用于低剂量CT降噪的伪影感知生成对抗网络

近年来，生成对抗网络（GAN）用于低剂量CT（LDCT）伪影抑制表现出一定性能优势，已成为该领域新的研究热点。由于伪影分布不规律且与正常组织位置息息相关，现有GAN网络的降噪性能受限。针对上述问题，提出了一种基于伪影感知GAN的LDCT降噪算法。首先，设计了伪影方向感知生成器，该生成器在U型残差编解码结构的基础上增加了伪影方向感知子模块（ADSS），从而提高生成器对伪影方向特征的敏感度；其次，设计了注意力判别器（AttD）来提高对噪声伪影的鉴别能力；最后，设计了与网络功能相对应的损失函数，通过多种损失函数协同作用来提高网络的降噪性能。实验结果表明，与高频敏感GAN（HFSGAN）相比，该降噪算法的平均峰值信噪比（PSNR）和结构相似度（SSIM）分别提升了4.9%和2.8%，伪影抑制效果良好。

Artifacts sensing generative adversarial network for low-dose CT denoising

In recent years， Generative Adversarial Network （GAN） has become a new research hotspot in Low-Dose Computed Tomography （LDCT） artifact suppression because of its performance advantages. Due to irregular distribution and strong relevance to the normal tissues of artifacts， denoising performance of the existing GAN-based denoising networks is limited. Aiming at this problem， a LDCT denoising algorithm based on artifacts sensing GAN was proposed. Firstly， an artifacts direction sensing generator was designed. In this generator， on the basis of U-residual encoding and decoding structure， an Artifacts Direction Sensing Sub-module （ADSS） was added to improve the generator’s sensitivity to artifacts direction features. Secondly， the Attention Discriminator （AttD） was designed to improve the ability of distinguishing noise and artifacts. Finally， the loss functions corresponding to the network functions were designed. Through the cooperation of multiple loss functions， the denoising performance of network was improved. Experimental results show that compared to the High-Frequency Sensitive GAN （HFSGAN）， the proposed denoising algorithm has the average Peak Signal-to-Noise Ratio （PSNR） and Structural SIMilarity （SSIM） improved by 4.9% and 2.8% respectively， and has good artifact suppression effect.