Deep Embedding-Attention-Refinement for Sparse-View CT Reconstruction

Abstract

Tomographic image reconstruction with deep learning is an emerging field of applied artificial intelligence. Reducing radiation dose with sparse views’ reconstruction is a significant task in cardiac imaging. Many efforts are contributing to sparse-view tomography imaging, but it is still a challenge for achieving good images from high sparse-view level, such as 60 views. In this study, we proposed a Deep Embedding-Attention-Refinement (DEAR) network to fundamentally address this challenge. DEAR consists of three modules including deep embedding, deep attention, and deep refinement. The measurement is extended by deep embedding network to generate artifact-reduction images. Then, the deep attention network is employed to remove sparse-view artifacts and correct wrong details introduced by deep embedding network. Finally, the deep refinement module is used to refine finer image features and structures. The results on clinical datasets demonstrate the efficiency of our proposed DEAR in edge preservation and feature recovery.