  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.
