Prediction of peripheral nerve stimulation thresholds of MRI gradient coils using coupled electromagnetic and neurodynamic simulations
Purpose As gradient performance increases, peripheral nerve stimulation (PNS) is becoming a significant constraint for fast MRI. Despite its impact, PNS is not directly included in the coil design process. Instead, the PNS characteristics of a gradient are assessed on healthy subjects after prototyp...
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| Main Authors: | , |
|---|---|
| Format: | Article (Journal) |
| Language: | English |
| Published: |
2019
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| In: |
Magnetic resonance in medicine
Year: 2018, Volume: 81, Issue: 1, Pages: 686-701 |
| ISSN: | 1522-2594 |
| DOI: | 10.1002/mrm.27382 |
| Online Access: | Verlag, Volltext: https://doi.org/10.1002/mrm.27382 Verlag, Volltext: https://onlinelibrary.wiley.com/doi/abs/10.1002/mrm.27382 
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| Author Notes: | Mathias Davids, Bastien Guérin, Axel vom Endt, Lothar R. Schad, Lawrence L. Wald |
MARC
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| 245 | 1 | 0 | |a Prediction of peripheral nerve stimulation thresholds of MRI gradient coils using coupled electromagnetic and neurodynamic simulations |c Mathias Davids, Bastien Guérin, Axel vom Endt, Lothar R. Schad, Lawrence L. Wald |
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| 520 | |a Purpose As gradient performance increases, peripheral nerve stimulation (PNS) is becoming a significant constraint for fast MRI. Despite its impact, PNS is not directly included in the coil design process. Instead, the PNS characteristics of a gradient are assessed on healthy subjects after prototype construction. We attempt to develop a tool to inform coil design by predicting the PNS thresholds and activation locations in the human body using electromagnetic field simulations coupled to a neurodynamic model. We validate the approach by comparing simulated and experimentally determined thresholds for 3 gradient coils. Methods We first compute the electric field induced by the switching fields within a detailed electromagnetic body model, which includes a detailed atlas of peripheral nerves. We then calculate potential changes along the nerves and evaluate their response using a neurodynamic model. Both a male and female body model are used to study 2 body gradients and 1 head gradient. Results There was good agreement between the average simulated thresholds of the male and female models with the experimental average (normalized root-mean-square error: <10% and <5% in most cases). The simulation could also interrogate thresholds above those accessible by the experimental setup and allowed identification of the site of stimulation. Conclusions Our simulation framework allows accurate prediction of gradient coil PNS thresholds and provides detailed information on location and “next nerve” thresholds that are not available experimentally. As such, we hope that PNS simulations can have a potential role in the design phase of high performance MRI gradient coils. | ||
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