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.

翻译：磁共振成像（MRI）在医学诊断中扮演着重要角色，大型医院每年产生PB级的图像数据。这一庞大数据流需要大量网络带宽和存储基础设施。此外，本地数据处理需要大量人力和硬件投入。不同医疗机构间的数据隔离阻碍了临床与科研中的跨机构协作。本研究展望了一种创新的MRI系统及其四代演进，该系统融合了新兴分布式云计算、6G带宽、边缘计算、联邦学习和区块链技术，称为Cloud-MRI。该系统旨在解决MRI数据存储安全、传输速度、AI算法维护、硬件升级及协同工作等难题。工作流程始于将k空间原始数据转换为标准化医学磁共振成像学会原始数据格式（ISMRMRD），随后将数据上传至云端或边缘节点进行快速图像重建、神经网络训练和自动分析，最后将结果无缝传输至诊所或研究机构用于诊断及其他服务。Cloud-MRI系统将保存原始成像数据、降低数据丢失风险、促进跨机构医疗协作，最终提升诊断准确性和工作效率。