Annu Int Conf IEEE Eng Med Biol Soc. 2021 Nov;2021:3053-3056. doi: 10.1109/EMBC46164.2021.9631011.
CT machines can be tuned in order to reduce the radiation dose used for imaging, yet reducing the radiation dose results in noisy images which are not suitable in clinical practice. In order for low dose CT to be used effectively in practice this issue must be addressed. Generative Adversarial Networks (GAN) have been used widely in computer vision research and have proven themselves as a powerful tool for producing images with high perceptual quality. In this work we use a cascade of two neural networks, the first is a Generative Adversarial Network and the second is a Deep Convolutional Neural Network. The first network generates a denoised sample which is then fine-tuned by the second network via residue learning. We show that our cascaded method outperforms related works and more effectively reconstructs fine structural details in low contrast regions of the image.
CT 机能被调整以减少成像时使用的辐射剂量，然而降低辐射剂量会导致图像噪点增加，而这些图像在临床实践中并不适用。为了在实践中有效地使用低剂量 CT，这个问题必须得到解决。生成式对抗网络（GAN）在计算机视觉研究中得到了广泛的应用，并且已经证明它们是生成具有高感知质量图像的强大工具。在这项工作中，我们使用了两个神经网络的级联，第一个是生成式对抗网络，第二个是深度卷积神经网络。第一个网络生成一个去噪的样本，然后第二个网络通过残差学习对其进行微调。我们表明，我们的级联方法优于相关工作，并且更有效地重建图像中低对比度区域的精细结构细节。
