Statistical Inversion Based on Nonlinear Weighted Anisotropic Total Variational Model and Its Application in Electrical Impedance Tomography

Electrical impedance tomography (EIT) aims to reconstruct the conductivity distribution using the boundary measured voltage potential. Traditional regularization based method would suffer from error propagation due to the iteration process. The statistical inverse problem method uses statistical inference to estimate unknown parameters. In this article, we develop a nonlinear weighted anisotropic total variation (NWATV) prior density function based on the recently proposed NWATV regularization method. We calculate the corresponding posterior density function, i.e., the solution of the EIT inverse problem in the statistical sense, via a modified Markov chain Monte Carlo (MCMC) sampling. We do numerical experiment to validate the proposed approach.

Nonlinear Electrical Impedance Tomography Method Using a Complete Electrode Model for the Characterization of Heterogeneous Domains

This paper presents an electrical impedance tomography(EIT)method using a partial-differential-equationconstrained optimization approach.The forward problem in the inversion framework is described by a complete electrodemodel(CEM),which seeks the electric potential within the domain and at surface electrodes considering the contact impedance between them.The finite element solution of the electric potential has been validated using a commercial code.The inverse medium problem for reconstructing the unknown electrical conductivity profile is formulated as an optimization problem constrained by the CEM.The method seeks the optimal solution of the domain’s electrical conductivity to minimize a Lagrangian functional consisting of a least-squares objective functional and a regularization term.Enforcing the stationarity of the Lagrangian leads to state,adjoint,and control problems,which constitute the Karush-Kuhn-Tucker(KKT)first-order optimality conditions.Subsequently,the electrical conductivity profile of the domain is iteratively updated by solving the KKT conditions in the reduced space of the control variable.Numerical results show that the relative error of the measured and calculated electric potentials after the inversion is less than 1%,demonstrating the successful reconstruction of heterogeneous electrical conductivity profiles using the proposed EIT method.This method thus represents an application framework for nondestructive evaluation of structures and geotechnical site characterization.

基于DREAM_ZS算法的EIT电阻率反演方法研究

针对电阻抗成像(EIT)中的电阻率反演及其不确定性量化问题,提出基于贝叶斯理论的不确定性分析方法.首先,利用反向传播(BP)神经网络模型作为正问题替代模型,取得了计算精度高的结果,并且大大提高计算效率.然后,采用基于贝叶斯理论的自适应差分进化Metropolis抽样(DREAM_ZS)算法对电阻率进行反演,并对不同激励模式和不同先验分布进行了对比分析.对模拟头部的4层同心圆模型的反演结果显示,DREAM_ZS抽样算法能够对4个参数进行准确识别,相对激励模式的反演效果最优.4个参数的不确定性程度不同,头皮电阻率不确定性最小,敏感性最强,其次是颅骨,大脑和脑脊液的不确定性较大.进而,对高维参数的圆模型进行仿真,采用相对激励模式,DREAM_ZS抽样算法能够准确反演二维圆模型的各个参数.参数的先验分布为正态分布时,与均匀分布相比,其反演结果不确定性小,对算法的识别效果更强.

电阻抗断层成像技术(EIT)的数学建模与其他应用场景

电阻抗断层成像(EIT)技术在医学成像和隐身技术等领域有着广泛的应用前景,本文首先对EIT的成像原理及其优缺点进行了论述;然后,对EIT在隐身技术和作为机械通气检测新技术的临床应用方面进行了介绍;最后,介绍了EIT的数学建模,尤其详细介绍了四种电极模型.EIT数学模型带来了偏微分方程的正问题、反问题、硬件系统设计以及图像去噪问题,成为这一技术的核心的“卡脖子”部分.

Advances in electrical impedance tomography-based brain imaging

Novel advances in the field of brain imaging have enabled the unprecedented clinical application of various imaging modalities to facilitate disease diagnosis and treatment. Electrical impedance tomography(EIT) is a functional imaging technique that measures the transfer impedances between electrodes on the body surface to estimate the spatial distribution of electrical properties of tissues. EIT offers many advantages over other neuroimaging technologies,which has led to its potential clinical use. This qualitative review provides an overview of the basic principles,algorithms, and system composition of EIT. Recent advances in the field of EIT are discussed in the context of epilepsy,stroke, brain injuries and edema, and other brain diseases. Further, we summarize factors limiting the development of brain EIT and highlight prospects for the field. In epilepsy imaging, there have been advances in EIT imaging depth,from cortical to subcortical regions. In stroke research, a bedside EIT stroke monitoring system has been developed for clinical practice, and data support the role of EIT in multi-modal imaging for diagnosing stroke. Additionally, EIT has been applied to monitor the changes in brain water content associated with cerebral edema, enabling the early identification of brain edema and the evaluation of mannitol dehydration. However, anatomically realistic geometry,inhomogeneity, cranium completeness, anisotropy and skull type, etc., must be considered to improve the accuracy of EIT modeling. Thus, the further establishment of EIT as a mature and routine diagnostic technique will necessitate the accumulation of more supporting evidence.

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_ II system are feasible and EIT is a promising technique in clinical monitoring application.

Lorentz force electrical impedance tomography using pulse compression technique

Lorentz force electrical impedance tomography （LFEIT） combines ultrasound stimulation and electromagnetic field detection with the goal of creating a high contrast and high resolution hybrid imaging modality. In this study, pulse compression working together with a linearly frequency modulated ultrasound pulse was investigated in LFEIT. Experiments were done on agar phantoms having the same level of electrical conductivity as soft biological tissues. The results showed that：（i） LFEIT using pulse compression could detect the location of the electrical conductivity variations precisely; （ii） LFEIT using pulse compression could get the same performance of detecting electrical conductivity variations as the traditional LFEIT using high voltage narrow pulse but reduce the peak stimulating power to the transducer by 25.5 dB; （iii） axial resolution of 1 mm could be obtained using modulation frequency bandwidth 2 MHz.

Digital biomedical electrical impedance tomography based on FPGA

A digital biomedical electrical impedance tomography (EIT) system is developed with the aid of FPGA. The key elements of EIT system are described specifically in the paper. The functions are realized to generate excitation source, switch electrode channels, deal collected signals, demodulate measured voltages etc. The system is tested by a circular tank with 16 stainless electrodes attached around the boundary. The adjacent incentive adjacent measurement mode is adapted to collect boundary voltages of the interesting field. By testing, the system works with 36 dB signal-to-noise ratio (SNR) when 1 mA 100 KHz current is applied into a homogenous tank.

Use of 3-D magnetic resonance electrical impedance tomography in detecting human cerebral stroke: a simulation study

We have developed a new three dimensional (3-D) conductivity imaging approach and have used it to detect human brain conductivity changes corresponding to acute cerebral stroke. The proposed Magnetic Resonance Electrical Impedance Tomography (MREIT) approach is based on the J-Substitution algorithm and is expanded to imaging 3-D subject conductivity distribution changes. Computer simulation studies have been conducted to evaluate the present MREIT imaging approach. Simulations of both types of cerebral stroke, hemorrhagic stroke and ischemic stroke, were performed on a four-sphere head model. Simulation results showed that the correlation coefficient (CC) and relative error (RE) between target and estimated conductivity distributions were 0.9245±0.0068 and 8.9997%±0.0084%, for hemorrhagic stroke, and 0.6748±0.0197 and 8.8986%±0.0089%, for ischemic stroke, when the SNR (signal-to-noise radio) of added GWN (Gaussian White Noise) was 40. The convergence characteristic was also evaluated according to the changes of CC and RE with different iteration numbers. The CC increases and RE decreases monotonously with the increasing number of iterations. The present simulation results show the feasibility of the proposed 3-D MREIT approach in hemorrhagic and ischemic stroke detection and suggest that the method may become a useful alternative in clinical diagnosis of acute cerebral stroke in humans.

Electrode models in electrical impedance tomography

This paper presents different views on electrode modelling, which include electrode electrochemistry models for modelling the effects of electrode-electrolyte interface, electric field electrode models for modelling electrode geometry, and electrode models for modelling the effects of electrode common mode voltage and double layer capacitance. Taking the full electrode models into consideration in electrical impedance tomography (EIT) will greatly help the optimised approach to a good solution and further understanding of the measurement principle.

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.

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.

Electrical Impedance Tomography Based on Direct Search Method

Solution to impedance distribution in electrical impedance tomography （EIT） is an ill-posed nonlinear inverse problem. It is especially difficult to reconstruct an EIT image in the center area of a measured object. Tikhonov regularization with some prior information is a sound regnlarization method for static electrical impedance tomography under the condition that some true impedance distribution information is known a priori. This paper presents a direct search method （DSM） as pretreatment of image reconstruction through which one not only can construct a regularization matrix which may locate in areas of impedance change, but also can obtain an initial impedance distribution more similar to the true impedance distribution, as well as better current modes which can better distinguish the initial distribution and the true distribution. Simulation results indicate that, by using DSM, resolution in the center area of the measured object can be improved significantly.

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.

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 based image reconstruction and feto-maternal monitoring in pregnancy

Standard methods of monitoring the fetus and maternal health during labor are cardioto-cogram, tocography, ultrasound and magneto-cardiograpghy. These methods have some limi-tations in real time continuous monitoring and cause some degree of inconvenience to the pa-tient and demand special attendance of the ob-stetrician also these methods cannot be used for continuous monitoring of the fetal well being. To overcome the limitations of above techniques, a non-invasive bioimpedance measuring method is proposed. This technique helps in monitoring and recording of the electrical field distribution of a closed object. The output variation on the outer surface is likely to provide information because of fetal movements and related physio-logical parameters. It will also help in the de-velopment of Electrical Impedance Tomography based imaging technique for a closed body system with special reference to fetal monitor-ing in-utero during pregnancy and labor. Also we have developed the data acquisition system of 16 electrodes with software for image recon-struction.

An Algorithm for Cavity Reconstruction in Electrical Impedance Tomography

We consider the inverse problem of finding cavities within some object from electrostatic measurements on the boundary. By a cavity we understand any object with a different electrical conductivity from the background material of the body. We give an algorithm for solving this inverse problem based on the output nonlinear least-square formulation and the regularized Newton-type iteration. In particular, we present a number of numerical results to highlight the potential and the limitations of this method.

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.

Applications of Symmetric Circulant Matrices to Isotropic Markov Chain Models and Electrical Impedance Tomography

Symmetric circulant matrices (or shortly symmetric circulants) are a very special class of matrices sometimes arising in problems of discrete periodic convolutions with symmetric kernel. First, we collect major properties of symmetric circulants scattered through the literature. Second, we report two new applications of these matrices to isotropic Markov chain models and electrical impedance tomography on a homogeneous disk with equidistant electrodes. A new special function is introduced for computation of the Ohm’s matrix. The latter application is illustrated with estimation of the resistivity of gelatin using an electrical impedance tomography setup.