High radiation dose in CT imaging is a major concern, which could result in increased lifetime risk of cancers. Therefore, to reduce the radiation dose at the same time maintaining clinically acceptable CT image quality is desirable in CT application. One of the most successful strategies is to apply statistical iterative reconstruction (SIR) to obtain promising CT images at low dose. Although the SIR algorithms are effective, they usually have three disadvantages: 1) desired-image prior design; 2) optimal parameters selection; and 3) high computation burden. To address these three issues, in this work, inspired by the deep learning network for inverse problem, we present a low-dose CT image reconstruction strategy driven by a deep dual network (LdCT-Net) to yield high-quality CT images by incorporating both projection information and image information simultaneously. Specifically, the present LdCT-Net effectively reconstructs CT images by adequately taking into account the information learned in dual-domain, i.e., projection domain and image domain, simultaneously. The experiment results on patients data demonstrated the present LdCT-Net can achieve promising gains over other existing algorithms in terms of noise-induced artifacts suppression and edge details preservation.