Electrical Impedance Tomography System Research Articles

Portable electrical impedance tomography (EIT) system stages non-alcoholic fatty liver disease for potential screening and monitoring at home.

This study demonstrates the feasibility of predicting NAFLD using multi-spectral electrical impedance tomography (EIT), group source separation, constant reference EIT and anthropometric measures. Vibration-controlled Transient Elastography (VCTE) Controlled Attenuated Parameter (CAP; n = 121) and magnetic resonance imaging-proton density fat fraction (MRI-PDFF; n = 34) achieved a sensitivity of 70.9% and specificity of 73.8% with our CAP predicting model and sensitivity of 77.8% and specificity of 80.0% with our MRI-PDFF predicting model. In summary, a portable EIT can be a cost-effective and self-administrable alternative for widespread home-based and community-based diagnostic screening and treatment monitoring of NAFLD.Clinical Relevance- Portable multi-spectral EIT system has the sensitivity and specificity to potentially unlock biomedical imaging in telemedicine for home-based and community-based screening, staging and monitoring for NAFLD.

A Comprehensive Noise Model for Wideband and High Frame Rate Electrical Impedance Tomography Systems

Clinical applications call for comprehensive performances of electrical impedance tomography (EIT) system, including sufficient signal-to-noise ratio (SNR), high frame-per-second (Fps), and wide bandwidth. In this article, thorough analyses of switching noise in an EIT system are presented. The energy of switching noise is split into two parts: spurs and extra quantization noise of analog-to-digital converter (ADC). Based on the characteristics of switching noise, a comprehensive noise model of EIT systems with digital matched filter, is proposed. The impacts of switching noise, frame rate, and bandwidth of an EIT system are considered in the noise model. Simulation and experiment conducted in a laboratory-built EIT system are employed to validate the noise model. Test results show that the EIT system can obtain an average SNR greater than 94dB with a frame rate of 31Fps and a bandwidth of 2MHz, and an average SNR greater than 90dB with a frame rate of 31 Fps and a bandwidth of 10MHz, under the guidance of the proposed model. Overall, this work provides a novel and comprehensive noise model by including the impacts of switching noise, frame rate, and bandwidth, which can serve as a guide to improve the comprehensive performance of an EIT system for clinical applications.

SAT-439 - Wearable technology utilizing an electrical impedance tomography (EIT) system for hepatic steatosis quantification and the role in ambulatory monitoring

SAT-439 - Wearable technology utilizing an electrical impedance tomography (EIT) system for hepatic steatosis quantification and the role in ambulatory monitoring

Wireless Electrical Impedance Tomography for Pleural Effusion Analysis

Lung diseases, such as pleural effusion, may seriously affect the life quality of the patients. In clinical, the lung auscultation method and some lung imaging approaches, such as chest X-ray, computed tomography (CT), positron emission tomography (PET), magnetic resonance imaging (MRI), are frequently used to evaluate the state of pleural effusion. However, most of the above imaging approaches may encounter the radiation issue, and require the experienced operator and the relative expensive cost. The technique of electrical impedance tomography (EIT) is a novel imaging approach providing the rapid, noninvasive and radiation-free measurement. Based on the EIT technique, a novel wireless EIT system was proposed for pleural effusion analysis in this study. Different from other EIT systems requiring many electrode-leads and disposable Ag/AgCl electrodes, conductive foam-based dry electrodes and the specific belt mechanism design were designed to improve the convenience of use and provide a good stretchability to fit different body sizes so that it can be suitable to get better human signals and long-term use. Moreover, several indexes related to pleural effusion were also defined from the change of the EIT image of the lungs during the breathing activity. Finally, the phantom and human experiments were tested to validate the system performance. The experimental results show the pleural effusion state exactly reflected on the defined indexes extracted from the EIT image.

An MSA-Net Algorithm for Direct Estimation of Gas Holdup Based on Electrical Impedance Tomography System

Electrical impedance tomography (EIT) is a nondestructive testing technique, which has great potential to be used for the detection of the cross-sectional gas-holdup ratio (CGR) of gas–liquid two-phase flow. Due to the nonlinear and ill-posed characteristics of the EIT image reconstruction, the accuracy of image-based CGR estimation methods is poor. To improve the detection accuracy, a learning-based direct CGR estimation method is proposed in this study, which applies a novel multiscale attention network (MSA-Net) to directly estimate the CGR from the voltage measurements. Multiscale feature extraction and residual structure are introduced into MSA-Net to fully extract the features, and attention unit (AU) is also used to capture high-frequency features. As a result, accurate and robust CGR estimation can be realized by MSA-Net. ResNet18 and single-scale attention network (SSA-Net) are selected for comparison. The simulation results indicate that the relative error (RE <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$_{\text {CG}}{)}$ </tex-math></inline-formula> of CGR estimated by the learning-based direct CGR estimation method is far lower than that of the image-based CGR estimation methods. Compared with ResNet18 and SSA-Net, the RECG of the CGR estimated by MSA-Net is lower, and the range is 0.07%–0.36%. Moreover, MSA-Net shows good noise robustness simultaneously. The experiment is also set to test MSA-Net. The range of RECG is 0.1%–1.12%, which further verifies the practicability of the proposed learning-based direct CGR estimation method.

An INN-Based Solution for the Underdetermined Image Reconstruction Problem in EIT

Electrical impedance tomography (EIT) has been widely concerned in online nondestructive testing of carbon fiber-reinforced polymers (CFRPs) due to its significant advantages of high real-time performance, zero radiation, simple structure, and low cost. However, its inverse problem is highly underdetermined and nonlinear, thus limiting its imaging quality and application scope. Based on the architecture of an invertible neural network (INN), an INN image reconstruction algorithm with latent output variables is proposed in this article. With latent output variables, the information lost during training is reused, and a unique input can be determined by sampling latent output variables and predicted outputs. In addition, the forward and reverse processes are combined together to weaken the dependency of effective output and latent output variables, reduce the errors between input and output domains, and solve the underdetermination and nonlinearity problems of EIT. The simulation and experimental results showed that compared with the traditional algorithms and the convolution neural network (CNN) network algorithm, the INN can accurately reconstruct the damage location, yield a clearer damaged edge, and reduce artifacts. Noise-added simulations and prototype experiments showed that the INN achieves both good imaging quality and enhanced noise immunity, to improve the performance of the EIT system in CFRP laminate inspection.

Electrical Impedance Tomography: From the Traditional Design to the Novel Frontier of Wearables.

Electrical impedance tomography (EIT) is a medical imaging technique based on the injection of a current or voltage pattern through electrodes on the skin of the patient, and on the reconstruction of the internal conductivity distribution from the voltages collected by the electrodes. Compared to other imaging techniques, EIT shows significant advantages: it does not use ionizing radiation, is non-invasive and is characterized by high temporal resolution. Moreover, its low cost and high portability make it suitable for real-time, bedside monitoring. However, EIT is also characterized by some technical limitations that cause poor spatial resolution. The possibility to design wearable devices based on EIT has recently given a boost to this technology. In this paper we reviewed EIT physical principles, hardware design and major clinical applications, from the classical to a wearable setup. A wireless and wearable EIT system seems a promising frontier of this technology, as it can both facilitate making clinical measurements and open novel scenarios to EIT systems, such as home monitoring.

Real-Time Wrist Motion Decoding with High Framerate Electrical Impedance Tomography (EIT).

Human wrist motion decoding with a biological-signal-based interface is a key technique in the upper-limb exoskeleton and prosthesis control. One critical issue in this field is achieving high recognition precision and fast time response while against external disturbances of sensor re-wearing. In this study, we proposed a high-framerate Electrical Impedance Tomography (EIT) system combined with an adaptive recognition algorithm for real-time wrist kinematics decoding. The high-framerate EIT system was developed by a parallel stimulation-measurement sequence, and the sampling rate was as high as 104 Hz. Compared to the most widely used myoelectric techniques, the EIT-based interface can provide extra deep muscular spatial information with similar surface electrodes. It greatly benefited the subsequent recognition algorithms, in which the key EIT regions indicating muscle morphology kept consistent after an arbitrary sensor re-donning. The designed adaptive algorithm achieved equally high performance with an automatic update of the classifier mean values with a fast self-operated calibration process. We validated the approach on 12 subjects with a 2-dimensional Fitts' law test. The wrist gestures and joint angles were mapped to the direction and speed of the cursor movement, respectively. The average throughputs (TPs) of Fitts' law tests were 1.0269 ± 0.0971 bits/s and 1.0095 ± 0.0931 bits/s without and with sensor re-donning, respectively, which were comparable to the TPs of sEMG-based studies. The results showed the promise of the EIT-based interface on real-time human motion intent recognition. Future endeavors are worth being paid in this direction for more complicated robotic tasks.

An Image Construction Based on the VTLF Fusion Technique for Oil–Water Two-Phase Flow of Noncontact Electrical Impedance Tomography

Electrical tomography (ET) has attracted extensive attention in the petroleum multiphase flow field due to its advantages of nonintrusion, nonradiation, and simple equipment structure. Some contactless impedance imaging methods, such as capacitively coupled electrical resistance tomography (CCERT) and phase-based dielectric spectroscopy tomography, or displacement current phase tomography (DCPT), have been studied for the parameter measurement of two-phase flow. The mentioned research mainly focuses on the separate imaging by real part, imaginary part, or phase information of the voltage/current. However, the distribution change of media in the region of interest (RoI), especially the presence of high permittivity and conductivity materials, involves the distribution change of conductivity, permittivity, and loss factor. Developing a proper image fusion technique based on the three components can provide more comprehensive image information for tomographic imaging. This article proposed an improved logic filter image fusion algorithm based on a variable threshold (VTLF), and a noncontact electrical impedance tomography (NEIT) system for imaging water–oil two-phase flow is carried out. Image reconstruction is implemented by the VTLF image fusion algorithm under different media distributions, conductivities, and excitation frequencies, and the image fusion algorithms of a weighted average (WA) and logic filter (LF) are also used for comparison. The simulation results show that the reconstructed images by the proposed fusion method of VTLF had the lowest artifacts and the highest correlation coefficient (above 0.7) compared with other methods. Also, in the experiment, the VTLF also got the best performance, so it is effective to improve the reconstruction accuracy.

Design and analysis of a multifrequency electrical impedance tomography-based pressure sensor

Electrical impedance tomography (EIT) is a nondestructive imaging technique for estimating the electrical impedance distribution within a conductive process. EIT is employed in many fields, including medical, geology, and industrial. The pressure sensor design is one of the most recent applications of this method. In this application, a thin and flexible conductor uses as the pressure sensor to apply physical pressure on a specific region and cause local variations in the electrical impedance of that region. We present the principles of designing and implementing a multifrequency EIT system for pressure sensing and imaging. Considering this, the theoretical topics and the hardware components of an EIT system are described. Subsequently, with the aim of examining the designed system, the conductive fabric was employed as the pressure sensor. In numerous experiments, the reconstructed electrical impedance images of the applied pressure demonstrated that there is a relatively linear relation between the pressure and the impedance variations at each frequency. The experimental results reveal that the designed system successfully detects the pressure area and determines its magnitude in the applied pressure region.

Compensation for Electrode Detachment in Electrical Impedance Tomography with Wearable Textile Electrodes.

Electrical impedance tomography (EIT) is a radiation-free and noninvasive medical image reconstruction technique in which a current is injected and the reflected voltage is received through electrodes. EIT electrodes require good connection with the skin for data acquisition and image reconstruction. However, detached electrodes are a common occurrence and cause measurement errors in EIT clinical applications. To address these issues, in this study, we proposed a method for detecting faulty electrodes using the differential voltage value of the detached electrode in an EIT system. Additionally, we proposed the voltage-replace and voltage-shift methods to compensate for invalid data from the faulty electrodes. In this study, we present the simulation, experimental, and in vivo chest results of our proposed methods to verify and evaluate the feasibility of this approach.

The influence of reference electrode in electrical impedance tomography

BackgroundSome electrical impedance tomography (EIT) devices equip reference electrodes. In practice, it is not uncommon to observe high contact impedance for the reference electrode. The influence of bad contact reference electrode on data quality is unknown. The study aimed to investigate the influence of reference electrode on EIT image reconstruction. MethodsThirty lung healthy volunteers were prospectively examined with EIT. The subjects were spontaneously breathing in supine position. Three scenarios were constructed: 1. Normal measurement; 2. Reference electrode disconnected without recalibration; 3. Reference electrode disconnected, and the measurement restarted after recalibration of the system. EIT-based parameters measuring spatial and temporal ventilation distributions were calculated and compared. A so-call deviation score was calculated to assess the differences in EIT parameters between scenarios 2 and 1, between 3 and 1. ResultsThe absolute differences for all parameters were significantly higher than zero (p < 0.01 for all parameters and scenarios). Deviation score for scenario 2 was 4.5 ± 3.5. Four subjects had a deviation score of 0 in scenario 2 and five subjects had a score of 1. The deviation in scenario 3 was higher (6.1 ± 3.1). No subjects had a score of 0 and only two subjects had a score of 1. ConclusionsFor EIT systems that equips with reference electrode, it is important to ensure the proper contact and functionality of the reference electrode. The EIT data quality would remain unchanged in only a small portion of subjects.

Affordable, portable and self-administrable electrical impedance tomography enables global and regional lung function assessment

Accessibility of diagnostic screening and treatment monitoring devices for respiratory diseases is critical in promoting healthcare and reducing sudden complications and mortality. Spirometry is the standard for diagnosing and monitoring several lung diseases. However, it lacks regional assessment capabilities necessary for detecting subtle regional changes in certain diseases. It also requires challenging breathing maneuvers difficult for elderlies, children, and diseased patients. Here, we actualized an affordable, portable, and self-administrable electrical impedance tomography (EIT) system for home-based lung function assessment and telemedicine. Through simultaneous EIT-spirometry trials on healthy subjects, we demonstrated that our device can predict spirometry indicators over a wide range and can provide regional mapping of these indicators. We further developed a close-to-effortless breathing paradigm and tested it by longitudinally monitoring a COVID-19 discharged subject and two healthy controls with results suggesting the paradigm can detect initial deterioration followed by recovery. Overall, the EIT system can be widely applicable for lung function screening and monitoring both at homes and clinics.

CLOSE-TO-EFFORTLESS BREATHING PARADIGM MAPS GLOBAL AND REGIONAL LUNG FUNCTION USING ELECTRICAL IMPEDANCE TOMOGRAPHY CROSS SECTIONALLY AND LONGITUDINALLY

CLOSE-TO-EFFORTLESS BREATHING PARADIGM MAPS GLOBAL AND REGIONAL LUNG FUNCTION USING ELECTRICAL IMPEDANCE TOMOGRAPHY CROSS SECTIONALLY AND LONGITUDINALLY

A Low-Power Recursive I/Q Signal Generator and Current Driver for Bioimpedance Applications

This brief presents a power-efficient quadrature signal generator and current driver application-specific integrated circuit (ASIC) for bioimpedance measurements in an electrical impedance tomography system for monitoring lung function. The signal generator is realized by a digital recursive signal oscillator with the ability of generating quadrature signals over a wide frequency range. The generated in-phase signal is applied to a current driver. It uses a balanced current-mode feedback architecture that monitors the output current through a feedback loop to minimize common-mode voltage build-up at the injection site. The quadrature signals can be used for I/Q demodulation of the measured bioimpedance. The ASIC was designed in TSMC 65 nm technology occupying an area of 0.21 mm2. The current driver can generate up to 0.7 mA <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$_{\text {p-p}}$ </tex-math></inline-formula> current up to 200 kHz and consumes 2.7 mW power using ±0.8 V supplies.

A new portable electrical impedance tomography system for measuring two-dimensional stem water content distribution

Stem water content (StWC) is an important parameter for characterizing plant–water relations. Most sensors and methods can measure only one-dimensional StWC, and those measuring two-dimensional (2D) StWC distributions are expensive. In this study, we developed a low-cost and portable electrical impedance tomography (EIT) system to measure 2D StWC distributions in vivo. The performance of the EIT system was tested in a laboratory setting. An analog stem made of gypsum and two true stems cut from apple trees were used to calibrate the relationship between the average StWC (θv¯) and average change in electrical conductivity (Δσ¯) determined by the EIT system during the stepwise drying procedure. Evaporation experiments were conducted in the laboratory with hollow analog and cut stems to detect variations in StWC (Δθv) and its 2D distribution. Finally, the EIT system was installed in an apple tree for the in vivo monitoring of the diurnal variation of the 2D StWC distribution. The results indicated that the optimal measurement frequency of the EIT system was 47 kHz. After correcting for the effect of temperature, the average relative error of the measurement was reduced (approximately 80.21%). The effective height of the volume sensitivity of the EIT system was approximately 6 cm. The relationship between θv¯ and Δσ¯ showed a strong positive correlation, with all R2 values higher than 0.9808 for the analog stem and two cut stems. Moreover, the actual and EIT-estimatedθv¯ of the analog and cut stems during the evaporation experiments agreed well (R2 = 0.9367 and 0.9610, respectively), which validated the feasibility of the EIT system for StWC measurements. Furthermore, the in vivo EIT-estimated Δθv distribution of the apple tree was highly consistent with the regulation of diurnal variation in StWC in a greenhouse under irrigation and drought stress conditions. The new portable EIT system was able to measure the 2D StWC distribution and has great potential for application in drought monitoring of plant in the near future.

Imaging spatial-temporal distribution of moisture in intact maize ear

The moisture distribution in maize ears during the growth period has a major impact on crop quality and post-harvest production costs and is an important indicator for plant breeding. An electrical impedance tomography system with a resilient electrode array was designed to fit around a maize ear, and featured an adjustable diameter that can be adapted to the growth of the ear. We used the system to monitor the dehydration of an intact maize ear in situ over 5 days and observe the changes in moisture distribution. The high-contrast images correlated with the moisture distribution in the maize ear. We also created a 3D model to visualize the distribution of moisture content within the maize ear. Our method provides a novel tool to monitor the spatial-temporal dynamics of the water content within the maize ear in situ.

Electrical Impedance Tomography Based on Grey Wolf Optimized Radial Basis Function Neural Network.

Electrical impedance tomography (EIT) is a non-invasive, radiation-free imaging technique with a lot of promise in clinical monitoring. However, since EIT image reconstruction is a non-linear, pathological, and ill-posed issue, the quality of the reconstructed images needs constant improvement. To increase image reconstruction accuracy, a grey wolf optimized radial basis function neural network (GWO-RBFNN) is proposed in this paper. The grey wolf algorithm is used to optimize the weights in the radial base neural network, determine the mapping between the weights and the initial position of the grey wolf, and calculate the optimal position of the grey wolf to find the optimal solution for the weights, thus improving the image resolution of EIT imaging. COMSOL and MATLAB were used to numerically simulate the EIT system with 16 electrodes, producing 1700 simulation samples. The standard Landweber, RBFNN, and GWO-RBFNN approaches were used to train the sets separately. The obtained image correlation coefficient (ICC) of the test set after training with GWO-RBFNN is 0.9551. After adding 30, 40, and 50 dB of Gaussian white noise to the test set, the attained ICCs with GWO-RBFNN are 0.8966, 0.9197, and 0.9319, respectively. The findings reveal that the proposed GWO-RBFNN approach outperforms the existing methods when it comes to image reconstruction.

High Frame Rate Electrical Impedance Tomography System for Monitoring of Regional Lung Ventilation.

This paper describes the development of a compact high frame rate passive electrical impedance tomography system. The injected current amplitude and frequency can be adjusted to fit any EIT application. Measured results show that the system is capable of high frame rate of 89 fps and has power consumption of 1.7 W. It has automatic gain control that reduces noise and improves the quality of the measured EIT image. A comparison is made with other EIT systems to show the potential of the developed system. Clinical Relevance- The developed EIT system has application in the clinical assessment of neonatal and SARS-Co V-2 patients. In these applications there is an urgent need for a low cost bedside non-invasive imaging system to continuously monitor dynamic changes in regional lung ventilation.

A Rapid, Low-Cost, and High-Precision Multifrequency Electrical Impedance Tomography Data Acquisition System for Plant Phenotyping

Plant phenotyping plays an important role for the thorough assessment of plant traits such as growth, development, and physiological processes with the target of achieving higher crop yields by the proper crop management. The assessment can be done by utilizing two- and three-dimensional image reconstructions of the inhomogeneities. The quality of the reconstructed image is required to maintain a high accuracy and a good resolution, and it is desirable to reconstruct the images with the lowest possible noise. In this work, an electrical impedance tomography (EIT) data acquisition system is developed for the reconstruction and evaluation of the inhomogeneities by utilizing a non-destructive method. A high-precision EIT system is developed by designing an electrode array sensor using a cylindrical domain for the measurements in different planes. Different edible plant slices along with multiple plant roots are taken in the EIT domain to assess and calibrate the system, and their reconstructed results are evaluated by utilizing an impedance imaging technique. A non-invasive imaging is carried out in multiple frequencies by utilizing a difference method of reconstruction. The performance and accuracy of the EIT system are evaluated by measuring impedances between 1 and 100 kHz using a low-cost and rapid electrical impedance spectroscopy (EIS) tool connected to the sensor. A finite element method (FEM) modeling is utilized for image reconstruction, which is carried out using electrical impedance and diffuse optical tomography reconstruction software (EIDORS). The reconstruction is made successfully with the optimized results obtained using Gauss–Newton (GN) algorithms.