Ecodesign and Operational Strategies to Reduce the Carbon Footprint                     of MRI for Energy Cost Savings

Woolen, Sean; Becker, Amy E; Martin, Alastair J.; Knoerl, Roland; Lam, Vincent; Folsom, Jerry; Eusemann, Christian D.; Hess, Christopher P.; Deshpande, Vibhas

doi:10.1148/radiol.230441

Cited by 26 publications

(14 citation statements)

References 3 publications

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“…In the field of diagnostic radiology, inhospital services are the main investigation’s setting (14/16, 87.5%), followed by radiology training areas (2/16, 12.5%). Diagnostic radiology devices were considered in 13 studies [ 13 , 14 , 18 , 25 – 28 , 30 – 35 ]. In particular, the devices whose energy consumption or environmental impact has been most studied are CT scanners (7/16, 43.7%), followed by MRI scanners (6/16, 37.5%), monitors and workstations (4/16, 25.0%), and ultrasound scanners (3/16, 18.7%).…”

Section: Resultsmentioning

confidence: 99%

“…A total of 11 studies [ 13 , 14 , 18 , 25 – 29 , 33 – 35 ] focused on energy consumption in radiology departments and identified potential areas for improvement. In 8 of these 11 studies [ 13 , 14 , 18 , 25 , 26 , 33 – 35 ], the authors used real-time meters electricity/power consumption, as a tool of measuring the electricity consumed. Alshqaqeeq et al [ 28 ] evaluated the appropriateness of imaging examinations in relation to energy consumption described for each type of imaging exam.…”

Section: Resultsmentioning

confidence: 99%

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The environmental impact of energy consumption and carbon emissions in radiology departments: a systematic review

Roletto,

Zanardo,

Bonfitto

et al. 2024

Eur Radiol Exp

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Objectives Energy consumption and carbon emissions from medical equipment like CT/MRI scanners and workstations contribute to the environmental impact of healthcare facilities. The aim of this systematic review was to identify all strategies to reduce energy use and carbon emissions in radiology. Methods In June 2023, a systematic review (Medline/Embase/Web of Science) was performed to search original articles on environmental sustainability in radiology. The extracted data include environmental sustainability topics (e.g., energy consumption, carbon footprint) and radiological devices involved. Sustainable actions and environmental impact in radiology settings were analyzed. Study quality was assessed using the QualSyst tool. Results From 918 retrieved articles, 16 met the inclusion criteria. Among them, main topics were energy consumption (10/16, 62.5%), life-cycle assessment (4/16, 25.0%), and carbon footprint (2/16, 12.5%). Eleven studies reported that 40–91% of the energy consumed by radiological devices can be defined as “nonproductive” (devices “on” but not working). Turning-off devices during idle periods 9/16 (56.2%) and implementing workflow informatic tools (2/16, 12.5%) were the sustainable actions identified. Energy-saving strategies were reported in 8/16 articles (50%), estimating annual savings of thousand kilowatt-hours (14,180–171,000 kWh). Cost-savings were identified in 7/16 (43.7%) articles, ranging from US $9,225 to 14,328 per device. Study quality was over or equal the 80% of high-quality level in 14/16 (87.5%) articles. Conclusion Energy consumption and environmental sustainability in radiology received attention in literature. Sustainable actions include turning-off radiological devices during idle periods, favoring the most energy-efficient imaging devices, and educating radiological staff on energy-saving practices, without compromising service quality. Relevance statement A non-negligible number of articles — mainly coming from North America and Europe — highlighted the need for energy-saving strategies, attention to equipment life-cycle assessment, and carbon footprint reduction in radiology, with a potential for cost-saving outcome. Key points • Energy consumption and environmental sustainability in radiology received attention in the literature (16 articles published from 2010 to 2023). • A substantial portion (40–91%) of the energy consumed by radiological devices was classified as “non-productive” (devices “on” but not working). • Sustainable action such as shutting down devices during idle periods was identified, with potential annual energy savings ranging from 14,180 to 171,000 kWh. Graphical Abstract

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

The environmental impact of energy consumption and carbon emissions in radiology departments: a systematic review

Roletto,

Zanardo,

Bonfitto

et al. 2024

Eur Radiol Exp

View full text Add to dashboard Cite

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“…On the other hand, system off refers to the minimum power‐mode and is operator‐initiated. Newer vendor‐specific modes designed to further reduce power during nonproductive time include power save mode 12 …”

Section: Strategies To Reduce Ghg Emissions From Mrimentioning

confidence: 99%

“…This reflects a currently untapped and easy to address potential for energy and cost savings with corresponding reductions in GHG emissions by reducing energy waste during non‐productive time periods. Woolen et al demonstrated that switching scanners from idle to off when not in use reduced energy consumption by 25%–33%, and implementing a power save option further reduced energy use by 22%–28% compared to off mode 12 . They estimated that implementing a power save mode during 12 overnight hours of nonproductive use across ambulatory MRI units in the US could result in total potential savings of 58,863–76,288 MWh and $8.2–$10.7 USD million in costs annually, equivalent to the annual energy usage of a town of 5519–7175 residential homes.…”

Section: Strategies To Reduce Ghg Emissions From Mrimentioning

confidence: 99%

“…However, changes in cooling technology may have other effects on energy use. For example, a 3T system with a typical helium cooling system consumes less energy in power save mode compared to a 0.55T system with a very low helium cooling system in off mode (6.4 kWh for 3T MRI vs. 7.1 kWh for 0.55T MRI), 12,94 potentially reflecting a trade‐off between lower helium requirements and slightly higher baseline energy use due to differences in how cooling is performed. Finally, the development of MRI systems using high temperature superconductors including magnesium diboride (Tc = 39 K), yttrium barium copper oxide (Tc = 93 K), and other materials would allow for higher temperature cryogens and potentially alleviate the need for helium entirely 91,95 …”

Section: Other Strategies To Address Sustainability In Mri: Gd Contam...mentioning

confidence: 99%

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Environmental Sustainability and MRI: Challenges, Opportunities, and a Call for Action

Chaban,

Vosshenrich,

McKee

et al. 2023

Magnetic Resonance Imaging

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The environmental impact of magnetic resonance imaging (MRI) has recently come into focus. This includes its enormous demand for electricity compared to other imaging modalities and contamination of water bodies with anthropogenic gadolinium related to contrast administration. Given the pressing threat of climate change, addressing these challenges to improve the environmental sustainability of MRI is imperative. The purpose of this review is to discuss the challenges, opportunities, and the need for action to reduce the environmental impact of MRI and prepare for the effects of climate change. The approaches outlined are categorized as strategies to reduce greenhouse gas (GHG) emissions from MRI during production and use phases, approaches to reduce the environmental impact of MRI including the preservation of finite resources, and development of adaption plans to prepare for the impact of climate change. Co‐benefits of these strategies are emphasized including lower GHG emission and reduced cost along with improved heath and patient satisfaction. Although MRI is energy‐intensive, there are many steps that can be taken now to improve the environmental sustainability of MRI and prepare for the effects of climate change. On‐going research, technical development, and collaboration with industry partners are needed to achieve further reductions in MRI‐related GHG emissions and to decrease the reliance on finite resources.Level of Evidence5Technical EfficacyStage 6

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

Hernandez,

Alizadeh,

Ghatpande

et al. 2024

Medical Physics

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Background and purposeMagnetic 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.MethodsAn 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).ResultsThe 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.ConclusionsA 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. 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.

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Ecodesign and Operational Strategies to Reduce the Carbon Footprint of MRI for Energy Cost Savings

Cited by 26 publications

References 3 publications

The environmental impact of energy consumption and carbon emissions in radiology departments: a systematic review

The environmental impact of energy consumption and carbon emissions in radiology departments: a systematic review

Environmental Sustainability and MRI: Challenges, Opportunities, and a Call for Action

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

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