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arXiv · 2607.10627

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

Haoqing Li·Jun Shi·Mingchao Li·Zehua Zhu·Qiwei Jia·Jiong Shi·Hong An
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Abstract

We present Spectral Consistent Flow (SC-Flow), a 3D medical image translation framework with a single function evaluation (1-NFE) in the latent space. This approach reformulates medical image translation as a stochastic Brownian bridge process that directly constructs a mapping between source and target modalities by predicting the support regularized mean velocity field. To mitigate modality entanglement, over-smoothing, and artifacts induced by the implicit low-pass modulation of the latent average velocity, we introduce a Spectral Consistency Corrector that dynamically regularizes the evolution of the power spectral density via learnable frequency-domain gain modulation. This mechanism establishes an explicit bridge between spatial textures and spectral energy flow, enabling the model to recover fine-grained anatomical fidelity while maintaining global structural coherence. Extensive experiments on four datasets demonstrate that SC-Flow delivers significantly more accurate, consistent, and robust performance across various translation scenarios.

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Haoqing Li, Jun Shi, Mingchao Li, Zehua Zhu, Qiwei Jia, Jiong Shi, Hong An. 2026-07-12. Spectral Consistent Flow for One-step 3D Medical Image Translation. https://arxiv.org/abs/2607.10627

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