  Multi-modal medical images provide complementary soft-tissue characteristics
that aid in the screening and diagnosis of diseases. However, limited scanning
time, image corruption and various imaging protocols often result in incomplete
multi-modal images, thus limiting the usage of multi-modal data for clinical
purposes. To address this issue, in this paper, we propose a novel unified
multi-modal image synthesis method for missing modality imputation. Our method
overall takes a generative adversarial architecture, which aims to synthesize
missing modalities from any combination of available ones with a single model.
To this end, we specifically design a Commonality- and Discrepancy-Sensitive
Encoder for the generator to exploit both modality-invariant and specific
information contained in input modalities. The incorporation of both types of
information facilitates the generation of images with consistent anatomy and
realistic details of the desired distribution. Besides, we propose a Dynamic
Feature Unification Module to integrate information from a varying number of
available modalities, which enables the network to be robust to random missing
modalities. The module performs both hard integration and soft integration,
ensuring the effectiveness of feature combination while avoiding information
loss. Verified on two public multi-modal magnetic resonance datasets, the
proposed method is effective in handling various synthesis tasks and shows
superior performance compared to previous methods.
