Abstract
The temporal interpolation task for 4D medical imaging, plays a crucial role in clinical practice of respiratory motion modeling. Following the simplified linear-motion hypothesis, existing approaches adopt optical flow-based models to interpolate intermediate frames. However, realistic respiratory motions should be nonlinear and quasi-periodic with specific frequencies. Intuited by this property, we resolve the temporal interpolation task from the frequency perspective, and propose a Fourier basis-guided Diffusion model, termed FB-Diff. Specifically, due to the regular motion discipline of respiration, physiological motion priors are introduced to describe general characteristics of temporal data distributions. Then a Fourier motion operator is elaborately devised to extract Fourier bases by incorporating physiological motion priors and case-specific spectral information in the feature space of Variational Autoencoder. Well-learned Fourier bases can better simulate respiratory motions with motion patterns of specific frequencies. Conditioned on starting and ending frames, the diffusion model further leverages well-learned Fourier bases via the basis interaction operator, which promotes the temporal interpolation task in a generative manner. Extensive results demonstrate that FB-Diff achieves state-of-the-art (SOTA) perceptual performance with better temporal consistency while maintaining promising reconstruction metrics. Codes are available.
Abstract (translated)
4D医学成像的时间插值任务在呼吸运动建模的临床实践中扮演着至关重要的角色。现有方法通常采用基于光流模型的方法来插值中间帧，而这些方法是根据简化的线性运动假设提出的。然而，在现实世界中，呼吸运动应该是非线性和准周期性的，并具有特定频率。鉴于这一特性，我们从频域角度解决时间插值任务，并提出了一种名为FB-Diff的傅里叶基引导扩散模型。 具体来说，考虑到呼吸运动具有的规则性，引入了生理运动先验来描述时序数据分布的一般特征。随后精心设计了一个傅立叶运动算子，通过在变分自动编码器的特征空间中结合生理运动先验和特定病例的频谱信息来提取傅里叶基。这些学习到的傅里叶基可以更好地模拟具有特定频率模式的呼吸运动。 基于起始帧和结束帧，在扩散模型的基础上进一步利用了这些好的傅立叶基，通过设计基础交互算子以生成方式促进了时间插值任务的发展。大量的实验结果表明，FB-Diff在感知性能方面达到了最先进的（SOTA）水平，并且具有更好的时序一致性同时保持了有前景的重建指标。 代码现已提供。
URL
https://arxiv.org/abs/2507.04547
