With the rapid development of medical imaging technology， medical images have been widely used in clinical detection and scientific research. In view of the insufficient clinical image data set， this paper proposes a generation model based on dense connection self-inverse generative adversarial network （GAN） to realize the mutual generation of T1- and T2-weighted MR images. Especially， the dense block is introduced into the generator module of self-inverse GAN model， and the multi-scale fusion framework of U-net is adopted to realize the mutual generation of T1 and T2 weighted MR images. The BraTS 2018 data set is used for validation and the peak signal-to-noise ratio and structure similarity of the generated images could reach 22.78 and 0.8， respectively. Contrast experimental results of different generators show that the model with the generator based on dense block has better performance than the model with the generator based on U-net or ResNet. The MR image generation method based on dense connection self-inverse GAN proposed in this paper can reduce the negative influence brought from missing T1 or T2 weighted images and provide more information for clinical judgment.