Abstract #2220

Cloud-Magnetic Resonance Imaging Systems: Prospects and Challenges

Yirong Zhou¹, Yanhuang Wu¹, Yuhan Su², Jing Li³, Jianyu Cai⁴, Yongfu You⁵, Di Guo⁶, and Xiaobo Qu¹

¹Department of Electronic Science, Biomedical Intelligent Cloud R&D Center, Fujian Provincial Key Laboratory of Plasma and Magnetic Resonance, National Institute for Data Science in Health and Medicine, Institute of Artificial Intelligence, Xiamen University, Xiamen, China, ²Department of Electronic Science, Key Laboratory of Digital Fujian on IoT Communication, Xiamen University, Xiamen, China, ³Shanghai Electric Group CO., LTD, Shanghai, China, ⁴China Telecom Group, Quanzhou, China, ⁵China Mobile Group, Xiamen, China, ⁶School of Computer and Information Engineering, Xiamen University of Technology, Xiamen, China

Synopsis

Keywords: AI/ML Software, Software Tools

Motivation: Magnetic Resonance Imaging (MRI) plays an important role in medical diagnosis, generating petabytes of image data annually in large hospitals. Local data processing demands substantial manpower and hardware investments. Data isolation across different healthcare institutions hinders cross-institutional collaboration.

Goal(s): Solve a series of problems existing in current hospitals.

Approach: Integrating cloud computing, 6G, edge computing, federated learning, and blockchain.

Results: Cloud-MRI transforms raw data to the Imaging Society for Magnetic Resonance in Medicine Raw Data (ISMRMRD) format and enables fast reconstruction, AI training, and analysis. Results are relayed to cloud radiologists.

Impact: This system safeguards data, promotes collaboration, and enhances diagnostic precision and efficiency.

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