ArticleIEEE transactions on electromagnetic compatibility2021
Machine learning-based prediction of MRI-induced power absorption in the tissue in patients with simplified deep brain stimulation lead models.
Article in IEEE transactions on electromagnetic compatibility, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed, 28 citations in OpenAlex.
- Roadmap towards personalized approaches and safety considerations in non-ionizing radiation: from dosimetry to therapeutic and diagnostic applications.Physics in medicine and biology · 2026Review
- Prediction of MRI-Induced Power Absorption in Patients with DBS Leads.Proceedings. IEEE International Symposium on Computer-Based Medical Systems · 2024Article
- A workflow for predicting radiofrequency-induced heating around bilateral deep brain stimulation electrodes in MRI.Medical physics · 2024Article
- Deep learning-based local SAR prediction using BMagnetic resonance in medicine · 2023Article
- Modifying the trajectory of epicardial leads can substantially reduce MRI-induced RF heating in pediatric patients with a cardiac implantable electronic device at 1.5T.Magnetic resonance in medicine · 2023Article
- Age Matters: A Comparative Study of RF Heating of Epicardial and Endocardial Electronic Devices in Pediatric and Adult Phantoms during Cardiothoracic MRI.Diagnostics (Basel, Switzerland) · 2023Article
- Rapid prediction of MRI-induced RF heating of active implantable medical devices using machine learning.Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference · 2023Article
- MRI of Implantation Sites Using Parallel Transmission of an Optimized Radiofrequency Excitation Vector.Tomography (Ann Arbor, Mich.) · 2023Article
- Article
- A comparative study of MRI-induced RF heating in pediatric and adult populations with epicardial and endocardial implantable electronic devices.Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference · 2022Article
Corrections and comments
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Authors and funding
8 authors at 1 institution in 1 country.
Funding
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
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Registered trials
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