A machine learning-based approach for RF transfer function modeling of active implantable medical electrodes at 3T MRI.

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.

Thermal Positioning Error Modeling of Servo Axis Based on Empirical Modeling Method.

In order to investigate the thermal effect of a servo axis' positioning error on the accuracy of machine tools, an empirical modeling method was proposed, which considers both the geometric and thermal positioning error. Through the analysis of the characteristics of the positioning error curves, the initial geometric positioning error was modeled with polynomial fitting, while the thermal positioning error was built with an empirical modeling method. Empirical modeling maps the relationship between the temperature points and thermal error directly, where the multi-collinearity among the temperature variables exists. Therefore, fuzzy clustering combined with principal component regression (PCR) is applied to the thermal error modeling. The PCR model can preserve information from raw variables and eliminate the effect of multi-collinearity on the error model to a certain degree. The advantages of this modeling method are its high-precision and strong robustness. Experiments were conducted on a three-axis machine tool. A criterion was also proposed to select the temperature-sensitivity points. The fitting accuracy of the comprehensive error modeling could reach about 89%, and the prediction accuracy could reach about 86%. The proposed modeling method was proven to be effective and accurate enough to predict the positioning error at any time during the machine tool operation.