Could AI-driven acceleration techniques be one answer to the issue of environmental sustainability in MRI?

Motivation: This project explored the integration of AI-powered Deep Resolve to enhance sustainability in Magnetic Resonance Imaging (MRI). With healthcare contributing significantly to global greenhouse emissions, reducing energy consumption in MRI operations is crucial. Goal(s): By accelerating imaging protocols through AI, scan times were reduced without compromising diagnostic quality, with the aim of producing significant energy savings. Approach: The approach highlighted AI's potential to drive sustainability in healthcare while improving operational efficiency. Results: This mini pilot study demonstrated a 42% reduction in scan time for brain and spinal cord imaging, translating to a decrease in carbon emissions equivalent to 2.4 tonnes annually. Impact: The overall impact of AI-powered MRI optimisation is significant: reduced scan times, lower energy consumption, cutting carbon emissions, and improved operational efficiency. This contributes to healthcare sustainability, reduces costs, and enhances patient comfort, while fostering environmental stewardship.