Nonsmooth nonconvex LDCT image reconstruction via learned descent algorithm
Qingchao Zhang, Xiaojing Ye, Yunmei Chen
Abstract
Deep neural network architectures based on unrolling optimization algorithms have been widely adopted in deep-learning based image reconstruction applications in recent years. However, these architectures only mimic the iterative schemes of the corresponding algorithms, but lack rigorous convergence guarantee; and the learned network layers are difficult to interpret. These issues have hindered their applications in clinical use. In this paper, we develop an efficient Learned Descent Algorithm with a Line Search strategy (LDA-LS) and apply it to the nonconvex nonsmooth optimization problem of low-dose CT (LDCT) reconstruction. We show that LDA-LS yields a highly interpretable neural network architecture, where the regularization parameterized as multilayer perception is explicitly integrated into the iterative scheme and learned during the training process. We demonstrate that LDA-LS retains convergence guarantee as classical optimization algorithms, while achieving improved efficiency and accuracy in LDCT image reconstruction problems.