Magnetic Resonance Imaging (MRI) plays an important role in medical
diagnosis, generating petabytes of image data annually in large hospitals. This
voluminous data stream requires a significant amount of network bandwidth and
extensive storage infrastructure. Additionally, local data processing demands
substantial manpower and hardware investments. Data isolation across different
healthcare institutions hinders cross-institutional collaboration in clinics
and research. In this work, we anticipate an innovative MRI system and its four
generations that integrate emerging distributed cloud computing, 6G bandwidth,
edge computing, federated learning, and blockchain technology. This system is
called Cloud-MRI, aiming at solving the problems of MRI data storage security,
transmission speed, AI algorithm maintenance, hardware upgrading, and
collaborative work. The workflow commences with the transformation of k-space
raw data into the standardized Imaging Society for Magnetic Resonance in
Medicine Raw Data (ISMRMRD) format. Then, the data are uploaded to the cloud or
edge nodes for fast image reconstruction, neural network training, and
automatic analysis. Then, the outcomes are seamlessly transmitted to clinics or
research institutes for diagnosis and other services. The Cloud-MRI system will
save the raw imaging data, reduce the risk of data loss, facilitate
inter-institutional medical collaboration, and finally improve diagnostic
accuracy and work efficiency.

Cloud-MRI 系统整合了分布式云计算、6G 带宽、边缘计算、联邦学习和区块链技术，旨在解决 MRI 数据存储安全、传输速度、AI 算法维护、硬件升级和协同工作的问题，从而提高诊断准确性和工作效率。