  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.
