FB-Diff: Fourier Basis-guided Diffusion for Temporal Interpolation of 4D Medical Imaging
Journal:
arXiv
Published Date:
Jul 6, 2025
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.
