An optimized block forward‐elimination and backward‐substitution algorithm for GPU accelerated ILU preconditioner in evaluating the induced electric field during transcranial magnetic stimulation

Li, Congsheng; Ye, Zhenfei; Wei, Yiwen; Wu, Tongning

doi:10.1002/bem.22178

Cited by 2 publications

(2 citation statements)

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“…The in-house ELF scalar potential finite difference (SPFD) solver was used to calculate the induced E-field distribution in the brains [29]. The solver used the incomplete lower- and upper-matrix preconditioner to speed up solution of the derived septa-diagonal matrix, where block Forward-Elimination and Backward-Substitution algorithms were developed to facilitate GPU-based multithread parallelization.…”

Section: Materials and Modelsmentioning

confidence: 99%

“…The solver used the incomplete lower- and upper-matrix preconditioner to speed up solution of the derived septa-diagonal matrix, where block Forward-Elimination and Backward-Substitution algorithms were developed to facilitate GPU-based multithread parallelization. This solver has been validated with commercial software and has been demonstrated to have high computational efficiency when processing ELF MF problems [29]. The solver's code is free to download at https://github.com/licongsheng/OpenSPFD.…”

Section: Materials and Modelsmentioning

confidence: 99%

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Particle Swarm Optimization for Positioning the Coil of Transcranial Magnetic Stimulation

Yang

et al. 2019

BioMed Research International

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The distribution of the induced electric field (E-field) during transcranial magnetic stimulation (TMS) depends on the individual anatomical structure of the brain as well as coil positioning. Inappropriate stimulation may degrade the efficacy of TMS or even induce adverse effects. Therefore, optimizing the E-field according to individual anatomy and clinical need has become a research focus. In this paper, particle swarm optimization (PSO) was applied for the first time to the positioning of TMS coils with anatomical head models. We discuss the parameters of the PSO algorithm, which were optimized to achieve a reasonable convergence time suitable for in-time treatment planning. The optimizer improved the distribution of the induced E-field strength at the dedicated cortical region, with a mean value of 48.31% compared with that from the conventional treatment position. The optimization terminated after 4–11 iterations for 13 head models. The applicability and performance of the optimizer for a large population are discussed in terms of cortical complexity. This study could benefit not only clinics but also research on brain modulation.

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Section: Materials and Modelsmentioning

confidence: 99%

Section: Materials and Modelsmentioning

confidence: 99%