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Comparative Performance of the Finite Element Method and the Boundary Element Fast Multipole Method for Problems Mimicking Transcranial Magnetic Stimulation (TMS)

Aung Thu Htet, Guilherme B. Saturnino, Edward H. Burnham, Gregory M. Noetscher, Aapo Nummenmaa, Sergey N. Makarov
doi: https://doi.org/10.1101/411082
Aung Thu Htet
1ECE Department, Worcester Polytechnic Inst., Worcester, MA 01609 USA
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Guilherme B. Saturnino
2Danish Research Centre for Magnetic Resonance, Centre for Functional and Diagnostic Imaging and Research, Copenhagen University Hospital Hvidovre, Hvidovre, DK-2650 Denmark
3Center for Magnetic Resonance, Department of Electrical Engineering, Technical University of Denmark, Kgs Lyngby, DK-2800 Denmark
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Edward H. Burnham
1ECE Department, Worcester Polytechnic Inst., Worcester, MA 01609 USA
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Gregory M. Noetscher
1ECE Department, Worcester Polytechnic Inst., Worcester, MA 01609 USA
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Aapo Nummenmaa
4Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA 02129 USA
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Sergey N. Makarov
1ECE Department, Worcester Polytechnic Inst., Worcester, MA 01609 USA
4Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA 02129 USA
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Abstract

A study pertinent to the numerical modeling of cortical neurostimulation is conducted in an effort to compare the performance of the finite element method (FEM) and an original formulation of the boundary element fast multipole method (BEM-FMM) at matched computational performance metrics. We consider two problems: (i) a canonic multi-sphere geometry and an external magnetic-dipole excitation where the analytical solution is available and; (ii) a problem with realistic head models excited by a realistic coil geometry. In the first case, the FEM algorithm tested is a fast open-source getDP solver running within the SimNIBS 2.1.1 environment. In the second case, a high-end commercial FEM software package ANSYS Maxwell 3D is used. The BEM-FMM method runs in the MATLAB® 2018a environment.

In the first case, we observe that the BEM-FMM algorithm gives a smaller solution error for all mesh resolutions and runs significantly faster for high-resolution meshes when the number of triangular facets exceeds approximately 0.25 M. We present other relevant simulation results such as volumetric mesh generation times for the FEM, time necessary to compute the potential integrals for the BEM-FMM, and solution performance metrics for different hardware/operating system combinations. In the second case, we observe an excellent agreement for electric field distribution across different cranium compartments and, at the same time, a speed improvement of three orders of magnitude when the BEM-FMM algorithm used.

This study may provide a justification for anticipated use of the BEM-FMM algorithm for high-resolution realistic transcranial magnetic stimulation scenarios.

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The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. All rights reserved. No reuse allowed without permission.
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Posted December 21, 2018.
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Comparative Performance of the Finite Element Method and the Boundary Element Fast Multipole Method for Problems Mimicking Transcranial Magnetic Stimulation (TMS)
Aung Thu Htet, Guilherme B. Saturnino, Edward H. Burnham, Gregory M. Noetscher, Aapo Nummenmaa, Sergey N. Makarov
bioRxiv 411082; doi: https://doi.org/10.1101/411082
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Comparative Performance of the Finite Element Method and the Boundary Element Fast Multipole Method for Problems Mimicking Transcranial Magnetic Stimulation (TMS)
Aung Thu Htet, Guilherme B. Saturnino, Edward H. Burnham, Gregory M. Noetscher, Aapo Nummenmaa, Sergey N. Makarov
bioRxiv 411082; doi: https://doi.org/10.1101/411082

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