Optimizing Energy Efficiency in MRI Scanners by Thomas Küstner, Ph.D. (MIDAS.lab); Saif Afat, M.D. (Universitätsklinikum Tübingen, Germany); Mike Notohamiprodjo (DIE RADIOLOGIE, Munich, Germany); and colleagues. MRI scanners are essential in clinical diagnostics—but also among the most energy-intensive devices in healthcare. Radiology departments account for over 4% of a hospital’s total energy use, and MRI scanners alone consume over 100 MWh per year, making them the top energy consumers. As demand for MRI grows, optimizing efficiency and operations has become a pressing priority. The main energy drivers are the magnet cooling and gradient systems, with magnet cooling alone making up ~42% of total energy use. Active scanner operation consumes more energy than idling, with up to 70% used during operation. Reducing energy consumption during active operation or shortening the duration of active scanner operations offers the biggest savings. Shorter acquisition times, without compromising image quality, is one promising approach. AI-accelerated sequences using undersampling and AI-based reconstruction can help balance energy efficiency with reduced scan times. However, freed-up time might increase patient throughput, offsetting gains. That’s why it is critical to observe the complete workflow and propose energy optimizations along the whole imaging chain. The authors present detailed data from a fleet of MRI scanners over several months, showing correlations of energy consumption with MRI sequences and various features. They highlight the potential of Eco Power Mode and of AI-accelerated sequences to significantly reduce energy consumption. Implementing these strategies will drive substantial energy and cost savings, and will support more sustainable MRI operation in clinical settings. Shoutout to the co-authors: Florian Raab, Fabian Wagner, Julian Wohlers, Shreeja Varadarajan, Jens Gühring, Rainer Schneider, Judith Herrmann, Konstantin Nikolaou
#MRI scanners are energy-hungry.
This study shows how Eco Power Mode + AI-accelerated sequences can cut energy use without compromising image quality.
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#AI #EnergyEfficiency #GreenRadiology #Sustainability @mritobi.bsky.social #RadSky
