Electrical Impedance Tomography (EIT)

Figure 3 EIT waveforms from a patient suffering septic shock due to peritonitis. The patient was initially ventilated on a pressure-control mode (settings: PEEP 10 mmH 2 O, FiO 2 0.7, PaO 2 /FiO 2 ratio 190). A recruitment maneuver was carried out, in a 4 cmH 2 O steps every two minutes, until PEEP value reached 18 cmH 2 O. A decremental PEEP trial was performed afterwards (using a 2 cmH 2 O stepdown) until PEEP value reached 2 cmH 2 O (driving pressure was kept on 12 cmH 2 O). Tidal volume and oxygen saturation were monitored during incremental and decremental PEEP trials. Optimal PEEP was interpreted as the PEEP value with the best possible compliance value. After both trials, PEEP value was set on 6 cmH 2 O; no derecruitment was observed, and driving pressure was decreased to 10 cmH 2 O, witnessing a decrease in impedance change on anterior ROI and an increase on posterior ROI. EIT, electrical impedance tomography; PEEP, positive end-expiratory pressure.

Conclusion Many of these new developments were presented at the 18th International Conference on Biomedical Applications of Electrical Impedance Tomography, which was held at Dartmouth College, Hanover, NH, US in June 2017. Authors of the 56 abstracts and 26 posters presented were invited to submit full-length papers to this focus collection. Of those submitted, this focus issue represents the most novel and exciting international research being conducted in Electrical Impedance Tomography. It is critical that advances in EIT hardware, reconstruction algorithms, and data analytics like the ones highlighted in this focus issue continue to be developed and optimized. Some of the most exciting work continues to stem from actual clinical deployments that highlight the benefits of EIT and direct the research community to address the challenges that remain in translating EIT to the clinic.

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Electrical Impedance Tomography (EIT) has recently been shown appropriate for estimating the values of tissue conductivities inside the head, particularly the skull-to-scalp conductivity ratio [1, 5, 7]. Its principle is to impose a (very small) source of current on the scalp, and to measure the resulting electrical potential on the scalp by EEG. The conductivity values are estimated by minimizing the difference between the measurements and the potential predicted by the forward EIT problem.

Imaging of neuronal depolarization in the brain is a major goal in neuroscience, but no technique currently exists that could image neural activity over milliseconds throughout the whole brain. Electrical impedance tomography (EIT) is an emerging medical imaging technique which can produce tomographic images of impedance changes with non-invasive surface electrodes. We report EIT imaging of impedance changes in rat somatosensory cerebral cortex with a resolution of 2 ms and b200 μm during evoked potentials using epicortical arrays with 30 elec- trodes. Images were validated with local field potential recordings and current source-sink density analysis.

Electrical Impedance Tomography (EIT) is a non-invasive technique employed to estimate the internal resistivity distribution within a subject or object. It uses an array of electrodes attached to the boundary (or skin). The electrodes are used to inject current and measure potentials in order to solve a non linear ill-posed inverse problem. EIT has both medical and industrial applications, such as monitoring lung function (Victorino et al, 2004), detect breast tumors (Bayford, 2006), obtain information on three-dimensional material distribution within process vessels (Heikkinen et al, 2006), monitoring mixing processes, etc.

Abstract Acute respiratory distress syndrome (ARDS) is a clinical entity that acutely affects the lung parenchyma, and is characterized by diffuse alveolar damage and increased pulmonary vascular permeability. Currently, computed tomography (CT) is commonly used for classifying and prognosticating ARDS. However, performing this examination in critically ill patients is complex, due to the need to transfer these patients to the CT room. Fortunately, new technologies have been developed that allow the monitoring of patients at the bedside. Electrical impedance tomography (EIT) is a monitoring tool that allows one to evaluate at the bedside the distribution of pulmonary ventilation continuously, in real time, and which has proven to be useful in optimizing mechanical ventilation parameters in critically ill patients. Several clinical applications of EIT have been developed during the last years and the technique has been generating increasing interest among researchers. However, among clinicians, there is still a lack of knowledge regarding the technical principles of EIT and potential applications in ARDS patients. The aim of this review is to present the characteristics, technical concepts, and clinical applications of EIT, which may allow better monitoring of lung function during ARDS.

Copyright © 2013 Hui Zhang et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. ABSTRACT Electrical impedance tomography (EIT) reconstructs the internal impedance distribution of the body from electrical measurements on body surface. The algorithm research is one of the main problems of the EIT. This paper presents the MPSO-MNR Algorithm, which is formed by combining the Modified Particle Swarm Optimization (MPSO) with Modified Newton-Raphson algorithm (MNR), gives the reconstruction results of certain configurations and analyzes the influence of the noise on the MPSO-MNR algorithm in the EIT. The numerical results show that the MPSO-MNR algo- rithm can reconstruct the resistivity distribution within the certain iterations. With the moving of the target to the centre of 2-D solution domain and the increase of noise, the border of the reconstruction objects becomes vague, and the fit- ness value and the total error increase gradually.

trodes EIT digital data acquisition system can complete the multi-channel voltage signal acquisition and pro- cessing. Although there are still some defects left to be further studied and solved. We hope it can take the ad- vantages of digital system to put forward the applica- tion of electrical impedance tomography.

Breast imaging together with other more advanced complementary methods focuses on improving early detection of the cancer cells and reduces the occurrence of missed cancers (Houssami et al. 2009). One suggested method is electrical impedance tomography (EIT). A potentially, new noninvasive diagnostic technique based on different electrical storage potential of normal and pathologically altered tissues allowing image differences in the tissue conductivity and permittivity inferred from the body surface electrical measurements. EIT consists of a hand-held scanning probe and a computer screen that displays two-dimensional images of the breast. The EIT examination is performed with the subject recumbent, with both arms raised above the head. The purpose of this position is to flatten the breast as much as possible, allowing optimal contact of the flat surface of

1 National University of Defense Technology, Changsha, 410073, P. R. China 2 University College London, London, WC1E 6BT, UK zhou.zhou.13@ucl.ac.uk Abstract. The applications of Total Variation (TV) algorithms for Electrical Impedance Tomography (EIT) have been investigated. The use of the TV regularisation technique helps to preserve discontinuities in reconstruction, such as the boundaries of perturbations and sharp changes in conductivity, which are unintentionally smoothed by traditional l norm 2 regularisation. However, the non-differentiability of TV regularisation has led to the use of different algorithms. Recent advances in TV algorithms such as Primal Dual Interior Point Method (PDIPM), Linearised Alternating Direction Method of Multipliers (LADMM) and Spilt Bregman (SB) method have all been demonstrated successfully for EIT applications, but no direct comparison of the techniques has been made. Their noise performance, spatial resolution and convergence rate applied to time difference EIT were studied in simulations on 2D cylindrical meshes with different noise levels, 2D cylindrical tank and 3D anatomically head-shaped phantoms containing vegetable material with complex conductivity. LADMM had the fastest calculation speed but worst resolution due to the exclusion of the second-derivative;

Keywords: nonlinear electrical impedance tomography, adaptive mesh refinement, h-refinement, p-refinement, efficiency, improved convergence 1. Introduction Electrical impedance tomography (EIT) can provide images with well defined characteristics only when the full nonlinear reconstruction process is constrained by a property of the image such as its local smoothness, applied in parallel with the requirement to fit the data to within clearly defined statistical criteria (Blott et al 1998, 2000). The finite element forward solution is a significant part of the computational cost of such a reconstruction (Yorkey et al 1987, Johnson and MacLeod 1994). This cost grows quickly when the image is subdivided into smaller and smaller elements to obtain an image whose accuracy is governed by the quality of the input data alone and not by the choice of discretization.

Electrical Department, VJTI, Mumbai, India. ABSTRACT Electrical Impedance Tomography is an imaging technique which images the resistivity distribution of the body. An alternating current is injected through the contact electrodes placed on the surface and resulting potentials are measured to image the current density distribution which is solved as Inverse Problem. The EIDORS (Electrical Impedance and Diffused Optical Reconstruction Software) is an open source software suite used for the reconstruction of Electrical Impedance Tomography (EIT) and Diffused Optical Tomography (DOT). This paper describes the EIT experiments conducted on a circular box phantom. Here a low magnitude current is applied in a neighboring current pattern and the boundary potentials are measured and the resistivity images are reconstructed using EIDORS for circular inhomogeneities used inside the phantom.

With the rapid development and the revolution of technologies in science, materials and biotechnologies in the last decade. Electrical Impedance tomography (EIT) is seen to be playing a big role to improve and establish a modern approach of supervision and imagining physiological state of materials that depends on internal impedance distribution. The impedance of a conducting domain may include physical objects, tissue samples or human bodies. Currents are injected directly into the domain that resulting a current-voltage boundary [1]. Thus, the data acquisition is conducted sequentially with time and the data sets are measured to determine the values of transfer impedance, from which the internal impedance distribution is reconstructed as a cross- sectional image. Electrical characteristic imagining of diverse materials have been investigated for a few years in order to reconstruct or form the image of impedance

Key words. Electrical impedance tomography, mathematical model, electrical conductivity, spatial distribution, image reconstruction, reconstruction algorithms, two-dimensional and three-dimensional visualization, internal organs, electrode system. Currently electrical impedance tomography (EIT) is one of the promising areas in diagnosing functional state of humans 1-6 . Advantages of this method are simplicity of hardware implementation, safety, non- invasiveness, the possibility to study dynamic processes, economic efficiency of its application in clinical practice. In this respect, the EIT technology has significant potential for obtaining necessary results in diagnosing common human functional state 7 , studying dynamic processes of cardiovascular activity 8,9 , observing cancer formations 10-12 , monitoring respiratory system 13, 14 ,

Electrical Impedance Tomography(EIT) is a non-invasive imaging technique which aims to provide the cross- sectional distribution of electrical impedance inside the human body. In EIT, we attach surface electrodes (typically 8 to 256) on the boundary of the subject, inject linearly independent patterns of sinusoidal currents in the frequency range of 50Hz to 500kHz, and measure the induced complex voltages. Since the relationship between the applied current and the resulting voltage data provide the electrical propensity of the subject, we use all available distributed current patterns and the measured voltage data set to reconstruct cross-sectional images of the conductivity and/or permittivity distribution inside the subject. This EIT technique has received considerable attention over the past two decades. Several review papers describe numerous aspects of the EIT technique [5, 8, 24, 29, 37], and mathematical theory was developed to support EIT system [1, 10, 16, 23, 26, 27, 34–36].

We investigate the potential of sparsity constraints in the electrical impedance tomography (EIT) inverse problem of inferring the distributed conductivity based on boundary potential measurements. In sparsity reconstruction, inhomogeneities of the conductivity are a priori assumed to be sparse with respect to a certain basis. This prior information is incorporated into a Tikhonov-type functional by including a sparsity-promoting ℓ 1 -penalty term. The functional is minimized with an iterative soft shrinkage-type algorithm. In this paper, the feasibility of the sparsity reconstruction approach is evaluated by experimental data from water tank measurements. The reconstructions are computed both with sparsity constraints and with a more conventional smoothness regularization approach. The results verify that the adoption of ℓ 1 -type constraints can enhance the quality of EIT reconstructions: in most of the test cases the reconstructions with sparsity constraints are both qualitatively and quantitatively more feasible than that with the smoothness constraint.

Electrical Impedance Tomography is an imaging technique that aims to reconstruct the inner conductivity distribution of a medium starting from a set of measured voltages registered by a series of electrodes that are posi- tioned on the surface of the medium. As Bayford reports in [5], such tech- nique was used for the rst time in geological studies in 1930 and then applied to industrial procedures such as detection of air bubbles in pipes or monitor- ing of mixing processes. The rst clinical use of EIT dates back to 1987. The promising advantages of this imaging procedure over X-Ray, CT and MRI are the fact that the device can be brought to the patient and no exposition to radiation is required for data collection. Examples of clinical applica- tions where EIT has been applied are: lung ventilation imaging, detection of pulmunary emboli in lungs, cardiac output measuring, detection of breast cancer, localization of epilectic foci and imaging of functional activity in hu- man brain. In 2018 Wu et alia validated the use of EIT in tissue engineering as an imaging and monitoring tool for cell distribution (cell growth, dieren- tiation and tissue formation) in 3D scaolds [38]. The main advantages are the possibiliy to monitor tissue formation with no need to cut, x or operate histology staining on the sample (which would make the sample useless for further studies) as state of the art techniques require, and a real-time and label-free valuation of cell growth in large samples.

Electrical impedance tomography by using 3D planar array provides a better option to detect industrial damaging compared with a traditional ring electrode testing system [9]. For some electrical impedance tomographic problems in areas of geophysics, archaeology, medical diagnosis and industrial plant control, the planar array is an appropriate electrode geometry [10]. For traditional EIT measurement with annulus electrodes, only 2D images can be obtained for each measurement. EIT by using 3D Planar Array can easily attach to the surface of objects and produces an 3D reconstructed structure within the tested domain. A constant current is injected, and voltages are collected on a four-electrode-by-four- electrode array placed on subsurface of the tank. Because of the general nonlinear and ill posed properties of EIT [11], finite element forward model and algorithms of total variation are used to solve the forward and inverse problem respectively. Tikhonov regularization based on 𝐿 2 -norm and least

Abstract— Using a quaternionic reformulation of the three- dimensional Electrical Impedance Equation, and a generalization of the Bers generating sets, as well as the Beltrami equation, we introduce a new class of exact solutions for the case when the conductivity is represented by a separable-variables function depending upon three spacial variables. Finally, we discuss the possible contribution of these results into the Electrical Impedance Tomography Theory.