Image synthesis from corrupted contrasts increases the diversity of diagnostic information available for many neurological diseases. Recently the image-to-image translation has experienced significant levels of interest within medical research, beginning with the successful use of the Generative Adversarial Network (GAN) to the introduction of cyclic constraint extended to multiple domains. However, in current approaches, there is no guarantee that the mapping between the two image domains would be unique or one-to-one. In this paper, we introduce a novel approach to unpaired image-to-image translation based on the invertible architecture. The invertible property of the flow-based architecture assures a cycle-consistency of image-to-image translation without additional loss functions. We utilize the temporal information between consecutive slices to provide more constraints to the optimization for transforming one domain to another in unpaired volumetric medical images. To capture temporal structures in the medical images, we explore the displacement between the consecutive slices using a deformation field. In our approach, the deformation field is used as a guidance to keep the translated slides realistic and consistent across the translation. The experimental results have shown that the synthesized images using our proposed approach are able to archive a competitive performance in terms of mean squared error, peak signal-to-noise ratio, and structural similarity index when compared with the existing deep learning-based methods on three standard datasets, i.e. HCP, MRBrainS13, and Brats2019.

翻译：从腐败对比中产生的图像合成增加了许多神经疾病的现有诊断信息的多样性。最近,图像到图像翻译在医学研究中经历了高度的兴趣,首先是成功使用基因反反影网络(GAN),到引入循环约束扩展到多个领域。然而,在目前的方法中,无法保证两个图像领域之间的映射将是独特的或一对一的。在本文件中,我们引入了一种新颖的方法,根据不可逆结构对不视线图像到图像的翻译。流基结构的不可逆属性确保了图像到图像的翻译周期的一致性,而没有额外的损失功能。我们利用连续切片之间的时间信息,为优化将一个领域转换为另一个领域提供了更多的限制。为了捕捉医学图像中的时间结构,我们用一个基于变形的字段来探索连续切片之间的偏移。在我们的方法中,变形字段被用来指导将幻灯片翻译的幻灯片保持现实性和一致性,而不是额外的损失功能。我们使用竞争性结构图解比的模型,在进行模拟时,将测试结果合成了现有标准的模型与标准的模型。