基于域无关循环生成对抗网络的跨模态医学影像生成OA

[Objective]To solve the problem of misaligned and inaccurate image structure generated by training with unpaired data in cross-modal medical image generation tasks,[Methods]this arti-cle proposes a cross-modal medical image generation model based on domain independent cy-clic generation adversarial network,which constrains the structural consistency of images be-fore and after modal transformation by aligning the intermediate features during cyclic genera-tion.[Results]The experimental results on the brain CT-MRI dataset show that the method pro-posed in this article can improve the consistency of image structure before and after cross-modal transformation,thereby improving the quality of cross-modal medical image generation.[Limitations]This study has conducted a large number of experiments on the multimodal brain dataset.Further verification of its universality is needed in other datasets.[Conclusions]The method proposed in this article outperforms the current best performing cross-modal medical image generation model in various metrics for measuring the quality of generated images.