Machine learning-based prediction of MRI-induced power absorption in the tissue in patients with simplified deep brain stimulation lead models.

ABSTRACT: Interaction of an active electronic implant such as a deep brain stimulation (DBS) system and MRI 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, 1gSAR_max, at the lead-tip during 1.5 T MRI was dete

SUBMITTER: Vu J 

PROVIDER: S-EPMC8654205 | biostudies-literature | 2021 Oct

REPOSITORIES: biostudies-literature