Deep Residual Convolutional Sparse Coding Networks for Low Dose CT Imaging

Abstract

With the explosion of deep learning algorithms, big data, and high-performance computing, deep learning has flourished in the fields of medical analysis and image processing. In this paper, we present a simple yet effective model for low dose computed tomography (CT) image processing procedure, by combining with the advantages of residual convolution network and convolutional sparse coding (DRCSC). Through the learned iterative shrinkage threshold algorithm (LISTA), we extend convolutional sparse coding to its convolutional learning from and entirely following the residual convolution network structure, which improves the network's interpretability. The network workflow consists of three components: input feature maps prepare, recursive manner for feature maps learning by convolutional sparse coding, and high-frequency information recover. Within the residual learning strategy, the deep network training become easier and preservation more detail feature. Experiments on AAPM datasets has shown the efficacy of our method. Network testing results identify that the proposed method can restrain of artifacts and noise oscillations for low dose CT imaging.