Paper Title
Low-Dose CT Restoration Using Deep Convolutional Neural Network

Abstract
Computed tomography (CT) is one of the most important imaging modalities in modern hospitals and clinics. Many approaches were proposed to improve the quality of low-dose CT image, which is an effective solution to alleviate radiation risk to patients, it also introduces additional noise and streak artifacts. In this paper, we introduce a deep Convolutional Neural Network framework designed for low-dose CT restoration. With the developments of new networkunits such as rectified linear unit(ReLU), max pooling and batch normalization, the classical training problems are solved and the networks are given deep structures. Experiments on both simulated and real dataset shows that the proposed method achieves improved performance in the quantitative assessment and the visual results are more appealing than the tested competitors.