CorGAN: Context aware Recurrent Generative Adversarial Network for Medical Image Generation

Multi-modal imaging plays a critical role in various clinical applications. However, due to the associated high cost and potential risk, the acquisition of multi-modal images could be limited. To address this issue, many cross-modality image synthesis methods have been proposed. The state-of-the-art methods are mainly based on traditional convolutional generative adversarial networks (GANs) for generating target images. In 3D medical image synthesis, an open problem is how to efficiently exploit the spatial correlations of the 3D image sequence to resolve the inter-slice discontinuity and unevenness artifacts. In this paper, we propose a novel Context aware Residual Recurrent Generative Adversarial Network (short for CorGAN) for sequential medical image generation, which jointly exploits the spatial dependencies of the sequences as well as the peer image generation with GANs. Experimental results show the robustness and accuracy of our method, which outperforms the-state-of-the-art methods in synthesizing target 3D images from the corresponding source images.