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.

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.

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.

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.

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.

Adaptive Noise Cancellation Algorithms Implemented onto FPGA-Based Electrical Impedance Tomography System

Electrical Impedance Tomography(EIT)as a non-invasive of electrical conductivity imaging method commonly employs the stationary-coefficient based filters(such as FFT)in order to remove the noise signal.In the practical applications,the stationary-coefficient based filters fail to remove the time-varying random noise which leads to the lack of impedance measurement sensitivity.In this paper,the implementation of adaptive noise cancellation(ANC)algorithms which are Least Mean Square(LMS)and Normalized Least Mean Square(NLMS)filters onto Field Programmable Gate Array(FPGA)-based EIT system is proposed in order to eliminate the time-varying random noise signal.The proposed method was evaluated through experimental studies with biomaterial phantom.The reconstructed EIT images with NLMS is better than the images with LMS by amplitude response AR=12.5%,position error PE=200%,resolution RES=33%,and shape deformation SD=66%.Moreover,the Analog-to-Digital Converter(ADC)performances of power spectral density(PSD)and the effective number of bit ENOB with NLMS is higher than the performances with LMS by SI=5.7%and ENOB=15.4%.The results showed that implementing ANC algorithms onto FPGA-based EIT system shows significantly more accurate image reconstruction as compared without ANC algorithms implementation.

基于小波多分辨分析和LSQR的快速EIT图像重建算法

针对LSQR(least square QR-factorization)算法在求解电阻抗层析成像(electrical impedance tomography,EIT)逆问题时,由于矩阵维数高、计算量大而导致重建速度较慢的问题,提出基于小波多分辨分析的LSQR算法(wavelet multi-resolution based least square QR-factorization,WALSQR)。该方法将EIT的图像重建过程投影到低维的尺度空间进行,通过提取有效信息减少数据计算量,明显提高了图像重建速度。同时由于去除了噪声和冗余信息,保证了成像质量。本文将SALSQR方法分别应用于二维、三维EIT成像实验,证明其有效性,并为三维动态EIT图像重建算法的研究奠定了基础。

一种基于加权频差阻尼最小二乘的准静态EIT算法

传统的准静态电阻抗成像算法不能减少背景在重构结果中造成的伪影,导致无法进行有效成像。针对这一问题,提出一种基于加权频差阻尼最小二乘法的成像算法。该算法对成像数据采用加权频差的处理方式,克服了背景对成像结果的影响;同时结合阻尼最小二乘重构算法的正则化方法,改善了EIT逆问题求解的病态性。算法功能在Visual C++环境下完成,并通过仿真实验和物理模型实验对该算法的有效性进行验证。结果表明,无论模型的背景阻抗分布是否随频率变化,该算法均能够准确地检测出扰动目标。因此,该算法是一种有效的准静态电阻抗成像算法。

信噪比对不同EIT图像重建算法的影响研究及评价

探索不同位置单目标和多目标电阻抗断层成像(EIT)中,信噪比对不同EIT图像重建算法应用效果的影响。针对16电极系统,通过仿真获取EIT场域理想边界电压作为理想EIT系统的采集信号,对理想系统所有通道采集的数据整体添加高斯白噪声,模拟信噪比为40、60、80 dB的EIT检测系统的测量电压。采用等位线反投影(LBP)、基于Newton-Raphson算法的Tikhonov正则化(NR Tikhonov)和Tikhonov-Noser组合正则化(NR Tikhonov-Noser)、Landweber迭代以及Newton-Raphson 5种算法,重建EIT图像。引入重建误差ER和结构相似度函数SSIM,定量评价成像效果。结果表明,当单目标由场域中心向场域边界移动时,5种算法的SSIM都增大;当目标位置靠近边界但个数增加时,5种算法的ER都增大。LBP和NR Tikhonov-Noser在3种信噪比的系统中均可对5种目标模型成像,5种算法中NR Tikhonov-Noser成像质量最好。对场域中心的目标A,Newton-Raphson算法在40 dB信噪比时无法成像,NR Tikhonov、Landweber在40和60 dB时均不能获得有效图像。在80 dB系统中,LBP成像效果不及另4种算法。不同算法成像对EIT检测系统信噪比的要求不一致,应依据构建的EIT检测系统信噪比指标和具体应用目标择优选取成像算法。NR Tikhonov-Noser可作为一般情况下的首选算法,信噪比低于60 dB的系统建议选择LBP,80 dB以上的系统不建议选择LBP。60 dB左右的系统还建议可选择Newton-Raphson算法成像。

Ag/AgCl粉末电极和镀层电极在脑部EIT测量中的比较研究

目的:针对一种新型的医用Ag/AgCl粉末电极和传统的医用Ag/AgCl镀层电极,比较两者在脑部电阻抗断层成像(Electrical Impedance Tomography,EIT)测量中的性能差异。方法:基于自行开发的EIT系统,分别利用Ag/AgCl粉末电极和镀层电极,分别对物理模型和人体对象进行脑部电阻抗断层成像测量,并且通过提出的两个指标(高频信噪比和低频信噪比)及接触阻抗来评价电极系统的特性。结果:相对于Ag/AgCl镀层电极,Ag/AgCl粉末电极具有更好的抗噪性和稳定性。结论:Ag/AgCl粉末电极能更有效地满足脑部EIT监护的要求。

胃区3层EIT模型构建和仿真

构建3层电阻抗成像技术(EIT)模型,研究不同电极激励模型下胃排空信息的规律,以及胃内容物电导率不同时胃阻抗测量信号的变化情况。利用COMSOL MULTIPHYSICS仿真软件,以人体解剖学为基础,构建包含胃、肝胆的人体腹腔模型,并在模型外构建3层电极模型,每层16个电极,以5 mA的安全电流为激励源进行仿真。分别设定相对和相邻电极激励模型,设定不同的胃内容物,即绝缘试餐0.054 S/m、导电试餐1 S/m和中性试餐0.5 S/m等3种条件,用以模拟不同食物在胃内的情况;设定胃体积由2倍变为1倍的过程,模拟胃排空过程,进行仿真实验研究。包括对测量电压进行数据分析,利用评价参数边界测量电压敏感性δ,利用边界电压测量动态范围U对测量电压进行稳定性和检测效果的评价。相对电极模型的测量电压敏感性δ较高,且其在绝缘试餐时较导电试餐时小;而相邻模型的δ在绝缘试餐时较导电试餐时大,边界测量动态范围U也相对更大。对测量的敏感性而言,在试餐电导率0.054 S/m时,两种激励模式分别为34.13与34.25;试餐电导率1 S/m时,两种激励模式分别为33.60与26.68。3层EIT模型可提供达24×23×3组水平测量数据组,以及根据测试需求的可扩展交叉激励数据组,能够得到更多的胃排空信息,有效地反映胃内容物情况,以及胃排空过程的信息关系。

基于传感器测量系统的EIT重构算法研究

在传感器进行电位测量的电阻抗成像(EIT)数据采集系统的基础上,通过在粒子群搜索策略中引入量子行为特性,提出一种自适应量子粒子群算法,该算法提高了最优解搜索的成功率。应用于求解EIT逆问题,仿真实验结果表明:与粒子群算法相比,量子粒子群优化算法能有效克服粒子群优化(PSO)算法易出现的早熟收敛问题,收敛速度快,并且能够有效地提高图像分辨率。

乳腺肿瘤同伦LM算法EIT图像重建

将同伦延拓方法与Levenberg-Marquardt(LM)迭代算法相结合,对不同的乳腺肿瘤模型进行图像重建,并与LM算法进行比较.在不同迭代初始值下,同伦LM算法与LM算法的平均电导率相对误差相比,最优值能降低接近7%,,运行时间最大能减少46%,的计算开销,电压相对误差要优于LM算法7%,以上.实验结果表明,同伦LM算法比LM算法能重建更好的图像,提高了重建图像的精度和收敛速度.

基于EIT技术的气流传感器及其实验研究

提出了一种新型的、基于电阻抗断层成像技术进行气流传感的微机电(MEMS)传感器结构,其基本构造为覆盖在加热器件之上的半导体硅薄膜以及位于硅薄膜四周的16个金属电极.首先,在金属电极对中注入电流,并根据相邻测试规则测量其他电极对间的电压;然后,改变电流注入位置并测量电压,得到208个电压值;最后,利用成像软件将电流和电压参量转换为电阻率分布图像.气流流动所导致的热场分布变化将引起薄膜电阻率的分布发生变化.气流传感实验结果表明,气流方向、速度的变化能够采用图像的方式清晰、直观地进行描述.研究结果为MEMS传感器设计提供了新的方案与技术.

新型头模型EIT实验装置的设计及应用

目的:研制能精确定位的头模型EIT实验装置,满足进一步研究的需要。方法:安装电极和支撑颅骨物理模型的半球状结构选用有机玻璃材料,电极用银电极,支撑悬挂结构材料选用硬度较大、方便取材的钢或铜材料。结果:新型头部物理模型在精度和稳定性方面均达到了要求,能够满足进一步实验及其相关分析的需要。结论:新型头部物理模型对扰动目标的水平位置和垂直位置定位均精确到mm,能够满足进一步实验及其相关分析的需要。

基于最小二乘回归模型的EIT数据分析

医学电阻抗成像技术(EIT)是当今生物医学工程领域重大研究课题之一。本文利用最小二乘回归模型分析了圆形场EIT扫描数据,实现了场域内干扰物质的相对尺寸及相对位置的确定,为实现EIT动态心肺监测活动奠定基础。

改进的稀疏网格配点法对EIT电导率分布的不确定性量化

针对电阻抗成像(EIT)研究中电导率分布的不确定性问题,提出基于灵敏度分析的改进稀疏网格配点法以量化不确定性.以4层同心圆头模型为算例,采用基于方差的全局灵敏度分析法对其进行分析,发现各层电导率的变化对输出电位的影响程度各不相同.考虑模型中各维输入变量对输出结果不同程度的影响,改进传统稀疏网格配点法.改进方法对各维输入变量配置不同的精度水平,将EIT模型的隐式表达式转化为显式表达式,构造出高精度的替代模型.与蒙特卡洛(MC)法、混沌多项式展开(PCE)法和传统稀疏网格配点法相比,改进方法能够以更少的计算成本获得较高精度的量化结果.仿真结果验证了所提改进方法的高效性.

一种新的EIT成像优化方法应用

针对现有的电阻抗断层成像(EIT)技术具有非常严重的不适定问题,提出了一种新的优化方法,通过将正则化进行改进,进而通过迭代运算,更有效地减少目标函数的损失值。理论分析和仿真结果均表明:新方法可以获得较现有方法更好的空间分辨率,特别是对于小目标的检测。

一种基于FPGA的数字EIT系统的实验研究

以FPGA芯片为核心的数字化生物电阻抗测量实验平台,主要完成直接数字频率合成(DDS)的电流源模块、V/I变换和数字解调模块的研究。采用DDS技术生成正弦信号发生器,由THS4021改进的Howland电路设计压控电流源,在不同负载条件下测量其输出电流幅值的频率响应,并在不同频率下测量电流源的输出阻抗,对恒流源性能的稳定性和精度进行测定。解调方法采用数字化解调,测试不同激励频率下的数字正交解调输出误差变化情况。最后采用盐水槽实验系统进行成像实验。结果表明,电流源可在6.1～390.6 kHz范围输出多频激励信号,在600 kHz前保持在190 kΩ的输出阻抗。当信号频率从200 kHz逐渐增加到1.6 MHz时,解调电路输出的I和Q路误差逐渐增加,对应的幅度误差从1.13%增加到7.19%,相位误差从1.03%增大到5.34%。采用相邻激励-相邻测量模式对环氧树脂棒进行盐水槽成像实验,成像结果表明系统能够对单个目标物体实现较准确的定位,验证了本研究平台的可行性。