A model-guided deep network for limited-angle computed tomography

TL;DR

This paper introduces a novel deep learning approach based on a variational model for limited-angle CT reconstruction, effectively reducing artifacts and enhancing image quality by integrating prior information in both frequency and spatial domains.

Contribution

It converts a variational model into an end-to-end deep network that jointly processes sinograms and CT images, improving reconstruction quality over existing methods.

Findings

Outperforms existing algorithms in limited-angle CT reconstruction

Effectively suppresses artifacts from incomplete data

Recovers fine structural details in CT images

Abstract

In this paper, we first propose a variational model for the limited-angle computed tomography (CT) image reconstruction and then convert the model into an end-to-end deep network.We use the penalty method to solve the model and divide it into three iterative subproblems, where the first subproblem completes the sinograms by utilizing the prior information of sinograms in the frequency domain and the second refines the CT images by using the prior information of CT images in the spatial domain, and the last merges the outputs of the first two subproblems. In each iteration, we use the convolutional neural networks (CNNs) to approxiamte the solutions of the first two subproblems and, thus, obtain an end-to-end deep network for the limited-angle CT image reconstruction. Our network tackles both the sinograms and the CT images, and can simultaneously suppress the artifacts caused by the incomplete data and recover fine structural information in the CT images. Experimental results show that our method outperforms the existing algorithms for the limited-angle CT image reconstruction.