Posterior Samplings are Missing Modalities Generators for Medical Image Translation

TL;DR - A unified flow-matching framework generates arbitrary missing MRI modalities through posterior sampling from a learned joint prior. It outperforms baselines in most tested scenarios and better preserves structures useful for tumor segmentation.

• Models missing-modality generation as a linear inverse problem under the joint distribution of all modalities.
• Enforces consistency with observed MRI modalities by guiding the sampling trajectory at inference time.
• Uses many-to-one sampling to reduce error propagation when synthesizing multiple modalities.
• Evaluations on BraTS and IXI show strong reconstruction and downstream segmentation performance.