Wavelet-Optimized Motion Artifact Correction in 3D MRI Using Pre-trained 2D Score Priors

TL;DR

This paper introduces a wavelet-optimized framework using pre-trained 2D score priors for efficient 3D MRI motion artifact correction, overcoming limitations of existing methods in speed and dependency on forward models.

Contribution

The authors propose a novel 3D MRI motion correction method that integrates wavelet diffusion and 2D score priors, enabling faster and more effective artifact removal.

Findings

Significant improvement in artifact correction accuracy.

Faster inference speed due to wavelet diffusion.

Robust performance on both simulated and real-world data.

Abstract

Motion artifacts in magnetic resonance imaging (MRI) remain a major challenge, as they degrade image quality and compromise diagnostic reliability. Score-based generative models (SGMs) have recently shown promise for artifact removal. However, existing 3D SGM-based approaches are limited in two key aspects: (1) their strong dependence on known forward operators makes them ineffective for correcting MRI motion artifacts, and (2) their slow inference speed hinders clinical translation. To overcome these challenges, we propose a wavelet-optimized end-to-end framework for 3D MRI motion correct using pre-trained 2D score priors (3D-WMoCo). Specifically, two orthogonal 2D score priors are leveraged to guide the 3D distribution prior, while a mean-reverting stochastic differential equation (SDE) is employed to model the restoration process of motion-corrupted 3D volumes to motion-free 3D distribution. Furthermore, wavelet diffusion is introduced to accelerate inference, and wavelet convolution is applied to enhance feature extraction. We validate the effectiveness of our approach through both simulated motion artifact experiments and real-world clinical motion artifact correction tests. The proposed method achieves robust performance improvements over existing techniques. Implementation details and source code are available at: https://github.com/ZG-yuan/3D-WMoCo.