A Huygens’ surface approach to rapid characterization of peripheral nerve stimulation
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 sin...
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| Hauptverfasser: | , , |
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| Dokumenttyp: | Article (Journal) |
| Sprache: | Englisch |
| Veröffentlicht: |
January 2022
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| In: |
Magnetic resonance in medicine
Year: 2022, Jahrgang: 87, Heft: 1, Pages: 377-393 |
| ISSN: | 1522-2594 |
| DOI: | 10.1002/mrm.28966 |
| Online-Zugang: | Verlag, lizenzpflichtig, Volltext: https://doi.org/10.1002/mrm.28966 Verlag, lizenzpflichtig, Volltext: https://onlinelibrary.wiley.com/doi/abs/10.1002/mrm.28966 
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| Verfasserangaben: | Mathias Davids, Bastien Guerin, Lawrence L. Wald |
| Zusammenfassung: | 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. |
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| Beschreibung: | First published: 24 August 2021 Gesehen am 14.09.2023 |
| Beschreibung: | Online Resource |
| ISSN: | 1522-2594 |
| DOI: | 10.1002/mrm.28966 |