发表机构
University of Science and Technology of China; The First Affiliated Hospital of USTC（中国科学技术大学; 中国科学技术大学附属第一医院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该研究提出频谱一致流（SC-Flow）框架用于3D医学图像翻译，通过将其 reformulate 为随机布朗桥过程，引入频谱一致性校正器减轻相关问题，经四个数据集实验验证，此方法在多场景下性能更优。
AI 中文摘要
我们提出了频谱一致流（SC-Flow），这是一个在潜在空间中具有单次函数评估（1-NFE）的3D医学图像翻译框架。该方法将医学图像翻译重新表述为一个随机布朗桥过程，通过预测支持正则化平均速度场直接构建源模态和目标模态之间的映射。为减轻由潜在平均速度的隐式低通调制引起的模态纠缠、过度平滑和伪影，我们引入了频谱一致性校正器，通过可学习的频域增益调制动态正则化功率谱密度的演化。该机制在空间纹理和频谱能量流之间建立了明确的桥梁，使模型能够在保持全局结构一致性的同时恢复细粒度的解剖逼真度。在四个数据集上的大量实验表明，SC-Flow在各种翻译场景中都具有显著更准确、一致和稳健的性能。
英文摘要
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.
