MotionDPS: Motion-Compensated 3D Brain MRI Reconstruction

2026-05-21

AI Summary

This work addresses the challenge of motion artifacts in 3D brain MRI, particularly under highly accelerated acquisitions where joint image reconstruction and motion parameter estimation remain difficult. The authors propose a unified Bayesian framework that unsupervisedly and jointly estimates the anatomical image, rigid-body motion parameters, and coil sensitivity maps directly from motion-corrupted k-space data. The key innovation lies in the first-time integration of a pre-trained 3D complex-valued diffusion model as an image prior within a physics-driven motion-compensated reconstruction pipeline. Efficient inference is achieved through alternating diffusion posterior sampling and proximal optimization. Experiments demonstrate that the proposed method significantly outperforms existing classical and learning-based approaches on both simulated and real motion-contaminated data, delivering superior image quality and robustness—especially under severe motion and high acceleration scenarios.

Abstract

Magnetic resonance imaging (MRI) is highly susceptible to patient motion due to its relatively long acquisition times and the fact that data are acquired sequentially in k-space. Even small patient movements introduce phase inconsistencies across measurements, leading to severe artifacts such as blurring, ghosting, and geometric distortions that can compromise diagnostic quality. Retrospective motion compensation remains challenging, particularly in accelerated acquisitions, due to the ill-posed nature of the joint reconstruction and motion estimation problem. In this work, we propose a unified Bayesian framework for motion-compensated 3D MRI that jointly estimates the anatomical image, rigid-body motion parameters, and coil sensitivity maps directly from motion-corrupted k-space data. Our approach integrates pretrained 3D complex-valued score-based diffusion models as expressive anatomical image priors within a physics-based forward model. Inference is performed by alternating diffusion posterior image updates with efficient proximal optimization steps for motion and coil sensitivity estimation, enabling fully unsupervised reconstruction without the need for paired motion-free training data. Experiments on simulated and real-motion brain MRI datasets demonstrate that the proposed method achieves improved image quality and motion robustness compared to state-of-the-art classical and learning-based motion correction techniques, particularly in the presence of severe motion and high acceleration.

Problem

• motion compensation
• 3D brain MRI
• image artifacts
• accelerated MRI
• retrospective correction

Innovation

• motion compensation
• diffusion model
• Bayesian reconstruction
• unsupervised MRI
• k-space