Institute of Nuclear and New Energy Technology, Tsinghua University, Beijing, China.
Beijing Key Laboratory of Nuclear Detection, Beijing, China.
J Xray Sci Technol. 2018;26(4):523-534. doi: 10.3233/XST-17356.
Statistical noise may degrade the x-ray image quality of digital radiography (DR) system. This corruption can be alleviated by extending exposure time of detectors and increasing the intensity of radiation. However, in some instances, such as the security check and medical imaging examination, the system demands rapid and low-dose detection. In this study, we propose and test a generative adversarial network (GAN) based x-ray image denoising method. Images used in this study were acquired from a digital radiography (DR) imaging system. Promising results have been obtained in our experiments with x-ray images for the security check application. The Experiment results demonstrated that the proposed new image denoising method was able to effectively remove the statistical noise from x-ray images, while kept sharp edge and clear structure. Thus, comparing with the traditional convolutional neural network (CNN) based method, the proposed new method generates more plausible-looking images, which contains more details.
统计噪声可能会降低数字射线照相术（DR）系统的 X 射线图像质量。通过延长探测器的曝光时间和增加辐射强度，可以减轻这种损坏。然而，在某些情况下，例如安全检查和医学成像检查，系统需要快速和低剂量检测。在本研究中，我们提出并测试了一种基于生成对抗网络（GAN）的 X 射线图像去噪方法。本研究中使用的图像是从数字射线照相术（DR）成像系统中获取的。对于安全检查应用，我们的实验结果得到了有希望的结果。实验结果表明，所提出的新的图像去噪方法能够有效地从 X 射线图像中去除统计噪声，同时保持锐利的边缘和清晰的结构。因此，与基于传统卷积神经网络（CNN）的方法相比，所提出的新方法生成的图像更逼真，包含更多细节。
