coronary artery, left circumflex coronary artery and other positions. OBJECTIVE:To perform coronary artery motion artifact correction on clinical CCTA images collected by Siemens 64-slice spiral CT and evaluate the artifact correction method. METHODS:We propose a novel method based on the generative adversarial network (GAN) to correct artifacts of CCTA clinical images. We use CCTA clinical images collected by 64-slice spiral CT as the original dataset. Pairs of regions of interest (ROIs) cropped from original dataset or images with and without motion artifacts are used to train the dual-zone GAN. When predicting the CCTA images, the network inputs only the clinical images with motion artifacts. RESULTS:Experiments show that this network effectively corrects CCTA motion artifacts. Regardless of ROIs or images, the peak signal to noise ratio (PSNR), structural similarity (SSIM), mean square error (MSE) and mean absolute error (MAE) of the generated images are greatly improved compared to those of the input data. In addition, based on scores from physicians, the average score for the coronary artery artifact correction of the output images is higher. CONCLUSIONS:This study demonstrates that the dual-zone GAN has the excellent ability to correct motion artifacts in the coronary arteries and maintain the overall characteristics of CCTA clinical images.
