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

• URL: http://arxiv.org/abs/2505.15057v3
• Date: Thu, 03 Jul 2025 04:39:51 GMT
• Title: Non-rigid Motion Correction for MRI Reconstruction via Coarse-To-Fine Diffusion Models
• Authors: Frederic Wang, Jonathan I. Tamir,
• Abstract summary: Motion artifacts can compromise diagnostic utility, particularly for dynamic imaging.<n>We propose a novel alternating minimization framework that leverages a bespoke diffusion model to jointly reconstruct and correct motion-corrupted k-space data.<n>We demonstrate the performance of our approach on both real-world cine cardiac MRI datasets and complex simulated rigid and non-rigid deformations.
• Score: 2.8189656701789816
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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