Keywords: Image Reconstruction, Machine Learning/Artificial IntelligenceGeneralization performance in learning-based MRI reconstruction relies on comprehensive model training on large, diverse datasets collected at multiple institutions. Yet, centralized training after cross-site transfer of imaging data introduces patient privacy risks. Federated learning (FL) is a promising framework that enables collaborative training without explicit data sharing across sites. Here, we introduce a novel FL method for MRI reconstruction based on a multi-site deep generative model. To improve performance and reliability against data heterogeneity across sites, the proposed method decentrally trains a generative image prior decoupled from the imaging operator, and adapts it to minimize data-consistency loss during inference.
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