In order to improve the visual quality of low-dose computed tomography (CT) images, an image denoising method based on convolutional neural network is proposed. The batch normalization is introduced to the network, and the mapping function between low-dose CT images and their corresponding noise images is learned. The dilated convolution is used to expand the receptive field without increasing the complexity. Besides, the feature maps from the front and back layers are concatenated, and all the feature maps of convolution layers ahead can be used as the input of a subsequent convolution layer.It encourages the reuse of feature maps in the network. The experimental results show that the proposed network architecture achieves better denoising performance and sharply reduces the network complexity when compared with the state-of-the-art method at present. So, it can quickly and significantly improve the visual quality of low-dose CT images.