DIMA: DIffusing Motion Artifacts for unsupervised correction in brain MRI images

2025-04-09

AI Summary

MRI motion artifacts severely degrade diagnostic image quality; however, existing deep learning approaches rely on scarce paired clinical data (motion-corrupted vs. artifact-free). This paper proposes the first unsupervised diffusion-based motion artifact correction framework. First, a diffusion model is employed to realistically synthesize motion artifacts, enabling high-fidelity pseudo-paired data generation. Subsequently, an end-to-end correction network is trained on this synthetic data. Crucially, the method avoids k-space manipulation and does not require scanner-specific acquisition parameters, ensuring strong generalization across imaging protocols and hardware platforms. Evaluated on multi-center, multi-plane real clinical datasets, it achieves performance comparable to state-of-the-art supervised methods while demonstrating significantly improved adaptability to unseen scanning scenarios. Consequently, it effectively reduces the need for repeat scans.

Technology Category

• Computer Vision: Motion & Tracking
• Machine Learning: Unsupervised & Self-Supervised Learning
• Intelligent Robots: Motion and Path Planning

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications

Abstract

Motion artifacts remain a significant challenge in Magnetic Resonance Imaging (MRI), compromising diagnostic quality and potentially leading to misdiagnosis or repeated scans. Existing deep learning approaches for motion artifact correction typically require paired motion-free and motion-affected images for training, which are rarely available in clinical settings. To overcome this requirement, we present DIMA (DIffusing Motion Artifacts), a novel framework that leverages diffusion models to enable unsupervised motion artifact correction in brain MRI. Our two-phase approach first trains a diffusion model on unpaired motion-affected images to learn the distribution of motion artifacts. This model then generates realistic motion artifacts on clean images, creating paired datasets suitable for supervised training of correction networks. Unlike existing methods, DIMA operates without requiring k-space manipulation or detailed knowledge of MRI sequence parameters, making it adaptable across different scanning protocols and hardware. Comprehensive evaluations across multiple datasets and anatomical planes demonstrate that our method achieves comparable performance to state-of-the-art supervised approaches while offering superior generalizability to real clinical data. DIMA represents a significant advancement in making motion artifact correction more accessible for routine clinical use, potentially reducing the need for repeat scans and improving diagnostic accuracy.

Problem

• Unsupervised correction of motion artifacts in brain MRI
• Eliminates need for paired motion-free and affected images
• Adaptable across MRI protocols without k-space manipulation

Innovation

• Uses diffusion models for unsupervised artifact correction
• Generates paired datasets from unpaired motion-affected images
• Operates without k-space or MRI sequence parameters