Department of Radiology, Kyung Hee University Hospital at Gangdong, College of Medicine, Kyung Hee University, Dongnam-ro, Gangdong-gu, Seoul 05278, Republic of Korea.
Department of Mathematics, Konkuk University, Neungdong-ro, Gwangjin-gu, Seoul 05029, Republic of Korea.
Neuroimage. 2021 Jan 15;225:117466. doi: 10.1016/j.neuroimage.2020.117466. Epub 2020 Oct 16.
Diffusion weighted imaging based on random Brownian motion of water molecules within a voxel provides information on the micro-structure of biological tissues through water molecule diffusivity. As the electrical conductivity is primarily determined by the concentration and mobility of ionic charge carriers, the macroscopic electrical conductivity of biological tissues is also related to the diffusion of electrical ions. This paper aims to investigate the low-frequency electrical conductivity by relying on a pre-defined biological model that separates the brain into the intracellular (restricted) and extracellular (hindered) compartments. The proposed method uses B1 mapping technique, which provides a high-frequency conductivity distribution at Larmor frequency, and the spherical mean technique, which directly estimates the microscopic tissue structure based on the water molecule diffusivity and neurite orientation distribution. The total extracellular ion concentration, which is separated from the high-frequency conductivity, is recovered using the estimated diffusivity parameters and volume fraction in each compartment. We propose a method to reconstruct the low-frequency dominant conductivity tensor by taking into consideration the extracted extracellular diffusion tensor map and the reconstructed electrical parameters. To demonstrate the reliability of the proposed method, we conducted two phantom experiments. The first one used a cylindrical acrylic cage filled with an agar in the background region and four anomalies for the effect of ion concentration on the electrical conductivity. The other experiment, in which the effect of ion mobility on the conductivity was verified, used cell-like materials with thin insulating membranes suspended in an electrolyte. Animal and human brain experiments were conducted to visualize the low-frequency dominant conductivity tensor images. The proposed method using a conventional MRI scanner can predict the internal current density map in the brain without directly injected external currents.
基于水分子在体素内随机布朗运动的扩散加权成像通过水分子扩散率提供了关于生物组织微观结构的信息。由于电导率主要由离子电荷载流子的浓度和迁移率决定，因此生物组织的宏观电导率也与电离子的扩散有关。本文旨在通过依赖于将大脑分为细胞内（受限）和细胞外（受阻）隔室的预定义生物模型来研究低频电导率。该方法使用 B1 映射技术，该技术在拉莫尔频率处提供高频电导率分布，以及球平均技术，该技术直接根据水分子扩散率和神经突取向分布来估计微观组织结构。通过在每个隔室中使用估计的扩散率参数和体积分数来恢复从高频电导率中分离出的总细胞外离子浓度。我们提出了一种通过考虑提取的细胞外扩散张量图和重建的电参数来重建低频主导电导率张量的方法。为了证明所提出方法的可靠性，我们进行了两个体模实验。第一个实验使用圆柱形丙烯酸笼子，在背景区域填充琼脂，并在四个异常区域中研究离子浓度对电导率的影响。另一个实验使用具有悬浮在电解质中的薄绝缘膜的类似细胞的材料来验证离子迁移率对电导率的影响。进行了动物和人脑实验以可视化低频主导电导率张量图像。该方法使用常规 MRI 扫描仪可以预测大脑中的内部电流密度图，而无需直接注入外部电流。
