跨中心心脏MRI重建新框架，高效适配不同扫描仪和协议差异
HierAdaptMR: Cross-Center Cardiac MRI Reconstruction with Hierarchical Feature Adapters
- 分层适配器分别处理序列和中心特异性差异
- 在5个以上中心、10种以上扫描仪上保持高质量重建
- 适合医疗影像跨机构部署，尤其适用于多中心研究
基于深度学习的心脏MRI重建在跨多个临床中心部署时，面临因扫描仪配置和成像协议异质性带来的显著域偏移挑战。本文提出HierAdaptMR，一种分层特征适配框架，通过参数高效的适配器应对多层次域变化。方法包含针对序列特性的协议级适配器、针对扫描仪差异的中心级适配器，以及基于随机训练学习中心无关适应的通用适配器，整体架构基于变分展开主干。优化过程中采用多尺度SSIM损失，结合频域增强与对比度自适应加权，提升鲁棒性。在涵盖5个以上中心、10种以上扫描仪、9种模态的CMRxRecon2025数据集上进行综合评估，结果表明该方法在保持重建质量的同时，显著提升了跨中心泛化能力。
原文摘要 · Abstract (English)
Deep learning-based cardiac MRI reconstruction faces significant domain shift challenges when deployed across multiple clinical centers with heterogeneous scanner configurations and imaging protocols. We propose HierAdaptMR, a hierarchical feature adaptation framework that addresses multi-level domain variations through parameter-efficient adapters. Our method employs Protocol-Level Adapters for sequence-specific characteristics and Center-Level Adapters for scanner-dependent variations, built upon a variational unrolling backbone. A Universal Adapter enables generalization to entirely unseen centers through stochastic training that learns center-invariant adaptations. The framework utilizes multi-scale SSIM loss with frequency domain enhancement and contrast-adaptive weighting for robust optimization. Comprehensive evaluation on the CMRxRecon2025 dataset spanning 5+ centers, 10+ scanners, and 9 modalities demonstrates superior cross-center generalization while maintaining reconstruction quality. code: https://github.com/Ruru-Xu/HierAdaptMR
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