Abstract：
Objective.The objective of this work is to propose a machine learning-based approach to rapidly and efficiently model the radiofrequency (RF) transfer function of active implantable medical (AIM) electrodes, and to overcome the limitations and drawbacks of traditional measurement methods when applied to heterogeneous tissue environments.Approach.AIM electrodes with different geometries and proximate tissue distributions were considered, and their RF transfer functions were modeled numerically. Machine learning algorithms were developed and trained with the simulated transfer function datasets for homogeneous and heterogeneous tissue distributions. The performance of the method was analyzed statistically and validated experimentally and numerically. A comprehensive uncertainty analysis was performed and uncertainty budgets were derived.Main results.The proposed method is able to predict the RF transfer function of AIM electrodes under different tissue distributions, with mean correlation coefficientsrof 0.99 and 0.98 for homogeneous and heterogeneous environments, respectively. The results were successfully validated by experimental measurements (e.g. the uncertainty of less than 0.9 dB) and numerical simulation (e.g. transfer function uncertainty <1.6 dB and power deposition uncertainty <1.9 dB). Up to 1.3 dBin vivopower deposition underestimation was observed near generic pacemakers when using a simplified homogeneous tissue model.Significance.Provide an efficient alternative of transfer function modeling, which allows a more realistic tissue distribution and the potential underestimation ofin vivoRF-induced power deposition near the AIM electrode can be reduced.
摘要：
目的：这项工作的目的是提出一种基于机器学习的方法，以快速有效地对有源植入式医疗（AIM）电极的射频（RF）传递函数进行建模，并克服了传统测量方法应用于异质组织环境时的局限性和弊端。
方法：考虑了具有不同几何形状和邻近组织分布的AIM电极，并对它们的射频传递函数进行了数值建模。开发了机器学习算法，并使用模拟的传递函数数据集进行了训练，以获得均匀和异质的组织分布。对该方法的性能进行了统计分析，并进行了实验和数值验证。进行了全面的不确定度分析，并得出了不确定度预算。
结果：所提出的方法是＆＃xD;能够预测不同组织分布下AIM电极的RF传递函数，同质和异质环境的平均相关系数r为0.99和0.98，分别。通过实验测量成功验证了结果(例如，小于0.9dB的不确定性)和数值模拟(例如，传递函数不确定性<1.6dB，功率沉积不确定性<1.9dB)。使用简化的均质组织模型时，在通用起搏器附近观察到高达1.3dB的体内功率沉积低估。
结论：提供传递函数建模的有效替代方案，这允许更真实的组织分布，并且可以减少AIM电极附近的体内RF感应功率沉积的潜在低估。 .
方法：考虑了具有不同几何形状和邻近组织分布的AIM电极，并对它们的射频传递函数进行了数值建模。开发了机器学习算法，并使用模拟的传递函数数据集进行了训练，以获得均匀和异质的组织分布。对该方法的性能进行了统计分析，并进行了实验和数值验证。进行了全面的不确定度分析，并得出了不确定度预算。
结果：所提出的方法是＆＃xD;能够预测不同组织分布下AIM电极的RF传递函数，同质和异质环境的平均相关系数r为0.99和0.98，分别。通过实验测量成功验证了结果(例如，小于0.9dB的不确定性)和数值模拟(例如，传递函数不确定性<1.6dB，功率沉积不确定性<1.9dB)。使用简化的均质组织模型时，在通用起搏器附近观察到高达1.3dB的体内功率沉积低估。
结论：提供传递函数建模的有效替代方案，这允许更真实的组织分布，并且可以减少AIM电极附近的体内RF感应功率沉积的潜在低估。 .
