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.

中文翻译：

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使用双区循环生成对抗网络校正冠状动脉计算机断层扫描血管造影图像中的运动伪影

背景：冠状动脉计算机断层扫描血管造影（CCTA）是一种检测和诊断冠状动脉疾病的无创成像方式。由于设备和患者生理条件的限制，部分64排螺旋CT（CT）采集的CCTA图像在右冠状动脉、左旋冠状动脉等部位存在运动伪影。目的：对西门子64排螺旋CT采集的临床CCTA图像进行冠状动脉运动伪影校正，并对伪影校正方法进行评价。方法：我们提出了一种基于生成对抗网络（GAN）的新方法来纠正 CCTA 临床图像的伪影。我们使用 64 层螺旋 CT 收集的 CCTA 临床图像作为原始数据集。从原始数据集或带有和不带有运动伪影的图像裁剪的感兴趣区域 (ROI) 对用于训练双区 GAN。在预测 CCTA 图像时，网络仅输入具有运动伪影的临床图像。结果：实验表明，该网络有效地纠正了 CCTA 运动伪影。无论 ROI 还是图像，与输入数据相比，生成图像的峰值信噪比 (PSNR)、结构相似性 (SSIM)、均方误差 (MSE) 和平均绝对误差 (MAE) 都有很大提高。此外，根据医生的分数，输出图像的冠状动脉伪影校正的平均分数更高。结论：