  Unsupervised deformable image registration is one of the challenging tasks in
medical imaging. Obtaining a high-quality deformation field while preserving
deformation topology remains demanding amid a series of deep-learning-based
solutions. Meanwhile, the diffusion model's latent feature space shows
potential in modeling the deformation semantics. To fully exploit the diffusion
model's ability to guide the registration task, we present two modules:
Feature-wise Diffusion-Guided Module (FDG) and Score-wise Diffusion-Guided
Module (SDG). Specifically, FDG uses the diffusion model's multi-scale semantic
features to guide the generation of the deformation field. SDG uses the
diffusion score to guide the optimization process for preserving deformation
topology with barely any additional computation. Experiment results on the 3D
medical cardiac image registration task validate our model's ability to provide
refined deformation fields with preserved topology effectively. Code is
available at: https://github.com/xmed-lab/FSDiffReg.git.
