光学学报, 2020, 40 (22): 2210002, 网络出版: 2020-10-25
基于改进型循环一致性生成对抗网络的低剂量CT去噪算法 下载: 1458次
Low-Dose CT Denoising Algorithm Based on Improved Cycle GAN
图像处理 低剂量CT 循环一致性生成对抗网络 密集型残差学习网络 图像去噪 image processing low-dose CT cycle generative adversarial networks DenseNet residual network image denoising
摘要
针对低剂量医学CT图像噪声大且配对数据集难以获得的问题,提出一种基于改进型循环一致性生成对抗网络的低剂量CT去噪算法。该算法使用循环一致性生成对抗网络,由未配对的数据集实现了从低剂量CT图像到标准剂量CT图像的端到端映射;同时将密集型残差学习网络模型引入到该网络生成器中,利用残差网络的特征复用性来恢复图像细节,使生成器输出图像更接近目标图像。实验研究表明,本文算法提升了去噪效果,并准确地恢复了图像细节及边缘结构,修复后的图像质量显著提升,有助于病灶的检测与分析。
Abstract
Low-dose medical computed tomography (CT) images are associated with noise problems, and it is difficult to obtain relevant paired datasets. To solve these issues, we propose a low-dose CT denoising algorithm, which is based on an improved cycle generative adversarial network. Our algorithm achieves end-to-end mapping from low-dose CT images to standard-dose CT images using unpaired datasets. In addition, to make the generator output image similar to the target image, we creatively put the DenseNet residual learning network model to the generator, wherein feature reusability is beneficial to restore the image details. Research confirms that this algorithm effectively improves the ability of edge keeping and denoising. The quality of the restored image is significantly improved, which is helpful for the detection and analysis of lesions.
朱斯琪, 王珏, 蔡玉芳. 基于改进型循环一致性生成对抗网络的低剂量CT去噪算法[J]. 光学学报, 2020, 40(22): 2210002. Siqi Zhu, Jue Wang, Yufang Cai. Low-Dose CT Denoising Algorithm Based on Improved Cycle GAN[J]. Acta Optica Sinica, 2020, 40(22): 2210002.