用参数引导解耦与自适应专家，统一矫正多对比度MRI运动伪影。
Multi-Contrast MRI Motion Correction via Parameter-Informed Disentanglement and Adaptive Experts
- 通过扫描参数生成对比度嵌入，分离解剖结构与对比风格。
- 在IXI和HCP上提升PSNR 0.75 dB，高伪影下效果更优。
- 零样本泛化能力强，适用于未知扫描参数的真实临床数据。
磁共振成像中的运动伪影会降低诊断可靠性。现有深度学习方法通常针对特定对比度，难以跨模态和伪影严重程度泛化。本文提出统一框架，结合参数引导的对比度解耦与严重性感知的自适应校正。ScanCLIP 在超过3万对MRI图文数据上预训练，从采集参数中提取对比度嵌入，实现对比风格与解剖内容的解耦，获得无对比度特征。视觉变换器估计运动严重性，并将特征路由至混合专家网络，实现针对性伪影校正。双路径解码器同时重建干净图像与残差伪影图，强化图像空间一致性。在IXI和HCP基准测试中，本方法相比现有最优方法，PSNR提升0.75 dB，SSIM最高提升0.0279，且在更高伪影严重性下增益更大。进一步在真实临床数据上展示出强零样本泛化能力，而现有方法或无法去除伪影，或引入新失真。
原文摘要 · Abstract (English)
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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