The radiation treatment plan system needs to calculate the dose distribution accurately based on CT images， but sometimes clinical MR images can only be obtained. Image synthesis effectively creates new modality images from another modality， which enhances image information. This paper presents a new method of synthesizing high precision and definition of CT images from MR images. To synthesize clearly pseudo CT images， an improved cycle-consistent generative adversarial network （CycleGAN） with densely connected convolutional network （DenseNet） is proposed. Avoiding the disappearance of input information and the vanishing of gradient information， the improved network can synthesize more credible CT images. Compared with the original method， the proposed method is reduced by 5.9% on mean absolute error， increased by 1.1% on structural similarity and increased by 4.4% on peak signal to ratio， which is trained and tested on the dataset of 18 patients. And compared with the deep convolutional neural network and the atlas-based method， the improved CycleGAN is reduced by 0.065% and 0.55% on relative error， respectively. The proposed method can synthesize more vivid CT images owing to the advantages of deep learning model， which better meets the requirements of dose calculation in radiation treatment planning system.