Learned Full-Sampling Reconstruction From Incomplete Data

Abstract

Sparse-view and limited-angle Computed Tomography (CT) are very challenging problems in real applications. Due to the high ill-posedness, both analytical and iterative reconstruction methods may present distortions and artifacts for such incomplete data problems. In this work, we propose a novel reconstruction model to jointly reconstruct a high-quality image and its corresponding high-resolution projection data. The model is built up by deploying regularization on both CT image and projection data, as well as by introducing a novel full-sampling condition to fuse information from both domains. Inspired by the success of deep learning methods in imaging, we utilize the convolutional neural networks to embed and learn both the interrelationship between raw data and reconstructed images and prior information such as regularization, which is implemented in an end-to-end training process. Numerical results demonstrate that the proposed approach outperforms both variational and popular learning-based reconstruction methods for the sparse-view and limited-angle CT problems.