Application Of Electrical Impedance Tomography Research Articles

A preliminary study on the application of electrical impedance tomography based on cerebral perfusion monitoring to intracranial pressure changes.

In intracranial pathologic conditions of intracranial pressure (ICP) disturbance or hemodynamic instability, maintaining appropriate ICP may reduce the risk of ischemic brain injury. The change of ICP is often accompanied by the change of intracranial blood status. As a non-invasive functional imaging technique, the sensitivity of electrical impedance tomography (EIT) to cerebral hemodynamic changes has been preliminarily confirmed. However, no team has conducted a feasibility study on the dynamic detection of ICP by EIT technology from the perspective of non-invasive whole-brain blood perfusion monitoring. In this study, human brain EIT image sequence was obtained by in vivo measurement, from which a variety of indicators that can reflect the tidal changes of the whole brain impedance were extracted, in order to establish a new method for non-invasive monitoring of ICP changes from the level of cerebral blood perfusion monitoring. Valsalva maneuver (VM) was used to temporarily change the cerebral blood perfusion status of volunteers. The electrical impedance information of the brain during this process was continuously monitored by EIT device and real-time imaging was performed, and the hemodynamic indexes of bilateral middle cerebral arteries were monitored by transcranial Doppler (TCD). The changes in monitoring information obtained by the two techniques were compared and observed. The EIT imaging results indicated that the image sequence showed obvious tidal changes with the heart beating. Perfusion indicators of vascular pulsation obtained from EIT images decreased significantly during the stabilization phase of the intervention (PAC: 242.94 ± 100.83, p < 0.01); perfusion index which reflects vascular resistance increased significantly in the stable stage of intervention (PDT: 79.72 ± 18.23, p < 0.001). After the intervention, the parameters gradually returned to the baseline level before compression. The changes of EIT indexes in the whole process are consistent with the changes of middle cerebral artery velocity related indexes shown in TCD results. The EIT image combined with the blood perfusion index proposed in this paper can reflect the decrease of cerebral blood flow under the condition of increased ICP in real time and intuitively. With the advantages of high time resolution and high sensitivity, EIT provides a new idea for non-invasive bedside measurement of ICP.

The application of electrical impedance tomography and surgical outcomes of thoracoscope-assisted surgical stabilization of rib fractures in severe chest trauma

Serious blunt chest trauma usually induces hemothorax, pneumothorax, and rib fractures. More studies have claimed that early video-assisted thoracoscopic surgery with surgical stabilization of rib fractures (SSRF) results in a good prognosis in patients with major trauma. This study aimed to verify the outcomes in patients with chest trauma whether SSRF was performed. Consecutive patients who were treated in a medical center in Taiwan, for traumatic events between January 2015 and June 2020, were retrospectively reviewed. This study focused on patients with major trauma and thoracic injuries, and they were divided into groups based on whether they received SSRF. We used electrical impedance tomography (EIT) to evaluate the change of ventilation conditions. Different scores used for the evaluation of trauma severity were also compared in this study. Among the 8396 patients who were included, 1529 (18.21%) had major trauma with injury severity score > 16 and were admitted to the intensive care unit initially. A total of 596 patients with chest trauma were admitted, of whom 519 (87%) survived. Younger age and a lower trauma score (including injury severity scale, new injury severity score, trauma and injury severity score, and revised trauma score) account for better survival rates. Moreover, 74 patients received SSRF. They had a shorter intensive care unit (ICU) stay (5.24, p = 0.045) and better performance in electrical impedance tomography (23.46, p < 0.001). In patients with major thoracic injury, older age and higher injury survival scale account for higher mortality rate. Effective surgical stabilization of rib fractures shortened the ICU stay and helped achieve better performance in EIT. Thoracoscope-assisted rib fixation is suggested in severe trauma cases.

Contribution of electrical impedance tomography to personalize positive end-expiratory pressure under ECCO2R

Extracorporeal Carbon Dioxide Removal (ECCO2R) is used in acute respiratory distress syndrome (ARDS) patients to facilitate lung-protective ventilatory strategies. Electrical Impedance Tomography (EIT) allows individual, non-invasive, real-time, bedside, radiation-free imaging of the lungs, providing global and regional dynamic lung analyses. To provide new insights for future ECCO2R research in ARDS, we propose a potential application of EIT to personalize End-Expiratory Pressure (PEEP) following each reduction in tidal volume (VT), as demonstrated in an illustrative case. A 72-year-old male with COVID-19 was admitted to the ICU for moderate ARDS. Monitoring with EIT was started to determine the optimal PEEP value (PEEPEIT), defined as the intersection of the collapse and overdistention curves, after each reduction in VT during ECCO2R. The identified PEEPEIT values were notably low (< 10 cmH2O). The decrease in VT associated with PEEPEIT levels resulted in improved lung compliance, reduced driving pressure and a more uniform ventilation pattern. Despite current Randomized Controlled Trials showing that ultra-protective ventilation with ECCO2R does not improve survival, the applicability of universal ultra-protective ventilation settings for all patients remains a subject of debate. Inappropriately set PEEP levels can lead to alveolar collapse or overdistension, potentially negating the benefits of VT reduction. EIT facilitates real-time monitoring of derecruitment associated with VT reduction, guiding physicians in determining the optimal PEEP value after each decrease in tidal volume. This original description of using EIT under ECCO2R to adjust PEEP at a level compromising between recruitability and overdistention could be a crucial element for future research on ECCO2R.

IoT-Driven Experimental Framework for Advancing Electrical Impedance Tomography

This research paper focuses on the current emphasis on the latest industrial revolution, particularly the innovative integration of artificial intelligence and the Internet of Things (IoT). The study explores the seamless integration of Electrical Impedance Tomography (EIT) with IoT, presenting a groundbreaking framework where impedance-based sensing plays a vital role in enhancing the dynamic and adaptable qualities of IoT ecosystems. This contribution facilitates intelligent decision-making and real-time monitoring. The research investigates the application of non-invasive Electrical Impedance Tomography for the rapid identification of minor changes in the electrical impedance of the body or a simulated object. Electrodes positioned at the ends of the phantom’s cylinder measure impedance changes through the application of a high-frequency, low-current signal. Image reconstruction employs both forward and inverse solutions, utilizing a triangular finite element method (FEM) mesh to determine conductivity distribution based on recommended phantom models. The integration of IoT enables data capture, enhancing accessibility through remote monitoring. The novel IoT system proves advantageous for various engineering research applications, providing easily monitored parameters in both commercial and clinical contexts.

A Shifting Current Mirror Driver Circuit for Electrical Impedance Tomography Applications

This brief describes a high power efficiency, high linearity current driver for wearable electrical impedance tomography (EIT). It is extremely important to improve the power efficiency of the current driver circuit for wearable EIT applications because it consumes the majority of the power. As such, we propose a multi-stage shifting current mirror (SCM) current-steering current driver circuit with customized dynamic element matching (DEM) techniques to suppress harmonic distortion (HD) to the greatest extent. Furthermore, the placement of switches in the current mirror circuit is optimized to reduce glitches during the switching phases. Operating between 14 MHz to 56 MHz, the power consumptions for the current mirror and the digital control logic are 21.6-141.6 lW and 64.8-438 μW, respectively. The proposed circuit has demonstrated an excellent energy efficiency of 0.3 μW/kHz while maintaining a total harmonic distortion (HD) of <-43 dB (0.7%).

Three-dimensional electrical impedance tomography (EIT) to study regional ventilation/perfusion (V/Q̇) ratios in anesthetized pigs.

This study aimed to develop a 3D method for assessing ventilation/perfusion (V/Q̇) ratios in a pig model of hemodynamic perturbations using electrical impedance tomography (EIT). In order to evaluate the physiological coherence of changes in EIT-derived V/Q̇ ratios, global EIT-derived V/Q̇ mismatches were compared with global gold-standards. The study found regional heterogeneity in the distribution of V/Q̇ ratios in both the ventrodorsal and craniocaudal directions. Although global EIT-derived indices of V/Q̇ mismatch consistently underestimated both low and high V/Q̇ mismatch compared to global gold standards, the direction of the change was similar. We made the software available at no cost for other researchers to use. Future studies should compare regional V/Q̇ ratios determined by our method against other regional, high-resolution methods.

Numerical analysis of aerospace plate damage via 3D electrical impedance tomography.

The carbon fiber reinforced plastic (CFRP) is widely used in the aerospace industry due to its high strength and lightweight characteristics, making it crucial to ensure the reliability of these materials. This has led to an increasing focus on research on the health monitoring technologies of aerospace materials. Electrical impedance tomography (EIT) is a non-invasive and cost-effective technology that has the potential to realize real-time health monitoring of materials by measuring changes in electrical parameters. This paper investigates the application of EIT for direct 3D reconstruction of damage in CFRP laminates with significant conductivity anisotropy distribution. Based on the corrected sensitivity matrix formula, the direct 3D image reconstruction method combined with the fast iterative shrinkage-thresholding algorithm (FISTA) is proposed to achieve damage imaging of CFRP laminates in the inverse problem. The fast convergence of the FISTA makes it possible to solve complex inverse problems. The numerical simulation results indicate that, compared with 2D EIT, the proposed method is more capable of providing damage information, especially in the depth direction. This research plays a constructive role in realizing 3D image reconstruction of CFRP material damage and has significant implications for improving the reliability and safety of CFRP materials in aerospace applications.

Effective Electrical Impedance Tomography Based on Enhanced Encoder–Decoder Using Atrous Spatial Pyramid Pooling Module

Electrical impedance tomography (EIT) is a noninvasive and radiation-free imaging method. As a "soft-field" imaging technique, in EIT, the target signal in the center of the measured field is frequently swamped by the target signal at the edge, which restricts its further application. To alleviate this problem, this study presents an enhanced encoder-decoder (EED) method with an atrous spatial pyramid pooling (ASPP) module. The proposed method enhances the ability to detect central weak targets by constructing an ASPP module that integrates multiscale information in the encoder. The multilevel semantic features are fused in the decoder to improve the boundary reconstruction accuracy of the center target. The average absolute error of the imaging results by the EED method reduced by 82.0%, 83.6%, and 36.5% in simulation experiments and 83.0%, 83.2%, and 36.1% in physical experiments compared with the errors of the damped least-squares algorithm, Kalman filtering method, and U-Net-based imaging method, respectively. The average structural similarity improved by 37.3%, 42.9%, and 3.6%, and 39.2%, 45.2%, and 3.8% in the simulation and physical experiments, respectively. The proposed method provides a practical and reliable means of extending the application of EIT by solving the problem of weak central target reconstruction under the effect of strong edge targets in EIT.

Emerging nanomaterials to enhance electrochemical impedance spectroscopy for biomedical applications

Over the last few decades, electrical impedance-based sensors have been investigated for clinical translation to detect changes in tissue conductivities, including cardiac output and pulmonary function. Recently, electrochemical impedance spectroscopy (EIS) provides metabolic measurements that occur at the electrode-tissue interface, and the 3-D EIS can be reconstructed to generate electrical impedance tomography (EIT) for detecting the impedimetric properties of the vascular wall or fatty liver disease. In both EIS and EIT applications, the electrochemical properties of the interface electrodes are essential to address the signal-to-noise ratio or sensitivity of measurements in the biological environment. To enhance the conductive properties, we will survey a series of carbon-based nanomaterials as the emerging candidates for coating the electrodes of bioimpedance sensors. In this review, we will provide a theoretical background on impedance-based measurements and highlight the current state of EIS and EIT, including their applications for cancer screening and detection of vulnerable atherosclerotic plaques. Next, we will focus on the strengths of different nanomaterials when used as an electrode coating to optimize charge transfer across the electric double layers and to enhance measurement sensitivity. We will also identify some unmet clinical needs, such as the ability to adapt to different hemodynamic conditions and blood vessel geometries, that can be realized by the novel biomaterials for the future EIS-based sensors.

An Optimization Algorithm H-GVSPM for Electrical Impedance Tomography

Electrical impedance tomography (EIT) is a novel industrial monitoring technology with the characteristics of safety, inexpensiveness, and real-time monitoring. Its imaging results are easily to generate artifacts due to the influence of various factors. Therefore, the image quality is low, and the contour of measured objects has a distortion phenomenon. An optimization algorithm H-GVSPM based on the Hadamard product is proposed for EIT to reduce the artifacts, and to improve the image quality. The relative conductivity difference between the measured object and the background becomes more distinguishable with the action of the Hadamard product. Simulation and experimental results show that H-GVSPM effectively reduces the influence of artifacts in the reconstructed images. The maximum increase of the correlation coefficient approximately is 15.56% and 22.80% respectively compared with GVSPM and TV method. The maximum reduction of the voltage relative residual is 22.12% in the simulation, and it is 8.12% in the experiment. The standard deviation of conductivity is about 1/10 of the original. This study provides a reference method for improving image quality, and it contributes to the application of EIT in industry, biomedicine, and other fields in the future.

Extracting reference voltages from measurement voltages for oil-water two-phase flow measurement of electrical impedance tomography

To apply electrical impedance tomography (EIT) for heterogeneous two-phase flow measurement, reference voltages have to be measured, which requires the entire pipeline filled with a homogeneous medium. Such process is often time-consuming, costly and infeasible in some cases. Reference voltages acquired initially could not be used later, because of electrical conductivity change of two-phase flow caused by temperature change or chemical reaction. It will be ideal if reference voltages could be extracted from measurement voltages. In this paper, a novel multiple measurements (MMs) method is proposed by data fitting and empirical formula to establish the mapping model from measurement voltages to reference voltages, and the effectiveness of the method is proved by the oil-water two-phase flow experiment. Compared with the measurement-scale feature (MSF) and best homogeneous (BH) methods, the proposed MMs method achieves 6.38% average relative error (RE) and 4.96% average volume fraction relative error (VFRE) in the test set. The work enables the accurate estimation of the reference voltage and thus the accurate measurement of the volume fraction of oil-water two-phase flows, which will broaden the application of EIT in the field of two-phase flow measurement.

Compensation of Contact Impedance Variation for Cerebral Electrical Impedance Tomography

Electrical impedance tomography (EIT) is a promising technique for conductivity distribution reconstruction. Considering the fact that cerebral disease causes variation of conductivity distribution, EIT technique can be used to reflect the pathological change in the cerebral region. This would offer an alternative for the timely diagnosis and treatment of cerebral disease. In cerebral EIT, contact impedance between the electrode and the scalp changes with time, which largely affects the reconstruction quality. To conquer this problem, a compensation method is proposed for voltage data correction when contact impedance varies. By analysis of boundary measurement, the electrode with contact impedance variation and how much contact impedance changes are determined. Together with a prior matrix, voltage data in the case of contact impedance variation can be compensated. Reconstruction of conductivity distribution is implemented by the L1 regularization method. The performance of the proposed method is evaluated under contact impedance variation of a single electrode, a pair of electrodes, and 16 electrodes. In addition, the influence of noise with a signal-to-noise ratio of 40 dB is studied. The results show that the proposed method can effectively suppress the effect of contact impedance change on image quality. Even under the impact of noise, reconstructed images show great improvement. With this method, it is able to monitor cerebral disease in the clinical application of EIT where contact impedance variation is inevitable.

Roles of electrical impedance tomography in lung transplantation.

Lung transplantation is the preferred treatment method for patients with end-stage pulmonary disease. However, several factors hinder the progress of lung transplantation, including donor shortages, candidate selection, and various postoperative complications. Electrical impedance tomography (EIT) is a functional imaging tool that can be used to evaluate pulmonary ventilation and perfusion at the bedside. Among patients after lung transplantation, monitoring the graft's pulmonary function is one of the most concerning issues. The feasible application of EIT in lung transplantation has been reported over the past few years, and this technique has gained increasing interest from multidisciplinary researchers. Nevertheless, physicians still lack knowledge concerning the potential applications of EIT in lung transplantation. We present an updated review of EIT in lung transplantation donors and recipients over the past few years, and discuss the potential use of ventilation- and perfusion-monitoring-based EIT in lung transplantation.

Applications of Electrical Impedance Tomography in Neurology.

Electrical impedance tomography (EIT) is a non-invasive technique utilized in various medical applications, including brain imaging and other neurological diseases. Recognizing the physiological and anatomical characteristics of organs based on their electrical properties is one of the main applications of EIT, as each variety of tissue structure has its own electrical characteristics. The high potential of brain EIT is established in real-time supervision and early recognition of cerebral brain infarction, hemorrhage, and other diseases. In this paper, we review the studies on the neurological applications of EIT. EIT calculates the internal electrical conductivity distribution of an organ by measuring its surface impedance. A series of electrodes are placed on the surface of the target tissue, and small alternating currents are injected. The related voltages are then observed and analyzed. The electrical permittivity and conductivity distributions inside the tissue are reconstructed by measuring the electrode voltages. The electrical characteristic of biological tissues is remarkably dependent on their structures. Some tissues are better electrical conductors than the others since they have more ions that can carry the electrical charges. This difference is attributed to changes in cellular water content, membrane properties, and destruction of tight junctions within cell membranes. EIT is an extremely practical device for brain imaging, capturing fast electrical activities in the brain, imaging epileptic seizures, detecting intracranial bleeding, detecting cerebral edema, and diagnosing stroke.

IPDC Network-Based Image Reconstruction for Damage Detection of CFRP With Electrical Impedance Tomography

The nonlinearity and ill-poseness of image reconstruction has hindered the application of electrical impedance tomography (EIT) in damage detection of Carbon Fiber-Reinforced Polymer (CFRP). To cope with this problem, a dual-channel Inception-Dense-Cbam (IPDC) network model integrated with attention mechanism is proposed. To combine the diversity of feature extraction with the sparseness of the true conductivity distribution, the whole frame is designed to be composed of two parallel branches: the Inception-resnet-cbam (IPRC) branch utilizing multi-scale feature fusion based on the Inception-resnet module and the Dense-Cbam (Dense-CB) branch utilizing feature reuse based on the dense module. Features extracted via the two branches are then fused as outputs. Additionally, to ensure extraction accuracy of damage boundary, convolutional block attention mechanism module (CBAM) is integrated into both branches. Both simulation results and prototype experiments showed that compared with traditional algorithms, the proposed method could effectively reduce the artifacts of damage images, improve accuracy of damage location, and enhance the definition of damage edges. Simulation data obtained with added noise and the data of prototype experiments demonstrated that the IPDC network had the good anti-noise performance in detecting different types of damages.

Machine learning enhanced electrical impedance tomography for 2D materials

Electrical impedance tomography (EIT) is a non-invasive imaging technique that reconstructs the interior conductivity distribution of samples from a set of voltage measurements performed on the sample boundary. EIT reconstruction is a non-linear and ill-posed inverse problem. Consequently, the non-linearity results in a high computational cost of solution, while regularisation and the most informative measurements must be used to overcome ill-posedness. To build the foundation of future research into EIT applications for 2D materials, such as graphene, we designed and implemented a novel approach to measurement optimisation via a machine learning adaptive electrode selection algorithm (A-ESA). Furthermore, we modified the forward solver of a python-based EIT simulation software, pyEIT, to include the complete electrode model (CEM) and employed it on 2D square samples (Liu B et al 2018 SoftwareX 7 304–8; Somersalo E et al 1992 SIAM J. Appl. Math. 52 1023–40). In addition, the deep D-Bar U-Net convolutional neural network architecture was applied to post-process conductivity map reconstructions from the GREIT algorithm (Hamilton and Hauptmann 2018 IEEE Trans. Med. Imaging 37 2367–77; Adler et al 2009 Physiol. Meas. 30 S35). The A-ESA offered around 20% lower reconstruction losses in fewer measurements than the standard opposite–adjacent electrode selection algorithm, on both simulated data and when applied to a real graphene-based device. The CEM enhanced forward solver achieved a 3% lower loss compared to the original pyEIT forward model. Finally, an experimental evaluation was performed on a graphene laminate film. Overall, this work demonstrates how EIT could be applied to 2D materials and highlights the utility of machine learning in both the experimental and analytical aspects of EIT.

Different electrical voltage responses of cement composites under forward and backward current directions

This study investigates the effect of input current direction on the electrical voltages of ultra-high-performance concrete containing fine steel slag aggregates (UA). For the application of electrical impedance tomography based on Laplace’s equation, electrical voltage should be identical under different input current directions. However, the electrical voltages of UA differ significantly corresponding to forward and backward current directions. The percentage differences of real voltages (VRE¯) corresponding to different input current directions are between 4.57% and 76.39% under different boundary conditions such as Wenner, same-boundary, and opposite-boundary conditions. VRE¯ originates from a different internal resistance of the internal path (Rip) corresponding to the input current direction. Among the investigated boundary conditions, the Wenner boundary condition provides the lowest VRE¯ because it leads to the generation of an almost uniform current density between the two inner probes used for measuring voltages than other conditions.

A Regularization-Guided Deep Imaging Method for Electrical Impedance Tomography

Electrical Impedance Tomography (EIT) is a promising technique for the process monitoring. However, due to the inherent nonlinearity and ill- posedness of the image reconstruction in EIT, it remains a great challenge for EIT application. A regularization-guided deep imaging (RGDI) method which combines the mathematical structure of the regularization reconstruction method with deep learning is proposed. As a &#x2018;gray-box&#x2019;, RGDI can not only solve the problem of parameter selection in regularization algorithms, but also depend less on the complete database. The structure of RGDI is composed of three sequentially connected modules, i.e. data pre-processing, feature extraction and image reconstruction. The feature extraction module contains five blocks, and each block consists of a regularization network and a convolutional neural network (CNN) with the function of an &#x2018;encoding-decoding&#x2019;. To obtain more accurate reconstruction images, a two-layer fully connected neural network (FCNN) is applied. During the training of RGDI network, prior information of structural features is added to avoid the overfitting problem and accelerate its convergence. The experimental results illustrate that the image quality of RGDI is better than that of numerical imaging methods and several reported deep learning methods.

Damage detection in non-planar carbon fiber-reinforced polymer laminates via electrical impedance tomography with surface-mounted electrodes and directional sensitivity matrices

Carbon fiber reinforced polymers (CFRPs) have emerged as promising alternatives to traditional metals and alloys in weight-conscious applications such as aerospace due to their superior strength-to-weight properties. However, these materials are vulnerable to complex and difficult-to-predict sub-surface damages. Therefore, damage detection modalities are important for assuring the safety of CFRP-based structures and components. Particularly for in-service structural health monitoring (SHM), it would be desirable to utilize an inherent property of these materials, such as electrical conductivity, as a means of detecting and localizing damages. In this respect, electrical impedance tomography (EIT) is a promising SHM modality. Studies to-date involving the application of EIT to CFRPs are limited due to the challenges associated with high electrical anisotropy. Furthermore, most studies have only considered flat laminates with unrealistic edge-placed electrodes. In this paper, we advance the state of the art by studying the application of EIT to non-planar CFRP geometries using surface-mounted electrodes. Additionally, we present a modified EIT sensitivity matrix formulation which intrinsically incorporates the electrical anisotropy of the material by forming the sensitivity matrix via three approaches – with respect to i) a scalar multiple of the conductivity tensor, ii) the in-plane conductivity, and iii) the through-thickness conductivity. It was found that both through-hole and impact damages can be adeptly identified with a combination of surface-mounted electrodes and a sensitivity matrix formed with respect to either a scalar multiple of the conductivity tensor or the in-plane conductivity. The results presented here are an important step towards the transition of EIT-based diagnostics to actual CFRP structures.

Technique Innovation and Clinical Application of Electrical Impedance Tomography: Bibliometric Research from 2001 to 2020.

BackgroundElectrical impedance tomography (EIT) is a new test that has been widely used by clinicians in recent years at bedside or in ICU wards. Studies and publications on EIT increased quickly and the hotspot trends changed; however, the overview and characteristics of such studies have not yet been reported. Therefore, we have attempted to interpret the evolution of EIT and to anticipate its possible future clinical use by conducting a statistical analysis of EIT articles over the past 20 years.Material/MethodsWe analyzed EIT-related articles from 2020 and the 20 years prior, sourced from the Web of Science database. The data collected included the number of articles published, the classification of the articles, basic information, and author affiliation.ResultsOur study retrieved a total of 1427 EIT-related articles through screening, with the most articles published from Chinese authors and the Chinese Air Force Military Medical University, and the most cited article type being EIT-related basic research. Most articles on EIT have been published in the journal Physiological Measurement. Furthermore, the hotspots and research trends of EIT have changed from basic innovation development to clinical application in the past 20 years.ConclusionsThis paper presents a statistical analysis of articles on EIT over the last 20 years, focusing on trends from the mechanisms of EIT to its clinical use.