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FastField: An Open-Source Toolbox for Efficient Approximation of Deep Brain Stimulation Electric Fields
NeuroImage ( IF 4.7 ) Pub Date : 2020-12-01 , DOI: 10.1016/j.neuroimage.2020.117330
Mehri Baniasadi 1 , Daniele Proverbio 2 , Jorge Gonçalves 2 , Frank Hertel 1 , Andreas Husch 2
Affiliation  

Deep brain stimulation (DBS) is a surgical therapy to alleviate symptoms of certain brain disorders by electrically modulating neural tissues. Computational models predicting electric fields and volumes of tissue activated are key for efficient parameter tuning and network analysis. Currently, we lack efficient and flexible software implementations supporting complex electrode geometries and stimulation settings. Available tools are either too slow (e.g. finite element method-FEM), or too simple, with limited applicability to basic use-cases. This paper introduces FastField, an efficient open-source toolbox for DBS electric field and VTA approximations. It computes scalable electric field approximations based on the principle of superposition, and VTA activation models from pulse width and axon diameter. In benchmarks and case studies, FastField is solved in about 0.2s, ∼ 1000 times faster than using FEM. Moreover, it is almost as accurate as using FEM: average Dice overlap of 92%, which is around typical noise levels found in clinical data. Hence, FastField has the potential to foster efficient optimization studies and to support clinical applications.

中文翻译:

FastField:用于有效逼近深部脑刺激电场的开源工具箱

深部脑刺激 (DBS) 是一种通过电调节神经组织来缓解某些脑部疾病症状的手术疗法。预测电场和激活组织体积的计算模型是有效参数调整和网络分析的关键。目前,我们缺乏支持复杂电极几何形状和刺激设置的高效和灵活的软件实现。可用的工具要么太慢(例如有限元方法-FEM),要么太简单,对基本用例的适用性有限。本文介绍了 FastField,这是一种用于 DBS 电场和 VTA 近似的高效开源工具箱。它根据叠加原理计算可扩展的电场近似值,并根据脉冲宽度和轴突直径计算 VTA 激活模型。在基准和案例研究中,FastField 在大约 0.2 秒内求解,比使用 FEM 快 1000 倍。此外,它几乎与使用 FEM 一样准确:平均 Dice 重叠率为 92%,接近临床数据中发现的典型噪声水平。因此,FastField 具有促进高效优化研究和支持临床应用的潜力。
更新日期:2020-12-01
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