Retrospective motion correction in MRI using disentangled embeddings

TL;DR

This paper introduces a hierarchical VQ auto-encoder that learns disentangled motion features in MRI, enabling robust, generalizable retrospective motion correction across different motion types and body regions without artifact-specific training.

Contribution

The proposed method uses a hierarchical VQ auto-encoder with a codebook and auto-regressive prior to achieve generalizable MRI motion correction by disentangling motion features.

Findings

Robust correction across varying motion severity in simulated data

Effective disentanglement of physical motion features

Generalization to unseen motion patterns

Abstract

Physiological motion can affect the diagnostic quality of magnetic resonance imaging (MRI). While various retrospective motion correction methods exist, many struggle to generalize across different motion types and body regions. In particular, machine learning (ML)-based corrections are often tailored to specific applications and datasets. We hypothesize that motion artifacts, though diverse, share underlying patterns that can be disentangled and exploited. To address this, we propose a hierarchical vector-quantized (VQ) variational auto-encoder that learns a disentangled embedding of motion-to-clean image features. A codebook is deployed to capture finite collection of motion patterns at multiple resolutions, enabling coarse-to-fine correction. An auto-regressive model is trained to learn the prior distribution of motion-free images and is used at inference to guide the correction process. Unlike conventional approaches, our method does not require artifact-specific training and can generalize to unseen motion patterns. We demonstrate the approach on simulated whole-body motion artifacts and observe robust correction across varying motion severity. Our results suggest that the model effectively disentangled physical motion of the simulated motion-effective scans, therefore, improving the generalizability of the ML-based MRI motion correction. Our work of disentangling the motion features shed a light on its potential application across anatomical regions and motion types.