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arXiv · 2606.00146

Multi-Contrast MRI Motion Correction via Parameter-Informed Disentanglement and Adaptive Experts

Honglin Xiong·Yuxian Tang·Feng Li·Yulin Wang·Lei Xiang·Dinggang Shen·Qian Wang
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Abstract

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.

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Honglin Xiong, Yuxian Tang, Feng Li, Yulin Wang, Lei Xiang, Dinggang Shen, Qian Wang. 2026-05-29. Multi-Contrast MRI Motion Correction via Parameter-Informed Disentanglement and Adaptive Experts. https://arxiv.org/abs/2606.00146

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