Speaker
Description
Magnetic Resonance Spectroscopy is a powerful, non-invasive tool for in vivo biochemical and metabolic tissue analysis, yet its widespread clinical application remains hindered by susceptibility to motion artifacts. Traditional retrospective corrections struggle with real-time constraints, limiting diagnostic precision in key medical scenarios such as neurodegenerative disease monitoring.
The Recentre project founded by the Italian Ministry of University and Research, pioneers an AI-driven, real-time motion correction system that dynamically adapts MR acquisition sequences using deep learning models deployed on fast hardware accelerators. By leveraging know-how developed in real-time inference techniques developed for high-energy physics experiments, particularly fast trigger systems used in particle detection at CERN, we introduce optimized deep neural networks capable of low latency corrections. In the talk we’ll explore the synergy between fundamental physics and application in medical imaging, highlighting how machine learning techniques developed for particle physics data pipelines can enhance real-time diagnostics in biomedical applications.
| AI keywords | motion correction:magnetic resonance:real-time AI |
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