Electrical Impedance Tomography Image Reconstruction Using Iterative Lavrentiev and L-Curve-Based Regularization Algorithm

Electrical impedance tomography (EIT) is a technique for determining the electrical conductivity and permittivity distribution inside a medium from measurements made on its surface. The impedance distribution reconstruction in EIT is a nonlinear inverse problem that requires the use of a regularization method. The generalized Tikhonov regularization methods are often used in solving inverse problems. However, for EIT image reconstruction, the generalized Tikhonov regularization methods may lose the boundary information due to its smoothing operation. In this paper, we propose an iterative Lavrentiev regularization and L-curve-based algorithm to reconstruct EIT images. The regularization parameter should be carefully chosen, but it is often heuristically selected in the conventional regularization-based reconstruction algorithms. So, an L-curve-based optimization algorithm is used for selecting the Lavrentiev regularization parameter. Numerical analysis and simulation results are performed to illustrate EIT image reconstruction. It is shown that choosing the appropriate regularization parameter plays an important role in reconstructing EIT images.

Model-data-driven P-wave impedance inversion using ResNets and the normalized zero-lag cross-correlation objective function

Model-driven and data-driven inversions are two prominent methods for obtaining P-wave impedance,which is significant in reservoir description and identification.Based on proper initial models,most model-driven methods primarily use the limited frequency bandwidth information of seismic data and can invert P-wave impedance with high accuracy,but not high resolution.Conventional data-driven methods mainly employ the information from well-log data and can provide high-accuracy and highresolution P-wave impedance owing to the superior nonlinear curve fitting capacity of neural networks.However,these methods require a significant number of training samples,which are frequently insufficient.To obtain P-wave impedance with both high accuracy and high resolution,we propose a model-data-driven inversion method using Res Nets and the normalized zero-lag cross-correlation objective function which is effective for avoiding local minima and suppressing random noise.By using initial models and training samples,the proposed model-data-driven method can invert P-wave impedance with satisfactory accuracy and resolution.Tests on synthetic and field data demonstrate the proposed method’s efficacy and practicability.

Threshold strategy to improve the images reconstructed by electrical impedance tomography

Because of the illposedness of soft field, the quality of EIT images is not satisfied as expected. This paper puts forward a threshold strategy to decrease the artifacts in the reconstructed images by modifying the solutions of inverse problem. Threshold strategy is a kind of post processing method with merits of easy, direct and efficient. Reconstructed by Gauss-Newton algorithm, the simulation image’s quality is improved evidently. We take two performance targets, image reconstruction error and correlation coefficient, to evaluate the improvement. The images and the data show that threshold strategy is effective and achievable.

Electrical impedance tomography using adaptive mesh refinement

In electrical impedance tomography （EIT）, distribution of the internal resistivity or conductivity of an unknown object is esti- mated using measured boundary voltage data induced by different current patterns with various reconstruction algorithms. The reconstruction algorithms usually employ the Newton-Raphson iteration scheme to visualize the resistivity distribution inside the object. Accuracy of the imaging process depends not only on the algorithm used, but also on the scheme of finite element discretization. In this paper an adaptive mesh refinement is used in a modified reconstruction algorithm for the regularized Err. The method has a major impact on efficient solution of the forward problem as well as on achieving improved image resolution. Computer simulations indicate that the Newton-Raphson reconstruction algorithm for Err using adaptive mesh refinement performs better than the classical Newton-Raphson algorithm in terms of reconstructed image resolution.

Lung Ventilation Functional Monitoring Based on Electrical Impedance Tomography

Medically, electrical impedance tomography (EIT) is a relatively inexpensive, safe, non-invasive and portable technique compared with computerized tomography (CT) and magnetic resonance imaging (MRI). In this paper, EIT_TJU_Ⅱ system is developed including both the data collection system and image reconstruction algo-rithm. The testing approach of the system performance, including spatial resolution and sensitivity, is described through brine tank experiments. The images of the thorax physical model verify that the system can reconstruct the interior resistivity distribution. Finally, the lung ventilation functional monitoring in vivo is realized by EIT, and the visualized images indicate that the configuration and performance of EIT_TJU_ Ⅱsystem are feasible and EIT is a promising technique in clinical monitoring application.

Electromagnetic Model and Image Reconstruction Algorithms Based on EIT System

An intuitive 2D model of circular electrical impedance tomography (EIT) sensor with small size electrodes is established based on the theory of analytic functions. The validation of the model is proved using the result from the solution of Laplace equation. Suggestions on to electrode optimization and explanation to the ill-condition property of the sensitivity matrix are provided based on the model, which takes electrode distance into account and can be generalized to the sensor with any simple connected region through a conformal transformation. Image reconstruction algorithms based on the model are implemented to show feasibility of the model using experimental data collected from the EIT system developed in Tianjin University. In the simulation with a human chest-like configuration, electrical conductivity distributions are reconstructed using equi-potential back-projection (EBP) and Tikhonov regularization (TR) based on a conformal transformation of the model. The algorithms based on the model are suitable for online image reconstruction and the reconstructed results are good both in size and position.

New Regularization Method in Electrical Impedance Tomography

Image reconstruction in electrical impedance tomography(EIT) is a highly ill posed inverse problem. Regularization techniques must be used in order to solve the problem. In this paper, a new regularization method based on the spatial filtering theory is proposed. The new regularized reconstruction for EIT is independent of the estimation of impedance distribution, so it can be implemented more easily than the maximum a posteriori(MAP) method. The regularization level in our proposed method varies spatially so as to be suited to the correlation character of the object's impedance distribution. We implemented our regularization method with two dimensional computer simulations. The experimental results indicate that the quality of the reconstructed impedance images with the descibed regularization method based on spatial filtering theory is better than that with Tikhonov method.

Locating Impedance Change in Electrical Impedance Tomography Based on Multilevel BP Neural Network

Electrical impedance tomography(EIT) is a new computer tomography technology, which reconstructs an impedance (resistivity, conductivity) distribution, or change of impedance, by making voltage and current measurements on the object's periphery. Image reconstruction in EIT is an ill-posed, non-linear inverse problem. A method for finding the place of impedance change in EIT is proposed in this paper, in which a multilevel BP neural network (MBPNN) is used to express the non-linear relation between the impedance change inside the object and the voltage change measured on the surface of the object. Thus, the location of the impedance change can be decided by the measured voltage variation on the surface. The impedance change is then reconstructed using a linear approximate method. MBPNN can decide the impedance change location exactly without long training time. It alleviates some noise effects and can be expanded, ensuring high precision and space resolution of the reconstructed image that are not possible by using the back projection method.

Improve Spatial Resolution of Electrical Impedance Tomography Image Based on Non-uniformity Fine Mesh

In electrical impedance tomography (EIT) an approximation for the internal resistivity distribution is computed based on the knowledge of the injected currents and measured voltages on the surface of the body. Several difficulties have been identified in EIT, where the main problem is the low spatial resolution. This paper presents a fining mesh method based on finite element method (FEM), by fining the sensitive element, the most actual signal is obtained in certain electrode number. Newton-Raphson reconstruction algorithm improves the spatial solution of image. The advantages of this method are the improvement of spatial resolution and ease of implementation.

Comparison of Image Reconstruction Algorithms in EIT Imaging

Electrical Impedance Tomography (EIT) is a medical imaging technique which can be used to monitor the regional ventilation in patients utilizing voltage measurements made at the thorax. Several reconstruction algorithms have been developed during the last few years. In this manuscript we compare a well-established algorithm and a re-cently developed method for image reconstruction regarding EIT indices derived from the differently reconstructed images.

基于全连接神经网络的颅脑电阻抗成像参考电压预测方法

电阻抗层析成像(EIT)作为一种新兴可视化技术,可通过电导率分布变化的重建图像获得人体组织病理变化信息,为疾病检测提供了一种选择。在基于EIT的颅脑疾病检测中,为了准确获取差分成像图像重建所需的参考电压,提出一种基于全连接神经网络(FCNN)的参考电压预测方法。通过研究所提方法在不同信噪比情况下的图像重建性能,验证所提方法对参考电压预测的准确性和泛化能力。此外,还研究了头皮、颅骨和脑组织电导率分别发生变化时所提方法的有效性,并通过计算模糊半径和相关系数对图像重建质量进行了定量评价。结果表明,所提方法在一定电导率范围内和不同噪声水平下能够有效预测参考电压。

基于深度学习的电阻抗断层成像图像重建算法研究综述

介绍了电阻抗断层成像(electrical impedance tomography,EIT)传统图像重建算法的局限性和深度学习在非线性图像重建中的优点,综述了基于深度学习的直接重建法、间接重建法和隐式重建法3种EIT图像重建算法的研究进展,分析了基于深度学习的EIT图像重建算法存在的不足,指出了基于深度学习的EIT图像重建算法应在提高数据集质量、增强图像可解释性以及改善成像分辨力等方面实现进一步的技术突破。

基于PT-PFM激励阻抗谱数字重构的电缆故障诊断定位

由于传统频谱测试仪器增益带宽积有限,高频区间内输出电压过低,在针对长距离电缆测试时信号衰减较为严重,导致阻抗谱在高频区间内存在一定失真问题。该文提出一种基于伪梯形波激励阻抗谱数字重构算法的电缆故障诊断定位算法。分析不同故障类型下电力电缆的局部故障阻抗对电缆输入阻抗谱的影响规律、首端激励电压信号的衰减特性和不同电压幅值的功率传输特性。设计一种基于Si C高速逆变器的大容量伪梯形励磁系统,用于测量最高频率7 MHz的阻抗谱信息,利用阻抗谱数字重构方法实现0.1 Hz~60 MHz宽频区间的全覆盖。与传统的低压正弦激励相比,采用该时空转换函数的定位结果具有更小的区间振荡和更快的收敛速度。缺陷定位精度提高了80%,验证了该方法的有效性和准确性。

基于电阻抗断层成像的亚健康人群经络原穴电特性监测技术

电阻抗断层成像(EIT)技术在无损伤、装置简单等方面有着巨大优势,但其存在重构结果质量不理想问题。鉴于此,利用深度学习方法建立基于电阻抗断层成像的映射网络模型并进行比较分析。结果显示,在结构相似性上,所提算法高于0.8,而比较算法均低于0.6,表明所提模型在图像重构质量具有优势,具备一定投用价值。

Stability Investigation and Improvement for DC Cascade Systems with Simplified Impedance-based Stability Criterion

In DC distributed power systems(DPSs),the complex impedance interactions possibly lead to DC bus voltage oscillation or collapse.In previous research,the stability analysis of DPSs is implemented based on mathematical analysis in control theory.The specific mechanisms of the instability of the cascade system have not been intuitively clarified.In this paper,the stability analysis of DPSs based on the traditional Nyquist criterion is simplified to the resonance analysis of the seriesconnected port impedance(Z=R+jX)of source and load converters.It reveals that the essential reason for impedance instability of a DC cascade system is that the negative damping characteristic(R<0)of the port the overall impedance amplifies the internal resonance source at reactance zero-crossing frequency.The simplified stability criterion for DC cascade systems can be concluded as:in the negative damping frequency ranges(R<0),there exists no zero-crossing point of the reactance component(i.e.,X=0).According to the proposed stability criterion,the oscillation modes of cascade systems are classified.A typical one is the internal impedance instability excited by the negative damping,and the other one is that the external disturbance amplified by negativity in a low stability margin.Thus,the impedance reshaping method for stability improvement of the system can be further specified.The validity of the simplified criterion is verified theoretically and experimentally by a positive damping reshaping method.

基于正则化参数优化和边界聚类的电阻抗成像研究

电阻抗成像是一种无损伤的功能成像技术,由于逆问题具有不适定性、不稳定性等特点,往往存在重构图像的分辨率不高、伪影较大等问题。将Tikhonov和全变量(TV)两种正则化算法的罚函数进行组合应用,提出将粒子群算法用于组合罚函数的正则化参数优化,把图像质量指标(artifact level, AL)作为粒子群算法的适应度值,从而确定最优正则化参数,通过牛顿迭代法获得电导率,为了进一步去除伪影,将Niblack算法与边界聚类算法相结合,对求得的电导率进行处理,得到最终的电导率分布。仿真和实测结果均表明,该方法重建的图像能够更加准确地反映电场内目标物体的位置信息,有效的抑制伪影,提高了重建效果。

电阻抗成像技术在肺功能检测的研究进展

电阻抗成像技术(EIT)通过对电极施加安全的交流激励电流信号,测量其余电极对的电压信号,借助重构图像算法,利用采集到的电压数据重构肺部阻抗分布情况。EIT技术具有实时、无创、便携的特点,能够动态监测肺部功能,有利于辅助肺疾病的诊断和治疗。概括了肺功能EIT技术的发展历程与原理,并对EIT硬件系统、图像重建算法和临床应用的研究进展进行了总结和分析;对肺功能EIT技术发展方向和趋势进行探讨与展望。

基于单电极激励模式的颅脑电阻抗图像重建方法研究

作为一种新兴的可视化技术,电阻抗层析成像(EIT)能够根据人体组织病理变化对其电导率分布进行图像重建,为疾病检测提供了一种选择.在基于EIT的脑部疾病检测中,为了改善被测区域的灵敏度分布并解决电阻抗成像中典型的不适定问题,在单电极激励数据采集模式下,提出了k阶有限差分L1正则化目标函数,并采用增广拉格朗日和交替方向算法对目标函数进行求解,实现电导率分布的重构.研究了单电极激励模式下,外接电阻对敏感场的影响;针对脑出血和脑缺血两种病情,对比了Landweber方法、Newton-Raphson方法、Tikhonov方法、广义总变分方法(TGV)和本文方法的图像重建性能.结果表明,在脑出血和脑缺血的图像重建中,采用单电极激励模式的ALAD-LR方法可有效提高图像重建质量,并具有较强的鲁棒性.

基于数学形态学和沃尔什变换的配电网高阻接地故障检测

配电网中高阻接地故障(high impedance grounding fault,HIGF)时常发生,故障一维零序电流信号特征模糊、微弱,极易与励磁涌流、电容器投切信号混淆。为此,提出一种基于相空间重构、数学形态学(mathematical morphology,MM)-沃尔什变换(Walsh transform,WT)的HIGF识别方法。首先,使用联合参数选取算法确定延迟时间和嵌入维数,利用坐标重构法对仿真零序电流信号进行相空间重构,获得重构轨迹图,以发掘在一维空间无法观测到的特征;然后,使用MM对重构图像进行去噪以及边缘特征提取;接着,采用MM-WT算法提取重构图像特征;最后,计算提取的图像特征的脉冲因子和对称因子,实现对HIGF和其他扰动事件的识别。在PSCAD/EMTDC和RSCAD/RTDS上进行仿真实验,结果验证了所提算法的可靠性和可行性。

基于改进自适应阈值EIT算法的CFRP损伤检测

电阻抗层析成像(EIT)具有快速、无辐射等众多优点,但EIT的逆问题严重的病态性导致FISTA等算法的重建图像中存在损伤边缘信息缺失的现象。针对该问题引入了一种与解向量稀疏度相关的自适应阈值算子和一种可变阈值函数,解决了软阈值函数边缘处不可导的问题。仿真实验表明改进算法与传统的算法相比,FIMSTA算法的表现最好,尤其是对于传统算法图像重建效果较差的中心裂纹损伤,FIMSTA算法的相关系数达到0.7038,较表现最好的FISTA算法提升了51.08%。