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Radiofrequency heating of retained cardiac leads during magnetic resonance imaging at 1.5 T and 3 T.

, , , and . EMBC, page 4986-4989. IEEE, (2021)

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On the accuracy of Tier 4 simulations to predict RF heating of wire implants during magnetic resonance imaging at 1.5 T., , , , , , and . EMBC, page 4982-4985. IEEE, (2021)Evaluating Accuracy of Numerical Simulations in Predicting Heating of Wire Implants During MRI at 1.5 T., , , and . EMBC, page 6107-6110. IEEE, (2020)Device Configuration and Patient's Body Composition Significantly Affect RF Heating of Deep Brain Stimulation Implants During MRI: An Experimental Study at 1.5T and 3T., , , , , , , , and . EMBC, page 5192-5197. IEEE, (2020)Radiofrequency heating of retained cardiac leads during magnetic resonance imaging at 1.5 T and 3 T., , , and . EMBC, page 4986-4989. IEEE, (2021)Multi-Segment Leads To Reduce RF Heating in MRI: A Computational Evaluation at 1.5T and 3T., , and . EMBC, page 1-4. IEEE, (2023)EM fields comparison between planar vs. solenoidal μMS coil designs for nerve stimulation., and . EMBC, page 3576-3579. IEEE, (2017)Analysis of fractal electrodes for efficient neural stimulation., , and . EMBC, page 791-794. IEEE, (2013)Analysis of the intended and actual orientations of directional deep brain stimulation leads across deep brain stimulation systems., , , , , and . EMBC, page 1725-1728. IEEE, (2022)A comparative study of MRI-induced RF heating in pediatric and adult populations with epicardial and endocardial implantable electronic devices., , , , , , , and . EMBC, page 4014-4017. IEEE, (2022)Rapid prediction of MRI-induced RF heating of active implantable medical devices using machine learning., , , , and . EMBC, page 1-4. IEEE, (2023)