生成对抗网络下的低剂量CT图像增强

为去除低剂量计算机断层扫描（LDCT）图像中的噪声，增强去噪后图像的显示效果，提出一种生成对抗网络（GAN）下的LDCT图像增强算法。首先，将GAN与感知损失、结构损失相结合对LDCT图像进行去噪；然后，对去噪后的图像分别进行动态灰度增强和边缘轮廓增强；最后，利用非下采样轮廓波变换（NSCT）将增强后的图像在频域上分解为具有多方向性的系数子图，并将配对的高低频子图使用卷积神经网络（CNN）进行自适应融合，以重构得到增强后的计算机断层扫描（CT）图像。使用AAPM比赛公开的真实临床数据作为实验数据集，进行图像去噪、增强、融合实验，所提方法在峰值信噪比（PSNR）、结构相似度（SSIM）和均方根误差（RMSE）上的结果分别为33.015 5 dB、0.918 5和5.99。实验结果表明，所提算法在去除噪声的同时能保留CT图像的细节信息，提高图像的亮度和对比度，有助于医生更加准确地分析病情。

Low dose CT image enhancement based on generative adversarial network

In order to remove the noise in Low Dose Computed Tomography （LDCT） images and enhance the display effect of the denoised images， an LDCT image enhancement algorithm based on Generative Adversarial Network （GAN） was proposed. Firstly， GAN was combined with perceptual loss and structure loss to denoise the LDCT image. Then， dynamic gray?scale enhancement and edge contour enhancement were performed to the denoised image respectively. Finally， Non?Subsampled Contourlet Transform （NSCT） was used to decompose the enhanced image into multi?directional coefficient sub?images in the frequency domain， and the paired high? and low?frequency sub?images were adaptively fused with Convolutional Neural Network （CNN） to reconstruct the enhanced Computed Tomography （CT） image. Using the real clinical data of the AAPM competition as the experimental dataset， the image denoising， enhancement， and fusion experiments were carried out. The results of the proposed method are 33.015 5 dB， 0.918 5， and 5.99 on Peak Signal?to?Noise Ratio （PSNR）， Structural Similarity Index Measure （SSIM）， and Root Mean Square Error （RMSE） respectively. Experimental results show that the proposed algorithm retains the detailed information of the CT image while removing noise， and improves the brightness and contrast of the image， which helps doctors analyze the patient’s condition more accurately.