基于扩散张量成像技术的大脑组织核磁共振电导率重构的仿真研究

将核磁共振电阻抗成像(MREIT)技术应用于人体头部大脑组织电导率重构上.采用基于径向基函数(RBF)神经网络的均质电导率重构MREIT算法,对建立在扩散张量核磁共振成像(DT-MRI)数据基础上的白质组织各向异性电导率和各向同性的灰质、脑脊液目标电导率进行重构.在五层真实形状头模型(包括头皮、颅骨、脑脊液、灰质和白质组织)上进行的仿真实验结果表明,该算法具有一定的抗噪声能力,重构的头部电导率分布图像具有较高的精确性.研究证明了MREIT技术用于头部复杂组织结构电导率重构上的合理性与可行性,在无创头部组织电导率检测领域具有潜在的应用价值.     

头部组织感应电流磁共振电阻抗成像仿真研究

将感应电流磁共振电阻抗成像(IC-MREIT)应用于人体头部组织电导率重构,并在三层球头模型上进行仿真研究.首先建立二次磁场Z方向磁感应强度摄动对有限元头模型中单元电导率摄动的灵敏度矩阵;然后分析系统中激励线圈个数、激励线圈与三层球头模型相对位置及激励线圈半径等因素对灵敏度矩阵性态的影响,为IC-MREIT系统激励线圈优化设计提供指导;最后采用灵敏度矩阵法对三层球头模型非均匀电导率分布进行重构,仿真结果证明IC-MREIT技术用于重构头部组织电导率分布是可行的,在无创头部组织电导率检测领域具有潜在的应用价值.     

点电流源激励下头颅球模型的电位分布解析算法研究

建立了头颅的球型仿真数学模型.用头皮、颅骨、脑脊髓和脑组织四层同心球结构仿真人体头颅.从拉普拉斯方程出发,用解析解的分离变量法求解头颅球模型在最外层(头皮层)表面施加点电流激励的情况下,各层的电位分布函数.根据电位分布的表达式,绘制出颅内的电位等位线图以及电流线图.分析了电流注入角度对电位分布和电流流向的影响.结果表明颅骨的低电导率对颅内的电位分布有很大的影响.研究结果可用于分析头部电阻抗成像等问题.     

基于三层球模型弱直流电刺激下脑白质各向异性对电场分布的影响

目的研究脑白质的各向异性电导率对经颅直流电刺激产生的电场分布的影响。方法依托经典三层同心球模型,通过基于有限元法的ANSYS软件建模,并求解头模型的电场强度、电流密度分布及初级皮质运动区的关键点数值。结果随着脑白质各向异性电导率的比率、空间分布不同,头模型中电场强度和电流密度的分布产生了较大区别。结论仿真结果说明脑白质各向异性对电场分布有显著影响,对临床应用有重要的借鉴意义。     

组织电导率性质对磁感应磁声耦合成像声源强度的影响

针对部分生物组织电导率分布具有各向异性的特点,对磁感应磁声耦合成像的声源强度特征进行理论分析,推导不同电导率性质的声源强度公式,并运用COMSOL Multiphysics5.5建立生物组织电导率模型,进行电磁场仿真分析求解,用Matlab 2016a计算其振动声源。结果证明,在同样磁场激励条件下,电导率各向同性和各向异性的声源分布都能反映生物组织的层析结构,但其强度不同。本研究为磁感应磁声耦合成像逆问题声源的精确重建提供了理论基础。     

基于不同几何模型的感应式磁声成像声源重建仿真

感应式磁声成像是一种新型功能成像技术,能够反映组织电导率变化信息.生物组织结构和电导率分布复杂,且电导率存在非突变的或者连续过渡的边界.为深入研究更为真实的生物组织电导率分布与变化特点,本研究从不规则的几何模型出发,建立了单线圈激励下的偏心球、正方体、椭球和电导率连续分布的球模型,借助有限元分析,研究仿真模型中的感应涡流分布并计算空间声场,应用时间反演法进行声源重建,同时分析了检测范围对重构图像的影响.仿真结果表明,感应式磁声成像能够有效地重构区分组织电特性的声源分布图像,探测器检测范围的有限性会使重构图像轮廓失真.本研究为感应式磁声成像实用技术的研究提供了较好的理论基础.     

基于EIT技术颅内电阻抗特性分析

电阻抗断层成像技术（EIT）是一种基于生物组织电学特性的成像技术。本研究基于EIT技术对二维四层同心圆头模型和基于MRI图片构造的脑电二维真实头模型的电阻抗特性进行了分析，给出了头部组织电导率参数变化对求解区域场内及头皮表面电位分布的影响，得出了有实际意义的结论，为实现颅内EIT逆问题求解和阻抗成像及脑内电特性的深入研究奠定了基础。     

人颅骨电阻抗频率特性及其与解剖结构关系研究

在电阻抗断层成像(EIT)技术应用于脑部成像的研究中，颅骨的电阻抗对驱动电流的屏蔽效应非常严重，严重影响了成像效果(特别是中心区域)。本研究采用离体人颅骨，二电极法，用频响分析仪(Solartron 1255B)经电阻抗接口(Solartron 1294)，在0．1Hz～1MHz频率范围内进行测量，描记出颅骨电阻抗频响特性曲线，初步观察了颅骨电阻抗随时间变化的趋势，研究了颅骨解剖结构同其电阻抗之间的关系。结果表明：颅骨电阻抗幅值随时间明显增大；其特征频率应在10KHz附近，颅骨组织的解剖结构对颅骨电阻抗有重要的影响，含有板障层的厚颅骨由于颅骨内、外板中富含毛细血管的原因，其电阻抗较不含板障层的薄颅骨电阻抗小。本研究结果为解决颅骨高阻抗的屏蔽效应问题提供了可能的参考和依据。     

基于斜位电流注入的磁共振电阻抗成像谐函数Bz重建算法及实验研究

针对斜位电流注入的磁共振电阻抗成像,提出基于斜位电流注入的谐函数Bz电导率重建算法,并建立相应的二维电导率重建方案。在构建磁共振电阻抗成像实验系统的基础上,分别进行电导率分布图像的仿真和测量磁通密度的实验。仿真结果表明,所提出的重建算法能够获得电导率分布的图像,并且硬件系统能够获得对成像物体注入电流时的磁共振图像数据,进一步可以得到成像体内的磁通密度。     

四层复杂球颅脑模型的构建

颅脑模型构建的研究是颅内成像的基础,也是磁感应断层成像(MIT)系统设计中正问题计算的必要条件。根据人体颅脑真实结构,通过Comsol Multiphysics有限元数值仿真软件的几何建模工具,构建近似真实颅脑结构的四层复杂球颅脑模型。首先,根据大脑体积和颅骨内径,构建脑实质模型;其次,根据人体解剖结构,构建颅骨模型,并进行枕骨修正、轮廓修正、额骨修正和眼眶修正;第三,通过对颅骨模型的缩放,构成头皮层、颅骨层、脊液层,并与脑实质模型共同构成具有4层结构的颅脑模型;最后,将模型置入10 MHz的交流磁场中,通过仿真计算获得头皮层、颅骨层、脊液层和脑实质层感应电流的分布,感应电流在脊液层最强,在皮肤层和脑实质层较弱,在颅骨层最弱,且各层感应电流密度值之比为32:1:190:21,与电导率之比相近。结果表明,该模型可以很好地显示出人体头部各组织的电磁特性差异,为MIT系统研究提供可靠的依据。     

Image reconstruction of anisotropic conductivity tensor distribution in MREIT: computer simulation study

We describe a novel method of reconstructing images of an anisotropic conductivity tensor distribution inside an electrically conducting subject in magnetic resonance electrical impedance tomography (MREIT). MREIT is a recent medical imaging technique combining electrical impedance tomography (EIT) and magnetic resonance imaging (MRI) to produce conductivity images with improved spatial resolution and accuracy. In MREIT, we inject electrical current into the subject through surface electrodes and measure the z-component Bz of the induced magnetic flux density using an MRI scanner. Here, we assume that z is the direction of the main magnetic field of the MRI scanner. Considering the fact that most biological tissues are known to have anisotropic conductivity values, the primary goal of MREIT should be the imaging of an anisotropic conductivity tensor distribution. However, up to now, all MREIT techniques have assumed an isotropic conductivity distribution in the image reconstruction problem to simplify the underlying mathematical theory. In this paper, we firstly formulate a new image reconstruction method of an anisotropic conductivity tensor distribution. We use the relationship between multiple injection currents and the corresponding induced Bz data. Simulation results show that the algorithm can successfully reconstruct images of anisotropic conductivity tensor distributions. While the results show the feasibility of the method, they also suggest a more careful design of data collection methods and data processing techniques compared with isotropic conductivity imaging.     

Anisotropic conductivity imaging with MREIT using equipotential projection algorithm

Magnetic resonance electrical impedance tomography (MREIT) combines magnetic flux or current density measurements obtained by magnetic resonance imaging (MRI) and surface potential measurements to reconstruct images of true conductivity with high spatial resolution. Most of the biological tissues have anisotropic conductivity; therefore, anisotropy should be taken into account in conductivity image reconstruction. Almost all of the MREIT reconstruction algorithms proposed to date assume isotropic conductivity distribution. In this study, a novel MREIT image reconstruction algorithm is proposed to image anisotropic conductivity. Relative anisotropic conductivity values are reconstructed iteratively, using only current density measurements without any potential measurement. In order to obtain true conductivity values, only either one potential or conductivity measurement is sufficient to determine a scaling factor. The proposed technique is evaluated on simulated data for isotropic and anisotropic conductivity distributions, with and without measurement noise. Simulation results show that the images of both anisotropic and isotropic conductivity distributions can be reconstructed successfully.     

A new magnetic resonance electrical impedance tomography (MREIT) algorithm: the RSM-MREIT algorithm with applications to estimation of human head conductivity

We have developed a new magnetic resonance electrical impedance tomography (MREIT) algorithm, the RSM-MREIT algorithm, for noninvasive imaging of the electrical conductivity distribution using only one component of magnetic flux density. The proposed RSM-MREIT algorithm uses the response surface methodology (RSM) algorithm for optimizing the conductivity distribution through minimizing the errors between the measured and calculated magnetic flux densities. A series of computer simulations has been conducted to assess the performance of the proposed RSM-MREIT algorithm to estimate electrical conductivity values of the scalp, the skull and the brain tissue, in a three-shell piecewise homogeneous head model. Computer simulation studies were conducted in both a spherical and realistic-geometry head model with a single variable (the brain-to-skull conductivity ratio) and three variables (the conductivity of the brain, the skull, and the scalp). The relative error between the target and estimated head conductivity values was less than 12% for both the single-variable and three-variable simulations. These promising simulation results demonstrate the feasibility of the proposed RSM-MREIT algorithm in estimating electrical conductivity values in a piecewise homogeneous head model of the human head, and suggest that the RSM-MREIT algorithm merits further investigation.     

Estimation of electrical conductivity distribution within the human head from magnetic flux density measurement

We have developed a new algorithm for magnetic resonance electrical impedance tomography (MREIT), which uses only one component of the magnetic flux density to reconstruct the electrical conductivity distribution within the body. The radial basis function (RBF) network and simplex method are used in the present approach to estimate the conductivity distribution by minimizing the errors between the 'measured' and model-predicted magnetic flux densities. Computer simulations were conducted in a realistic-geometry head model to test the feasibility of the proposed approach. Single-variable and three-variable simulations were performed to estimate the brain-skull conductivity ratio and the conductivity values of the brain, skull and scalp layers. When SNR = 15 for magnetic flux density measurements with the target skull-to-brain conductivity ratio being 1/15, the relative error (RE) between the target and estimated conductivity was 0.0737 +/- 0.0746 in the single-variable simulations. In the three-variable simulations, the RE was 0.1676 +/- 0.0317. Effects of electrode position uncertainty were also assessed by computer simulations. The present promising results suggest the feasibility of estimating important conductivity values within the head from noninvasive magnetic flux density measurements.     

Measurement of ion diffusion using magnetic resonance electrical impedance tomography

In magnetic resonance electrical impedance tomography (MREIT), currents are applied to an object, the resulting magnetic flux density measured using MRI and the conductivity distribution reconstructed using these MRI data. In this study, we assess the ability of MREIT to monitor changes in the conductivity distribution of an agarose gel phantom, using injected current pulses of 900 microA. The phantom initially contained a distinct region of high sodium chloride concentration which diffused into the background over time. MREIT data were collected over a 12 h span, and conductivity images were reconstructed using the iterative sensitivity matrix method with Tikhonov regularization. The results indicate that MREIT was able to monitor the changing conductivity and concentration distributions resulting from the diffusion of ions within the agarose gel phantom.     

乳腺电阻抗成像非均匀介质电场的初步分析

目的：在验证旋转电极法对乳腺电阻抗断层成像的方法的可行性之后，进一步对非均匀电场分布进行分析．为此后的成像算法修正提供依据，以便能获得更接近实际的阻抗分布图像。方法：基于EIT实验平台，利用NaCI溶液模拟均匀介质，铁棒模拟引入到均匀介质中电导率不同的非均匀介质，研究铁棒在NaCI溶液中不同位置对其电场分布的影响。结果：得到铁棒在NaCl溶液中的轴对称位置上对中间测量电极下的电流值影响及铁棒在NaCl溶液中不同的位置对中间测量电极的电流值影响。结论：在研究电导率不同的铁棒对均匀介质NaCl溶液电场影响的实验中，该实验结果为下一步对成像算法的修正及提高重建的图像的分辨率提供了指导。     

A finite difference method with reciprocity used to incorporate anisotropy in electroencephalogram dipole source localization

Many implementations of electroencephalogram (EEG) dipole source localization neglect the anisotropical conductivities inherent to brain tissues, such as the skull and white matter anisotropy. An examination of dipole localization errors is made in EEG source analysis, due to not incorporating the anisotropic properties of the conductivity of the skull and white matter. First, simulations were performed in a 5 shell spherical head model using the analytical formula. Test dipoles were placed in three orthogonal planes in the spherical head model. Neglecting the skull anisotropy results in a dipole localization error of, on average, 13.73 mm with a maximum of 24.51 mm. For white matter anisotropy these values are 11.21 mm and 26.3 mm, respectively. Next, a finite difference method (FDM), presented by Saleheen and Kwong (1997 IEEE Trans. Biomed. Eng. 44 800-9), is used to incorporate the anisotropy of the skull and white matter. The FDM method has been validated for EEG dipole source localization in head models with all compartments isotropic as well as in a head model with white matter anisotropy. In a head model with skull anisotropy the numerical method could only be validated if the 3D lattice was chosen very fine (grid size < or = 2 mm).     

头部组织电阻抗成像研究进展

脑部疾病和脑功能活动期间常伴随脑组织电阻抗的变化,利用电阻抗成像技术可以对大脑疾病和脑功能活动进行临床诊断和监护。首先对人体头部组织阻抗测量技术的优缺点及其在头部组织阻抗成像上的应用前景进行简介,然后重点介绍了几种基于磁场测量的电阻抗成像方法,最后给出了目前头部阻抗成像研究存在的问题及该领域下一步的研究方向。     

A DTI-based model for TMS using the independent impedance method with frequency-dependent tissue parameters

Accurate simulations on detailed realistic head models are necessary to gain a better understanding of the response to transcranial magnetic stimulation (TMS). Hitherto, head models with simplified geometries and constant isotropic material properties are often used, whereas some biological tissues have anisotropic characteristics which vary naturally with frequency. Moreover, most computational methods do not take the tissue permittivity into account. Therefore, we calculate the electromagnetic behaviour due to TMS in a head model with realistic geometry and where realistic dispersive anisotropic tissue properties are incorporated, based on T1-weighted and diffusion-weighted magnetic resonance images. This paper studies the impact of tissue anisotropy, permittivity and frequency dependence, using the anisotropic independent impedance method. The results show that anisotropy yields differences up to 32% and 19% of the maximum induced currents and electric field, respectively. Neglecting the permittivity values leads to a decrease of about 72% and 24% of the maximum currents and field, respectively. Implementing the dispersive effects of biological tissues results in a difference of 6% of the maximum currents. The cerebral voxels show limited sensitivity of the induced electric field to changes in conductivity and permittivity, whereas the field varies approximately linearly with frequency. These findings illustrate the importance of including each of the above parameters in the model and confirm the need for accuracy in the applied patient-specific method, which can be used in computer-assisted TMS.