Zhang Hao, Gomez Luis, Guilleminot Johann
Department of Civil and Environmental Engineering, Duke University, 121 Hudson Hall, Durham, 27708-0187, UNITED STATES.
Elmore Family School of Electrical and Computer Engineering, Purdue University, 465 Northwestern Ave., West Lafayette, Indiana, 47907-2050, UNITED STATES.
J Neural Eng. 2022 Feb 8. doi: 10.1088/1741-2552/ac52d1.
Transcranial Magnetic Stimulation (TMS) is a non-invasive brain stimulation method that is used to study brain function and conduct neuropsychiatric therapy. Computational methods that are commonly used for electric field (E-field) dosimetry of TMS are limited in accuracy and precision because of possible geometric errors introduced in the generation of head models by segmenting medical images into tissue types. This paper studies E-field prediction fidelity as a function of segmentation accuracy.
The errors in the segmentation of medical images into tissue types are modeled as geometric uncertainty in the shape of the boundary between tissue types. For each tissue boundary realization, we then use an in-house boundary element method to perform a forward propagation analysis and quantify the impact of tissue boundary uncertainties on the induced cortical E-field.
Our results indicate that predictions of E-field induced in the brain are negligibly sensitive to segmentation errors in scalp, skull and white matter, compartments. In contrast, E-field predictions are highly sensitive to possible CSF segmentation errors. Specifically, the segmentation errors on the CSF and gray matter interface lead to higher E-field uncertainties in the gyral crowns, and the segmentation errors on CSF and white matter interface lead to higher uncertainties in the sulci. Furthermore, the uncertainty of the average cortical E-fields over a region exhibits lower uncertainty relative to point-wise estimates.
The accuracy of current cortical E-field simulations is limited by the accuracy of CSF segmentation accuracy. Other quantities of interest like the average of the E-field over a cortical region could provide a dose quantity that is robust to possible segmentation errors.
经颅磁刺激（TMS）是一种用于研究脑功能和进行神经精神治疗的非侵入性脑刺激方法。由于将医学图像分割成组织类型时在头部模型生成过程中可能引入几何误差，常用于TMS电场（E场）剂量测定的计算方法在准确性和精确性方面受到限制。本文研究了E场预测保真度作为分割精度的函数。
将医学图像分割成组织类型时的误差建模为组织类型之间边界形状的几何不确定性。对于每个组织边界实现，我们然后使用内部边界元方法进行正向传播分析，并量化组织边界不确定性对诱发的皮质E场的影响。
我们的结果表明，大脑中诱发的E场预测对头皮、颅骨和白质隔室的分割误差敏感度可忽略不计。相比之下，E场预测对脑脊液（CSF）可能的分割误差高度敏感。具体而言，CSF与灰质界面的分割误差会导致脑回顶部的E场不确定性更高，而CSF与白质界面的分割误差会导致脑沟中的不确定性更高。此外，相对于逐点估计，区域上平均皮质E场的不确定性较低。
当前皮质E场模拟的准确性受CSF分割精度的限制。其他感兴趣的量，如皮质区域上E场的平均值，可以提供对可能的分割误差具有鲁棒性的剂量量。
