Multi-Contrast MRI Motion Correction via Parameter-Informed Disentanglement and Adaptive Experts
This work addresses the need for a single motion correction method that works across diverse MRI contrasts and artifact severities, which is important for clinical deployment.
The paper introduces a unified framework for multi-contrast MRI motion correction that uses parameter-informed contrast disentanglement and severity-aware adaptive correction, achieving up to 0.75 dB PSNR and 0.0279 SSIM improvement over state-of-the-art methods on IXI and HCP benchmarks, with robust zero-shot generalization to unseen clinical data.
Motion artifacts in magnetic resonance imaging (MRI) degrade diagnostic reliability. Existing deep learning methods are typically contrast-specific and fail to generalize across diverse modalities and artifact severities. We propose a unified framework combining parameter-informed contrast disentanglement with severity-aware adaptive correction. ScanCLIP, pretrained on over 30,000 MRI text-image pairs, derives contrast embeddings from acquisition parameters to disentangle contrast style from anatomical content, yielding contrast-free features. A Vision Transformer then estimates motion severity and routes features through a Mixture-of-Experts network, enabling targeted artifact correction. A dual-pathway decoder reconstructs both the clean image and residual artifact map, enforcing image-space consistency. On IXI and HCP benchmarks, our method improves PSNR by 0.75 dB and SSIM by up to 0.0279 over state-of-the-art approaches, with larger gains at higher artifact severities. It further demonstrates robust zero-shot generalization on real-world clinical data acquired with unseen scanning parameters, where existing methods either fail to remove artifacts or introduce additional distortions.