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Verfasser:Davids, Mathias [VerfasserIn]   i
 Malzacher, Matthias [VerfasserIn]   i
 Schad, Lothar R. [VerfasserIn]   i
Titel:Predicting magnetostimulation thresholds in the peripheral nervous system using realistic body models
Verf.angabe:Mathias Davids, Bastien Guérin, Matthias Malzacher, Lothar R. Schad & Lawrence L. Wald
Fussnoten:Gesehen am 05.09.2017
Titel Quelle:Enthalten in: Scientific reports
Jahr Quelle:2017
Band/Heft Quelle:7(2017) Artikel-Nummer 5316, 29 Seiten
ISSN Quelle:2045-2322
Abstract:Rapid switching of applied magnetic fields in the kilohertz frequency range in the human body induces electric fields powerful enough to cause Peripheral Nerve Stimulation (PNS). PNS has become one of the main constraints on the use of high gradient fields for fast imaging with the latest MRI gradient technology. In recent MRI gradients, the applied fields are powerful enough that PNS limits their application in fast imaging sequences like echo-planar imaging. Application of Magnetic Particle Imaging (MPI) to humans is similarly PNS constrained. Despite its role as a major constraint, PNS considerations are only indirectly incorporated in the coil design process, mainly through using the size of the linear region as a proxy for PNS thresholds or by conducting human experiments after constructing coil prototypes. We present for the first time, a framework to simulate PNS thresholds for realistic coil geometries to directly address PNS in the design process. Our PNS model consists of an accurate body model for electromagnetic field simulations, an atlas of peripheral nerves, and a neurodynamic model to predict the nerve responses to imposed electric fields. With this model, we were able to reproduce measured PNS thresholds of two leg/arm solenoid coils with good agreement.
DOI:doi:10.1038/s41598-017-05493-9
URL:Kostenfrei: Verlag: http://dx.doi.org/10.1038/s41598-017-05493-9
 Kostenfrei: Verlag: https://www.nature.com/articles/s41598-017-05493-9
 DOI: 10.1038/s41598-017-05493-9
Datenträger:Online-Ressource
Sprache:eng
SWB-PPN:493214712
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