CCN-CL: A content-noise complementary network with contrastive learning for low-dose computed tomography denoising
CCN-CL: A content-noise complementary network with contrastive learning for low-dose computed tomography denoising
复制标题
CCN - CL：一种用于低剂量计算机断层扫描去噪的具有对比学习的内容 - 噪声互补网络
DOI：
10.1016/j.compbiomed.2022.105759
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发表时间：
2022-06
影响因子：
7.7
通讯作者：
Yufei Tang;Qiang Du;Jiping Wang;Zhongyi Wu;Yunxiang Li;Ming Li;Xiaodong Yang;Jian Zheng
中科院分区：
文献类型：
--
作者：
Yufei Tang;Qiang Du;Jiping Wang;Zhongyi Wu;Yunxiang Li;Ming Li;Xiaodong Yang;Jian Zheng
In recent years, low-dose computed tomography (LDCT) has played an increasingly important role in the diagnosis CT to reduce the potential adverse effects of x-ray radiation on patients while maintaining the same diagnostic image quality. Current deep learning-based denoising methods applied to LDCT imaging only use normal dose CT (NDCT) images as positive examples to guide the denoising process. Recent studies on contrastive learning have proved that the original images as negative examples can also be helpful for network learning. Therefore, this paper proposes a novel content-noise complementary network with contrastive learning for an LDCT denoising task. First, to better train our proposed network, a contrastive learning loss, taking the NDCT image as a positive example and the original LDCT image as a negative example to guide the network learning is added. Furthermore, we also design a network structure that combines content-noise complementary learning strategy, attention mechanism, and deformable convolution for better network performance. In an evaluation study, we compare the performance of our designed network with some of the state-of-the-art methods in the 2016 NIH-AAPM-Mayo Clinic Low Dose CT Grand Challenge dataset. The quantitative and qualitative evaluation results demonstrate the feasibility and effectiveness of applying our proposed CCN-CL network model as a new deep learning-based LDCT denoising method.
