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Electric field calculation and peripheral nerve stimulation prediction for head and body gradient coils
Author(s) -
Roemer Peter B.,
Wade Trevor,
Alejski Andrew,
McKenzie Charles A.,
Rutt Brian K.
Publication year - 2021
Publication title -
magnetic resonance in medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.696
H-Index - 225
eISSN - 1522-2594
pISSN - 0740-3194
DOI - 10.1002/mrm.28853
Subject(s) - electromagnetic coil , head (geology) , field (mathematics) , computation , electric field gradient , finite element method , computer science , electric field , algorithm , physics , mathematics , quantum mechanics , geomorphology , pure mathematics , thermodynamics , geology
Purpose To demonstrate and validate electric field (E‐field) calculation and peripheral nerve stimulation (PNS) prediction methods that are accurate, computationally efficient, and that could be used to inform regulatory standards. Methods We describe a simplified method for calculating the spatial distribution of induced E‐field over the volume of a body model given a gradient coil vector potential field. The method is easily programmed without finite element or finite difference software, allowing for straightforward and computationally efficient E‐field evaluation. Using these E‐field calculations and a range of body models, population‐weighted PNS thresholds are determined using established methods and compared against published experimental PNS data for two head gradient coils and one body gradient coil. Results A head‐gradient‐appropriate chronaxie value of 669 µs was determined by meta‐analysis. Prediction errors between our calculated PNS parameters and the corresponding experimentally measured values were ~5% for the body gradient and ~20% for the symmetric head gradient. Our calculated PNS parameters matched experimental measurements to within experimental uncertainty for 73% of ∆ G min estimates and 80% of SR min estimates. Computation time is seconds for initial E‐field maps and milliseconds for E‐field updates for different gradient designs, allowing for highly efficient iterative optimization of gradient designs and enabling new dimensions in PNS‐optimal gradient design. Conclusions We have developed accurate and computationally efficient methods for prospectively determining PNS limits, with specific application to head gradient coils.

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