Three steps to lower energy consumption in MRI
Among modern imaging modalities, magnetic resonance imaging (MRI) is probably the most power-hungry technology that is regularly used in clinical routine. One radiology practice network used a three-step approach to energy saving in their MRI departments.
If the healthcare industry were a country, it would be the fifth largest greenhouse gas emitter in the world [1]. For freestanding centers, imaging equipment alone can represent more than 19 percent of energy costs [2].
Carbon dioxide emissions and energy costs are two sides of the same coin. Already in 2019, when a new MRI system was installed at one of the network鈥檚 Munich sites, the team of DIE RADIOLOGIE implemented productivity solutions to get the most out of every kilowatt hour (kWh). When energy prices soared in Europe after the outbreak of the Ukrainian war, they took the project one step further.
We looked at where the main energy consumption was in the radiology department鈥攁nd most of it is MRI.
Prof. Mike Notohamiprodjo, MD, Managing Director, DIE RADIOLOGIE, Munich, Germany
DIE RADIOLOGIE
We could see that with Eco Power Mode, the energy consumption was about 30 percent less. This was very convincing, and we now upgraded all our systems to the new software.
Prof. Mike Notohamiprodjo, MD, Managing Director, DIE RADIOLOGIE, Munich, Germany
[1] Last accessed Jan 15, 2024.
[2] Last accessed Jan 15, 2024.
[3] Heye T, Knoerl R, Wehrle T, Mangold D, Cerminara A, Loser M, et al. The Energy Consumption of Radiology: Energy- and Cost-saving Opportunities for CT and MRI Operation. Radiology 2020; 295:593鈥605.
CO2 calculations based on Last accessed Jan 18, 2024.
1 Turning off the system does not affect the magnetic field of the scanner, which remains at its nominal strength. Therefore, please continue to operate local MRI safety within the department.
Dr. Notohamiprodjo receives financial support from Siemens Healthineers for collaborations.
The statements by customers of Siemens Healthineers described herein are based on results that were achieved in the customer鈥檚 unique setting. Since there is no 鈥渢ypical鈥 hospital and many variables exist (e.g., hospital size, case mix, level of IT adoption) there can be no guarantee that other customers will achieve the same results.