A Personalized Federated Learning Approach for Multi-Contrast MRI Translation

MRI contrast translation enables image imputation for missing sequences given acquired sequences in a multi-contrast protocol. Training of learning-based translation models requires access to large, diverse datasets that are challenging to aggregate centrally due to patient privacy risks. Federated learning (FL) is a promising solution that mitigates privacy concerns, but naive FL methods suffer from performance losses due to implicit and explicit data heterogeneities. Here, we introduce a novel FL-based personalized MRI translation method (pFLSynth) that effectively addresses implicit and explicit heterogeneity in multi-site datasets. FL experiments conducted on multi-contrast MRI datasets show the effectiveness of the proposed approach.