A semi–automatic analytical methodology for characterizing the energy consumption of MRI systems using load duration curves

Abstract

Background and purpose: Magnetic resonance imaging (MRI) scanners are a major contributor to greenhouse gas emissions from the healthcare sector, and efforts to improve energy efficiency and reduce energy consumption rely on quantification of the characteristics of energy consumption. The purpose of this work was to develop a semi-automatic analytical methodology for the characterization of the energy consumption of MRI systems using only the load duration curve (LDC). LDCs are a fundamental tool used across various fields to analyze and understand the behavior of loads over time. Methods: An electric current transformer sensor and data logger were installed on two 3T MRI scanners from two vendors, termed M1 (outpatient scanner) and M2 (inpatient/emergency scanner). Data was collected for 1 month (7/11/2023 to 8/11/2023). Active power was calculated, assuming a balanced three-phase system, using the average current measured across all three phases, a 480 V reference voltage for both machines, and vendor-provided power factors. An LDC was constructed for each system by sorting the active power values in descending order and computing the cumulative time (in units of percentage) for each data point. The first derivative of the LDC was then computed (LDC’), smoothed by convolution with a window function (sLDC’), and used to detect transitions between different system modes including (in descending power levels): scan, prepared-to-scan, idle, low-power, and off. The final, segmented LDC was used to measure time (% total time), total energy (kWh), and mean power (kW) for each system mode on both scanners. The method was validated by comparing mean power values, computed using the segmented 1-month LDC, for each nonproductive system mode (i.e., prepared-to-scan, idle, lower-power, and off) against power levels measured after a deliberate system shutdown was performed for each scanner (1 day worth of data). Results: The validation revealed differences in mean power values <1.4% for all nonproductive modes and both scanners. In the scan system mode, the mean power values ranged from 29.8 to 37.2 kW and the total energy consumed for 1 month ranged from 11 106 to 14 466 kWh depending on the scanner. Over the course of 1 month, the portion of time the scanners were in nonproductive modes ranged from 76% to 80% across scanners and the nonproductive energy consumption ranged from 8010 to 6722 kWh depending on the scanner. The M1 (outpatient) scanner consumed 99.9 and 183.9 kWh/day in idle mode for weekdays and weekends, respectively, because the scanner spent 23% more time proportionally in idle mode on the weekends. Conclusions: A semi-automatic method for quantifying energy consumption characteristics of MRI scanners was introduced and validated. This method is relatively simple to implement as it requires only power data from the scanners and avoids the technical challenges associated with extracting and processing scanner log files. Finally, the methodology enables quantitative evaluation of the power, time, and energy characteristics of MRI scanners in scan and nonproductive system modes, providing baseline data and the capability of identifying potential opportunities for enhancing the energy efficiency of MRI scanners.