Cloud-Magnetic Resonance Imaging Systems: Prospects and Challenges

Abstract

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.