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.

翻译：我们提出了一种可辨别的趋同方法,称为高效的从源测算法(ELDA),用于低剂量CT(LDCT)重建;ELDA是一个高度可解释的神经网络结构,具有丰富的参数,同时保留趋同保证作为经典优化算法;为了提高重建质量,拟议的ELDA还采用了新的非本地地物绘图和相关的常规化器;我们比较ELDA与一些最先进的深层图像方法,如RED-CNN和Chnew Primal-Dual,涉及一组最不发达国家重建问题;数字实验表明,仅用19个层次的ELDA改进了重建质量,表明ELDA在溶解精度和参数效率方面表现良好。