Multi-Contrast MRI Motion Correction via Parameter-Informed Disentanglement and Adaptive Experts
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| Main Authors: | , , , , , , |
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| Format: | Preprint |
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2026
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| _version_ | 1866913174737911808 |
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| author | Xiong, Honglin Tang, Yuxian Li, Feng Wang, Yulin Xiang, Lei Shen, Dinggang Wang, Qian |
| author_facet | Xiong, Honglin Tang, Yuxian Li, Feng Wang, Yulin Xiang, Lei Shen, Dinggang Wang, Qian |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2606_00146 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Multi-Contrast MRI Motion Correction via Parameter-Informed Disentanglement and Adaptive Experts Xiong, Honglin Tang, Yuxian Li, Feng Wang, Yulin Xiang, Lei Shen, Dinggang Wang, Qian Image and Video Processing Artificial Intelligence Computer Vision and Pattern Recognition 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. |
| title | Multi-Contrast MRI Motion Correction via Parameter-Informed Disentanglement and Adaptive Experts |
| topic | Image and Video Processing Artificial Intelligence Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2606.00146 |