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头体梯度线圈的电场计算和周围神经刺激预测。

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

机构信息

Roemer Consulting, Lutz, Florida, USA.

Imaging Research Laboratories, Robarts Research Institute, London, Ontario, Canada.

出版信息

Magn Reson Med. 2021 Oct;86(4):2301-2315. doi: 10.1002/mrm.28853. Epub 2021 Jun 3.

DOI:10.1002/mrm.28853
PMID:34080744
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC8651073/
Abstract

PURPOSE

To demonstrate and validate electric field (E-field) calculation and peripheral nerve stimulation (PNS) prediction methods that are accurate, computationally efficient, and that could be used to inform regulatory standards.

METHODS

We describe a simplified method for calculating the spatial distribution of induced E-field over the volume of a body model given a gradient coil vector potential field. The method is easily programmed without finite element or finite difference software, allowing for straightforward and computationally efficient E-field evaluation. Using these E-field calculations and a range of body models, population-weighted PNS thresholds are determined using established methods and compared against published experimental PNS data for two head gradient coils and one body gradient coil.

RESULTS

A head-gradient-appropriate chronaxie value of 669 µs was determined by meta-analysis. Prediction errors between our calculated PNS parameters and the corresponding experimentally measured values were ~5% for the body gradient and ~20% for the symmetric head gradient. Our calculated PNS parameters matched experimental measurements to within experimental uncertainty for 73% of ∆G estimates and 80% of SR estimates. Computation time is seconds for initial E-field maps and milliseconds for E-field updates for different gradient designs, allowing for highly efficient iterative optimization of gradient designs and enabling new dimensions in PNS-optimal gradient design.

CONCLUSIONS

We have developed accurate and computationally efficient methods for prospectively determining PNS limits, with specific application to head gradient coils.

摘要

目的

展示和验证电场 (E-field) 计算和周围神经刺激 (PNS) 预测方法,这些方法既准确又计算效率高,可用于为监管标准提供信息。

方法

我们描述了一种简化方法,用于计算给定梯度线圈向量势场的体模体积上感应 E 场的空间分布。该方法无需有限元或有限差分软件即可轻松编程,从而可以进行简单且计算效率高的 E 场评估。使用这些 E 场计算和一系列体模,使用既定方法确定加权人群的 PNS 阈值,并将其与针对两个头部梯度线圈和一个体部梯度线圈的已发表的实验 PNS 数据进行比较。

结果

通过荟萃分析确定了适合头部梯度的 chronaxie 值为 669 µs。我们计算的 PNS 参数与相应的实验测量值之间的预测误差对于体部梯度约为 5%,对于对称头部梯度约为 20%。我们计算的 PNS 参数与实验测量值相匹配,对于 ∆G 估计值的 73%和 SR 估计值的 80%,在实验不确定度范围内。对于不同的梯度设计,初始 E 场图的计算时间为秒,E 场更新的计算时间为毫秒,从而允许高效地迭代优化梯度设计,并在 PNS 最佳梯度设计方面开辟新的维度。

结论

我们已经开发了用于前瞻性确定 PNS 限制的准确且计算效率高的方法,特别适用于头部梯度线圈。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9a57/8651073/d8b0f8ebbbae/nihms-1701118-f0003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9a57/8651073/f304927bc4dc/nihms-1701118-f0001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9a57/8651073/38870164b380/nihms-1701118-f0002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9a57/8651073/d8b0f8ebbbae/nihms-1701118-f0003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9a57/8651073/f304927bc4dc/nihms-1701118-f0001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9a57/8651073/38870164b380/nihms-1701118-f0002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9a57/8651073/d8b0f8ebbbae/nihms-1701118-f0003.jpg

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