The number of electrodes and basis functions in EIT image reconstruction

Sensitivity volume as figure-of-merit for maximizing data importance in electrical impedance tomography

Objective.Electrical impedance tomography (EIT) is a noninvasive imaging method whereby electrical measurements on the periphery of a heterogeneous conductor are inverted to map its internal conductivity. The EIT method proposed here aims to improve computational speed and noise tolerance by introducing sensitivity volume as a figure-of-merit for comparing EIT measurement protocols.Approach.Each measurement is shown to correspond to a sensitivity vector in model space, such that the set of measurements, in turn, corresponds to a set of vectors that subtend a sensitivity volume in model space. A maximal sensitivity volume identifies the measurement protocol with the greatest sensitivity and greatest mutual orthogonality. A distinguishability criterion is generalized to quantify the increased noise tolerance of high sensitivity measurements.Main result.The sensitivity volume method allows the model space dimension to be minimized to match that of the data space, and the data importance to be increased within an expanded space of measurements defined by an increased number of contacts.Significance.The reduction in model space dimension is shown to increasecomputational efficiency, accelerating tomographic inversion by several orders of magnitude, while the enhanced sensitivitytolerates higher noiselevels up to several orders of magnitude larger than standard methods.

Pulmonary response prediction through personalized basis functions in a virtual patient model

BACKGROUND AND OBJECTIVE

Recruitment maneuvers with subsequent positive-end-expiratory-pressure (PEEP) have proven effective in recruiting lung volume and preventing alveoli collapse. However, determining a safe, effective, and patient-specific PEEP is not standardized, and this more optimal PEEP level evolves with patient condition, requiring personalised monitoring and care approaches to maintain optimal ventilation settings.

METHODS

This research examines 3 physiologically relevant basis function sets (exponential, parabolic, cumulative) to enable better prediction of elastance evolution for a virtual patient or digital twin model of MV lung mechanics, including novel elements to model and predict distension elastance. Prediction accuracy and robustness are validated against recruitment maneuver data from 18 volume-controlled ventilation (VCV) patients at 7 different baseline PEEP levels (0 to 12 cmH2O) and 14 pressure-controlled ventilation (PCV) patients at 4 different baseline PEEP levels (6 to 12 cmH2O), yielding 623 and 294 prediction cases, respectively. Predictions were made up to 12 cmH2O of added PEEP ahead, covering 6 × 2 cmH2O PEEP steps.

RESULTS

The 3 basis function sets yield median absolute peak inspiratory pressure (PIP) prediction error of 1.63 cmH2O for VCV patients, and median peak inspiratory volume (PIV) prediction error of 0.028 L for PCV patients. The exponential basis function set yields a better trade-off of overall performance across VCV and PCV prediction than parabolic and cumulative basis function sets from other studies. Comparing predicted and clinically measured distension prediction in VCV demonstrated consistent, robust high accuracy with R2 = 0.90-0.95.

CONCLUSIONS

The results demonstrate recruitment mechanics are best captured by an exponential basis function across different mechanical ventilation modes, matching physiological expectations, and accurately capture, for the first time, distension mechanics to within 5-10 % accuracy. Enabling the risk of lung injury to be predicted before changing ventilator settings. The overall outcomes significantly extend and more fully validate this digital twin or virtual mechanical ventilation patient model.

Tumor location on electroporation therapies by means of multi-electrode structures and machine learning

Electroporation is a phenomenon produced in the cell membrane when it is exposed to high pulsed electric fields that increases its permeability. Among other application fields, this phenomenon can be exploited in a clinical environment for tumor ablation therapies. In this context to achieve optimum results, it is convenient to focus the treatment on the tumor tissue to minimize side effects. In this work, a pre-treatment tumor location method is developed, with the purpose of being able to precisely target the therapy. This is done by taking different impedance measurements with a multi-output electroporation generator in conjunction with a multi-electrode structure. Data are processed by means of a vector of independent artificial neural networks, trained and tested with simulation data, and validated with phantom gels. This algorithm proved to provide suitable accuracy in spite of the low electrode count compared to the number of electrodes of a standard electrical impedance tomography device.

Influence of Excitation Frequency on the Performance of Peripheral Blood Flow Imaging using Electrical Impedance Tomography

This paper presents a method for selecting the efficient excitation frequency of Electrical Impedance Tomography (EIT) for imaging peripheral blood flow with high spatial-temporal performance. Using a simulation study, we selected the excitation frequency of 16 kHz to visualize the pulsation of arteries with a high sensitivity. We then conducted a subjective study using 16 electrodes and showed that the conductivity distribution is similar to the anatomical structure of the forearm. Moreover, the integrated conductivity spectrum showed a peak corresponding to a heart rate measurement obtained using a PPG sensor at the fingertip. Therefore, we conclude that this system can capture the spatial-temporal signals related to peripheral artery blood flow by using the selected excitation frequency.

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.

A Review of EMG-, FMG-, and EIT-Based Biosensors and Relevant Human–Machine Interactivities and Biomedical Applications

Wearables developed for human body signal detection receive increasing attention in the current decade. Compared to implantable sensors, wearables are more focused on body motion detection, which can support human–machine interaction (HMI) and biomedical applications. In wearables, electromyography (EMG)-, force myography (FMG)-, and electrical impedance tomography (EIT)-based body information monitoring technologies are broadly presented. In the literature, all of them have been adopted for many similar application scenarios, which easily confuses researchers when they start to explore the area. Hence, in this article, we review the three technologies in detail, from basics including working principles, device architectures, interpretation algorithms, application examples, merits and drawbacks, to state-of-the-art works, challenges remaining to be solved and the outlook of the field. We believe the content in this paper could help readers create a whole image of designing and applying the three technologies in relevant scenarios.

Electrical Impedance Tomography Technical Contributions for Detection and 3D Geometric Localization of Breast Tumors: A Systematic Review

Impedance measuring acquisition systems focused on breast tumor detection, as well as image processing techniques for 3D imaging, are reviewed in this paper in order to define potential opportunity areas for future research. The description of reported works using electrical impedance tomography (EIT)-based techniques and methodologies for 3D bioimpedance imaging of breast tissues with tumors is presented. The review is based on searching and analyzing related works reported in the most important research databases and is structured according to the Preferred Reporting Items for Systematic Reviews and Meta-analysis (PRISMA) parameters and statements. Nineteen papers reporting breast tumor detection and location using EIT were systematically selected and analyzed in this review. Clinical trials in the experimental stage did not produce results in most of analyzed proposals (about 80%), wherein statistical criteria comparison was not possible, such as specificity, sensitivity and predictive values. A 3D representation of bioimpedance is a potential tool for medical applications in malignant breast tumors detection being capable to estimate an ap-proximate the tumor volume and geometric location, in contrast with a tumor area computing capacity, but not the tumor extension depth, in a 2D representation.

Over-distension prediction via hysteresis loop analysis and patient-specific basis functions in a virtual patient model

BACKGROUND AND OBJECTIVE

Recruitment maneuvers (RMs) with subsequent positive-end-expiratory-pressure (PEEP) have proven effective in recruiting lung volume and preventing alveolar collapse. However, a suboptimal PEEP could induce undesired injury in lungs by insufficient or excessive breath support. Thus, a predictive model for patient response under PEEP changes could improve clinical care and lower risks.

METHODS

This research adds novel elements to a virtual patient model to identify and predict patient-specific lung distension to optimise and personalise care. Model validity and accuracy are validated using data from 18 volume-controlled ventilation (VCV) patients at 7 different baseline PEEP levels (0-12cmH₂O), yielding 623 prediction cases. Predictions were made up to ΔPEEP = 12cmH₂O ahead covering 6x2cmH₂O PEEP steps.

RESULTS

Using the proposed lung distension model, 90% of absolute peak inspiratory pressure (PIP) prediction errors compared to clinical measurement are within 3.95cmH₂O, compared with 4.76cmH₂O without this distension term. Comparing model-predicted and clinically measured distension had high correlation increasing to R² = 0.93-0.95 if maximum ΔPEEP ≤ 6cmH₂O. Predicted dynamic functional residual capacity (V_frc) changes as PEEP rises yield 0.013L median prediction error for both prediction groups and overall R² of 0.84.

CONCLUSIONS

Overall results demonstrate nonlinear distension mechanics are accurately captured in virtual lung mechanics patients for mechanical ventilation, for the first time. This result can minimise the risk of lung injury by predicting its potential occurrence of distension before changing ventilator settings. The overall outcomes significantly extend and more fully validate this virtual mechanical ventilation patient model.

Electrical impedance tomography: A compass for the safe route to optimal PEEP

Setting the proper level of positive end-expiratory pressure (PEEP) is a cornerstone of lung protective ventilation. PEEP keeps the alveoli open at the end of expiration, thus reducing atelectrauma and shunt. However, excessive PEEP may contribute to alveolar overdistension. Electrical impedance tomography (EIT) is a non-invasive bedside tool that monitors in real-time ventilation distribution. Aim of this narrative review is summarizing the techniques for EIT-guided PEEP titration, while providing useful insights to enhance comprehension on advantages and limits of EIT for current and future users. EIT detects thoracic impedance to alternating electrical currents between pairs of electrodes and, through the analysis of its temporal and spatial variation, reconstructs a two-dimensional slice image of the lung depicting regional variation of ventilation and perfusion. Several EIT-based methods have been proposed for PEEP titration. The first described technique estimates the variations of regional lung compliance during a decremental PEEP trial, after lung recruitment. The optimal PEEP value is represented by the best compromise between lung collapse and overdistension. Later on, a second technique assessing alveolar recruitment by variation of the end-expiratory lung impedance was validated. Finally, the global inhomogeneity index and the regional ventilation delay, two EIT-derived parameters, showed promising results selecting the optimal PEEP value as the one that presents the lowest global inhomogeneity index or the lowest regional ventilation delay. In conclusion EIT represents a promising technique to individualize PEEP in mechanically ventilated patients. Whether EIT is the best technique for this purpose and the overall influence of personalizing PEEP on clinical outcome remains to be determined.

An Optimal Electrical Impedance Tomography Drive Pattern for Human-Computer Interaction Applications

In this article, we presented an optimal Electrical Impedance Tomography (EIT) drive pattern based on feature selection and model explanation, and proposed a portable EIT system for applications in human-computer interaction for gesture recognition and contact detection, which can reduce the measurement time and realize a performance trade-off between the accuracy and the time response. In our experiment, eleven hand gestures were designed to verify the proposed approach and EIT system. Compared to the traditional eight-electrode method, the optimal electrode drive pattern achieved a recognition accuracy of 97.5% with seven electrodes and the measurement time was reduced by 60%. To illustrate the universality of this method, we performed a contact detection experiment. By setting seven labels on the conductive panel and using optimal electrode drive pattern, the detection accuracy reached 100% with seven electrodes and the measurement time was reduced by 85%.

A theoretical study on real time monitoring of single cell mitosis with micro electrical impedance tomography

Real time monitoring of cell division, mitosis, at the single cell level, has value for many biomedical applications; such as developing optimal cancer treatments that target the cell division process. The goal of this theoretical study is to explore the feasibility of using Micro Electrical Impedance Tomography (MEIT) for real time monitoring of mitosis in a single cell, through imaging. MEIT employs a micro (single cell) scale electrode cage with electrodes placed around the cell. The electrodes deliver subsensory current and the consequential voltages on the electrodes are measured. An inverse image reconstruction algorithm uses the electric data from the electrodes to generate a map of electrical conductivity distribution in the chamber, which is the image. EIT is a well-known medical imaging technology that is simple to use but lacks good resolution. Therefore, it is not a-priori obvious that EIT has sufficient resolution to monitor single cell mitosis. To accomplish the goal of this study we have developed a mathematical model of MEIT of single cell mitosis, in which an in silico experiment provided the data for the MEIT image reconstruction. This theoretical study shows that MEIT can detect the outlines of the dividing cell during the various stages of mitosis (metaphase, anaphase and telophase) and, therefore, has potential as a technology for real time monitoring of single cell mitosis.

Evaluation of measurement and stimulation patterns in open electrical impedance tomography with scanning electrode

The reconstruction quality in electrical impedance tomography is limited by the current injection amplitude, the injection and measurement patterns, and the measurement accuracy as well as the number and placement of electrodes. This paper dedicates to increase the number of independent voltage measurements by scanning electrode (SE), and design an optimal measurement and stimulation pattern for open electrical impedance tomography (OEIT). Firstly, several measurement patterns are, performed in OEIT, aiming to evaluate the right number of the measurement points for the imaged target in a certain depth. The results indicate that the image quality gets higher with the number of measurement point increased to some extent. Thus, it can guide the optimum design for the electrode system in OEIT. Secondly, through the numerical calculation and salt water tank experiment, in contrast to adjacent current injection pattern, cross-current-injection pattern achieves better reconstruction with higher imaging quality and penetration depth, and is more robust against data noise in deep domain. Lastly, the experiments also indicate that the electrode contact area affects the reconstruction quality and investigation depth. Therefore, OEIT with SE can improve the application in clinic, such as the detection and monitoring of vascular, breast, and pulmonary diseases.

Reducing computational costs in large scale 3D EIT by using a sparse Jacobian matrix with block-wise CGLS reconstruction

Electrical impedance tomography (EIT) is a fast and cost-effective technique to provide a tomographic conductivity image of a subject from boundary current-voltage data. This paper proposes a time and memory efficient method for solving a large scale 3D EIT inverse problem using a parallel conjugate gradient (CG) algorithm. The 3D EIT system with a large number of measurement data can produce a large size of Jacobian matrix; this could cause difficulties in computer storage and the inversion process. One of challenges in 3D EIT is to decrease the reconstruction time and memory usage, at the same time retaining the image quality. Firstly, a sparse matrix reduction technique is proposed using thresholding to set very small values of the Jacobian matrix to zero. By adjusting the Jacobian matrix into a sparse format, the element with zeros would be eliminated, which results in a saving of memory requirement. Secondly, a block-wise CG method for parallel reconstruction has been developed. The proposed method has been tested using simulated data as well as experimental test samples. Sparse Jacobian with a block-wise CG enables the large scale EIT problem to be solved efficiently. Image quality measures are presented to quantify the effect of sparse matrix reduction in reconstruction results.

Note: Two-dimensional resistivity mapping method for characterization of thin films and nanomaterials

A two-dimensional resistivity mapping method is presented as an analysis tool for thin films. The spatial distribution of resistivity in the interior of the film is reconstructed with the data measured on its periphery. A square window with four electrodes on each side is fabricated as the test vehicle. While the current is applied to one electrode, the potentials on the other electrodes are monitored and an iterative method generates the resistivity map. The technique is demonstrated by measurements on a homogeneous organic PEDOT:PSS film and an inhomogeneous ZnO nanoparticle coating.

Frequency-division multiplexing for electrical impedance tomography in biomedical applications

Electrical impedance tomography (EIT) produces an image of the electrical impedance distribution of tissues in the body, using electrodes that are placed on the periphery of the imaged area. These electrodes inject currents and measure voltages and from these data, the impedance can be computed. Traditional EIT systems usually inject current patterns in a serial manner which means that the impedance is computed from data collected at slightly different times. It is usually also a time-consuming process. In this paper, we propose a method for collecting data concurrently from all of the current patterns in biomedical applications of EIT. This is achieved by injecting current through all of the current injecting electrodes simultaneously, and measuring all of the resulting voltages at once. The signals from various current injecting electrodes are separated by injecting different frequencies through each electrode. This is called frequency-division multiplexing (FDM). At the voltage measurement electrodes, the voltage related to each current injecting electrode is isolated by using Fourier decomposition. In biomedical applications, using different frequencies has important implications due to dispersions as the tissue's electrical properties change with frequency. Another significant issue arises when we are recording data in a dynamic environment where the properties change very fast. This method allows simultaneous measurements of all the current patterns, which may be important in applications where the tissue changes occur in the same time scale as the measurement. We discuss the FDM EIT method from the biomedical point of view and show results obtained with a simple experimental system.

Linear superposition electrical impedance tomography imaging with multiple electrical/biopsy probes

In medical diagnostics, tissue is often examined with multiple discrete biopsies taken under ultrasound placement. In a previous theoretical study, we have suggested that the linear nature of the equations used in electrical impedance tomography (EIT) can be employed with the conventional practice of biopsy sampling to produce an image of the tissue between the biopsy samplings. Specifically, the biopsy probes can be used to record EIT-type electrical data during the discrete tissue sampling. The location of the discrete biopsy needle insertions available from the ultrasound placement of the probes can be combined with the electrical measurement data and used with linear superposition to produce a complete EIT image of the tissue between the sampled sites. In this study, we explore the concept experimentally using gel phantoms to simulate tissue and heterogeneities in the tissue. The experiments are performed in 2-D and 3-D configurations, and data are taken discretely, one at a time, through single electrical probe insertions. In the 2-D configuration, we were able to produce images of reasonable quality for heterogeneities with a diameter larger than 3 mm (conductivity ratio 1:5) and with relative conductivity differences above 50% (diameter 5 mm).

Electrical impedance tomography guided ventilation therapy

PURPOSE OF REVIEW

Computed tomography (CT) in patients with acute respiratory distress syndrome has shown that intrapulmonary gas is not homogeneously distributed. Although regional ventilation can be studied by isotope and magnetic resonance techniques while aeration of the lungs can be imaged using CT, these techniques are not available at the bedside. Recently, electrical impedance tomography has been introduced as a true bedside technique which provides information on regional ventilation distribution.

RECENT FINDINGS

Electrical impedance tomography can reliably determine regional ventilation in healthy lungs and various models of induced lung injury when compared with CT, electron beam CT, and single photon emission CT. In healthy volunteers and patients with acute lung injury, relative impedance changes on the electrical impedance tomography image demonstrate an excellent correlation with regional changes in lung air content detected by CT. In a limited number of patients with respiratory dysfunction, gas exchange was found to improve when electrical impedance tomography was used to adjust ventilator settings, improving regional ventilation and avoiding tidal alveolar collapse.

SUMMARY

In view of recently published data, it can be concluded that, in critically ill patients, electrical impedance tomography determines reliable regional ventilation. Therefore, this technique has the potential to become a valuable bedside tool.

Multiple biopsy probe sampling enabled minimally invasive electrical impedance tomography

Biopsies are a reliable method for examining tissues and organs inside the body, in particular for detection of tumors. However, a single biopsy produces only limited information on the site from which it is taken. Therefore, tumor detection now employs multiple biopsy samplings to examine larger volumes of tissue. Nevertheless, even with multiple biopsies, the information remains discrete, while the costs of biopsy increase. Here we propose and evaluate the feasibility of using minimally invasive medical imaging as a means to overcome the limitations of discrete biopsy sampling. The minimally invasive medical imaging technique employs the biopsy probe as electrodes for measurements of electrical impedance tomography relevant data during each biopsy sampling. The data from multiple samplings are combined and used to produce an EIT image of the tissue. Two- and three-dimensional mathematical simulations confirm that the minimally invasive medical imaging technique can produce electrical impedance tomography images of the tissues between the biopsy probe insertion sites. We show that these images can detect tumors that would be missed with multiple biopsy samplings only, and that the technique may facilitate the detection of tumors with fewer biopsies, thereby reducing the cost of cancer detection.

Bioimpedance tomography (electrical impedance tomography)

Electrical impedance tomography (EIT) is a relatively new imaging method that has evolved over the past 20 years. It has the potential to be of great value in clinical diagnosis; however, EIT is a technically difficult problem to solve in terms of developing hardware for data capture and the algorithms to reconstruct the images. This review looks at the development of EIT and how it has evolved. It focuses on its clinical applications, examining hardware for the collection of data and reconstruction algorithms to generate images. Finally, this review looks at future developments that are evolving from EIT. These new variations use mixed modalities that may produce interesting new clinical imaging tools.

Monitoring of lung edema using focused impedance spectroscopy: a feasibility study

Currently only ionizing or invasive methods are used in clinical applications for the monitoring of extracellular lung water. Alternatively a method called focused conductivity spectroscopy (FCS) is suggested, which aims at reconstructing a pulmonary edema index (PEIX) by measuring the electrical conductivity of the region of interest (ROI) at several frequencies. In contrast to electrical impedance tomography (EIT) a minimum number of strategically placed electrodes is used. The goals of this study were the analysis of the sensitivity for the PEIX, an estimate of the optimal electrode configuration and the determination of the required frequencies. In order to calculate the solution of the FCS forward problem a realistic 3D model of a human torso was developed containing both lungs, the heart, the liver and the thorax musculature. The bioelectrical properties for each compartment were described with appropriate tissue models which relate the conductivity spectra to physiological parameters. The PEIX was defined as the interstitial volume fraction of the alveolar septa. Furthermore the model includes 48 electrodes subdivided into three layers. The optimal electrode configuration was selected by minimizing the number of electrodes, among certain subsets of these electrodes. The analysis shows that eight to ten electrodes and six frequencies are theoretically sufficient to obtain a coefficient of variation.

Imbalances in Regional Lung Ventilation

Imbalances in regional lung ventilation, with gravity-dependent collapse and overdistention of nondependent zones, are likely associated to ventilator-induced lung injury. Electric impedance tomography is a new imaging technique that is potentially capable of monitoring those imbalances. The aim of this study was to validate electrical impedance tomography measurements of ventilation distribution, by comparison with dynamic computerized tomography in a heterogeneous population of critically ill patients under mechanical ventilation. Multiple scans with both devices were collected during slow-inflation breaths. Six repeated breaths were monitored by impedance tomography, showing acceptable reproducibility. We observed acceptable agreement between both technologies in detecting right-left ventilation imbalances (bias = 0% and limits of agreement = -10 to +10%). Relative distribution of ventilation into regions or layers representing one-fourth of the thoracic section could also be assessed with good precision. Depending on electrode positioning, impedance tomography slightly overestimated ventilation imbalances along gravitational axis. Ventilation was gravitationally dependent in all patients, with some transient blockages in dependent regions synchronously detected by both scanning techniques. Among variables derived from computerized tomography, changes in absolute air content best explained the integral of impedance changes inside regions of interest (r(2) > or = 0.92). Impedance tomography can reliably assess ventilation distribution during mechanical ventilation.

Effects of incompatible boundary information in EIT on the convergence behavior of an iterative algorithm

In electrical impedance tomography, currents are applied to the body through electrodes that are attached to the surface and the corresponding surface voltages are measured. Based on these boundary measurements, the internal admittivity distribution of the body can be reconstructed. In order to improve the image quality it is necessary and useful to apply physiologically meaningful prior information into the image reconstruction. Such prior information usually can be obtained from other sources. For example, information on the object's boundary shape and internal structure can be obtained from computed tomography and magnetic resonance imaging scan. However, this type of prior information may change from time to time and from person to person. As these changes are limited anatomically and physiologically, the prior information including the possible changes can be presented in a number of variational forms. The aim of this paper is to find which form of prior information is more compatible for a specific imaged object at the time of imaging. This paper proposes a new method for selecting the most appropriate form of prior information, through the procedure of iterative image reconstruction by using the information obtained from boundary measurements. The method is based on the principle that incompatible prior information causes errors which are able to affect the image reconstruction's convergence behavior. In this method, according to the various forms of prior information available, several image reconstruction configurations are designed. Then, through monitoring the convergence behavior in an iterative image reconstruction, the configuration with compatible prior information can be found among those different configurations. As an example, the prior information regarding the imaged object's boundary shape and internal structure was studied by computer simulation. Results were shown and discussed.