Non-rigid Motion Correction for MRI Reconstruction via Coarse-To-Fine Diffusion Models

Magnetic Resonance Imaging (MRI) is highly susceptible to motion artifacts due to the extended acquisition times required for k-space sampling. These artifacts can compromise diagnostic utility, particularly for dynamic imaging. We propose a novel alternating minimization framework that leverages a bespoke diffusion model to jointly reconstruct and correct non-rigid motion-corrupted k-space data. The diffusion model uses a coarse-to-fine denoising strategy to capture large overall motion and reconstruct the lower frequencies of the image first, providing a better inductive bias for motion estimation than that of standard diffusion models. We demonstrate the performance of our approach on both real-world cine cardiac MRI datasets and complex simulated rigid and non-rigid deformations, even when each motion state is undersampled by a factor of 64x. Additionally, our method is agnostic to sampling patterns, anatomical variations, and MRI scanning protocols, as long as some low frequency components are sampled during each motion state.

@article{wang2025_2505.15057,
  title={ Non-rigid Motion Correction for MRI Reconstruction via Coarse-To-Fine Diffusion Models },
  author={ Frederic Wang and Jonathan I. Tamir },
  journal={arXiv preprint arXiv:2505.15057},
  year={ 2025 }
}