Machine Learning-Based Prediction of MRI-Induced Power Absorption in the Tissue in Patients With Simplified Deep Brain Stimulation Lead Models

Interaction of an active electronic implant such as a deep brain stimulation (DBS) system and magnetic resonance imaging (MRI) radiofrequency (RF) fields can induce excessive tissue heating, limiting MRI accessibility. Efforts to quantify RF heating mostly rely on electromagnetic (EM) simulations to assess individualized specific absorption rate (SAR), but such simulations require extensive computational resources. Here, we investigate if a predictive model using machine learning (ML) can predict the local SAR in the tissue around tips of implanted leads from the distribution of the tangential component of the MRI incident electric field,
E
_tan
. A dataset of 260 unique patient-derived and artificial DBS lead trajectories was constructed, and the 1 g-averaged SAR, 1 g SAR
_max
, at the lead-tip during 1.5 T MRI was determined by EM simulations.
E
_tan
values along each lead's trajectory and the simulated SAR values were used to train and test the ML algorithm. The resulting predictions of the ML algorithm indicated that the distribution of
E
_tan
could effectively predict 1 g SAR
_max
at the DBS lead-tip (
R
= 0.82). Our results indicate that ML has the potential to provide a fast method for predicting MR-induced power absorption in the tissue around tips of implanted leads such as those in active electronic medical devices.