ArticleMagnetic resonance letters2025
Cloud-magnetic resonance imaging system: In the era of 6G and artificial intelligence.
Article in Magnetic resonance letters, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
6 citing papers in PubMed.
- The perspective of artificial intelligence for NMR.Magnetic resonance letters · 2027Review
- A hybrid deep learning framework based on VGG19 and U-Net for accurate brain tumor segmentation in MRI images.Magnetic resonance letters · 2026Article
- Radiology research platform: automating multicenter radiological research workflows using a cloud-native platform.European radiology · 2026Article
- A cloud-edge adaptive lightweight network with dynamic inference enables real-time ultrasound image segmentation.Scientific reports · 2026Article
- Comparative evaluation of a deep learning method QNet and LCModel for MRS quantification on the cloud computing platform CloudBrain-MRS.BMC medical imaging · 2026Article
- Web based AI-driven framework combining multi-modal data with CNN and LLM for Parkinson's disease diagnosis.Scientific reports · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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, artificial intelligence (AI) algorithm maintenance, hardware upgrading, and collaborative work. The workflow commences with the transformation of
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.