AUTHOR=Erturk M. Arcan , Panken Eric , Conroy Mark J. , Edmonson Jonathan , Kramer Jeff , Chatterton Jacob , Banerjee S. Riki 

TITLE=Predicting in vivo MRI Gradient-Field Induced Voltage Levels on Implanted Deep Brain Stimulation Systems Using Neural Networks

JOURNAL=Frontiers in Human Neuroscience

VOLUME=Volume 14 - 2020

YEAR=2020

URL=https://www.frontiersin.org/journals/human-neuroscience/articles/10.3389/fnhum.2020.00034

DOI=10.3389/fnhum.2020.00034

ISSN=1662-5161

ABSTRACT=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.