Abstract
The interaction between deep brain stimulation (DBS) systems and magnetic resonance imaging (MRI) can induce tissue heating in patients. While electromagnetic (EM) simulations can be used to estimate the specific absorption rate (SAR) values in the presence of an implanted DBS system, they are computationally expensive. To address this drawback, we predict local SAR values in the tips of DBS leads with machine learning based efficient algorithms, specifically XgBoost and deep learning. We significantly outperformed the previous state of the art, and adapted new machine learning models based on Residual Networks family as well as XgBoost models. We observed that already extracted limited features are better suited for ensemble learning via XgBoost than deep networks due the small-data regime. Although we conclude that boosting gradient algorithm is more suitable for this non-linear regression problem due to structured nature of the data and small data regime, we found that width plays a more critical role than depth in network design and it has a strong potential for future research. Our experimental results, using a dataset of 260 instances that are patient-derived and artificial, reached an outstanding RMSE of 17.8 W/kg with XgBoost, 78 W/kg with deep networks, given that the previous study on this problem reached a state-of-the-art root mean square error value (RMSE) of 168 W/kg.