One Model to Unite Them All: Personalized Federated Learning of Multi-Contrast MRI Synthesis

Understand

Multi-institutional collaborations are key for learning generalizable MRI synthesis models that translate source- onto target-contrast images.

• To facilitate collaboration, federated learning (FL) adopts decentralized training and mitigates privacy concerns by avoiding sharing of imaging data.
• However, FL-trained synthesis models can be impaired by the inherent heterogeneity in the data distribution, with domain shifts evident when common or variable translation tasks are prescribed across sites.
• Here we introduce the first personalized FL method for MRI Synthesis (pFLSynth) to improve reliability against domain shifts.