ArticlePhysics in medicine and biology2020
The effect of simulation strategies on prediction of power deposition in the tissue around electronic implants during magnetic resonance imaging.
Article in Physics in medicine and biology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed.
- Weight-Guided Constraints for Body Model and Lead Selection in Pediatric CIED MRI Safety Simulations.ArXiv · 2026Article
- A workflow for predicting radiofrequency-induced heating around bilateral deep brain stimulation electrodes in MRI.Medical physics · 2024Article
- Low-field MRI's Spark on Implant Safety: A Closer Look at Radiofrequency Heating.Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference · 2023Article
- Application of Machine learning to predict RF heating of cardiac leads during magnetic resonance imaging at 1.5 T and 3 T: A simulation study.Journal of magnetic resonance (San Diego, Calif. : 1997) · 2023Article
- Article
- Safety of MRI in patients with retained cardiac leads.Magnetic resonance in medicine · 2022Article
- A comparative study of RF heating of deep brain stimulation devices in vertical vs. horizontal MRI systems.PloS one · 2022Article
- Predicting RF Heating of Conductive Leads During Magnetic Resonance Imaging at 1.5 T: A Machine Learning ApproachAnnual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference · 2021Article
- Machine learning-based prediction of MRI-induced power absorption in the tissue in patients with simplified deep brain stimulation lead models.IEEE transactions on electromagnetic compatibility · 2021Article
- Vertical open-bore MRI scanners generate significantly less radiofrequency heating around implanted leads: A study of deep brain stimulation implants in 1.2T OASIS scanners versus 1.5T horizontal systems.Magnetic resonance in medicine · 2021Article
- Patient's body composition can significantly affect RF power deposition in the tissue around DBS implants: ramifications for lead management strategies and MRI field-shaping techniques.Physics in medicine and biology · 2021Article
- Safety and image quality at 7T MRI for deep brain stimulation systems: Ex vivo study with lead-only and full-systems.PloS one · 2021Article
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3 authors.
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Abstract
Numerical simulations are increasingly employed in safety assessment of high-field magnetic resonance imaging (MRI) in patients with conductive medical implants such as those with deep brain stimulation (DBS) devices. Performing numerical simulations with realistic patient models and implant geometry is the preferred method as it provides the most accurate results; however, in many cases such an approach is infeasible due to limitation of computational resources. The difficulties in reconstructing realistic patient and device models and obtaining accurate electrical properties of tissue have compelled researchers to adopt compromises, either to exceedingly simplify implant structure and geometry, or the complexity of the body model. This study examines the effect of variations in anatomical details of the human body model and implant geometry on predicted values of specific absorption rate (SAR) values during MRI in a patient with a DBS implant. We used a patient-derived model of a fully implanted DBS implant and performed numerical simulations to calculate the maximum SAR during MRI at 1.5T (64 MHz) and 3T (127 MHz). We then assessed the effect of uncertainties in dielectric properties of tissue, complexity of body model, truncation of body/DBS model, and DBS lead geometry on SAR. Our results showed that 40% variation in the conductivity of individual tissues in a heterogeneous body model caused a peak of 7% variation in maximum SAR value at 64 MHz, and 10.6% variation in SAR at 127 MHz. SAR predictions from a homogeneous body model with a conductivity range of [Formula: see text] could cover the full range of SAR variations predicted by the heterogeneous body model. Truncation of body model below the implanted pulse generator changed the predicted SAR by 16% at 1.5T and 32% at 3T while saving 250% and 148% in computational time and memory allocation, respectively. In contrast, variation in DBS lead geometry significantly changed the SAR by up to 51% at 64 MHz and 67% at 127 MHz. These results suggest that the error introduced by simplifying the implant's geometry could negate the benefit of using a realistic body model, should such model be used at the expense of oversimplifying implant geometry.
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