HierAdaptMR: Cross-Center Cardiac MRI Reconstruction with Hierarchical Feature Adapters

TL;DR

HierAdaptMR introduces a hierarchical, parameter-efficient framework for improving cross-center cardiac MRI reconstruction, effectively handling domain shifts across diverse scanners and protocols with a universal adapter for unseen centers.

Contribution

The paper presents HierAdaptMR, a novel hierarchical feature adaptation framework with universal adapters, enhancing multi-center MRI reconstruction generalization and robustness.

Findings

Outperforms existing methods on multi-center datasets

Maintains high reconstruction quality across diverse scanners

Effective adaptation to unseen centers through stochastic training

Abstract

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