Predicting RF-Induced Heating for Deep Brain Stimulator System Using an Artificial Neural Network

Guo, Ran; Zheng, Jianfeng; Kainz, Wolfgang; Chen, Ji

doi:10.1109/iccsi55536.2022.9970664

Cited by 3 publications

(2 citation statements)

References 11 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…30 On the other hand, a fast, patient-specific safety assessment workflow that predicts electrode heating prior to an MR scan can be used to adjust scan protocols on an individual patient basis and reduce restrictions due to DBS manufacturers' MR guidelines. 21 Some groups have focused on computational methods (i.e., full-wave EM simulations [31][32][33][34][35] as well as machine learning-based SAR estimation methods [36][37][38] ) to predict RF heating. In general, these methods utilize realistic human models and detailed electrical models of the MR hardware and DBS electrode.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A workflow for predicting radiofrequency‐induced heating around bilateral deep brain stimulation electrodes in MRI

Zulkarnain,

Sadeghi‐Tarakameh,

Thotland

et al. 2023

Medical Physics

View full text Add to dashboard Cite

BackgroundHeating around deep brain stimulation (DBS) in magnetic resonance imaging (MRI) occurs when the time‐varying electromagnetic (EM) fields induce currents in the electrodes which can generate heat and potentially cause tissue damage. Predicting the heating around the electrode contacts is important to ensure the safety of patients with DBS implants undergoing an MRI scan. We previously proposed a workflow to predict heating around DBS contacts and introduced a parameter, equivalent transimpedance, that is independent of electrode trajectories, termination, and radiofrequency (RF) excitations. The workflow performance was validated in a unilateral DBS system.PurposeTo predict RF heating around the contacts of bilateral (DBS) electrodes during an MRI scan in an anthropomorphic head phantom.MethodsBilateral electrodes were fixed in a skull phantom filled with hydroxyethyl cellulose (HEC) gel. The electrode shafts were suspended extracranially, in a head and torso phantom filled with the same gel material. The current induced on the electrode shaft was experimentally measured using an MR‐based technique 3 cm above the tip. A transimpedance value determined in a previous offline calibration was used to scale the shaft current and calculate the contact voltage. The voltage was assigned as a boundary condition on the electrical contacts of the electrode in a quasi‐static (EM) simulation. The resulting specific absorption rate (SAR) distribution became the input for a transient thermal simulation and was used to predict the heating around the contacts. RF heating experiments were performed for eight different lead trajectories using circularly polarized (CP) excitation and two linear excitations for one trajectory. The measured temperatures for all experiments were compared with the simulated temperatures and the root‐mean‐squared errors (RMSE) were calculated.ResultsThe RF heating around the contacts of both bilateral electrodes was predicted with ≤ 0.29°C of RMSE for 20 heating scenarios.ConclusionThe workflow successfully predicted the heating for different bilateral DBS trajectories and excitation patterns in an anthropomorphic head phantom.

show abstract

Section: Introductionmentioning

confidence: 99%

“…Some groups have focused on computational methods (i.e., full‐wave EM simulations 31–35 as well as machine learning‐based SAR estimation methods 36–38 ) to predict RF heating. In general, these methods utilize realistic human models and detailed electrical models of the MR hardware and DBS electrode.…”

Section: Introductionmentioning

confidence: 99%