Time-frequency domain prior constrained deep unfolding network for low-dose CT reconstruction.

Low-dose computed tomography (LDCT) effectively reduces the risk of malignant disease; however, reducing the radiation dose introduces additional noise and stripe artifacts in the CT imaging process. While Convolutional Neural Networks (CNN) have demonstrated performance advantages in LDCT imaging tasks, their end-to-end network architecture limits adaptability to CT reconstruction tasks, leaving room for further performance improvement. To propose a low-dose CT reconstruction network based on the iterative algorithms, incorporating an interpretable network architecture to achieve superior reconstruction performance. To better adapt to CT reconstruction tasks, we proposed an interpretable deep unfolding network leveraging time-frequency and image domain priors to fully exploit the features extracted in the transform domain. The iterative optimization process of the proposed algorithm is mapped into a deep unfolding network, and a Stage Information Memory Network (SIMN) is designed to address information loss between adjacent stages and within each stage. Experimental results on Mayo and Piglet datasets show that the proposed model outperforms state-of-the-art techniques in both quantitative metrics and visual quality. The proposed network effectively removes artifacts and noise from low-dose CT images, achieving excellent reconstruction performance.