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

Jasmine Vu*, Bach T. Nguyen, Bhumi Bhusal, Justin Baraboo, Joshua Rosenow, Ulas Bagci, Molly G. Bright, Laleh Golestanirad

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

Abstract

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, Etan. A dataset of 260 unique patient-derived and artificial DBS lead trajectories was constructed, and the 1 g-averaged SAR, 1 g SARmax, at the lead-tip during 1.5 T MRI was determined by EM simulations. Etan 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 Etan could effectively predict 1 g SARmax 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.

Original languageEnglish (US)
Pages (from-to)1757-1766
Number of pages10
JournalIEEE Transactions on Electromagnetic Compatibility
Volume63
Issue number5
DOIs
StatePublished - Oct 1 2021

Keywords

  • Active implantable medical device
  • deep brain stimulation (DBS)
  • implants
  • machine learning (ML)
  • neural networks
  • radiofrequency (RF) safety

ASJC Scopus subject areas

  • Atomic and Molecular Physics, and Optics
  • Condensed Matter Physics
  • Electrical and Electronic Engineering

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