FB-Diff: Fourier Basis-guided Diffusion for Temporal Interpolation of 4D Medical Imaging

• URL: http://arxiv.org/abs/2507.04547v1
• Date: Sun, 06 Jul 2025 21:39:48 GMT
• Title: FB-Diff: Fourier Basis-guided Diffusion for Temporal Interpolation of 4D Medical Imaging
• Authors: Xin You, Runze Yang, Chuyan Zhang, Zhongliang Jiang, Jie Yang, Nassir Navab,
• Abstract summary: The temporal task for 4D medical imaging plays a crucial role in clinical practice of respiratory motion modeling.<n>We propose a Fourier basis-guided Diffusion model, termed FB-Diff.<n>We show that FB-Diff achieves state-of-the-art metrics with better temporal consistency while maintaining promising reconstruction metrics.
• Score: 38.70420710947938
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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