Reducing energy consumption in musculoskeletal MRI using shorter scan protocols, optimized magnet cooling patterns, and deep learning sequences

Author:

Afat Saif,Wohlers Julian,Herrmann Judith,Brendlin Andreas S.,Gassenmaier Sebastian,Almansour Haidara,Werner Sebastian,Brendel Jan M.,Mika Alexander,Scherieble Christoph,Notohamiprodjo Mike,Gatidis Sergios,Nikolaou Konstantin,Küstner ThomasORCID

Abstract

Abstract Objectives The unprecedented surge in energy costs in Europe, coupled with the significant energy consumption of MRI scanners in radiology departments, necessitates exploring strategies to optimize energy usage without compromising efficiency or image quality. This study investigates MR energy consumption and identifies strategies for improving energy efficiency, focusing on musculoskeletal MRI. We assess the potential savings achievable through (1) optimizing protocols, (2) incorporating deep learning (DL) accelerated acquisitions, and (3) optimizing the cooling system. Materials and methods Energy consumption measurements were performed on two MRI scanners (1.5-T Aera, 1.5-T Sola) in practices in Munich, Germany, between December 2022 and March 2023. Three levels of energy reduction measures were implemented and compared to the baseline. Wilcoxon signed-rank test with Bonferroni correction was conducted to evaluate the impact of sequence scan times and energy consumption. Results Our findings showed significant energy savings by optimizing protocol settings and implementing DL technologies. Across all body regions, the average reduction in energy consumption was 72% with DL and 31% with economic protocols, accompanied by time reductions of 71% (DL) and 18% (economic protocols) compared to baseline. Optimizing the cooling system during the non-scanning time showed a 30% lower energy consumption. Conclusion Implementing energy-saving strategies, including economic protocols, DL accelerated sequences, and optimized magnet cooling, can significantly reduce energy consumption in MRI scanners. Radiology departments and practices should consider adopting these strategies to improve energy efficiency and reduce costs. Clinical relevance statement MRI scanner energy consumption can be substantially reduced by incorporating protocol optimization, DL accelerated acquisition, and optimized magnetic cooling into daily practice, thereby cutting costs and environmental impact. Key Points Optimization of protocol settings reduced energy consumption by 31% and imaging time by 18%. DL technologies led to a 72% reduction in energy consumption of and a 71% reduction in time, compared to the standard MRI protocol. During non-scanning times, activating Eco power mode (EPM) resulted in a 30% reduction in energy consumption, saving 4881€ ($5287) per scanner annually.

Publisher

Springer Science and Business Media LLC

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