Rethinking Diffusion Bridge Model with Dual Alignments for Medical Image Synthesis

Medical image synthesis is crucial in clinical workflows, enabling the generation of missing modalities from available imaging data. While recent diffusion-based models show promise in medical image synthesis, they face two key limitations: progressive distribution drift from coarse intermediate samples and structural granularity loss due to missing high-frequency constraints. To address these challenges, we propose Dual Diffusion Bridge (DualDB), a framework integrating implicit distribution alignment and explicit structural constraints within a unified diffusion bridge paradigm. First, implicit distribution alignment employs optimal transport-guided adversarial learning to minimize statistical discrepancies between intermediate and target distributions, mitigating global distribution drift. Second, explicit structural alignment applies gradient-driven constraints to preserve high-frequency anatomical features, preventing structural degradation during reverse diffusion. This complementary design ensures both global statistical consistency and local anatomical precision in the synthesized results. Extensive experiments on multi-contrast MRI and MRI-CT translation show that DualDB outperforms state-of-the-art methods in quantitative performance and visual fidelity, maintaining superior anatomical accuracy even under noisy conditions.