Sustainable MRI: Interpretable Deep Learning for estimating energy and power consumption, revealing key acquisition parameters and their interactions

Abstract

Magnetic resonance imaging (MRI) is among the most energy-intensive medical imaging modalities. To facilitate the development of more energy-efficient MRI protocols and sequences, we developed an interpretable data-driven deep learning (DL) framework to characterize the factors driving energy and power consumption. The aim was to identify the most influential acquisition parameters and their interactions on MRI energy and power demand, and to prospectively predict energy and power consumption on a per-sequence level. Two separate DL models were trained to predict energy and power consumption from MRI acquisition parameters. Attention weights were analyzed to identify the most influential parameters and their interactions. The proposed DL models successfully captured the variability in the data (energy model: $R^2 = 0.963$, power model: $R^2 = 0.843$). Energy was primarily driven by temporal parameters, whereas power depended on more complex sequence, gradient, and RF-related interactions. These results demonstrate that energy and power consumption can be accurately predicted from MRI acquisition parameters alone. The proposed framework reveals that a small subset of parameters governs energy and power demand, providing interpretable insights to support the development of more energy-efficient scanning protocols and sequences.