Investigating Data Acquisition Strategies for High Efficiency Clinical MRI and a More Sustainable Future in Radiology

Motivation: Recognizing Radiology’s role in the medical healthcare environmental impact, we investigate strategies to reduce MRI scanning energy consumption and carbon footprint of Radiology. Goal(s): To demonstrate the achievable savings in time and energy during clinical MRI scans. Approach: Power meters were connected to three clinical MRI scanners from different field strengths to collect power data while phantom scans were acquired with typical clinical sequences and a range of common acceleration methods. Results: The application of acceleration techniques resulted in decreased scan duration, energy consumption, and carbon footprint. Deep Learning (DL) acceleration emerged as the technique with the most savings. Impact: This research paves the way for adoption of high-efficiency MRI techniques, which promise to substantially lower the carbon footprint and scan duration associated with MRI exams.