School of Biomedical Engineering (Suzhou), Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, 230026, China; Medical Imaging Department, Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou, 215163, China.
Medical Imaging Department, Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou, 215163, China; Institute of Electronic Information Engineering, Changchun University of Science and Technology, Changchun, 130013, China.
Comput Biol Med. 2022 Aug;147:105759. doi: 10.1016/j.compbiomed.2022.105759. Epub 2022 Jun 20.
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.
近年来，低剂量计算机断层扫描（LDCT）在诊断 CT 中发挥了越来越重要的作用，旨在降低 X 射线辐射对患者的潜在不良影响，同时保持相同的诊断图像质量。目前，应用于 LDCT 成像的基于深度学习的去噪方法仅将正常剂量 CT（NDCT）图像用作正例来指导去噪过程。最近关于对比学习的研究已经证明，原始图像作为负例也可以帮助网络学习。因此，本文提出了一种用于 LDCT 去噪任务的具有对比学习的新型内容-噪声互补网络。首先，为了更好地训练我们提出的网络，我们添加了一个对比学习损失，将 NDCT 图像作为正例，原始 LDCT 图像作为负例来指导网络学习。此外，我们还设计了一种网络结构，结合内容-噪声互补学习策略、注意力机制和可变形卷积，以提高网络性能。在评估研究中，我们将我们设计的网络的性能与 2016 年 NIH-AAPM-Mayo 诊所低剂量 CT 大挑战数据集的一些最先进的方法进行了比较。定量和定性评估结果证明了应用我们提出的 CCN-CL 网络模型作为一种新的基于深度学习的 LDCT 去噪方法的可行性和有效性。
