发表机构
Tübingen University Hospital; Eberhard Karls University of Tübingen; Siemens Healthineers AG（蒂宾根大学医院; 蒂宾根大学; 西门子医疗股份公司）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究提出可解释深度学习框架，从MRI采集参数预测能量和功率消耗，识别关键参数及交互，助力开发节能扫描协议。
AI 中文摘要
磁共振成像（MRI）是最耗能的医学成像模态之一。为促进开发更节能的MRI协议和序列，我们开发了一个可解释的数据驱动深度学习（DL）框架，以表征驱动能量和功率消耗的因素。目标是识别对MRI能量和功率需求最具影响的采集参数及其相互作用，并在序列级别前瞻性地预测能量和功率消耗。我们训练了两个独立的DL模型，分别从MRI采集参数预测能量和功率消耗。通过分析注意力权重来识别最具影响的参数及其相互作用。所提出的DL模型成功捕获了数据中的变异性（能量模型：$R^2 = 0.963$，功率模型：$R^2 = 0.843$）。能量主要由时间参数驱动，而功率则依赖于更复杂的序列、梯度和射频相关的相互作用。这些结果表明，仅凭MRI采集参数即可准确预测能量和功率消耗。所提出的框架揭示了一小部分参数主导能量和功率需求，为开发更节能的扫描协议和序列提供了可解释的见解。
英文摘要
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.
