Posterior Samplings are Missing Modalities Generators for Medical Image Translation

Magnetic resonance imaging comes in various modality con-trasts that provide complementary anatomical and pathological informa-tion. Complete multimodal acquisitions are often unavailable due to timeand protocol constraints. This leads to real-world datasets with missingmodalities, where conventional medical image translation methods aretypically limited to fixed source-target settings or require retraining foreach observed source-target pair. We propose a unified framework thatformulates missing-modality generation as a linear inverse problem un-der a joint distribution and solves it via posterior sampling with a flowmatching model. By learning a joint prior over the complete modalityset, our method can reconstruct arbitrary missing modalities at inferencetime by guiding the sampling trajectory to enforce measurement consis-tency with observed modalities. We further mitigate inter-modality er-ror propagation in multi-target generation by adopting a many-to-onesampling strategy. Experiments on BraTS and IXI datasets show thatour method achieves the best performance over baselines across mostmissing-modality scenarios. In downstream tumor segmentation, synthe-sized images from our method result in higher segmentation performance,indicating better preservation of clinically relevant structures. Our codeis available at github.com/jongdory/PS-MIT.