Motion artifact removal in coronary CT angiography based on generative adversarial networks
Motion artifact removal in coronary CT angiography based on generative adversarial networks
复制标题
基于生成对抗网络的冠状动脉CT血管造影运动伪影去除
DOI：
10.1007/s00330-022-08971-5
复制
发表时间：
2022-07-12
影响因子：
5.9
通讯作者：
Xie, Xueqian
中科院分区：
文献类型：
--
作者：
Zhang, Lu;Jiang, Beibei;Xie, Xueqian
目的冠状动脉运动伪影影响冠状动脉CT血管成像(CCTA)诊断的准确性，尤其是右冠状动脉中段(MRCA)。其目的是使用生成性对抗网络(GAN)来校正MRCA的CCTA运动伪影。方法313例CCTA患者在同一心动周期内的不同R-R间期有运动影响和无运动参考图像配对，另53例CCTA患者与有创冠状动脉造影(ICA)对照。Pix2Pix是一种图像到图像转换的GaN，它由受运动影响和无运动的参考对训练，以从受运动影响的图像生成无运动图像。计算峰值信噪比(PSNR)、结构相似性(SSIM)、芯片相似性系数(DSC)和Hausdorff距离(HD)来评价GaN生成图像的质量。结果在图像水平上，GaN图像的PSNR24.4~27.5四分位数0.860(0.830~0.882)0.783(0.714~0.825)，HD中位数4.47(3.00~4.47)，明显好于运动影响图像(p<；0.001)。在患者层面上，图像质量结果是相似的。与受运动影响的图像相比，GAN生成的图像改善了运动伪影缓解分数(4比1，p<；0.001)和整体图像质量分数(4比1，p<；0.001)。在与ICA对照的患者中，GAN生成的图像在识别NO、<；50%和>；=50%狭窄方面的准确率分别为81%、85%和70%，高于运动影响图像的66%、72%和68%。结论与运动影响图像相比，生成性对抗性网络生成的CCTA图像大大提高了图像质量和诊断准确率。
Objectives Coronary motion artifacts affect the diagnostic accuracy of coronary CT angiography (CCTA), especially in the mid right coronary artery (mRCA). The purpose is to correct CCTA motion artifacts of the mRCA using a GAN (generative adversarial network). Methods We included 313 patients with CCTA scans, who had paired motion-affected and motion-free reference images at different R-R interval phases in the same cardiac cycle and included another 53 CCTA cases with invasive coronary angiography (ICA) comparison. Pix2pix, an image-to-image conversion GAN, was trained by the motion-affected and motion-free reference pairs to generate motion-free images from the motion-affected images. Peak signal-to-noise ratio (PSNR), structural similarity (SSIM), Dice similarity coefficient (DSC), and Hausdorff distance (HD) were calculated to evaluate the image quality of GAN-generated images. Results At the image level, the median of PSNR, SSIM, DSC, and HD of GAN-generated images were 26.1 (interquartile: 24.4-27.5), 0.860 (0.830-0.882), 0.783 (0.714-0.825), and 4.47 (3.00-4.47), respectively, significantly better than the motion-affected images (p < 0.001). At the patient level, the image quality results were similar. GAN-generated images improved the motion artifact alleviation score (4 vs. 1, p < 0.001) and overall image quality score (4 vs. 1, p < 0.001) than those of the motion-affected images. In patients with ICA comparison, GAN-generated images achieved accuracy of 81%, 85%, and 70% in identifying no, < 50%, and >= 50% stenosis, respectively, higher than 66%, 72%, and 68% for the motion-affected images. Conclusion Generative adversarial network-generated CCTA images greatly improved the image quality and diagnostic accuracy compared to motion-affected images.