Hallez Hans, Vanrumste Bart, Van Hese Peter, Delputte Steven, Lemahieu Ignace
Ghent University, Department of Electronics and Information Systems, Medical Image and Signal Processing, Ghent University Hospital-IBITECH, De Pintelaan 185 B-9000 Ghent, Belgium.
Phys Med Biol. 2008 Apr 7;53(7):1877-94. doi: 10.1088/0031-9155/53/7/005. Epub 2008 Mar 10.
To improve the EEG source localization in the brain, the conductivities used in the head model play a very important role. In this study, we focus on the modeling of the anisotropic conductivity of the white matter. The anisotropic conductivity profile can be derived from diffusion weighted magnetic resonance images (DW-MRI). However, deriving these anisotropic conductivities from diffusion weighted MR images of the white matter is not straightforward. In the literature, two methods can be found for calculating the conductivity from the diffusion weighted images. One method uses a fixed value for the ratio of the conductivity in different directions, while the other method uses a conductivity profile obtained from a linear scaling of the diffusion ellipsoid. We propose a model which can be used to derive the conductivity profile from the diffusion tensor images. This model is based on the variable anisotropic ratio throughout the white matter and is a combination of the linear relationship as stated in the literature, with a constraint on the magnitude of the conductivity tensor (also known as the volume constraint). This approach is stated in the paper as approach A. In our study we want to investigate dipole estimation differences due to using a more simplified model for white matter anisotropy (approach B), while the electrode potentials are derived using a head model with a more realistic approach for the white matter anisotropy (approach A). We used a realistic head model, in which the forward problem was solved using a finite difference method that can incorporate anisotropic conductivities. As error measures we considered the dipole location error and the dipole orientation error. The results show that the dipole location errors are all below 10 mm and have an average of 4 mm in gray matter regions. The dipole orientation errors ranged up to 66.4 degrees, and had a mean of, on average, 11.6 degrees in gray matter regions. In a qualitative manner, the results show that the orientation and location error is dependent on the orientation of the test dipole. The location error is larger when the orientation of the test dipole is similar to the orientation of the anisotropy, while the orientation error is larger when the orientation of the test dipole deviates from the orientation of the anisotropy. From these results, we can conclude that the modeling of white matter anisotropy plays an important role in EEG source localization. More specifically, accurate source localization will require an accurate modeling of the white matter conductivity profile in each voxel.
为了改善大脑中脑电图源定位,头部模型中使用的电导率起着非常重要的作用。在本研究中,我们专注于白质各向异性电导率的建模。各向异性电导率剖面可从扩散加权磁共振图像(DW-MRI)中得出。然而,从白质的扩散加权磁共振图像中得出这些各向异性电导率并非易事。在文献中,可以找到两种从扩散加权图像计算电导率的方法。一种方法对不同方向的电导率比值使用固定值,而另一种方法使用从扩散椭球体的线性缩放获得的电导率剖面。我们提出了一个可用于从扩散张量图像中得出电导率剖面的模型。该模型基于白质中可变的各向异性比值,是文献中所述的线性关系与电导率张量大小约束(也称为体积约束)的结合。本文将此方法称为方法A。在我们的研究中,我们想研究由于对白质各向异性使用更简化模型(方法B)而导致的偶极子估计差异,同时使用对白质各向异性采用更真实方法的头部模型来推导电极电位(方法A)。我们使用了一个真实的头部模型,其中正向问题通过可纳入各向异性电导率的有限差分法求解。作为误差度量,我们考虑了偶极子位置误差和偶极子方向误差。结果表明,在灰质区域,偶极子位置误差均低于10毫米,平均为4毫米。偶极子方向误差高达66.4度,在灰质区域平均为11.6度。定性地说,结果表明方向和位置误差取决于测试偶极子的方向。当测试偶极子的方向与各向异性方向相似时,位置误差较大,而当测试偶极子的方向偏离各向异性方向时,方向误差较大。从这些结果中,我们可以得出结论,白质各向异性建模在脑电图源定位中起着重要作用。更具体地说,准确的源定位需要对每个体素中的白质电导率剖面进行准确建模。