基于TwIST-TV正则化算法的肺萎陷电阻抗成像仿真研究

肺部电阻抗成像(EIT)通过配置于人体体表一组阵列电极,利用边界测量信息重建胸腔内部二维断面电导率分布,具有非侵入、无辐射等特点,可用于临床监护.针对EIT逆问题求解的欠定性和病态性,提出一种基于总变差正则化(TV)的两步迭代(TwIST)算法.该算法利用迭代引入TV去噪算子,达到解的双重正则化效果.通过仿真构建不同程度肺痿陷EIT模型,利用该算法进行呼吸状态差分图像再构;同时基于图像提出肺通气总量指数作为客观评价指标.结果表明,与传统Tikhonov正则化算法相比,该算法可达到较好的图像重建质量和鲁棒性,其相应肺通气量指数也更接近于仿真肺痿陷模型变化.从而验证了该算法用于临床肺痿陷EIT通气量监测的可行性.     

一种基于频谱约束的多频动态电阻抗断层成像算法

本文旨在提出一种基于频谱约束的多频动态电阻抗断层成像(EIT)算法。在已知成像域内各组分电导率频谱的情况下,通过重构独立于频率的参数——体积分数变化,同时利用多个激励频率下的测量电压差重构一帧时差图像,从而大大增加测量数据量以改善逆问题的病态性。数值仿真实验显示,该算法较传统阻尼最小二乘算法具有更小的图像伪影,且在低信噪比情形下具有更小的位置误差和形变误差。本研究有望为动态EIT提供一种有效利用多频信息的方法,并为在已知各组分电导率频谱情况下的动态EIT发展提供一个新思路。     

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。     

磁感应断层成像磁力线反投影算法研究

磁感应断层成像利用涡流原理,在成像区域边界对内部磁场变化进行测量,再经过重建,获得内部电导率分布图像。其中,反投影作为一种快速重建算法,经常被应用于断层成像技术。但是,由于成像区域内磁场分布的非线性,大大降低了反投影成像的定位精度。研究检测线圈测得的相位差与成像区域磁场分布之间的关系,根据测量相位差即为成像区域内电导率分布沿磁力线路径投影的集合这一结论,设计一套适用于磁感应断层成像的反投影算法,利用成像区域磁力线的分布,构建反投影路径和区域,改善直线反投影应用于非线性场定位不准的问题。重建实验结果表明,在直径为200 mm的成像区域内,所提出算法的最小定位误差为1 mm,相对直线反投影算法提高了2～26 mm,同时重建图像在视觉质量上也有很大提高,为磁感应断层成像技术提供一种有效的重建算法。     

基于先验信息的肺部电阻抗成像算法

电阻抗层析成像 (EIT) 系统逆问题求解的欠定性是导致重构图像分辨率低的主要原因之一.结合先验信息,改善逆问题的不适定性,提高算法鲁棒性是提高成像质量可行的办法.本研究基于Tikhonov正则化算法,结合肺部组织结构、器官电导率分布参数以及肺部呼吸动态结构变化等先验信息,构建正则化矩阵,进行EIT图像重构.研究结果表明,结合先验信息的Tikhonov正则化图像重构算法减小了成像相对误差,改进了图像质量,EIT用于区域性肺部通气变化的检测和监护是可行的.     

肺部三维EIT模型构建与图像重建研究

肺部三维电阻抗图像重建是电阻抗成像技术的重要应用之一。利用3D光学云点技术对人体胸腔区域扫描,并融合X图像提供的肺部结构构建肺部三维EIT仿真模型。根据肺部膨胀及收缩时的电导率先验知识,由COMSOL软件求解获得三维灵敏度矩阵,并在Matlab平台下由共轭梯度迭代算法重建肺部EIT断层图像,进行三维立体重构,获得3D EIT图像。为研究电流敏感场均匀性,设置4组不同电极层间距进行比较仿真实验,结果表明,对于33 cm胸腔区域,电极层间距为8 cm时,成像效果最佳,其最大相关系数为0.810 3,敏感场均匀性为1.869 6×10³,结构相似度为0.482 5,灵敏场的均匀性明显改善,有利于图像重建质量的提高。     

基于磁声耦合效应的电导率图像重建研究

磁声耦合成像技术利用电磁声三物理场耦合作用的原理,通过声检测方式重建电导率存在明显变化的组织轮廓,实现恶性肿瘤的早期检测。本文建立双层同心球模型,模拟被正常组织包裹的病变组织,利用声场中的时间反转法以及电磁场方程,求解组织中的电导率分布,研究了由检测声压计算电导率分布的重建算法,并进行了仿真分析。仿真结果显示重建的电导率分布图像在电导率边界处清晰可见,进而验证了应用磁声耦合成像对电导率边界进行功能成像的可行性,为恶性肿瘤的早期检测提供了一种新的功能成像方法。     

基于Deep CG的肺部电阻抗成像方法

针对电阻抗图像重建空间分辨率不足问题，基于深度学习理论提出一种共轭梯度快速预重建与深度堆栈式自编码器后处理的电阻抗成像方法(Deep CG)。该方法的核心思想是：融合数值重建算法与深度学习算法，使胸腔内肺部的结构和电导率分布更加精准。首先采用共轭梯度算法进行图像预重建，获得边界电压与胸腔内部电导率分布的预映射关系；再采用深度堆栈式自编码器，将编码和解码层级连接，充分利用不同空间特征信息，实现特征提取和图像重建；最后根据公开的80名临床患者的CT结构图像构建了数据集，采用混合式监督训练方法调参，不仅避免了深度网络中信息流和梯度流弥散问题，而且优化了算法模型。采用图像相对误差、相关系数进行量化指标评价，并与常用的数值图像重建算法和全连接神经网络模型进行对比。结果显示，Deep CG算法的比常用图像重建算法图像和全连接神经网络模型相对误差从0.50和0.24降低到0.11,相关系数由0.80和0.90提高到0.96。该方法获得了空间分辨率高，尤其边界更清晰的电阻抗图像，有望进一步推动EIT技术在临床的应用研究。     

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。     

数字式生物电阻抗与电阻抗断层成像测量系统

构建了以ATmega16单片机为控制核心,8个AD5933为数字电极,nRF24L01为无线通信模块的数字式生物电阻抗与电阻抗断层成像(EIT)测量系统。详细阐述了系统结构、电路设计、系统测试、动物组织阻抗测量及EIT成像。经测试,系统相对测量误差可达0.42%,信噪比达76.3dB。该系统不仅可采用二电极法在1~100kHz范围内进行扫频式阻抗测量,而且能实现EIT成像。对SD幼鼠解剖后的组织进行阻抗测量,测量结果显示,数据有效,经最小二乘法拟合,符合Cole-Cole理论,验证了该系统阻抗测量的可行性与有效性;通过盐水槽实验,对场域中物体的分布图像进行重构,重建图像与实际分布吻合,成像效果良好。该系统很大程度上提高了前端测量信号的有效性以及系统的稳定性。     

基于遗传算法的电阻抗图像重建

电阻抗图像重建是一个严重病态的非态线性的逆问题， Newton-Raphson迭代算法是目前理论上最为完善的静态电阻抗图像重建算法，它是一种基于最小化目标函数的搜索算法，在实际阻抗图像重建过程中对噪声非常敏感，即使使用正则化技术其稳定性和图像重建精度仍较差，本文提出一种基于遗传算法的图像重建新方法。实验结果表明这种方法具有较强的抗噪能力，其重建的静态电阻抗图像精度和空间分辨率都大大好于改进的Newton-Raphson重建算法。     

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。     

Finite-element method in electrical impedance tomography

In electrical impedance tomography (EIT), current patterns are injected into a subject and boundary voltages are measured to reconstruct a cross-sectional image of resistivity distribution. Static EIT image reconstruction requires a computer model of a subject, an efficient data-collection method and robust and fast reconstruction algorithms. The finite-element method is used as the computer model. The paper describes the finite-element analysis software package developed, including an interactive graphical mesh generator and fast algorithms for solving linear systems of equations using sparse-matrix and vector techniques. Various models of irregularly shaped subjects are developed using mesh-design tools, including automatic mesh generation and optimisation using the Delaunay algorithm. Even though the software package is customised for use in electrical impedance tomography, it can be used for other biomedical research areas, such as impedance cardiography, cardiac defibrillation and impedance pneumography.     

Current constrained voltage scaled reconstruction (CCVSR) algorithm for MR-EIT and its performance with different probing current patterns

Conventional injected-current electrical impedance tomography (EIT) and magnetic resonance imaging (MRI) techniques can be combined to reconstruct high resolution true conductivity images. The magnetic flux density distribution generated by the internal current density distribution is extracted from MR phase images. This information is used to form a fine detailed conductivity image using an Ohm's law based update equation. The reconstructed conductivity image is assumed to differ from the true image by a scale factor. EIT surface potential measurements are then used to scale the reconstructed image in order to find the true conductivity values. This process is iterated until a stopping criterion is met. Several simulations are carried out for opposite and cosine current injection patterns to select the best current injection pattern for a 2D thorax model. The contrast resolution and accuracy of the proposed algorithm are also studied. In all simulation studies, realistic noise models for voltage and magnetic flux density measurements are used. It is shown that, in contrast to the conventional EIT techniques, the proposed method has the capability of reconstructing conductivity images with uniform and high spatial resolution. The spatial resolution is limited by the larger element size of the finite element mesh and twice the magnetic resonance image pixel size.     

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.     

医学EIT复合电极结构参数优化设计

目的探索医学EIT复合电极结构参数变化对系统性能的影响。方法采用笔者建立的EIT电极结构及参数优化设计方法对复合电极进行了仿真研究，研究了位于成像区域不同位置的成像目标，复合电极各结构参数改变对成像质量和系统检测灵敏度影响的规律。结果和结论各结构参数的影响错综复杂，采用多参数仿真实验的方法是一种有效的方法。     

EIT image reconstruction with four dimensional regularization

Electrical impedance tomography (EIT) reconstructs internal impedance images of the body from electrical measurements on body surface. The temporal resolution of EIT data can be very high, although the spatial resolution of the images is relatively low. Most EIT reconstruction algorithms calculate images from data frames independently, although data are actually highly correlated especially in high speed EIT systems. This paper proposes a 4-D EIT image reconstruction for functional EIT. The new approach is developed to directly use prior models of the temporal correlations among images and 3-D spatial correlations among image elements. A fast algorithm is also developed to reconstruct the regularized images. Image reconstruction is posed in terms of an augmented image and measurement vector which are concatenated from a specific number of previous and future frames. The reconstruction is then based on an augmented regularization matrix which reflects the a priori constraints on temporal and 3-D spatial correlations of image elements. A temporal factor reflecting the relative strength of the image correlation is objectively calculated from measurement data. Results show that image reconstruction models which account for inter-element correlations, in both space and time, show improved resolution and noise performance, in comparison to simpler image models.     

Application of Electrical Impedance Tomography for Continuous Monitoring of Retroperitoneal Bleeding After Blunt Trauma

Retroperitoneal bleeding is commonly associated with blunt trauma to the abdomen. Current medical tools cannot be used for continuous monitoring of the bleeding. In the study, electrical impedance tomography (EIT) was applied to monitoring the retroperitoneal bleeding of an animal model. Six healthy swine were used. The process of retroperitoneal bleeding was simulated by the continuous injection of anticoagulated blood. For each subject, total blood of 200 mL or more was injected within different time periods ranging from tens of minutes to two hours. The bleeding was detected and monitored continuously by EIT system with 16 electrodes at a rate of one image per second. EIT images were reconstructed by dynamic back-projection algorithm. Mean resistivity value (MRV) of the bleeding region in EIT images was calculated and plotted over time. We found that impedance changes caused by the bleeding could be revealed by EIT images and MRV curves. MRV curve varied approximately linearly with the quantity of blood injected using regression analysis (R ² = 0.90 to 0.99, p < 0.05). In total, 20 mL of blood volume changes could be identified by EIT. The progression of the retroperitoneal bleeding can be monitored by EIT in the proposed animal model. It suggests EIT is potential as a useful tool for continuous monitoring of retroperitoneal bleeding after blunt trauma.     

阻抗断层成像中的图像重建技术

阻抗层成像是近年发展起来的一种新的医学影技术人有成本低廉,无电离辐射１可以动态显示组织生理活动等优点,ＥＩＴ图像重建一直是ＥＩＴ技术中的重要研究课题,本文对ＥＩＴ图像重建算法的发展作了评述。