Distribution Matching Based Personalized Federated Learning for Multi-Contrast Liver MRI Synthesis and Registration

Motivation: The combined diagnosis of diffusion-weighted imaging (DWI) and dynamic contrast-enhanced (DCE)-MRI is of significant importance for liver diseases, but accurate registration between these two modalities remains a substantial challenge. Goal(s): Our goal was to design a deep learning model for accurate registration between DCE and DCE-MRI, and conduct multicenter studies based on federated learning. Approach: We proposed a multi-task synthesis-registration network (SynReg) and a personalized decentralized distribution matching federated framework (PDMa) based on SynReg. Results: The proposed SynReg and PDMa method increased the registration accuracy in most centers both in liver region and liver tumor region. Impact: Accurate and rapid registration of DWI and DCE can effectively assist clinicians in leveraging multimodal imaging for efficient diagnosis. Personalized federated learning can effectively aid single-center with limited data to leverage the abundant data from multiple centers for model development.