Literature DB >> 23128377

Fast multigrid-based computation of the induced electric field for transcranial magnetic stimulation.

Ilkka Laakso1, Akimasa Hirata.   

Abstract

In transcranial magnetic stimulation (TMS), the distribution of the induced electric field, and the affected brain areas, depends on the position of the stimulation coil and the individual geometry of the head and brain. The distribution of the induced electric field in realistic anatomies can be modelled using computational methods. However, existing computational methods for accurately determining the induced electric field in realistic anatomical models have suffered from long computation times, typically in the range of tens of minutes or longer. This paper presents a matrix-free implementation of the finite-element method with a geometric multigrid method that can potentially reduce the computation time to several seconds or less even when using an ordinary computer. The performance of the method is studied by computing the induced electric field in two anatomically realistic models. An idealized two-loop coil is used as the stimulating coil. Multiple computational grid resolutions ranging from 2 to 0.25 mm are used. The results show that, for macroscopic modelling of the electric field in an anatomically realistic model, computational grid resolutions of 1 mm or 2 mm appear to provide good numerical accuracy compared to higher resolutions. The multigrid iteration typically converges in less than ten iterations independent of the grid resolution. Even without parallelization, each iteration takes about 1.0 s or 0.1 s for the 1 and 2 mm resolutions, respectively. This suggests that calculating the electric field with sufficient accuracy in real time is feasible.

Mesh:

Year:  2012        PMID: 23128377     DOI: 10.1088/0031-9155/57/23/7753

Source DB:  PubMed          Journal:  Phys Med Biol        ISSN: 0031-9155            Impact factor:   3.609


  18 in total

Review 1.  The development and modelling of devices and paradigms for transcranial magnetic stimulation.

Authors:  Stefan M Goetz; Zhi-De Deng
Journal:  Int Rev Psychiatry       Date:  2017-04-26

2.  Conditions for numerically accurate TMS electric field simulation.

Authors:  Luis J Gomez; Moritz Dannhauer; Lari M Koponen; Angel V Peterchev
Journal:  Brain Stimul       Date:  2019-10-03       Impact factor: 8.955

3.  Comparative performance of the finite element method and the boundary element fast multipole method for problems mimicking transcranial magnetic stimulation (TMS).

Authors:  Aung Thu Htet; Guilherme B Saturnino; Edward H Burnham; Gregory M Noetscher; Aapo Nummenmaa; Sergey N Makarov
Journal:  J Neural Eng       Date:  2019-01-03       Impact factor: 5.379

4.  A Quasi-Static Boundary Element Approach With Fast Multipole Acceleration for High-Resolution Bioelectromagnetic Models.

Authors:  Sergey N Makarov; Gregory M Noetscher; Tommi Raij; Aapo Nummenmaa
Journal:  IEEE Trans Biomed Eng       Date:  2018-03-07       Impact factor: 4.538

5.  The influence of sulcus width on simulated electric fields induced by transcranial magnetic stimulation.

Authors:  A M Janssen; S M Rampersad; F Lucka; B Lanfer; S Lew; U Aydin; C H Wolters; D F Stegeman; T F Oostendorp
Journal:  Phys Med Biol       Date:  2013-06-21       Impact factor: 3.609

6.  Electric field calculation and peripheral nerve stimulation prediction for head and body gradient coils.

Authors:  Peter B Roemer; Trevor Wade; Andrew Alejski; Charles A McKenzie; Brian K Rutt
Journal:  Magn Reson Med       Date:  2021-06-03       Impact factor: 3.737

7.  Prediction of Force Recruitment of Neuromuscular Magnetic Stimulation From 3D Field Model of the Thigh.

Authors:  Stefan Goetz; Joerg Kammermann; Florian Helling; Thomas Weyh; Zhongxi Li
Journal:  IEEE Trans Neural Syst Rehabil Eng       Date:  2022-03-28       Impact factor: 4.528

8.  Study of the influence of the orientation of a 50-Hz magnetic field on fetal exposure using polynomial chaos decomposition.

Authors:  Ilaria Liorni; Marta Parazzini; Serena Fiocchi; Paolo Ravazzani
Journal:  Int J Environ Res Public Health       Date:  2015-05-27       Impact factor: 3.390

9.  Multiscale Computational Model Reveals Nerve Response in a Mouse Model for Temporal Interference Brain Stimulation.

Authors:  Jose Gomez-Tames; Akihiro Asai; Akimasa Hirata
Journal:  Front Neurosci       Date:  2021-06-30       Impact factor: 4.677

10.  ECG Localization Method Based on Volume Conductor Model and Kalman Filtering.

Authors:  Yuki Nakano; Essam A Rashed; Tatsuhito Nakane; Ilkka Laakso; Akimasa Hirata
Journal:  Sensors (Basel)       Date:  2021-06-22       Impact factor: 3.576

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