采用带对比正则化机制的先验知识迁移技术进行全身低剂量CT图像去噪
Total-Body Low-Dose CT Image Denoising using Prior Knowledge Transfer Technique with Contrastive Regularization Mechanism
AI总结：
针对全身低剂量CT去噪中现有深度学习方法重建图像过度平滑的问题，提出带对比正则化机制的师生一致性网络TSC-Net，结合动态增强模块提升去噪效果与纹理保留能力。
摘要：
降低全身CT扫描中患者受到的辐射剂量已引起医学影像学界的广泛关注。低辐射剂量可能导致噪声和伪影增多，这一事实会极大影响临床诊断。为获取高质量的全身低剂量CT（LDCT）图像，以往基于深度学习的研究工作引入了多种网络架构。但这类方法大多仅将正常剂量CT（NDCT）图像作为真值来指导去噪网络的训练，这种简单的约束会导致模型效果不佳，且重建后的图像存在过度平滑的问题。本文提出一种新型的任务内知识迁移方法，利用从NDCT图像中提取的知识辅助LDCT图像的训练过程，所得架构被称为师生一致性网络（TSC-Net），由架构完全相同的教师网络和学生网络组成。通过中间特征之间的监督，引导学生网络模仿教师网络，从而获得丰富的纹理细节。此外，为进一步挖掘CT扫描中包含的信息，本文引入了基于对比学习构建的对比正则化机制（CRM），CRM的作用是在潜在空间中拉近恢复后的CT图像与NDCT样本的距离，同时推远其与LDCT样本的距离。另外，基于注意力机制与可变形卷积机制，我们设计了动态增强模块（DEM）以提升网络的变换能力。
英文摘要：
Reducing the radiation exposure for patients in Total-body CT scans has attracted extensive attention in the medical imaging community. Given the fact that low radiation dose may result in increased noise and artifacts, which greatly affected the clinical diagnosis. To obtain high-quality Total-body Low-dose CT (LDCT) images, previous deep-learning-based research work has introduced various network architectures. However, most of these methods only adopt Normal-dose CT (NDCT) images as ground truths to guide the training of the denoising network. Such simple restriction leads the model to less effectiveness and makes the reconstructed images suffer from over-smoothing effects. In this paper, we propose a novel intra-task knowledge transfer method that leverages the distilled knowledge from NDCT images to assist the training process on LDCT images. The derived architecture is referred to as the Teacher-Student Consistency Network (TSC-Net), which consists of the teacher network and the student network with identical architecture. Through the supervision between intermediate features, the student network is encouraged to imitate the teacher network and gain abundant texture details. Moreover, to further exploit the information contained in CT scans, a contrastive regularization mechanism (CRM) built upon contrastive learning is introduced.CRM performs to pull the restored CT images closer to the NDCT samples and push far away from the LDCT samples in the latent space. In addition, based on the attention and deformable convolution mechanism, we design a Dynamic Enhancement Module (DEM) to improve the network transformation capability.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
