HierAdaptMR: Cross-Center Cardiac MRI Reconstruction with Hierarchical Feature Adapters
This addresses domain shift challenges in cardiac MRI reconstruction for clinical applications, but it is incremental as it builds on existing variational unrolling and adapter methods.
The paper tackles the problem of domain shift in deep learning-based cardiac MRI reconstruction across multiple clinical centers with heterogeneous scanners and protocols, proposing HierAdaptMR, a hierarchical feature adaptation framework that achieves superior cross-center generalization while maintaining reconstruction quality, as demonstrated on the CMRxRecon2025 dataset spanning 5+ centers, 10+ scanners, and 9 modalities.
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