Keywords: Data Acquisition, Parallel Transmit & Multiband, Sustainability, Energy
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.
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