  As an effective way to integrate the information contained in multiple
medical images under different modalities, medical image synthesis and fusion
have emerged in various clinical applications such as disease diagnosis and
treatment planning. In this paper, an invertible and variable augmented network
(iVAN) is proposed for medical image synthesis and fusion. In iVAN, the channel
number of the network input and output is the same through variable
augmentation technology, and data relevance is enhanced, which is conducive to
the generation of characterization information. Meanwhile, the invertible
network is used to achieve the bidirectional inference processes. Due to the
invertible and variable augmentation schemes, iVAN can not only be applied to
the mappings of multi-input to one-output and multi-input to multi-output, but
also be applied to one-input to multi-output. Experimental results demonstrated
that the proposed method can obtain competitive or superior performance in
comparison to representative medical image synthesis and fusion methods.
