Towards Sustainable Magnetic Resonance Imaging: Insights from long-term, high-resolution energy recordings across an entire scanner fleet
Authors: Florian Leonhard Raab, Fiona Mankertz, Nour Maalouf, Josephine Berger, Andreas Lingg, Reza Dehdab, Sebastian Werner, Judith Herrmann, +12 more
Organizations: Medical Image and Data Analysis (MIDAS.lab), Tübingen University Hospital · Department of Diagnostic and Interventional Radiology, Tübingen University Hospital · Faculty of Computer Science, Eberhard Karls University of Tübingen · Magnetic Resonance, Siemens Healthineers AG
Magnetic resonance imaging (MRI) is among the most energy-intensive diagnostic modalities in healthcare, yet its energy consumption and the factors influencing it remain insufficiently understood. This study aims to establish a comprehensive baseline of MRI energy consumption by characterizing energy demand across a scanner fleet, examining scanner utilization and operating patterns in clinical practice. Concurrently, it investigates the relationships between energy consumption and relevant operational and acquisition features. On average, a single MRI measurement consumed 0.43 kWh, while a complete examination consumed 13.50 kWh. In general, substantial differences in energy consumption were observed between MRI scanners and their corresponding operating modes (scan, idle, and eco-power mode). These variations may be related to differences in scanner operating patterns, employed examination protocols, and their resulting acquisition parameters. Idle and eco-power modes accounted for more cumulative energy consumption than active scanning. However, these energy shares should always be interpreted in relation to scanner occupancy, as utilization patterns strongly influence the distribution of energy across those operating modes. Lastly, linear regression analysis showed that energy consumption was more strongly associated with scan duration than with average power, suggesting that scan duration may be an important factor influencing MRI energy consumption. ...
Figures & tables
Figure 1: An overview of the data processing architecture. Power meters recorded the energy and power consumption of the MRI scanner and its corresponding reconstruction workstation, while MRI system log files were acquired. These complementary data sources were subsequently integrated into a unified dataset.
Figure 2: Daily scanner-specific examination and measurement volumes over the study period. MRI scanners are color-coded for comparison. Seven MRI scanners across two sites (CRONA and UFK) were consecutively onboarded. On average, 42.80 examinations and 1,329.34 measurements were performed per day. Inter-scanner differences in workload and periods of missing data due to data recording interruptions can be seen. (A) Average daily number of MRI examinations. (B) Average daily number of MRI measurements.
Figure 3: Scanner-specific MRI energy consumption aggregated across weekdays. Average energy consumption amounted to 13.02 kWh per examination and 0.50 kWh per measurement. Energy consumption varied considerably between MRI scanners, reflecting differences in scanner characteristics, applied protocols, and the number of measurements. Temporal differences reflected scanner-specific operating schedules, with reduced consumption during weekends. (A) Average energy consumption per MRI examination. (B) Average energy consumption per MRI measurement.
Figure 4: Scanner-specific occupancy. Scanner occupancy was defined as the cumulative daily scan time relative to the total time per day ( 24 hours⋅60 minutes⋅60 seconds ). (A) Mean occupancy by weekday demonstrates temporal variations in scanner utilization and reduced occupancy during weekends. (B) Mean occupancy by scanner over the study period. Averaged Pie chart slice sizes are proportional to the average occupancy of each scanner. Differences in scanner occupancy indicate variations in workload distribution and patient throughput, which influence energy consumption patterns.
Figure 5: (A) Average scanner-specific energy consumption by operating mode and weekday. Total energy consumption is shown for each MRI scanner, and fill patterns indicate the operating mode: solid bars represent scan mode, striped bars represent idle mode, and dotted bars represent eco-power mode. Temporal variations in energy consumption across weekdays and weekends were observed, primarily driven by differences in scan-mode utilization, while idle and eco-power energy consumption remained relatively stable due to continuous cooling requirements. (B) Average scanner-specific energy consumption by operating mode. Energy consumption is expressed as the proportion of the total energy consumption for each scanner. Blue slices represent scan mode, red slices represent idle mode, and green slices represent eco-power mode. Idle and eco-power modes cumulatively accounted for a larger proportion of total energy consumption than active scanning, highlighting the substantial contribution of non-scanning periods to overall MRI energy demand.
Figure 6: Average scanner-specific energy consumption per measurement grouped by examined body region (i.e. scan mode). Pie charts illustrate the distribution of energy consumption across body regions for each MRI scanner. Energy consumption varied substantially between both body regions and scanners, indicating scanner-specific differences in energy demand across examination types. While body-region-based grouping provides an overview of energy demand across clinical applications, it does not fully account for variations caused by differences in sequences and acquisition parameters, highlighting the need for future sequence-based, data-driven analyses.
Figure 7: Scanner-specific scatter plots were used to illustrate the relationship between the energy consumption of individual MRI sequences and their average scan duration and active power consumption. A: Relationship between average scan duration and average energy consumption. Linear regression demonstrated a strong positive relationship between scan duration and energy consumption ( y=0.008x+0.020 , R2=0.769 ) over all scanners. For improved visualization, the y-axis limits were determined based on the regression results, resulting in the exclusion of outliers. B: Relationship between average active power and average energy consumption. A weaker positive relationship was observed based on linear regression ( y=0.048x−0.669 , R2=0.426 ). The same y-axis limits as in A were used to ensure comparability between the two plots.
Figure 8: Screenshots of the hosted energy dashboard. (A) Energy consumption for each measurement. The buttons below the plot allow users to individually adjust the aggregation of the displayed data. Hovering over the measurements provides additional information, while clicking on entries in the legend allows specific groups to be selected and analyzed. (B) Relationship between energy consumption and scan duration for the individual sequences. By selecting a specific area within the plot, users can zoom in on that region and investigate the corresponding sequences in greater detail. (C) Live data streamed directly from the hospital, showing the power consumption of various MRI scanners over the last three hours.
Department of Radiology, University of Calgary · Child and Adolescent Imaging Research (CAIR) Program · Alberta Children’s Hospital Research Institute +1
NYU Center for Data Science, NY, USA · Center for Advanced Imaging Innovation and Research (CAI²R), Department of Radiology, NYU Grossman School of Medicine, NY, USA · Courant Institute of Mathematical Sciences, NY, USA +1
Department of Radiography, Faculty of Health Professions, College of Medicine, University of Lagos, Nigeria. · Medical Artificial Intelligence Laboratory, Crestview Radiology Ltd., Lagos, Nigeria. · Department of Neurology and Neurosurgery, Montreal Neurological Institute, McGill University, Montreal, Canada. +3