Influence of segmentation accuracy in structural MR head scans on electric field computation for TMS and tES
Influence of segmentation accuracy in structural MR head scans on electric field computation for TMS and tES
复制标题
结构 MR 头部扫描分割精度对 TMS 和 tES 电场计算的影响
DOI：
10.1088/1361-6560/abe223
复制
发表时间：
2021
影响因子：
3.5
通讯作者：
Hirata Akimasa
中科院分区：
文献类型：
--
作者：
Rashed Essam A;Gomez-Tames Jose;Hirata Akimasa
在一些基于电刺激效应的诊断和治疗过程中，与刺激相关的内部物理量是感应电场。为了估计个体人体模型中的感应电场，需要将相应身体部位的解剖成像（诸如磁共振图像（MRI）扫描）分割成组织。然后，将与不同注释组织相关联的电特性分配给数字模型以生成体积导体。然而，不同组织的分割是一项繁琐的任务，具有若干相关联的挑战，特别是组织出现在解剖图像中的有限区域和/或低对比度。一个悬而未决的问题是不同组织的分割精度将如何影响感应电场的分布。在这项研究中，我们应用不同组织的参数分割来利用可用MRI的分割，使用深度学习神经网络架构（称为ForkNet）生成不同质量的头部模型。然后，比较感应电场以评估模型分割变化的影响。计算结果表明，分割误差的影响与组织有关。在大脑中，对于经颅磁刺激（TMS）和经颅电刺激（tES），脑脊液（CSF）对分割准确度的敏感性相对较高，灰质（GM）对分割准确度的敏感性中等，而白色物质对分割准确度的敏感性较低。CSF分割精度在Dice系数（DC）方面降低10%，导致两种应用中的归一化感应电场降低高达4%。然而，GM分割精度降低5.6%DC导致归一化感应电场增加高达6%。TMS和tES的CSF和GM之间的电场变化趋势相反。这里得到的发现将是有用的量化计算结果的潜在不确定性。
In several diagnosis and therapy procedures based on electrostimulation effect, the internal physical quantity related to the stimulation is the induced electric field. To estimate the induced electric field in an individual human model, the segmentation of anatomical imaging, such as magnetic resonance image (MRI) scans, of the corresponding body parts into tissues is required. Then, electrical properties associated with different annotated tissues are assigned to the digital model to generate a volume conductor. However, the segmentation of different tissues is a tedious task with several associated challenges specially with tissues appear in limited regions and/or low-contrast in anatomical images. An open question is how segmentation accuracy of different tissues would influence the distribution of the induced electric field. In this study, we applied parametric segmentation of different tissues to exploit the segmentation of available MRI to generate different quality of head models using deep learning neural network architecture, named ForkNet. Then, the induced electric field are compared to assess the effect of model segmentation variations. Computational results indicate that the influence of segmentation error is tissue-dependent. In brain, sensitivity to segmentation accuracy is relatively high in cerebrospinal fluid (CSF), moderate in gray matter (GM) and low in white matter for transcranial magnetic stimulation (TMS) and transcranial electrical stimulation (tES). A CSF segmentation accuracy reduction of 10% in terms of Dice coefficient (DC) lead to decrease up to 4% in normalized induced electric field in both applications. However, a GM segmentation accuracy reduction of 5.6% DC leads to increase of normalized induced electric field up to 6%. Opposite trend of electric field variation was found between CSF and GM for both TMS and tES. The finding obtained here would be useful to quantify potential uncertainty of computational results.
登录
查看更多内容
使用深度学习开发用于个性化电磁剂量测量的精确人体头部模型
影响因子：
5.7
作者：
E. Rashed;J. Gómez;A. Hirata
通讯作者：
A. Hirata
影响因子：
4
作者：
Huang, Yu;Datta, Abhishek;Parra, Lucas C.
通讯作者：
Parra, Lucas C.
影响因子：
7.8
作者：
Rashed, Essam A.;Gomez-Tames, Jose;Hirata, Akimasa
通讯作者：
Hirata, Akimasa
影响因子：
2
作者：
Bikson, Marom;Rahman, Asif;Datta, Abhishek
通讯作者：
Datta, Abhishek