A Huygens’ surface approach to rapid characterization of peripheral nerve stimulation

• Verfasst von: Davids, Mathias [VerfasserIn]
• Verfasst von: Guerin, Bastien [VerfasserIn]
• Verfasst von: Wald, Lawrence [VerfasserIn]
• Titel: A Huygens’ surface approach to rapid characterization of peripheral nerve stimulation
• Verf.angabe: Mathias Davids, Bastien Guerin, Lawrence L. Wald
• E-Jahr: 2022
• Jahr: January 2022
• Umfang: 17 S.
• Fussnoten: First published: 24 August 2021 ; Gesehen am 14.09.2023
• Titel Quelle: Enthalten in: Magnetic resonance in medicine
• Ort Quelle: New York, NY [u.a.] : Wiley-Liss, 1984
• Jahr Quelle: 2022
• Band/Heft Quelle: 87(2022), 1 vom: Jan., Seite 377-393
• ISSN Quelle: 1522-2594
• DOI: doi:10.1002/mrm.28966
• Datenträger: Online-Ressource
• Sprache: eng
• Sach-SW: electromagnetic field simulation
• Sach-SW: gradient coil design
• Sach-SW: magneto-stimulation thresholds
• Sach-SW: MRI safety
• Sach-SW: neurodynamic nerve model
• Sach-SW: peripheral nerve stimulation
• K10plus-PPN: 1859496504
• K10plus-PPN:
  Universitätsbibliothek
• Lokale URL UB: Zum Volltext

Abstract

Purpose Peripheral nerve stimulation (PNS) modeling has a potential role in designing and operating MRI gradient coils but requires computationally demanding simulations of electromagnetic fields and neural responses. We demonstrate compression of an electromagnetic and neurodynamic model into a single versatile PNS matrix (P-matrix) defined on an intermediary Huygens’ surface to allow fast PNS characterization of arbitrary coil geometries and body positions. Methods The Huygens’ surface approach divides PNS prediction into an extensive pre-computation phase of the electromagnetic and neurodynamic responses, which is independent of coil geometry and patient position, and a fast coil-specific linear projection step connecting this information to a specific coil geometry. We validate the Huygens’ approach by performing PNS characterizations for 21 body and head gradients and comparing them with full electromagnetic-neurodynamic modeling. We demonstrate the value of Huygens’ surface-based PNS modeling by characterizing PNS-optimized coil windings for a wide range of patient positions and poses in two body models. Results The PNS prediction using the Huygens’ P-matrix takes less than a minute (instead of hours to days) without compromising numerical accuracy (error ≤ 0.1%) compared to the full simulation. Using this tool, we demonstrate that coils optimized for PNS at the brain landmark using a male model can also improve PNS for other imaging applications (cardiac, abdominal, pelvic, and knee imaging) in both male and female models. Conclusion Representing PNS information on a Huygens’ surface extended the approach’s ability to assess PNS across body positions and models and test the robustness of PNS optimization in gradient design.