Posterior Samplings are Missing Modalities Generators for Medical Image Translation
Merged summary
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.
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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.