In this paper, we propose HybridGAN – a new medical MR image synthesis methods via generative adversarial learning. Specifically, our synthesizer generates MRI data in a sequential manner: first in order to improve the robustness of image synthesis, an input full-size real MR image is divided into an array of sub-images. Then, to avoid overfitting limited MRI encodings, these sub-images and an unlimited amount of random latent noise vectors become the input of automatic encoder for learning the marginal image distributions of real images. Finally, pseudo patches with constrained noise vectors are put into RU-NET which is a component of our HybridGAN to generate a large number of synthetic MR images. In RU-NET, A SpliceLayer is then employed to fuse sub-images together in an interlaced manner into a full-size image. The experimental results show that HybridGAN can effectively synthesize a large variety of MR images and display a good visual quality. Compared to the state-of-the-art synthesis methods, our method achieves a significant improvement in terms of both visual and quantitative evaluation metrics.

在本文中，我们提出了HybridGAN –一种通过生成对抗性学习的医学MR图像合成新方法。具体来说，我们的合成器按顺序生成MRI数据：首先，为了提高图像合成的鲁棒性，将输入的全尺寸真实MR图像划分为子图像阵列。然后，为了避免过度拟合有限的MRI编码，这些子图像和无限量的随机潜噪声矢量成为自动编码器的输入，用于学习真实图像的边缘图像分布。最后，与约束噪声向量的伪贴剂放入RU- NET这是我们的组分HybridGAN以生成大量的合成MR图像。在RU-NET，A然后使用SpliceLayer以隔行方式将子图像融合在一起，成为完整尺寸的图像。实验结果表明，HybridGAN可以有效地合成各种MR图像，并具有良好的视觉质量。与最新的综合方法相比，我们的方法在视觉和定量评估指标方面均取得了显着改善。