基于直接寻找法的电阻抗断层成像

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.     

二维电阻抗断层成像算法研究

粒子群算法是一种随机、智能、全局优化算法,近年来越来越多的被应用于电磁学领域。提出修正的粒子群算法,并应用其进行电阻抗断层成像研究。对研究的圆形求解域采用有限元法进行剖分,电流注入采用三角电流法,并用修正的粒子群算法对园域内电导率目标进行介质重构。数值仿真结果表明:该方法对求解域内的目标位置定位准确,并能够准确地反映场域内电导率的分布。     

基于多级BP神经网络的EIT阻抗变化位置的确定

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     

基于自适应网格的荧光分子断层图像重建

荧光分子断层成像涉及成像区域的网格化。基于先验信息产生自适应网格,并基于自适应网格、利用有限元方法进行了二维数值模拟,并将其结果与传统方法进行比较。实验结果表明,该方法可提升荧光分子断层图像重建效率。     

采用非均匀细分网格提高电阻抗成象图象精度的方法

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.     

基于电阻抗成像的食品中异物检测仿真研究

为了研究电阻抗成像系统参数对食品内异物成像品质的影响,验证有限元仿真方法能够模拟电阻抗成像硬件系统获取成像数据,利用16电极电阻抗成像系统和有限元仿真方法,通过相邻激励测量模式得到实测和仿真电压成像数据,采用牛顿-拉夫逊算法实现了异物的图像重建,并用图像质量函数D和结构相似度函数SSIM对重建图像进行定量评价。结果表明,计算机仿真方法能模拟电阻抗成像硬件系统,优化成像条件,提高研究效率;迭代次数小时重建图像锐度好,迭代次数到达一定值后,增加迭代次数对成像品质没有影响;对直径为100mm的圆形域,电极宽度为7.5mm时成像效果最好;正则化系数有与电阻抗成像系统条件相匹配的最优值。     

电阻抗图象重建的新正则化方法

Image reconstruction in elecrical 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 maxiumum 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.     

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 algorithm.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.     

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

1 IntroductionIll electrical impedance tomography (EIT) an ~ ofelectrodes (for example, 16, 32 or 64 electrodes) isattached around the Object. Small alternating current areinjected into these electrodes, and the resulting voltagesare measured on the surface. Usually, Two of theelectrodes are used to inject a constallt-aml)litudesinusoidal current, and the others measure the voltagedeveloped on them. An image reconstrUchon algorithm isaPPlied tO the measured data to Produce a cross-sectiona…     

Electromagnetic Model and Image Reconstruction Algorithms Based on EIT System

Electrical impedance tomography (EIT) tech-niques have been investigated extensively during thepast decades, as a kind of visible multiphase flowmeasurement technique in industry and medical in-strument[1—5]. EIT has several advantages over othertomographic techniques, e. g. low cost, rapid re-sponse, portability, non-invasion, and robustness. Inindustrial field, EIT has found many applications,such as measurement of two-phase ( e. g. gas/liq-uid[6]and gas/solid[7,8]) flows in pipelines or…