Abstract #0652

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

Rencheng Zheng¹, Hang Yu², Ruokun Li³, Qidong Wang⁴, Caizhong Chen⁵, Fei Dai¹, Boyu Zhang¹, Ying-Hua Chu⁶, Weibo Chen⁷, Chengyan Wang⁸, and He Wang¹

¹Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China, ²Institute of Science and Technology for Brain-inspired Intelligence, Fudan University, Shanghai, China, ³Department of Radiology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China, ⁴Department of Radiology, The First affiliated Hospital, School of Medicine, Zhejiang University, Shanghai, China, ⁵Department of Radiology, Zhongshan Hospital, Fudan University, Shanghai, China, ⁶Siemens Healthineers, Shanghai, China, ⁷Philips Healthcare, Shanghai, China, ⁸Human Phenome Institute, Fudan University, Shanghai, China

Synopsis

Keywords: AI/ML Image Reconstruction, Liver

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.

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