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一种适应性H精炼方法用于边界元素快速多极方法用于准静态电磁建模.
William A Wartman1, Konstantin Weise2,3, Manas Rachh4
1Electrical and Computer Engineering Department, Worcester Polytechnic Inst., Worcester, MA 01609 USA.
bioRxiv : the preprint server for biology
|August 30, 2023
概括
适应性网状精细化 (AMR) 显著提高了大脑刺激和神经生理学模型的准确性. 这种方法减少了大量的电场,并减少了跨电刺激 (TES) 和电脑电图 (EEG) 建模中的潜在错误.
科学领域:
- 计算神经科学是一种计算神经科学.
- 生物医学工程 生物医学工程
- 电子生理学 电子生理学
背景情况:
- 标准的多隔间头部模型可以在电场和潜在的脑刺激计算和神经生理记录方面表现出重大错误.
- 这些错误影响了对跨磁刺激 (TMS),跨电刺激 (TES) 和电脑电图 (EEG) 预测问题的准确性.
研究的目的:
- 量化用于大脑刺激和神经生理记录的标准头部模型中的建模错误.
- 开发和验证一个自适应网状精炼 (AMR) 算法,以消除这些错误.
- 为了提高TES,TMS和EEG前问题建模的准确性.
主要方法:
- 开发并研究了一种AMR方法,使用快速多极加速 (BEM-FMM) 的边界元素方法作为数值解答器.
- 该AMR方法有效地将额外的计算资源分配给关键模型区域,以提高解决方案的准确性.
- 准确性被评估在人类结合体项目的头部模型上,比较非适应性,适应性精细化和全球精细化解决方案.
主要成果:
- 该AMR方法与标准头型号的"银标准"解决方案表现出了很好的一致性.
- 对于准确的TES和EEG前问题建模,AMR被证明是必不可少的,平均网状元件增加不到25%.
- 与适应性精炼模型相比,未精炼模型的平均电场/电位误差超过60%.
结论:
- 在TES和EEG中,AMR对于准确的建模至关重要,大大减少了标准模型中存在的错误.
- 这些发现对精确的TES剂量预测和EEG领先场准确性具有关键意义.
- 预计开发的AMR方法将适用于各种数值建模包的精确电磁模拟,并且对于精确的电磁模拟是必要的.
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