Electric field calculation and peripheral nerve stimulation prediction for head and body gradient coils
Trevor Wade
Imaging Research Laboratories, Robarts Research Institute, London, Ontario, Canada
Search for more papers by this authorAndrew Alejski
Imaging Research Laboratories, Robarts Research Institute, London, Ontario, Canada
Search for more papers by this authorCharles A. McKenzie
Department of Medical Biophysics, Western University, London, Ontario, Canada
Search for more papers by this authorCorresponding Author
Brian K. Rutt
Department of Radiology, Stanford University, Stanford, California, USA
Correspondence
Brian K. Rutt, Department of Radiology, Stanford University, 1201 Welch Road, Stanford, CA 94305, USA.
Email: [email protected]
Search for more papers by this authorTrevor Wade
Imaging Research Laboratories, Robarts Research Institute, London, Ontario, Canada
Search for more papers by this authorAndrew Alejski
Imaging Research Laboratories, Robarts Research Institute, London, Ontario, Canada
Search for more papers by this authorCharles A. McKenzie
Department of Medical Biophysics, Western University, London, Ontario, Canada
Search for more papers by this authorCorresponding Author
Brian K. Rutt
Department of Radiology, Stanford University, Stanford, California, USA
Correspondence
Brian K. Rutt, Department of Radiology, Stanford University, 1201 Welch Road, Stanford, CA 94305, USA.
Email: [email protected]
Search for more papers by this authorFunding information
National Institutes of Health, Grant/Award Numbers: P41 EB015891, R01 EB025131, U01 EB025144
Abstract
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 ∆Gmin estimates and 80% of SRmin 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.
CONFLICT OF INTEREST
Dr. Roemer was an employee of GE Healthcare during the preparation of this manuscript.
Supporting Information
| Filename | Description |
|---|---|
| mrm28853-sup-0001-Supinfo.pdfPDF document, 1.1 MB |
FIGURE S1 Basis function shapes in v direction for calculation of charge distribution FIGURE S2 Convergence of E-field calculations was assessed by calculating the fractional error in normal E-field, defined as the peak normal component of E-field found anywhere on the body model surface divided by the peak magnitude of E-field found anywhere on the surface. Peak E-field magnitude converges more rapidly than the normal E-field (typical 1 to 2 orders of magnitude faster) because of the fact that the normal E-field adds quadratically to the desired tangential E-field, and hence, convergence error is dominated by the error in the normal E-field. Plot shows the convergence behavior of the error in normal E-field for a typical solution as the number of basis functions is increased. Only a few terms in angle (number of u direction basis functions) are required for full convergence. A cylindrically symmetric body model would only require a single term, and the elliptical shape of our body cross section requires only a small number of u direction basis functions for convergence. The v direction needs enough terms to adequately represent the field variation in the z direction; plot shows ~50 terms required to achieve 1% error in the normal E-field |
Please note: The publisher is not responsible for the content or functionality of any supporting information supplied by the authors. Any queries (other than missing content) should be directed to the corresponding author for the article.
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