A model-based deep network for limited-angle computed tomography image reconstruction

In this paper, we propose a variational model for the limited-angle computed tomography (CT) image reconstruction and convert it into an end-to-end deep network. We first use the penalty method to solve the model and divide it into three iterative subproblems, where the first subproblem completes the sinograms, the second refines the CT images, and the last merges the outputs of the first two subproblems. In each iteration, we use the convolutional neural networks (CNNs) to approximate 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 related methods for the limited-angle CT image reconstruction.