Electrical Impedance Tomography Technique Research Articles

Development of a Low-Cost and Compact Medical Image Reconstruction Platform Based on EIT Technique

Electrical impedance tomography (EIT) is an important imaging modality in non-invasive and radiation-free image reconstruction techniques. With the advantages of real-time imaging capabilities, portability, and safety features, EIT has acquired substantial significance as a valuable technique for lung patients during the COVID-19 pandemic. Nevertheless, a significant challenge arises when many COVID-19 patients need to utilize EIT devices for monitoring and treatment. To address this problem, a low-cost and compact wireless communication EIT platform was developed for impedance image reconstruction and real-time monitoring of human lung operations. Since the system was structured as a platform, it can be readily replicated and scaled up in emergencies. The proposed system consisted of 16 electrodes placed on the chest of the lung patients. A stable current was injected into the object through a pair of adjacent electrodes, while the voltage was measured on the remaining electrode pairs using a lock-in amplifier. The electrical impedance between each electrode pair was determined based on Ohm’s law. Experimental results showed that the system could reconstruct the shape and the position of objects inside the medium with high accuracy. The average blur radius and percentage of the location error of the reconstructed image were 1.01 ± 0.28 and 5.18 ± 1.35%, respectively. Furthermore, images showing the operation of the human lung were also observed by the proposed system, confirming the possibility of using the system in clinical applications. The proposed EIT system with wireless communication is a potential study for large-scale implementation, particularly in response to the COVID-19 pandemic.

Development of an Electrical Impedance Tomography Coupled Surgical Stapler for Tissue Characterization.

This study explores the feasibility of coupling Electrical Impedance Tomography (EIT) to a standard-of-care laparoscopic surgical stapler, stapler+EIT, with the long-term goal of enabling intraoperative tissue differentiation for tumor margin detection. Two custom printed-circuit-board-based electrode arrays with 60 and 8 electrodes, respectively, matching the stapler geometry, served as the jaws of an electrode-integrated surrogate stapler+EIT device. The device was evaluated through a series of simulations and bench-top imaging experiments of agar-gel phantoms and bovine tissue samples to evaluate the technique and determine optimal imaging parameters. Electrodes localized to only one jaw (the 60-electrode side) of the stapler outperformed a dual-jaw distribution of electrodes. Using this one-sided electrode array, reconstructions achieved an Area-Under-the-Curve (AUC) ≥ 0.94 for inclusions with conductivity contrasts of ≥30% for any size considered on measured agar-gel tests, and an AUC of 0.80 for bovine tissue samples. A stapler+EIT algorithm has been tuned and evaluated on challenging phantom tests and demonstrated to produce accurate reconstructions. This work is an important step in the development of a surgical stapler+EIT technique for margin assessment.

Finite element modeling of the electrical impedance tomography technique driven by machine learning

To create a human-like skin for a robotic application, current touch sensor technologies have a few drawbacks. Electrical Impedance Tomography (EIT) is a candidate for this application due to its applicability over complex geometries; nevertheless, it has accuracy concerns. This study employs artificial neural networks (ANNs) to investigate the accuracy and capability of EIT-based touch sensors. A finite element (FE) model is utilized to solve the forward EIT problem while simultaneously determining the system’s comprehensive mechanical response. The FE model is comprised of a polyurethane (PU) foam domain, a conductive spray layer and a set of sixteen electrodes. To replicate the process of touching the sensor body, a punch of varying diameters and touch forces is utilized. The mechanical response of the sensor body is modeled using the hyperfoam material model calibrated through experimental uniaxial and shear test data, while the electric conductivity of the sprayed skin surface is obtained experimentally as function of applied strain. The viscoelastic behavior of the PU foam material is also obtained experimentally. These experimental data were implemented in the FE model through user subroutines to model the mechanical and electrical properties of the sensor in the EIT forward problem. The traditional EIT inverse problem image reconstruction was replaced utilizing ANNs as an alternative to extract mechanics based parameters. The ANNs were created to predict the spatial coordinates of the touch point, and they were proven to be extremely accurate. Using the EIT voltage readings as input, the ANNs were utilized to forecast the system’s mechanical behavior such as contact pressure, contact area, indentation depth, and touching force.

Piezoresistive Performance of a Compliant Scalable Sensor Made of Low‐Cost Exfoliated Graphite Polymer Composites

Compliant sensors have drawn considerable interests in human–robot interactions. However, it is still challenging to obtain reliable and reproducible performance at reduced costs, especially for scaled‐up sensing applications. This work investigates the piezoresistive performance of a low‐cost, scalable sensor, which is made by spray coating exfoliated graphite natural rubber latex composites (EG/latex) over an elastomeric substrate. The EG/latex sensor provides a uniform distribution in the sheet resistance (variation below 4%) over a large 10 cm × 10 cm area. The stabilized piezoresistive response under cyclic tests is found highly related to both the filler concentration and the strain region. The 8 wt% and the 10 wt% sensors show nonlinear piezoresistive response, while the 25 wt% sensor exhibits the best linearity with a high gauge factor (7.6) and the lowest hysteresis (0.043) under a maximum strain of 60%. The distinct piezoresistive behavior is found attributed to the different levels of microbreakage under the applied strain. Scalable sensing performance is demonstrated on a hand‐size glove spray‐coated with 25 wt% EG/latex. Effective localization of contacts over the continuous sensing glove is achieved via the technique of electrical impedance tomography, validating the potential of the cost‐competitive EG/latex sensor for scalable sensing applications.

Image Reconstruction Using Supervised Learning in Wearable Electrical Impedance Tomography of the Thorax.

Electrical impedance tomography (EIT) is a non-invasive technique for visualizing the internal structure of a human body. Capacitively coupled electrical impedance tomography (CCEIT) is a new contactless EIT technique that can potentially be used as a wearable device. Recent studies have shown that a machine learning-based approach is very promising for EIT image reconstruction. Most of the studies concern models containing up to 22 electrodes and focus on using different artificial neural network models, from simple shallow networks to complex convolutional networks. However, the use of convolutional networks in image reconstruction with a higher number of electrodes requires further investigation. In this work, two different architectures of artificial networks were used for CCEIT image reconstruction: a fully connected deep neural network and a conditional generative adversarial network (cGAN). The training dataset was generated by the numerical simulation of a thorax phantom with healthy and illness-affected lungs. Three kinds of illnesses, pneumothorax, pleural effusion, and hydropneumothorax, were modeled using the electrical properties of the tissues. The thorax phantom included the heart, aorta, spine, and lungs. The sensor with 32 area electrodes was used in the numerical model. The ECTsim custom-designed toolbox for Matlab was used to solve the forward problem and measurement simulation. Two artificial neural networks were trained with supervision for image reconstruction. Reconstruction quality was compared between those networks and one-step algebraic reconstruction methods such as linear back projection and pseudoinverse with Tikhonov regularization. This evaluation was based on pixel-to-pixel metrics such as root-mean-square error, structural similarity index, 2D correlation coefficient, and peak signal-to-noise ratio. Additionally, the diagnostic value measured by the ROC AUC metric was used to assess the image quality. The results showed that obtaining information about regional lung function (regions affected by pneumothorax or pleural effusion) is possible using image reconstruction based on supervised learning and deep neural networks in EIT. The results obtained using cGAN are strongly better than those obtained using a fully connected network, especially in the case of noisy measurement data. However, diagnostic value estimation showed that even algebraic methods allow us to obtain satisfactory results.

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.

Evaluation of lung function in a German single center cohort of young patients with sickle cell disease using EIT and standard techniques.

Sickle cell disease (SCD) is a very common autosomal recessive hemoglobinopathy leading to multiple pulmonary complications that are closely associated with mortality. The pathophysiology of chronic pulmonary involvement is not yet fully understood and no specific therapies are available. The aim of this cross-sectional study was to characterize the lung function of children and young adolescents with SCD in a German single-center cohort and to extend conventional lung function testing by the use of a new imaging method. We performed spirometry and body plethysmography in 35 children and young adults with hemoglobin SS, SC, S/β-thalassemia as well as 50 controls. These data were compared with clinical characteristics and typical laboratory parameters of hemolysis and disease activity in SCD. To identify lung inhomogeneities, for example due to atelectasis, hyperinflation, air trapping or vascular occlusions, we used the promising new method of electrical impedance tomography (EIT) and calculated global inhomogeneity indices. Lung function of patients with SCD was significantly reduced compared to that of healthy controls. When the result was found to be pathological, the most commonly observed type of breathing disorder was classified as restrictive. Laboratory parameters showed typical features of SCD including decreased levels of hemoglobin and hematocrit and elevated levels of leucocytes, platelets, lactate dehydrogenase and total bilirubin. However, there was no correlation between blood values and reduced lung function. Electrical impedance tomography (EIT) revealed no abnormalities in SCD patients compared to healthy controls. In particular, we were unable to demonstrate any regional inhomogeneities in lung ventilation. In our study, SCD patients showed impaired lung function, with a relevant percentage of patients suffering from restrictive breathing disorder. Signs of obstruction could not be detected. Electrical impedance tomography (EIT) measurements revealed no unevenness that would suggest air entrapment, blockage of blood vessels, excessive inflation, obstruction, or other forms of lung disease. Additionally, the reduction in lung function observed in SCD patients was not related to the disease severity or laboratory test results.

Selection of optimal hyperparameter for detecting multiple contacts from large-area tactile sensors based on electrical impedance tomography

Large-area tactile sensors based on the technique of electrical impedance tomography (EIT) has drawn considerable interest in human-robot interactions. However, due to the ill-posed condition, it is challenging to differentiate between the real contacts and the artifacts from the reconstructed image. To address this issue, a new method to select an optimal hyperparameter that tunes the amount of regularization is developed in the context of tactile sensing. The optimal hyperparameter is determined to be the minimum value to obtain a stabilized number of sub-regions in the reconstructed image. The proposed method not only guarantees a correct detection on the number of multiple contacts at the minimum amount of regularization, but also provides a proper range of hyperparameters. The optimal hyperparameter is found in a chair-shape relation with the boundary signal-to-noise ratio (SNR), by varying the noise level of the hardware in simulation. The optimal hyperparameter decreases significantly when the boundary SNR increases between 5 ∼ 10 dB and 25 ∼ 35 dB, and keeps almost unchanged when SNR is between 10 ∼ 25 dB. The chair-shape relation also holds for contact conditions with varied intensities and sizes. Experimental validations on the proposed method are conducted on a compliant piezoresistive tactile sensor made of exfoliated graphite polymer composites. By varying the number of contacts in experiments, the relation between the optimal hyperparameter and the boundary SNR is consistent with the chair-shape curve. The investigation made in this work helps improve the performance of identifying multiple contacts from tactile sensors based on electrical impedance tomography.

Feasibility Study of Early Pressure Ulcer Detection Using Electrical Impedance Tomography

Pressure ulcers are chronic skin wounds with a particularly high morbidity and a relatively low recovery rate. At present, there is still a lack of a portable and visual real-time monitoring system for early pressure ulcers. Electrical impedance tomography (EIT) is a kind of functional imaging technique that can diagnose the healthy condition in the measured tissues by visualizing the distribution of bioelectrical impedance parameters. According to the principle of electrical characteristic of pressure ulcers, i.e., changes in physiological and pathological information of tissues caused by pressure lead to changes in electrical impedance of tissues, a non-invasive imaging system of pressure ulcers based on EIT with novel flexible electrode array is proposed. Two kinds of 3D finite element simulation models are constructed to discuss the measurement effects of the flexible array on simulated pressure ulcers at different locations, depths, and with different sizes. The effects of different imaging algorithms on imaging results are also compared and analyzed. Physical phantom model experiments and preliminary experiments on human skin are conducted. Both simulations and physical experiments indicate that EIT technique is excepted to realize a safer, portable, low-cost and visual real-time monitoring system for early pressure ulcers.

Imaging of conductivity distribution based on a combined reconstruction method in brain electrical impedance tomography

Electrical impedance tomography (EIT) is a promising technique in medical imaging. With this technique, pathology-related conductivity variation can be visualized. Nevertheless, reconstruction of conductivity distribution is a severely ill-posed inverse problem which poses a great challenge for the EIT technique. Especially in brain EIT, irregular and multi-layered head structure along with low-conductivity skull brings more difficulties for accurate reconstruction. To address such problems, a novel reconstruction method which combines Tikhonov regularization with denoising algorithm is proposed for imaging conductivity distribution in brain EIT. With the proposed method, image reconstruction of intracerebral hemorrhage in different brain lobes of a three-layer head model is conducted. Besides, simultaneous reconstruction of intracerebral hemorrhage and secondary ischemia is performed. Meanwhile, the impact of noise is investigated to evaluate the anti-noise performance. In addition, image reconstructions under head shape deformation are performed. The proposed reconstruction method is also quantitatively estimated by calculating blur radius and structural similarity. Phantom experiments are carried out to further verify the effectiveness of the proposed method. Both qualitative and quantitative results have demonstrated that the proposed combined method is superior to Tikhonov regularization in imaging conductivity distribution. This work would provide an alternative for accurate reconstruction in EIT based medical imaging.

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.

Application of constrained coefficient fuzzy linear programming in medical electrical impedance tomography

ABSTRACT Electrical impedance tomography (EIT) is an imaging technique that realizes the image reconstruction of conductivity distribution in the field. The existing EIT algorithms ignore the hidden fuzzy features during imaging, making the EIT technique exhibit a high degree of uncertainty, imprecision, incompleteness, and inconsistency in the actual use process, resulting in a low spatial resolution of the reconstructed images. In order to solve this problem, we introduce fuzzy linear programming into EIT imaging. On the basis of analyzing the fuzzy features of EIT in detail, a new model of fuzzy optimization is built up, whose optimal solution is obtained by using constrained coefficient fuzzy linear programming. To this end, we devise two types of simulation experiments to verify the performance of the optimization algorithm. Experimental results prove that compared with the traditional Tikhonov regularization algorithm, the correlation coefficient of the reconstructed image of the proposed algorithm is higher, the relative error value is smaller.

Damage localization on CFRP composites by electrical impedance tomography

This work exploited the use of electrical impedance tomography (EIT) with one-step difference Gaussian-Newton (GN) algorithm to detect different types of damage on unidirectional carbon fibre/epoxy composite laminates. The major challenge concerning the implementation of EIT on composite materials has to do with their anisotropy. To assess this issue, this study was conducted on carbon fibre composites having different layup configurations with different degrees of anisotropy: a quasi-isotropic layup, to approximate as much as possible these layered materials to an isotropic material, and an unbalanced layup, with further degree of anisotropy. Damage detection in the highly anisotropic unbalanced laminates is a major challenge for EIT technique, which has not been assessed before in the literature. Severe damage, in the form of through-thickness holes, was created in the laminates with different diameters and at two locations of the specimen to evaluate the sensitivity of this technique to damage size and its capacity to detect multiple damages. EIT showed progressive decrease of electrical conductivity as the diameter of through-thickness holes increased. Impact damages of different severities were also created. The EIT technique was able to distinguish different damage shapes in the laminates with different anisotropy. EIT identified elongated shaped damages, produced by impact events of different impact energies, on unbalanced laminates. However, the EIT images overestimate the damaged area, as compared to non-destructive ultrasonic inspections. The EIT images of the quasi-isotropic laminates revealed damage in the central area of the specimens, but a well-defined damage shape could not be distinguished.

Majorization–Minimization Total Variation Solution Methods for Electrical Impedance Tomography

Inverse problems arise in many areas of science and engineering, such as geophysics, biology, and medical imaging. One of the main imaging modalities that have seen a huge increase in recent years is the noninvasive, nonionizing, and radiation-free imaging technique of electrical impedance tomography (EIT). Other advantages of such a technique are the low cost and ubiquitousness. An imaging technique is used to recover the internal conductivity of a body using measurements from electrodes from the body’s surface. The standard procedure is to obtain measurements by placing electrodes in the body and measuring conductivity inside the object. A current with low frequency is applied on the electrodes below a threshold, rendering the technique harmless for the body, especially when applied to living organisms. As with many inverse problems, EIT suffers from ill-posedness, i.e., the reconstruction of internal conductivity is a severely ill-posed inverse problem and typically yields a poor-quality solution. Moreover, the desired solution has step changes in the electrical properties that are typically challenging to be reconstructed by traditional smoothing regularization methods. To counter this difficulty, one solves a regularized problem that is better conditioned than the original problem by imposing constraints on the regularization term. The main contribution of this work is to develop a general ℓp regularized method with total variation to solve the nonlinear EIT problem through a iteratively reweighted majorization–minimization strategy combined with the Gauss–Newton approach. The main idea is to majorize the linearized EIT problem at each iteration and minimize through a quadratic tangent majorant. Simulated numerical examples from complete electrode model illustrate the effectiveness of our approach.

A Large-Area Flexible Tactile Sensor for Multi-Touch and Force Detection Using Electrical Impedance Tomography

For large area robot skin design, the distribution of rigid components and wires in traditional array sensors leads to the decrease of the flexibility and extensibility of sensors. The flexible sensors based on non-invasive electrical impedance tomography (EIT) can avoid these shortcomings. However, design of a large-area flexible tactile sensors using EIT technique is still challenging due to the tradeoff between manufacturability and spatial resolution. In this study, we proposed a novel non-array EIT-based tactile sensor which is made of a porous elastic polymer and ionic liquid. The sensor free from internal array electrodes is straightforward to manufacture and can cover a large area with low cost. To improve the spatial resolution and touch sensitivity, we adopted a deep learning scheme Pyramid Scene Parsing Network (PSPNet) to postprocess the originally reconstructed images to enhance the sensor tactile perception. With this data-driven method, we achieved a single-point position detection error of 7.5± 4.5 mm without using internal electrodes. To overcome the location dependency of EIT sensing problems, we proposed a method of sub-regional fitting to calibrate the distributed forces for the large-area flexible tactile sensor and obtained a quantitative relationship between the touch force and EIT measurement for the entire sensing area for continuous sensing. The real-time performance of the proposed prototype sensor system demonstrated that we can achieve more accuracy of multi-touch and distributed force detection on the non-array sensors with a temporal resolution of about 1.3 frames/sec for potential applications in human-robot interfaces.

Location-Dependent Performance of Large-Area Piezoresistive Tactile Sensors Based on Electrical Impedance Tomography

The technique of electrical impedance tomography (EIT) has been recognized as a promising method to design tactile sensors with continuous sensing capability over a large area. The mechanism of electrical impedance tomography allows reconstructing tactile information within the sensing area based on measurements made only at the boundary. However, spatial performance of EIT-based tactile sensors has demonstrated location dependency, which severely affects correct interpretation of tactile stimuli. Here, we analyzed the effect of hyperparameter on the spatial performance, in terms of amplitude, size, position error, and shape deformation in the reconstructed images. To obtain uniform sensitivity throughout the entire sensing area, we developed an intensity scaling method to correct reconstructed amplitudes based on simulation studies. A diagonal scaling matrix was developed for a symmetric circular sensing area, and the scaling value were constructed according to the radial positions of the finite elements. The correction method was further evaluated on a compliant EIT-based touch sensor made of polymer filled composites with underlying paddings. We found that the developed method effectively produced a more uniform sensitivity distribution, and improved spatial profiles of shape deformation. The findings shown here help better interpret the strength information of tactile stimuli located at different positions of large area EIT-based sensors.

Sparse image reconstruction of intracerebral hemorrhage with electrical impedance tomography.

Purpose: Intracerebral hemorrhage (ICH) is a common disease that is known for its high morbidity, high mortality, and high disability. The fast and accurate detection of ICH is essential for the acute care of patients. Electrical impedance tomography (EIT) offers an alternative with which pathological tissues can be detected by reconstructing conductivity variation. Nevertheless, the sensitive field of EIT is greatly affected by medium distribution, which is referred to as soft-field effect. In addition, the image reconstruction is a severely ill-posed inverse problem. Furthermore, due to the low conductivity of skull, the sensitivity in the sensing area is extremely low. Therefore, the reconstruction of ICH with EIT is great challenge. Approach: A sparse image reconstruction method is proposed for EIT to visualize the conductivity variation caused by ICH. To reduce the impact of soft-field effect, the normalization of sensitivity distribution is conducted for monolayer and three-layer head model. In addition, a constrained sparse -norm minimization model is developed for the image reconstruction. Augmented Lagrangian multiplier method and alternating minimization scheme are adopted to solve the proposed model. Results: The results show that the sensitivity in the sensing area is largely enhanced. Numerical simulation based on monolayer head model and three-layer head model is respectively carried out. Both the reconstructed images and the quantitative evaluations show that image reconstructed by the proposed method is much better than that reconstructed by traditional Tikhonov method. The reconstructions evaluated under the impact of noise also show that the proposed method has superior anti-noise performance. Conclusions: With the proposed method, the quality of the reconstructed image would be greatly improved. It is an effective approach for imaging ICH with EIT technique.

A narrative review of electrical impedance tomography in lung diseases with flow limitation and hyperinflation: methodologies and applications

Electrical impedance tomography (EIT) is a functional radiation-free imaging technique that measures regional lung ventilation distribution by calculating the impedance changes in the corresponding regions. The aim of the present review was to summarize the current literature concerning the methodologies and applications of EIT in lung diseases with flow limitation and hyperinflation. PubMed was searched up to May 2020 to identify studies investigating the use of EIT in patients with asthma, bronchiectasis, bronchitis, bronchiolitis, chronic obstructive pulmonary disease, and cystic fibrosis. The extracted data included study design, EIT methodologies, interventions, validation and comparators, population characteristics, and key findings. Of the 44 included studies, seven were related to simulation, animal experimentation, or reconstruction algorithm development with evaluation on patients; 27 studies had the primary objective of validating EIT technique and measures including regional ventilation distribution, regional EIT-spirometry parameters, end-expiratory lung impedance, and regional time constants; and 10 studies had the primary objective of applying EIT to monitor the response to therapeutic interventions, including various ventilation supports, patient repositioning, and airway suctioning. In pediatric and adult patients, EIT has been successfully validated for assessing spatial and temporal ventilation distribution, measuring changes in lung volume and flow, and studying regional respiratory mechanics. EIT has also demonstrated potential as an alternative or supplement to well-established measurement modalities (e.g., conventional pulmonary function testing) to monitor the progression of obstructive lung diseases, although the existing literature lacks prediction values as references and lacks clinical outcome evidence.

A Non-Convex L₁-Norm Penalty-Based Total Generalized Variation Model for Reconstruction of Conductivity Distribution

Electrical impedance tomography (EIT) is a potential imaging technique for reconstructing the interior conductivity distribution within an object. The reconstruction is realized by processing the boundary voltage measured from the EIT. Mathematically, the reconstruction of conductivity distribution is a highly nonlinear ill-posed inverse problem as the solution is not unique and is sensitive to the noise, which largely hinders the practical application of the EIT technique. In this paper, a novel modified non-convex ${L}_{1}$ -norm penalty based total generalized variation (NCP-TGV) model is proposed to solve the inverse problem and reconstruct the conductivity distribution. An iteratively reweighted ${L}_{1}$ algorithm is developed to convert the non-convex model to convex function and the Chambolle-Pock primal-dual algorithm is then applied to solve the minimization problem. To demonstrate the performance of the proposed NCP-TGV model in reconstructing the conductivity distribution, extensive numerical simulation and experimental work have been carried out. Additionally, the proposed approach has been validated by comparing its performance with the convex ${L}_{1}$ -norm penalty based total variation (TV) and TGV inverse models. The results indicate that the images reconstructed by the proposed NCP-TGV model show better quality with the advantages of simultaneous reduction of staircase effect and preservation of edge in comparison to the TV and TGV approaches.

Noninvasive, simultaneous, and continuous measurements of stroke volume and tidal volume using EIT: feasibility study of animal experiments

Currently, there is no noninvasive method available for simultaneous measurements of tidal volume and stroke volume. Electrical impedance tomography (EIT) has been used for regional lung ventilation imaging. Cardiac EIT imaging, however, has not been successful due to the technical difficulty in extracting weak cardiogenic components. Instead of regional imaging, in this paper, we use the EIT technique to simultaneously measure two global variables of tidal volume and stroke volume. Time-varying patterns of boundary voltage data originating from lung ventilation and cardiac blood flow were extracted from measured boundary voltage data using the principal component analysis (PCA) and independent component analysis (ICA). The source consistency theory was adopted to separately synthesize time-series of boundary voltage data associated with lung ventilation and cardiac blood flow. The respiratory volume signal (RVS) and cardiac volume signal (CVS) were extracted from reconstructed time-difference EIT images of lung ventilation and cardiac blood flow, respectively. After calibrating the volume signals using the mechanical ventilator and the invasive transpulmonary thermodilution (TPTD) method, tidal volume and stroke volume were computed as valley-to-peak values of the RVS and CVS, respectively. The difference in the tidal volume data between EIT and mechanical ventilator was within ± 20 ml from six pigs. The difference in the stroke volume data between EIT and TPTD was within ± 4.7 ml from the same animals. The results show the feasibility of the proposed method as a new noninvasive cardiopulmonary monitoring tool for simultaneous continuous measurements of stroke volume and tidal volume that are two most important vital signs.