  Multimodal MRI provides complementary and clinically relevant information to
probe tissue condition and to characterize various diseases. However, it is
often difficult to acquire sufficiently many modalities from the same subject
due to limitations in study plans, while quantitative analysis is still
demanded. In this work, we propose a unified conditional disentanglement
framework to synthesize any arbitrary modality from an input modality. Our
framework hinges on a cycle-constrained conditional adversarial training
approach, where it can extract a modality-invariant anatomical feature with a
modality-agnostic encoder and generate a target modality with a conditioned
decoder. We validate our framework on four MRI modalities, including
T1-weighted, T1 contrast enhanced, T2-weighted, and FLAIR MRI, from the
BraTS'18 database, showing superior performance on synthesis quality over the
comparison methods. In addition, we report results from experiments on a tumor
segmentation task carried out with synthesized data.
