生物电阻抗脑功能成像研究现状

生物电阻抗成像技术在脑功能和脑疾病检测与监护中具有潜在的应用价值,并且具有无创伤、功能性、价格低、操作简便等优点,是目前生物医学工程的研究热点。本文主要介绍了生物电阻抗成像技术在脑功能和脑疾病成像的研究现状,并着重讨论了EIT(electrical impedance tomography,生物电阻抗断层成像)、MIT(magnetic induction tomography,磁感应电阻抗成像)、MREIT(magnetic resonanceelectrical impedance tomography,磁共振电阻抗成像)、MAT-MI(magnetoacoustic tomography with magneticinduction,磁感应磁声成像)技术在成像过程中的区别及今后有待近一步解决的理论与技术难题。     

生物阻抗的测量方法

通过对生物阻抗的测量来监测生理功能和检测病理事件 ,已成为近年来研究者非常关注的一个研究方向。本文介绍三种生物阻抗的测量方法 ,它们分别是阻抗断层摄影成像技术 ( electricalimpedance tomography,EIT)、基于磁共振成像 ( magnetic resonance imaging,MRI)的方法和阻抗谱测量 ( impedance spectrometry)技术 ,并分别简要介绍了各自的应用     

磁共振电阻抗成像技术

本文介绍了一种把磁共振成像(magnetic resonance imaging,MRI)技术和电阻抗成像(electrical impedance tomography,EIT)技术融合的新型成像技术--磁共振电阻抗成像技术.阐述了二者结合的关键点:磁共振电流密度成像技术.分析了硬件系统构建的特点,着重回顾了当前研究概况并介绍了几种主要模型和算法,指出了各自特点.最后对该技术的主要问题和发展方向进行了探讨.     

心脏瓣膜病的多层螺旋CT、MR研究进展

分析多层螺旋CT(Multi-detector computed tomography, MDCT)、MR(Magnetic resonance imaging, MRI)检查技术在心脏瓣膜病中的应用,并探讨常规CT、电影CT(Cine CT)、最大密度投影(Maximum intensity projection, MIP)、多平面重组(Multiple planar reconstruction, MPR)、容积再现(Volume rendering technique, VRT)及黑血序列、电影MRI(Cine MRI)等CT、MRI技术对心脏瓣膜病的诊断价值.     

前列腺电特性成像研究现状及展望

本文对前列腺电特性成像的研究现状进行综述,并对其发展前景进行展望。在简要介绍电阻抗成像(EIT)及磁共振电阻抗成像(MREIT)基本原理的基础上,详述两种方法在前列腺电特性成像中的应用情况;展望了感应电流磁共振电阻抗成像(IC-MREIT)及磁共振电特性成像(MREPT)在前列腺癌诊断中的应用前景。     

CT、MRI检查在中枢神经系统神经母细胞瘤中的临床诊断价值

目的:研究X线计算机断层摄影(Computed tomography,CT)和磁共振成像(Magnetic resonance imaging,MRI)检查在中枢神经系统神经母细胞瘤临床诊断中的应用价值。方法:收集本院2018年10月至2020年1月收治的75例高度怀疑中枢神经系统神经母细胞瘤患者的临床资料,并对所有患者行CT与MRI检查,观察患者病变形态、密度、信号及强化方式等特征;最终以手术病理结果为金标准,对比分析MRI、CT检查诊断敏感性、特异性及准确性。结果:术后病理检查结果显示,所有患者中67例为中枢神经系统神经母细胞瘤。CT检查诊断中枢神经系统神经母细胞瘤的敏感性、特异性及准确性分别为86.57%、75.00%、85.33%;MRI检查分别为88.06%、87.50%、88.00%,组间比较无统计学差异(P>0.05)。结论:CT与MRI检查均有效诊断中枢神经系统神经母细胞瘤,具有较高的诊断准确性,临床可根据患者个人情况进行选择。     

电流密度成像及其在生物电阻抗成像中的应用

本首先介绍了生物电阻抗成像(electrical impedance tomography，EIT)的数学模型、系统设计、算法分类及其存在的问题。然后介绍了由加拿大Toronto大学的Joy、Scott等人提出的电流密度成像(current density imaging，CDI)的基本原理及发展现状，提出将CDI应用到EIT可以得到精确度与分辨率都较高的阻抗图像及实现的基本思路。最后讨论了将CDI应用到EIT的发展前景及需要解决的问题。     

电流密度成像及其在生物电阻抗成像中的应用

本文首先介绍了生物电阻抗成像(electrical impedance tomography,EIT)的数学模型、系统设计、算法分类及其存在的问题。然后介绍了由加拿大Toronto大学的Joy、Scott等人提出的电流密度成像(current density imaging,CDI)的基本原理及发展现状,提出将CDI应用到EIT可以得到精确度与分辨率都较高的阻抗图像及实现的基本思路。最后讨论了将CDI应用到EIT的发展前景及需要解决的问题。     

磁共振成像检查中Bl场作用下人体头部比吸收率分布的研究

应用2D—阻抗法和由人体头部横断面切片获得的头部二维模型，计算人体头部在磁共振成像(magnetic resonance imaging,MRI)检查中由RF线圈产生的B1场作用下比吸收率(specific absorption rates,SAR)的分布，并对在MRI检查中人体头部的SAR只分布及相关问题进行了探讨。     

分子影像及其在活体内应用的研究进展

分子影像是用应用正电子发射断层扫描（positron emission tomography，PET）、单光子发射计算机体层摄影术（single photon emission computed tomography，SPECT）、PET／计算机体层摄影术（computed tomography，CT）（PET／CT）、PET／磁共振成像（magnetic resonance imaging，MRI）（PET／MRI）、超声仪（ultrasound）和光学成像仪（optical imaging）等成像系统，直接或者间接非侵袭性对活体组织细胞的生化、生理、诊断和治疗等分子事件的成像技术。     

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.     

Basic setup for breast conductivity imaging using magnetic resonance electrical impedance tomography

We present a new medical imaging technique for breast imaging, breast MREIT, in which magnetic resonance electrical impedance tomography (MREIT) is utilized to get high-resolution conductivity and current density images of the breast. In this work, we introduce the basic imaging setup of the breast MREIT technique with an investigation of four different imaging configurations of current-injection electrode positions and pathways through computer simulation studies. Utilizing the preliminary findings of a best breast MREIT configuration, additional numerical simulation studies have been carried out to validate breast MREIT at different levels of SNR. Finally, we have performed an experimental validation with a breast phantom on a 3.0 T MREIT system. The presented results strongly suggest that breast MREIT with careful imaging setups could be a potential imaging technique for human breast which may lead to early detection of breast cancer via improved differentiation of cancerous tissues in high-resolution conductivity images.     

Measurement of ion diffusion using magnetic resonance electrical impedance tomography

In magnetic resonance electrical impedance tomography (MREIT), currents are applied to an object, the resulting magnetic flux density measured using MRI and the conductivity distribution reconstructed using these MRI data. In this study, we assess the ability of MREIT to monitor changes in the conductivity distribution of an agarose gel phantom, using injected current pulses of 900 microA. The phantom initially contained a distinct region of high sodium chloride concentration which diffused into the background over time. MREIT data were collected over a 12 h span, and conductivity images were reconstructed using the iterative sensitivity matrix method with Tikhonov regularization. The results indicate that MREIT was able to monitor the changing conductivity and concentration distributions resulting from the diffusion of ions within the agarose gel phantom.     

Magnetic resonance electrical impedance tomography (MREIT) based on the solution of the convection equation using FEM with stabilization

Most algorithms for magnetic resonance electrical impedance tomography (MREIT) concentrate on reconstructing the internal conductivity distribution of a conductive object from the Laplacian of only one component of the magnetic flux density (?(2)B(z)) generated by the internal current distribution. In this study, a new algorithm is proposed to solve this ?(2)B(z)-based MREIT problem which is mathematically formulated as the steady-state scalar pure convection equation. Numerical methods developed for the solution of the more general convection-diffusion equation are utilized. It is known that the solution of the pure convection equation is numerically unstable if sharp variations of the field variable (in this case conductivity) exist or if there are inconsistent boundary conditions. Various stabilization techniques, based on introducing artificial diffusion, are developed to handle such cases and in this study the streamline upwind Petrov-Galerkin (SUPG) stabilization method is incorporated into the Galerkin weighted residual finite element method (FEM) to numerically solve the MREIT problem. The proposed algorithm is tested with simulated and also experimental data from phantoms. Successful conductivity reconstructions are obtained by solving the related convection equation using the Galerkin weighted residual FEM when there are no sharp variations in the actual conductivity distribution. However, when there is noise in the magnetic flux density data or when there are sharp variations in conductivity, it is found that SUPG stabilization is beneficial.     

Phase artefact reduction in magnetic resonance electrical impedance tomography (MREIT)

Cross-sectional conductivity imaging in magnetic resonance electrical impedance tomography (MREIT) requires the measurement of internal magnetic flux density using an MRI scanner. Current injection MRI techniques have been used to induce magnetic flux density distributions that appear in phase parts of the obtained MR signals. Since any phase error, as well as noise, deteriorates the quality of reconstructed conductivity images, we must minimize them during the data acquisition process. In this paper, we describe a new method to correct unavoidable phase errors to reduce artefacts in reconstructed conductivity images. From numerical simulations and phantom experiments, we found that the zeroth- and first-order phase errors can be effectively minimized to produce better conductivity images. The promising results suggest that this technique should be employed together with improved MREIT pulse sequences in future studies of high-resolution conductivity imaging.     

基于头部组织不同电导率特性的阻抗成像正问题仿真研究

目的 通过建立5层有限元真实头模型,研究了各层组织非均质和颅骨、脑白质各向异性电特性对电阻抗成像问题中电磁场分布的影响.方法 对头部各组织建立4种电导率分布模型:均质分布、非均质分布以及颅骨和脑白质各向异性电导率模型;通过正问题数值求解得到不同模型下的磁场分布和电场分布,并通过定量的统计分析研究非均质和各向异性电导率特...     

Anisotropic conductivity imaging with MREIT using equipotential projection algorithm

Magnetic resonance electrical impedance tomography (MREIT) combines magnetic flux or current density measurements obtained by magnetic resonance imaging (MRI) and surface potential measurements to reconstruct images of true conductivity with high spatial resolution. Most of the biological tissues have anisotropic conductivity; therefore, anisotropy should be taken into account in conductivity image reconstruction. Almost all of the MREIT reconstruction algorithms proposed to date assume isotropic conductivity distribution. In this study, a novel MREIT image reconstruction algorithm is proposed to image anisotropic conductivity. Relative anisotropic conductivity values are reconstructed iteratively, using only current density measurements without any potential measurement. In order to obtain true conductivity values, only either one potential or conductivity measurement is sufficient to determine a scaling factor. The proposed technique is evaluated on simulated data for isotropic and anisotropic conductivity distributions, with and without measurement noise. Simulation results show that the images of both anisotropic and isotropic conductivity distributions can be reconstructed successfully.     

MREIT experiments with 200 µA injected currents: a feasibility study using two reconstruction algorithms, SMM and harmonic B(Z)

Magnetic resonance electrical impedance tomography (MREIT) is a technique that produces images of conductivity in tissues and phantoms. In this technique, electrical currents are applied to an object and the resulting magnetic flux density is measured using magnetic resonance imaging (MRI) and the conductivity distribution is reconstructed using these MRI data. Currently, the technique is used in research environments, primarily studying phantoms and animals. In order to translate MREIT to clinical applications, strict safety standards need to be established, especially for safe current limits. However, there are currently no standards for safe current limits specific to MREIT. Until such standards are established, human MREIT applications need to conform to existing electrical safety standards in medical instrumentation, such as IEC601. This protocol limits patient auxiliary currents to 100?μA for low frequencies. However, published MREIT studies have utilized currents 10-400?times larger than this limit, bringing into question whether the clinical applications of MREIT are attainable under current standards. In this study, we investigated the feasibility of MREIT to accurately reconstruct the relative conductivity of a simple agarose phantom using 200?μA total injected current and tested the performance of two MREIT reconstruction algorithms. These reconstruction algorithms used are the iterative sensitivity matrix method (SMM) by Ider and Birgul (1998 Elektrik 6 215-25) with Tikhonov regularization and the harmonic B(Z) proposed by Oh et al (2003 Magn. Reason. Med. 50 875-8). The reconstruction techniques were tested at both 200?μA and 5?mA injected currents to investigate their noise sensitivity at low and high current conditions. It should be noted that 200?μA total injected current into a cylindrical phantom generates only 14.7?μA current in imaging slice. Similarly, 5?mA total injected current results in 367?μA in imaging slice. Total acquisition time for 200?μA and 5?mA experiments was about 1?h and 8.5?min, respectively. The results demonstrate that conductivity imaging is possible at low currents using the suggested imaging parameters and reconstructing the images using iterative SMM with Tikhonov regularization, which appears to be more tolerant to noisy data than harmonic B(Z).     

Conductivity imaging with low level current injection using transversal J-substitution algorithm in MREIT

An aim of magnetic resonance electrical impedance tomography (MREIT) is to visualize the internal current density and conductivity of the electrically imaged object by injecting current through electrodes attached to it. Due to a limited amount of injection current, one of the most important factors in MREIT is how to control the noise contained in the measured magnetic flux density data. This paper describes a new iterative algorithm called the transversal J-substitution algorithm which is robust to measured noise. As a result, the proposed transversal J-substitution algorithm considerably improves the quality of the reconstructed conductivity image under a low injection current. The relation between the reconstructed contrast of conductivity and the measured noise in the magnetic flux density is analyzed. We show that the contrast of first update of the conductivity with a homogeneous initial guess using the proposed algorithm has sufficient distinguishability to detect the anomaly. Results from numerical simulations demonstrate that the transversal J-substitution algorithm is robust to the noise. For practical implementations of MREIT, we tested real experiments in an agarose gel phantom using low current injection with amplitudes 1 mA and 5 mA to reconstruct the interior conductivity distribution.