AI RESEARCH
Multi-Contrast MRI Motion Correction via Parameter-Informed Disentanglement and Adaptive Experts
arXiv CS.AI
•
ArXi:2606.00146v1 Announce Type: cross 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.