Predicting in vivo MRI Gradient-Field Induced Voltage Levels on Implanted Deep Brain Stimulation Systems Using Neural Networks - Erturk, M. Arcan; Panken, Eric; Conroy, Mark J.; Edmonson, Jonathan; Kramer, Jeff; Chatterton, Jacob; Banerjee, S. Riki (Frontiers in Human Neuroscience, 2020)
Introduction: MRI gradient-fields may induce extrinsic voltage between electrodes and conductive neurostimulator enclosure of implanted deep brain stimulation (DBS) systems, and may cause unintended stimulation and/or malfunction. Electromagnetic (EM) simulations using detailed anatomical human models, therapy implant trajectories and gradient coil models can be used to calculate clinically-relevant induced voltage levels. Incorporating additional anatomical human models into the EM simulation library can help to achieve more clinically-relevant and accurate induced voltage levels, however adding new anatomical human models and developing implant trajectories is time-consuming, expensive and not always feasible. Methods: MRI gradient-field induced voltage levels are simulated in six adult human anatomical models, along clinically-relevant DBS implant trajectories to generate the dataset. Predictive artificial neural network (ANN) regression models are trained on the simulated dataset. Leave-one-out cross validation is performed to assess the performance of ANN regressors and quantify model prediction errors. Results: More than 180,000 unique gradient-induced voltage levels are simulated. ANN algorithm with two fully connected layers is selected due to its superior generalizability compared to support vector machine and tree-based algorithms in this particular application. The ANN regression model is capable of producing thousands of gradient-induced voltage predictions in less than a second with mean-squared-error less than 200 mV. Conclusion: We have integrated machine learning with computational modelling and simulations and developed an accurate predictive model to determine MRI gradient-field induced voltage levels on implanted DBS systems.