单次前向传播实现3D医学影像跨模态转换，精度更高更稳定。
Spectral Consistent Flow for One-step 3D Medical Image Translation
- 将影像转换建模为布朗桥过程，仅一次采样即完成映射
- 在四组数据上均显著提升转换精度与鲁棒性
- 适合需要快速、高保真医学图像生成的研究场景
我们提出Spectral Consistent Flow（SC-Flow），一种在隐空间中仅需一次函数评估（1-NFE）的3D医学图像转换框架。该方法将医学图像转换重新建模为随机布朗桥过程，通过预测受支持正则化的平均速度场，直接构建源模态与目标模态之间的映射。为缓解模态纠缠、过度平滑及由隐空间平均速度的隐式低通调制引起的伪影问题，我们引入频谱一致性校正器，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。该机制在空间纹理与频谱能量流间建立显式关联，使模型在保持全局结构一致性的前提下恢复细粒度解剖细节。在四个数据集上的大量实验表明，SC-Flow在多种转换场景中均表现出显著更高的准确性、一致性和鲁棒性。
原文摘要 · Abstract (English)
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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