Spectral Consistent Flow for One-step 3D Medical Image Translation

We present Spectral Consistent Flow (SC-Flow), a 3D med-ical image translation framework with a single function evaluation (1-NFE) in the latent space. This approach reformulates medical imagetranslation as a stochastic Brownian bridge process that directly con-structs a mapping between source and target modalities by predictingthe support regularized mean velocity field. To mitigate modality entan-glement, over-smoothing, and artifacts induced by the implicit low-passmodulation of the latent average velocity, we introduce a Spectral Consis-tency Corrector that dynamically regularizes the evolution of the powerspectral density via learnable frequency-domain gain modulation. Thismechanism establishes an explicit bridge between spatial textures andspectral energy flow, enabling the model to recover fine-grained anatom-ical fidelity while maintaining global structural coherence. Extensive ex-periments on four datasets demonstrate that SC-Flow delivers signifi-cantly more accurate, consistent, and robust performance across varioustranslation scenarios.