Cloud-magnetic resonance imaging system in the 6G and AI era

Magnetic Resonance Imaging (MRI) has played an important role in modern medical diagnosis, generating petabytes of crucial data annually across healthcare facilities worldwide. However, the challenges in big data storage, data accessibility, data security, etc., have impeded its potential in further enhancing global healthcare.

To that end, Professor Xiaobo Qu and his research team at Xiamen University have developed the Cloud-MRI system. This new platform facilitates seamless data sharing and improve diagnostic capabilities across healthcare institutions.

"Traditional methods of managing MRI data face significant limitations, from storage constraints to barriers in collaborative research," Professor Qu explains. "Our Cloud-MRI system will address these challenges by harnessing the power of distributed cloud computing, ultra-fast 6G bandwidth, edge computing, federated learning, and blockchain technology."

The core of the Cloud-MRI system is its capability to upload k-space raw data, essential for MRI reconstruction, to unified servers or local edge nodes in the ISMRMRD format, a standard vendor-neutral file format for MRI research and development. This facilitates rapid image reconstruction and enables advanced artificial intelligence (AI)-driven tasks, significantly enhancing diagnostic efficiency.

"The first generation of Cloud-MRI system has been setup up at https://csrc.xmu.edu.cn/CloudBrain.html , enabling the multiple vendor data reading, AI-based MRI image reconstruction, radiologists’ blind image quality evaluation, metabolic spectrum analysis, and visualized AI programming (without coding)," Professor Qu emphasizes "We anticipate successful Cloud-MRI system will lead to transformative impacts on medical diagnostics and patient care."

The team published their study in the KeAI journal Magnetic Resonance Letters.  

Contact author name, affiliation, email address: 

Xiaobo Qu, Department of Electronic Science, Fujian Provincial Key Laboratory of Plasma and Magnetic Resonance, Institute of Artificial Intelligence, National Institute for Data Science in Health and Medicine, Intelligent Instruments and Equipment Discipline, Xiamen University, Xiamen 361005, China.

E-mail address: quxiaobo@xmu.edu.cn

Funder: 

The National Natural Science Foundation of China (62122064, 62331021, 62371410), the Natural Science Foundation of Fujian Province of China (2023J02005 and 2021J011184), the President Fund of Xiamen University (20720220063), and Nanqiang Outstanding Talent Program of Xiamen University.

Conflict of interest:

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

See the article:

Yirong Zhou., et al., Cloud-magnetic resonance imaging system: In the era of 6G and artificial intelligence, Magnetic Resonance Letters, DOI: 10.1016/j.mrl.2024.200138, 2024.