  We propose a provably convergent method, called Efficient Learned Descent
Algorithm (ELDA), for low-dose CT (LDCT) reconstruction. ELDA is a highly
interpretable neural network architecture with learned parameters and meanwhile
retains convergence guarantee as classical optimization algorithms. To improve
reconstruction quality, the proposed ELDA also employs a new non-local feature
mapping and an associated regularizer. We compare ELDA with several
state-of-the-art deep image methods, such as RED-CNN and Learned Primal-Dual,
on a set of LDCT reconstruction problems. Numerical experiments demonstrate
improvement of reconstruction quality using ELDA with merely 19 layers,
suggesting the promising performance of ELDA in solution accuracy and parameter
efficiency.
