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.
@article{arxiv.2104.12939,
title = {Provably Convergent Learned Inexact Descent Algorithm for Low-Dose CT Reconstruction},
author = {Qingchao Zhang and Mehrdad Alvandipour and Wenjun Xia and Yi Zhang and Xiaojing Ye and Yunmei Chen},
journal= {arXiv preprint arXiv:2104.12939},
year = {2021}
}