Pressure mapping using a smart nanocomposite foam and electrical impedance tomography

This paper introduces a new method of simultaneous acquisition of electrocardiogram (ECG) signals and electrical impedance tomography (EIT) measurements on active electrodes, i.e. electrodes that are applying current, in an EIT system. Howland noise reduction followed by an integrate-and-dump filter and adaptive filtering are used to extract the ECG signal without loss of amplitude. Results of simultaneous ECG signals and EIT reconstructions on human subjects are shown. It is also demonstrated that the recovered ECG waveforms from 32 electrodes can be used to reconstruct the heart current source vectors during a cardiac cycle, yielding an approximate solution to the inverse problem of electrocardiography. Acquiring ECG signals from all electrodes attached to the body simultaneously with EIT data acquisition eliminates the need for a separate system to collect ECG signals. Time-aligned ECG signals are helpful in interpreting pulsatile perfusion images by providing exact timing of each image in relation to the cardiac cycle. Additionally, collecting ECG signals from many or all of the EIT electrodes enables reconstruction of the heart's moving dipole moment at the same time as the EIT images.

Objective. Electrical impedance tomography (EIT) has been used to determine regional lung ventilation distribution in humans for decades, however, the effect of biological sex on the findings has hardly ever been examined. The aim of our study was to determine if the spatial distribution of ventilation assessed by EIT during quiet breathing was influenced by biological sex. Approach. 219 adults with no known acute or chronic lung disease were examined in sitting position with the EIT electrodes placed around the lower chest (6th intercostal space). EIT data were recorded at 33 images/s during quiet breathing for 60 s. Regional tidal impedance variation was calculated in all EIT image pixels and the spatial distribution of the values was determined using the established EIT measures of centre of ventilation in ventrodorsal (CoVvd) and right-to-left direction (CoVrl), the dorsal and right fraction of ventilation, and ventilation defect score. Main results. After exclusion of one subject due to insufficient electrode contact, 218 data sets were analysed (120 men, 98 women) (age: 53 ± 18 vs 50 ± 16 yr (p = 0.2607), body mass index: 26.4 ± 4.0 vs 26.4 ± 6.6 kg m−2 (p = 0.9158), mean ± SD). Highly significant differences in ventilation distribution were identified between men and women between the right and left chest sides (CoVrl: 47.0 ± 2.9 vs 48.8 ± 3.3% of chest diameter (p < 0.0001), right fraction of ventilation: 0.573 ± 0.067 vs 0.539 ± 0.071 (p = 0.0004)) and less significant in the ventrodorsal direction (CoVvd: 55.6 ± 4.2 vs 54.5 ± 3.6% of chest diameter (p = 0.0364), dorsal fraction of ventilation: 0.650 ± 0.121 vs 0.625 ± 0.104 (p = 0.1155)). Ventilation defect score higher than one was found in 42.5% of men but only in 16.6% of women. Significance. Biological sex needs to be considered when EIT findings acquired in upright subjects in a rather caudal examination plane are interpreted. Sex differences in chest anatomy and thoracoabdominal mechanics may explain the results.

Electrical impedance tomography (EIT) is gaining importance in the field of geophysics and there is increasing interest for accurate borehole EIT measurements in a broad frequency range (mHz to kHz) in order to study subsurface properties. To characterize weakly polarizable soils and sediments with EIT, high phase accuracy is required. Typically, long electrode cables are used for borehole measurements. However, this may lead to undesired electromagnetic coupling effects associated with the inductive coupling between the double wire pairs for current injection and potential measurement and the capacitive coupling between the electrically conductive shield of the cable and the electrically conductive environment surrounding the electrode cables. Depending on the electrical properties of the subsurface and the measured transfer impedances, both coupling effects can cause large phase errors that have typically limited the frequency bandwidth of field EIT measurements to the mHz to Hz range. The aim of this paper is to develop numerical corrections for these phase errors. To this end, the inductive coupling effect was modeled using electronic circuit models, and the capacitive coupling effect was modeled by integrating discrete capacitances in the electrical forward model describing the EIT measurement process. The correction methods were successfully verified with measurements under controlled conditions in a water-filled rain barrel, where a high phase accuracy of 0.8 mrad in the frequency range up to 10 kHz was achieved. The corrections were also applied to field EIT measurements made using a 25 m long EIT borehole chain with eight electrodes and an electrode separation of 1 m. The results of a 1D inversion of these measurements showed that the correction methods increased the measurement accuracy considerably. It was concluded that the proposed correction methods enlarge the bandwidth of the field EIT measurement system, and that accurate EIT measurements can now be made in the mHz to kHz frequency range. This increased accuracy in the kHz range will allow a more accurate field characterization of the complex electrical conductivity of soils and sediments, which may lead to the improved estimation of saturated hydraulic conductivity from electrical properties. Although the correction methods have been developed for a custom-made EIT system, they also have potential to improve the phase accuracy of EIT measurements made with commercial systems relying on multicore cables.

This study aims to improve the accuracy of Electrical Impedance Tomography (EIT) measurements for monitoring ventilation and cardiac signal in medical imaging by proposing a new signal separation approach that does not require contrast agents. Conventionally, contrast agents like high-conductive saline solutions are used for signal separation in EIT measurements. This study uses a harmonic analysis on EIT raw voltage data to separate the ventilation- and cardiac-related signals (early separation). It evaluates its efficacy with a simulation model at low (1%) and high (10%) superimposed additive noise levels against the already published harmonic analysis at pixel level after EIT image reconstruction (late separation). The findings indicate that the voltage-based harmonic analysis approach, i.e., early separation, provides reliable signal separation, especially under high noise conditions, compared to the late separation. This method enables the possibility of incorporating independent cardiac-specific or ventilation-specific prior knowledge into the image reconstruction process, potentially improving the resulting images.

Purpose – The aim of this paper is to introduce and to evaluate the performance of a multiple frequency complex impedance reconstruction for fabric-based EIT pressure sensor. Pressure mapping is an important and challenging area of modern sensing technology. It has many applications in areas such as artificial skins in Robotics and pressure monitoring on soft tissue in biomechanics. Fabric-based sensors are being developed in conjunction with electrical impedance tomography (EIT) for pressure mapping imaging. This is potentially a very cost-effective pressure mapping imaging solution in particular for imaging large areas. Fabric-based EIT pressure sensors aim to provide a pressure mapping image using current carrying and voltage sensing electrodes attached on the boundary of the fabric patch. Design/methodology/approach – Recently, promising results are being achieved in conductivity imaging for these sensors. However, the fabric structure presents capacitive behaviour that could also be exploited for pressure mapping imaging. Complex impedance reconstructions with multiple frequencies are implemented to observe both conductivity and permittivity changes due to the pressure applied to the fabric sensor. Findings – Experimental studies on detecting changes of complex impedance on fabric-based sensor are performed. First, electrical impedance spectroscopy on a fabric-based sensor is performed. Secondly, the complex impedance tomography is carried out on fabric and compared with traditional EIT tank phantoms. Quantitative image quality measures are used to evaluate the performance of a fabric-based sensor at various frequencies and against the tank phantom. Originality/value – The paper demonstrates for the first time the useful information on pressure mapping imaging from the permittivity component of fabric EIT. Multiple frequency EIT reconstruction reveals spectral behaviour of the fabric-based EIT, which opens up new opportunities in exploration of these sensors.

Objective: Cystic fibrosis (CF) causes airway obstruction that can be detected in a spirometry test. We hypothesized that with Electrical Impedance Tomography (EIT), not only the information of global but also that of regional obstruction can be captured. Method: In this preliminary study, totally three CF patients and one healthy subject were examined. The subjects were asked to take deepest breath and exhale as hard as possible three times. EIT measurements were performed during spirometry tests. Flow-volume curves obtained from spirometer were compared to that from EIT. Ratio of maximum expiratory flow between 25% and 75% of vital capacity (MEF <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">25</sub> /MEF <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">75</sub> ) were calculated for lung regions in EIT imaging. Results: Relative low MEF <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">25</sub> values in CF patients were detected in global flow-volume curves, both in spirometry and EIT measurements. Further, the EIT measurement revealed a variation of MEF <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">25</sub> /MEF <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">75</sub> from 0.05 to 0.4 in different lung regions. Conclusion: Compared to the global parameters provided by spirometer, EIT is able to deliver both global and regional information to assess the airway obstruction in CF patients. The regional information may be useful for long term monitoring of disease progression.

To estimate the severity of airway abnormalities in cystic fibrosis (CF) Brody et al. developed a computed tomography (CT) scoring system. Each pulmonary lobe is analyzed separately considering various morphological defects. A study from Zhao et al. demonstrates that this CTbased score correlates with regional airway obstruction (RAO) measured by the real-time imaging method Electrical Impedance Tomography (EIT). Zhao et al. performed EIT measurements at the 5th intercostal space (ICS) and median RAO, including both lungs, was correlated with the associated score. In the present feasibility study, it was investigated if RAO determined by EIT within the left and right lung respectively at the 3rd and 5th ICS corresponds with the scores of the left and right lobes. EIT measurements and CT-based scoring were carried out on two CF patients. RAO was identified by ratios of impedance values associated to the maximal forced expiratory flow at 25% and 75% of the forced vital capacity. Mean RAO of each lung within both thorax sections was compared with the lobar scores. Airway abnormalities within upper lobes are assigned to RAO measured within the 3rd ICS, whereas abnormalities of the right middle lobe, both lower lobes and the lingula are mainly represented by EIT images of the 5th ICS. Results show that differences in the CT-based score between the left and right lung concur with differences in EIT derived RAO. The regional information provided by EIT might be used for a more targeted therapy of CF-related lung diseases.

Objective: This study was performed to assess the value of electrical impedance tomography (EIT) as an indicator of tidal (VT) and end expiratory lung volume (EELV).Methods: EIT measurements were performed in seven healthy piglets during constant tidal volume ventilation at incremental and decremental positive end-expiratory pressure (PEEP) levels. Tidal impedance changes were calibrated to volume using VT calculated from flow at the airway opening. Simultaneously, calibrated respiratory inductive plethysmography was used to measure EELV changes, and used as a reference standard.Results: EIT systematically underestimated both VT and EELV changes when EELV deviated from the level at which it was calibrated. Calculated over the entire pressure–volume curve, EIT systematically underestimated VT by 28 ml, with a precision from −16 to 72 ml. EELV was systemically underestimated by 406 ml, with a precision of −38 to 849 ml. Nonlinear recruitment in the ventral regions of the lungs was the main cause of this underestimation.Conclusions: Tidal and end-expiratory changes in pulmonary impedance reflect corresponding changes in lung volume, but the increasing underestimation with increasing lung volume should be taken into account in the analysis of EIT data.

In order to assess and optimize the effect of new therapies for acute lung injury (ALI) in rodent models, a monitoring technique that continuously assesses the functional state of the lung is mandatory. Electrical impedance tomography (EIT) has been suggested as a technique for quantifying lung inflammation in ALI. However, EIT has not been evaluated in a rodent model of ALI. EIT measurements were compared in ventilated Sprague-Dawley rats (n = 14), randomly subjected to intratracheal administration of endotoxin (LPS) or saline (control). Lung mechanics, lung weight wet/dry ratio and inflammatory markers in bronchoalveolar lavage fluid were also evaluated. LPS caused a significant decrease in lung compliance and TLC as compared with control (-42.0%, P = 0.04, and -27.9%, P = 0.02, respectively). These changes were paralleled by differences in mean impedance changes as detected by EIT (Spearman's rank correlation coefficient: rho = 0.66 and 0.73, respectively, P < 0.01). LPS increased the lung weight wet/dry ratio (6.35 +/- 0.42 vs 5.15 +/- 0.07, P = 0.003), and the bronchoalveolar lavage total WCC (8.96 +/- 1.87 vs 1.16 +/- 0.10 x 10(9)/L, P = 0.002) as compared with control. The lung weight wet/dry ratio was inversely related to the mean impedance change (rho = -0.76, P < 0.01). This study has demonstrated for the first time that eight-electrode EIT readily tracks the inflammatory response of lung tissue in a rodent model of ALI. EIT may thus provide a promising, non-invasive technique for monitoring the time-course of ALI in rodent models, and for testing novel pharmacological strategies to counter it.

Electrical impedance tomography (EIT) uses electrical measurements at electrodes placed around the thorax to image changes in the conductivity distribution within the thorax. This technique is well suited to studying pulmonary function because the movement of air, blood, and extravascular fluid induces significant conductivity changes within the thorax. We conducted three experimental protocols in a total of 19 dogs to assess the accuracy with which EIT can quantify changes in the volumes of both gas and fluid in the lungs. In the first protocol, lung volume increments from 50 to 1,000 ml were applied with a large syringe. EIT measured these volume changes with an average error of 27 +/- 6 ml. In the second protocol, EIT measurements were made at end expiration and end inspiration during regular ventilation with tidal volume ranging from 100 to 1,000 ml. The average error in the EIT estimates of tidal volume was 90 +/- 43 ml. In the third protocol, lung liquid volume was measured by instilling 5% albumin solution into a lung lobe in increments ranging from 10 to 100 ml. EIT measured these volume changes with an average error of 10 +/- 10 ml and was also able to detect into which lobe the fluid had been instilled. These results indicate that EIT can noninvasively measure changes in the volumes of both gas and fluid in the lungs with clinically useful accuracy.

A brain haemorrhage constitutes a serious medical scenario with a need for rapid, accurate detection to facilitate treatment initiation. Machine learning (ML) techniques applied to such medical diagnostic problems can improve the rate and accuracy of bleed detection leading to improved patient outcomes. In this chapter we examine the potential role of support vector machine (SVM) type classifiers in detecting such haemorrhagic lesions (bleeds) using electrical impedance tomography (EIT) measurement frames as the source of training and test data. A two-layer computational model of the head is designed, with EIT frame generation simulated from electrodes placed on the surface of the head model. A wide variety of test scenarios are modelled, including variations in measurement noise, bleed size and location, electrode position, and anatomy. Initial results using a linear SVM classifier applied to test scenarios, with and without pre-processing of the EIT measurement frame, are summarised. The classifier returned detection accuracies >90% with signal-to-noise ratios of ≥60 dB; was independent of bleed location, capable of detecting bleeds as small as 10 ml; and was unaffected by slight variances of ±2 mm in electrode position. However, the performance was degraded with anatomical variations. Options for improvement of performance, including selection of a different kernel and pre-processing of the frames prior to implementing the classifier, are then examined. This analysis demonstrated that using the radial basis function as the kernel for the SVM classifier and principal component analysis (PCA) to select specific features leads to the most accurate and robust performance. The analysis and results indicate that the coupling of EIT with ML has potential for improvement in the detection of bleeds such as brain haemorrhages.

&lt;div class="section abstract"&gt;&lt;div class="htmlview paragraph"&gt;For 2D surface temperature monitoring applications, a variant of Electrical Impedance Tomography (EIT) was evaluated computationally in this study. Literature examples of poor sensor performance in the center of the 2D domains away from the side electrodes motivated these efforts which seek to overcome some of the previously noted shortcomings. In particular, the use of ‘sensing skins’ with novel tailored baseline conductivities was examined using the EIDORS package for EIT. It was found that the best approach for detecting a temperature hot spot depends on several factors such as the current injection (stimulation) patterns, the measurement patterns, and the reconstruction algorithms. For well-performing combinations of these factors, customized baseline conductivities were assessed and compared to the baseline uniform conductivity.&lt;/div&gt;&lt;div class="htmlview paragraph"&gt;It was discovered that for some EIT applications, a tailored distribution needs to be smooth and that sudden changes in the conductivity gradients should be avoided to prevent the introduction of artifacts in the reconstructed conductivity field. Still, the benefits in terms of improved EIT performance were small for conditions for which the EIT measurements had been ‘optimized’ for the uniform baseline case. Within the limited scope of this study, only two specific cases showed benefits from customized distributions. For one case, a smooth tailored distribution with increased baseline conductivity in the center provided a better separation of two centrally located hot spots. For another case, a smooth tailored distribution with reduced conductivity in the center provided better estimates of the magnitudes of two hot spots near the center of the sensing skin.&lt;/div&gt;&lt;div class="htmlview paragraph"&gt;Overall, the results at hand suggest that improved 2D surface temperature measurement are best served by continued development of measurements and reconstruction algorithms rather than the use of sensing skins with tailored baseline conductivity distributions.&lt;/div&gt;&lt;/div&gt;

The inverse problem solution in electrical impedance tomography (EIT) is sensitive to measurement noise. Due to modeling errors and contact impedance, EIT measurement data are often contaminated by noise, which affects the accuracy of image reconstruction. This study introduces a novel denoising method combining Discrete Wavelet Transform (DWT) with a Denoising Autoencoder (DAE) to effectively address measurement noise. The DWT-DAE method was systematically evaluated on various EIT electrode pairs data, including simulated data with different SNR levels (20-60 dB), water tank measurement data, and KIT4 public dataset. Results show that the DWTDAE method significantly reduces noise in EIT measurement data, improving the SNR from approximately 20-30 dB to over 55 dB, thereby enhancing the quality of reconstructed images. To validate the practical efficacy of our approach, we applied it to the challenging case of intact maize ears. The DWT-DAE method was used to denoise the EIT measurements, followed by image reconstruction using the Convex-Concave Procedure-based Block Sparse Bayesian Learning Algorithm (CCPBSBL). Results indicate that this integrated methodology substantially enhances the accuracy of reconstructed conductivity distributions in maize ears, demonstrating its practical value for agricultural applications.

In this paper, we consider imaging of moving fluids with electrical impedance tomography (EIT). In EIT, the conductivity distribution is reconstructed on the basis of electrical boundary measurements. In the case of time-varying targets—such as moving fluids in process industry—it is advantageous to formulate the reconstruction problem as a state-estimation problem, because the state-estimation approach allows incorporation of target evolution models in the reconstruction. The reconstruction algorithms consist of recursions in which the state predictions given by the evolution model are updated with the information provided by measurements. When monitoring single-phase flow, the evolution of the substance concentration can be described with the convection–diffusion model. The convection–diffusion model includes the fluid velocity field. In our previous studies, we have assumed that the velocity field is known. In this paper, we extend the approach to cases of unknown velocity fields. The velocity field is reconstructed simultaneously with the conductivity distribution by using an extended Kalman filter. The numerical results indicate that estimating the velocity field from EIT measurements is possible—at least to some extent.

Electrical impedance tomography (EIT) is a rising and emerging imaging technique with great potential in many areas, especially in functional brain imaging applications. An EIT system with high speed and accuracy can have many applications to medical devices supporting in diagnosis and treatment of neurological disorders and diseases. In this research, EIT algorithms and hardware are developed and improved to increase reconstructed images' accuracy and decrease the reconstruction time. Due to multiplexer design limitations, EIT measurements are subject to strong capacitive effects from charging and discharging in switching cycles around 300 to 400 samples per 1280 samples (in 10 milliseconds sampling). We developed an algorithm to choose data in steady-state condition only selectively. This method improves the signal-to-noise ratio and results in better reconstruction images. An algorithm to effectively synchronize the beginning points of data was developed to increase the system's speed. This presentation also presents the EIT system's hardware architecture based on Texas Instruments Fixed-Point Digital Signal Processor - TMS320VC5509A, which is low-cost, high potential in popularity the community in the future. For high operation speed, we propose the EIT system used Sitara™ AM57x processors of Texas Instruments.