摘要
为提升在低辐射剂量条件下能谱式计算机断层扫描(CT)的重建图像质量,提出了一种基于复合感知损失函数的生成对抗网络去噪模型。此方法将像素空间与人类感知的特征空间结合到网络的生成对抗过程,并引入残差学习解决网络层数加深导致的图像细节丢失问题。通过采用多个能段的CT图像作为输入,同时利用了能段内的空间相关性和能段间的能量相关性,提高了能谱CT图像的视觉灵敏度。实验结果表明:该方法将峰值信噪比提高约5 dB,结构相似性指数提高约0.2,特征相似性指数提高约0.06。与当前的低剂量CT影像去噪算法相比,本文模型可实现更好的噪声去除效果,同时能保留诊断必要的细节信息,显著提高了低剂量能谱CT图像的质量。
The continuous development and widespread use of computed tomography(CT) in medical practice have caused patients and doctors’ attention to the risk of cancer due to radiation dose. However,spectral CT can generate severe Poisson noise as a result of the division of multiple energy bins while reducing the radiation dose,which has an adverse effect on clinical diagnosis. Aiming at improving the reconstructed image quality of spectral CT under low radiation dose,a denoising model based on a generative adversarial network with the hybrid perceptual loss was proposed. The method combines pixel space with the human-perceived feature space into generative adversarial network,and introduces the residual learning to reuse features from different levels to avoid the loss of important details that come from the increase in the number of network layers. Then the Wasserstein distance is chosen instead of JensenShannon to improve the stability of training. The inputs are from multiple energy bins,so that the spatial correlation of the reconstructed image from a single energy bin and the energy correlation between different reconstructed images from multiple energy bins can be utilized to improve the visual sensitivity of low dose spectral CT images. The experiments were performed on the simulated phantom data sets from Duke University Medical Center. The results show that the proposed method increases the PSNR by about 5 dB,the structure similarity(SSIM)value by about 0.2,and the feature similarity(FSIM)value about by 0.06.Compared with current advanced low-dose CT denoising method, the hybrid loss function based generative and adversarial network achieves a better noise removal,while retaining the necessary details for diagnosis. Moreover,the spectral information also plays a positive role in the recovery of noise images,and the use of hybrid perceptual loss is superior to any single loss. Overall,the proposed generative adversarial network with a hybrid loss method can significantly improve the quality of low-dose spectra
作者
史再峰
李金卓
曹清洁
李慧龙
胡起星
SHI Zai-feng;LI Jin-zhuo;CAO Qing-jie;LI Hui-long;HU Qi-xing(School of Microelectronics,Tianjin University,Tianjin 300072,China;Tianjin Key Laboratory of Imaging and Sensing Microelectronic Technology,Tianjin 300072,China;School of Mathematical Sciences,Tianjin Normal University,Tianjin 300387,China)
出处
《吉林大学学报（工学版）》
EI
CAS
CSCD
北大核心
2020年第5期1755-1764,共10页
Journal of Jilin University:Engineering and Technology Edition
基金
国家自然科学基金项目(61674115)
天津市自然科学基金项目(17JCYBJC15900)。
关键词
信息处理技术
低剂量
生成对抗网络
残差学习
information processing technology
low dose
generative adversarial network
residual learning
