Multi-frequency Electrical Impedance Research Articles

Complex-valued multi-frequency electrical impedance tomography based on deep neural networks

The utilization of multi-frequency electrical impedance tomography (mfEIT), a non-invasive imaging technique, allows for the visualization of the conductivity distribution in biological tissues across different frequencies. However, the analysis of phase angle information within complex impedance remains a challenge, as most existing deep learning-based mfEIT algorithms are limited to real number processing. To mitigate this limitation, this study proposes a comlex reconstruction method which is inspired by the idea of combining deep learning with traditional reconstruction algorithm. It uses a spare Bayesian learning algorithm in the preprocessing stage that can perform complex arithmetic operations, and fully learns and makes use of the correlation between the real and imaginary parts to reconstruc the distribution of complex-valued conductivity in the measurement area. After that, an altered UNet network is used to further optimize the pre-reconstruction outcomes. The experimental outcomes validate the efficacy of the proposed algorithm in accurately reconstructing the complex-valued conductivity distributions of diverse biological tissues, such as potato and pig kidney, across different frequencies. Furthermore, the algorithm exhibits exceptional performance in mitigating the presence of image artifacts during the reconstruction process.

Non-invasive biomarkers for detecting progression toward hypovolemic cardiovascular instability in a lower body negative pressure model

Occult hemorrhages after trauma can be present insidiously, and if not detected early enough can result in patient death. This study evaluated a hemorrhage model on 18 human subjects, comparing the performance of traditional vital signs to multiple off-the-shelf non-invasive biomarkers. A validated lower body negative pressure (LBNP) model was used to induce progression towards hypovolemic cardiovascular instability. Traditional vital signs included mean arterial pressure (MAP), electrocardiography (ECG), plethysmography (Pleth), and the test systems utilized electrical impedance via commercial electrical impedance tomography (EIT) and multifrequency electrical impedance spectroscopy (EIS) devices. Absolute and relative metrics were used to evaluate the performance in addition to machine learning-based modeling. Relative EIT-based metrics measured on the thorax outperformed vital sign metrics (MAP, ECG, and Pleth) achieving an area-under-the-curve (AUC) of 0.99 (CI 0.95–1.00, 100% sensitivity, 87.5% specificity) at the smallest LBNP change (0–15 mmHg). The best vital sign metric (MAP) at this LBNP change yielded an AUC of 0.6 (CI 0.38–0.79, 100% sensitivity, 25% specificity). Out-of-sample predictive performance from machine learning models were strong, especially when combining signals from multiple technologies simultaneously. EIT, alone or in machine learning-based combination, appears promising as a technology for early detection of progression toward hemodynamic instability.

A CV weighted method for oil-water two-phase flow of non-contact multi-frequency el7ectrical impedance tomography

Electrical tomography (ET) technology has attracted extensive attention in the field of scientific research on oil–water two-phase flow due to its advantages of non-intrusion, non-radiation, and simple equipment structure. Affected by Maxwell–Wagner-Sillars (MWS) effect, more imaging information can be obtained by changing the excitation frequency, especially for the mixtures with conductive phases. In this paper, a CV (coefficient of variation )-weighted method of non-contact multi-frequency electrical impedance tomography (NM-EIT) system is carried out for water-oil two-phase flow imaging. Firstly, the frequency response of the measurement system is studied and the electrical impedance information of medium in the region of interest (RoI) is observed. Secondly, the measured electrical impedance information with with different excitation frequencies are weighted by CV method. Finally, combined with the classic image reconstruction algorithm, the simulation and static experiment are carried out to verify the reconstruction effect of the proposed method. The imaging accuracies under different frequencies and flow patterns are analyzed, and a measurement experimental platform is set up to verify the reconstruction effect of the proposed method. The experimental results show that the CV-based multi-frequency weighted method maintains a higher imaging accuracy than single-frequency imaging.

Multi-Path Fusion in SFCF-Net for Enhanced Multi-Frequency Electrical Impedance Tomography.

Multi-frequency electrical impedance tomography (mfEIT) offers a nondestructive imaging technology that reconstructs the distribution of electrical characteristics within a subject based on the impedance spectral differences among biological tissues. However, the technology faces challenges in imaging multi-class lesion targets when the conductivity of background tissues is frequency-dependent. To address these issues, we propose a spatial-frequency cross-fusion network (SFCF-Net) imaging algorithm, built on a multi-path fusion structure. This algorithm uses multi-path structures and hyper-dense connections to capture both spatial and frequency correlations between multi-frequency conductivity images, which achieves differential imaging for lesion targets of multiple categories through cross-fusion of information. According to both simulation and physical experiment results, the proposed SFCF-Net algorithm shows an excellent performance in terms of lesion imaging and category discrimination compared to the weighted frequency-difference, U-Net, and MMV-Net algorithms. The proposed algorithm enhances the ability of mfEIT to simultaneously obtain both structural and spectral information from the tissue being examined and improves the accuracy and reliability of mfEIT, opening new avenues for its application in clinical diagnostics and treatment monitoring.

MMV-Net: A Multiple Measurement Vector Network for Multifrequency Electrical Impedance Tomography.

Multifrequency electrical impedance tomography (mfEIT) is an emerging biomedical imaging modality to reveal frequency-dependent conductivity distributions in biomedical applications. Conventional model-based image reconstruction methods suffer from low spatial resolution, unconstrained frequency correlation, and high computational cost. Deep learning has been extensively applied in solving the EIT inverse problem in biomedical and industrial process imaging. However, most existing learning-based approaches deal with the single-frequency setup, which is inefficient and ineffective when extended to the multifrequency setup. This article presents a multiple measurement vector (MMV) model-based learning algorithm named MMV-Net to solve the mfEIT image reconstruction problem. MMV-Net considers the correlations between mfEIT images and unfolds the update steps of the Alternating Direction Method of Multipliers for the MMV problem (MMV-ADMM). The nonlinear shrinkage operator associated with the weighted l2,1 regularization term of MMV-ADMM is generalized in MMV-Net with a cascade of a Spatial Self-Attention module and a Convolutional Long Short-Term Memory (ConvLSTM) module to better capture intrafrequency and interfrequency dependencies. The proposed MMV-Net was validated on our Edinburgh mfEIT Dataset and a series of comprehensive experiments. The results show superior image quality, convergence performance, noise robustness, and computational efficiency against the conventional MMV-ADMM and the state-of-the-art deep learning methods.

Exploratory study of a multifrequency EIT-based method for detecting intracranial abnormalities.

The purpose of this paper is to compare the differences in the features of multifrequency electrical impedance tomography (MFEIT) images of human heads between healthy subjects and patients with brain diseases and to explore the possibility of applying MFEIT to intracranial abnormality detection. Sixteen healthy volunteers and 8 patients with brain diseases were recruited as subjects, and the cerebral MFEIT data of 9 frequencies in the range of 21 kHz - 100 kHz of all subjects were acquired with an MFEIT system. MFEIT image sequences were obtained according to certain imaging algorithms, and the area ratio of the ROI (AR_ROI) and the mean value of the reconstructed resistivity change of the ROI (MVRRC_ROI) on both the left and right sides of these images were extracted. The geometric asymmetry index (GAI) and intensity asymmetry index (IAI) were further proposed to characterize the symmetry of MFEIT images based on the extracted indices and to statistically compare and analyze the differences between the two groups of subjects on MFEIT images. There were no significant differences in either the AR_ROI or the MVRRC_ROI between the two sides of the brains of healthy volunteers (p > 0.05); some of the MFEIT images mainly in the range of 30 kHz - 60 kHz of patients with brain diseases showed stronger resistivity distributions (larger area or stronger signal) that were approximately symmetric with the location of the lesions. However, statistical analysis showed that the AR_ROI and the MVRRC_ROI on the healthy sides of MFEIT images of patients with unilateral brain disease were not significantly different from those on the affected side (p > 0.05). The GAI and IAI were higher in all patients with brain diseases than in healthy volunteers except for 80 kHz (p < 0.05). There were significant differences in the geometric symmetry and the signal intensity symmetry of the reconstructed targets in the MFEIT images between healthy volunteers and patients with brain diseases, and the above findings provide a reference for the rapid detection of intracranial abnormalities using MFEIT images and may provide a basis for further exploration of MFEIT for the detection of brain diseases.

Electronic Immunoassay Using Enzymatic Metallization on Microparticles.

We present here an inexpensive method for generating a sensitive direct electronic readout in bead-based immunoassays without the use of any intermediate optical instrumentation (e.g., lasers, photomultipliers, etc.). Analyte binding to capture antigen-coated beads or microparticles is converted to probe-directed enzymatically amplified silver metallization on microparticle surfaces. Individual microparticles are then rapidly characterized in a high-throughput manner via single-bead multifrequency electrical impedance spectra captured using a simple and inexpensive microfluidic impedance spectrometry system we develop here, where they flow through a three-dimensional (3D)-printed plastic microaperture sandwiched between plated through-hole electrodes on a printed circuit board. Metallized microparticles are found to have unique impedance signatures distinguishing them from unmetallized ones. Coupled with a machine learning algorithm, this enables a simple electronic readout of the silver metallization density on microparticle surfaces and hence the underlying analyte binding. Here, we also demonstrate the use of this scheme to measure the antibody response to the viral nucleocapsid protein in convalescent COVID-19 patient serum.

The Assessment of Body Composition and Nutritional Awareness of Football Players According to Age.

The optimal body composition for health is an individual trait and is determined by genetic factors, sex, age, somatotype, physical activity, and individual variability. The present study aims to assess how professional football players' body composition has changed over the training macrocycle in various age groups and to determine the correlation between nutritional awareness and body composition maintenance. Thirty-eight football players participated in the study, with 16 players classified in the younger age group (19-25) and 22 in the older age group (26-31). Using the direct segmented multi-frequency electrical impedance analysis technique, the athletes' body composition was assessed six times across a training macrocycle made up of preparatory, competitive, and transitional periods. The Sports Nutrition Knowledge Questionnaire was used to evaluate nutrition knowledge. The above correlations show that both younger and older athletes with higher awareness are better able to adjust their nutrition to meet the goals of the preparation period and can achieve greater gains in muscle mass and greater reductions in body fat. According to the study's results, athletes who are better conscious of their nutritional needs during competition experience less muscle loss and exhibit more consistent body weight and BMI levels. Football players' body composition suffers detrimental alterations throughout the transition period. Higher body mass, lean body mass content, and skeletal muscle mass are traits of older players. Higher nutritional knowledge reduces the negative modifications of body composition consisting of muscle mass reduction and fat gain. Nutritional knowledge influences the stability of body composition in both age groups during all the analyzed periods: Preparation, competition, and transition.

Multi-frequency complex conductivity sparse imaging of plant root zone based on space-frequency correlation

The root zone provides indispensable water and nutrients for plants and monitoring the dynamics of the root zone is highly important. However, due to the opacity and complexity of this zone, in situ characterization of the root zone poses many challenges. A sparse Bayesian learning complex model based on spatial frequency correlation (SF-SBLC) was proposed for fast nondestructive imaging of the growth trends in the root zone of plants, and multi-frequency electrical impedance tomography (mfEIT) was used to reconstruct the complex conductivity distribution of the root zone. To achieve the best sparse coefficients and enhance the quality of the reconstructed images, the SF-SBLC learned and utilized the spatial and frequency correlations of the complex conductivities at different frequencies. The reconstruction algorithm was further optimized using a concave-convex process method and a multi-measure vector model, which decreased its time complexity. When compared to conventional mfEIT reconstruction algorithms, the experimental findings demonstrated that the SF-SBLC could greatly reduce imaging artifacts, generate higher reconstruction performance (in terms of the image correlation coefficient, relative image error, resolution, position error, shape deformation, and ringing), and shorten imaging time. Moreover, the SF-SBLC was able to recreate the root zone's shape, size, and location in both a nutrient solution and a horticulture substrate. Finally, the effective volume of the root zone is defined and calculated based on the 3D EIT image. This technique offers a prospective tool that might be useful for monitoring the root growth trends and expansion rates of potted plants.

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

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

Variational Mode Decomposition-based Synchronous Multi-Frequency Electrical Impedance Tomography

Electrical Impedance Tomography (EIT) can perform non-invasive, low-cost, safe, fast, and simple system structure and functional imaging to map the distribution and changes of root zone. Multi frequency EIT solves the problem that single-frequency EIT can only carry more impedance information than a given single excitation frequency. It still remains challenges to simultaneously obtain multi-frequency electrical impedance tomography. To address the problem, a mixed signal superimposed by multiple frequencies is injected to the object. Essentially, separating the measured mixed voltage signals, which can be used to obtain electrical impedance information at different frequencies at the same time quickly. Since the measurement signal is a multi-frequency signal, the effect of decomposing the multi-frequency signal directly affects the accuracy of imaging. In order to obtain more accurate data, this article used the variational mode decomposition (VMD) method to decompose the measured multi-frequency signal. Accurate amplitude and phase information could be obtained simultaneously at the same time in multi-frequency excitation, and these data could be used to reconstruct electrical impedance distribution The results showed that the proposed method can achieve the expected imaging effect. It was concluded that using the variational modal decomposition method to process the data of multi-frequency signals is more accurate and the imaging effect is better, and it can be applied to multi-frequency electrical impedance imaging in practice.

Using machine learning algorithms to enhance the diagnostic performance of electrical impedance myography.

We assessed the classification performance of machine learning (ML) using multifrequency electrical impedance myography (EIM) values to improve upon diagnostic outcomes as compared to those based on a single EIM value. EIM data was obtained from unilateral excised gastrocnemius in eighty diseased mice (26 D2-mdx, Duchenne muscular dystrophy model, 39 SOD1G93A ALS model, and 15 db/db, a model of obesity-induced muscle atrophy) and 33 wild-type (WT) animals. We assessed the classification performance of a ML random forest algorithm incorporating all the data (multifrequency resistance, reactance and phase values) comparing it to the 50 kHz phase value alone. ML outperformed the 50 kHz analysis as based on receiver-operating characteristic curves and measurement of the area under the curve (AUC). For example, comparing all diseases together versus WT from the test set outputs, the AUC was 0.52 for 50 kHz phase, but was 0.94 for the ML model. Similarly, when comparing ALS versus WT, the AUCs were 0.79 for 50 kHz phase and 0.99 for ML. Multifrequency EIM using ML improves upon classification compared to that achieved with a single-frequency value. ML approaches should be considered in all future basic and clinical diagnostic applications of EIM.

Multi-frequency Electrical Impedance Pneumography System as Point-Of-Care Device.

In this work we present the development of a multi-frequency electrical impedance pneumography (EIP) system based on a portable acquisition device and a mobile platform. This design is intended as an upgrade to our previous device for clinical use in the screening of patients with pulmonary diseases. The acquisition device uses the bioimpedance analog front end MAX30001, a mux/demux stage, Bluetooth 4.0 communication and an ESP32 microcontroller unit. It generates an excitation current of 8 μApp in a range of selectable frequencies from 1 kHz to 130 kHz. The mobile platform provides a real-time respiration signal at 64 S/s and allows the configuration of the device. Results show less error in higher frequencies, which are the most common in these applications, ensuring the feasibility of the system for use in humans. Some hardware constraints related to EIP integrated circuits are discussed, an their effect in the signal acquisition.

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

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

Mask-Guided Spatial–Temporal Graph Neural Network for Multifrequency Electrical Impedance Tomography

Multi-frequency Electrical Impedance Tomography (mfEIT) is an emerging biomedical imaging modality that exploits frequency-dependent electrical properties. The mfEIT-image-reconstruction problem for cell imaging is particularly challenging due to weak signals from miniaturized sensors and high sensitivity to modelling errors. Existing approaches are primarily based on the linearized model and few are applied to the miniaturized setup. Here we report a Mask-guided Spatial-Temporal Graph Neural Network (M-STGNN) to reconstruct mfEIT images in cell culture imaging. The M-STGNN captures simultaneously spatial and frequency correlations, and the spatial correlation is further constrained by geometric structures from auxiliary binary masks, such as CT or microscopic images. We validate the mfEIT approach through numerical simulations and experiments on MCF-7 human breast cancer cell aggregates. The results demonstrate the superiority of M-STGNN over the state of the art with an improvement of approximately 10.7% under the experimental setup. It can be readily extended to multi-modal biomedical imaging applications.

Design of multi-frequency electrical impedance tomography system based on multisine excitation and integer-period sampling

Starting from the principle that the integer-period sampling (IPS) of periodic signals is free of spectrum leakage, in this paper we propose the multisine-IPS theory, deduce theoretically the sampling rate setting formula of multisine-IPS condition for the first time, and build its realization method based on field-programmable gate array (FPGA) plus digital-to-analog converter (DAC) plus analog-to-digital converter (ADC). A new multi-frequency electrical impedance tomography (mfEIT) system based on multisine excitation and its IPS theory is developed, and a dual-target imaging model including a carrot stick and a cucumber stick is designed. The experiments of multi-frequency time-difference imaging and frequency-difference imaging are carried out on the mfEIT system. The experimental results show that the newly-designed mfEIT system can achieve full-band impedance measurements on multiple objective tissue boundary at 20 frequency points (2–997 kHz) within one fundamental period (1 ms), and the structure and position of biological tissues with different electrical properties can also be distinguished from the resulting images. The proposed multisine-IPS theory and its implementation method can complete a full-band impedance measurement within one multisine fundamental period, which lays a theoretical and technical foundation for developing high-speed mfEIT system.

Multifrequency Electrical Impedance Tomography With Ratiometric Preprocessing for Imaging Human Body Compartments

Reconstructing the structural images of human body compartments as point-of-care imaging could be possible using electrical impedance tomography (EIT), but the reconstructed image deteriorates due to high conductivity contrast <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mu $ </tex-math></inline-formula> between anomaly and background. In this study, a multifrequency EIT (mf-EIT) with a ratiometric preprocessing (ratiometric EIT) has been developed in order to minimize <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mu $ </tex-math></inline-formula> while maintaining high distinguishability without any <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$a$ </tex-math></inline-formula> <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">priori</i> information. The preprocessing of ratiometric EIT is achieved by extending the framework of ratiometric methods that uses the ratio of two measurement signals. Based on the proposed ratiometric preprocessing, ratiometric frequency-difference adjacent EIT (rfda-EIT) is newly derived. The rfda-EIT is qualitatively and quantitatively evaluated by numerical simulation under the variant conditions of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mu $ </tex-math></inline-formula> (=95%–10%) and experiment with eight subjects’ calves. As the results, the rfda-EIT scores the area ratio error of subcutaneous adipose tissue (SAT) 17.09% < ARE <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$^{\mathrm {SAT}} &lt; 28.13$ </tex-math></inline-formula> %, the position error of bone tibia 0.14% < PE <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$^{\mathrm {tibia}} &lt; 10.19$ </tex-math></inline-formula> %, the position error of bone fibula 9.51% < PE <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$^{\mathrm {fibula}} &lt; 1.74$ </tex-math></inline-formula> %, and the correlation coefficients <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$0.79&lt; $ </tex-math></inline-formula> CC < 0.91 in the numerical simulation. The total mean area ratio error <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\langle $ </tex-math></inline-formula> ARE <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$^{\mathrm {SAT}}\rangle $ </tex-math></inline-formula> by the rfda-EIT is lower than the error by the classical frequency-difference adjacent EIT (fda-EIT) from 12.70% to 5.18% in the experiment. Moreover, the true positive rate TPR <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$^{\mathrm {tibia}}$ </tex-math></inline-formula> for the bone tibia detection by the rfda-EIT is increased from 50.0% to 87.5% compared with the rate by the fda-EIT.

Experimental dependences of measurement data on the volume of inhaled air in multi-frequency electrical impedance tomography

This paper proposes an approach to modeling the process of artificial ventilation of human lungs by their controlled filling with a fixed volume of air, using an incentive spirometer Coach 2. This makes it possible to simulate the ventilation process for a healthy person and to link the assigned respiratory volume to measurement data. The results of experimental studies of the developed system of multifrequency electric impedance tomography are presented. The tests were performed for the frequency range from 50 kHz to 400 kHz (with a pitch of 50 kHz) at assigned respiratory volumes from 500 ml to 4,000 ml (with a pitch of 500 ml) for five inhalation/exhalation cycles. The scheme of research: active inhalation ‒ passive exhalation, the number of tested volunteers ‒ 3 people from the developers of the system. As a result, the dependences of the measured values of changes in potentials on the frequency of injected current for different respiratory volumes in three test participants without pathologies of the respiratory function and the external respiration function were obtained. The obtained results of the experimental studies show that there is a dependence of the value of the measurement data both on the volume of inhaled air and on the frequency of the injected current. This feature can be used to develop a number of medical devices for personalized monitoring of human lung function. It was also revealed that there are frequencies at which the maximum spread of measurement data according to the results of a series of repeated experiments is observed. At the same time, the nature of the change in the measurement data of the EIT at an increase in the volume of inhaled air is the same for all test participants. It is assumed that this feature can also be used to increase the EIT personalization degree

Retrieval of the conductivity spectrum of tissues in vitro with novel multimodal tomography

Objective: Imaging of tissue engineered three-dimensional (3D) specimens is challenging due to their thickness. We propose a novel multimodal imaging technique to obtain multi-physical 3D images and the electrical conductivity spectrum of tissue engineered specimens in vitro. Approach: We combine simultaneous recording of rotational multifrequency electrical impedance tomography (R-mfEIT) with optical projection tomography (OPT). Structural details of the specimen provided by OPT are used here as geometrical priors for R-mfEIT. Main results: This data fusion enables accurate retrieval of the conductivity spectrum of the specimen. We demonstrate experimentally the feasibility of the proposed technique using a potato phantom, adipose and liver tissues, and stem cells in biomaterial spheroids. The results indicate that the proposed technique can distinguish between viable and dead tissues and detect the presence of stem cells. Significance: This technique is expected to become a valuable tool for monitoring tissue engineered specimens’ growth and viability in vitro.

Designing a bench for testing medically and technically an information and measuring system for multi-frequency electrical impedance tomography of human lungs

This paper solves the problem of developing and creating multifunctional tools for conducting research into systems of multi-frequency electrical impedance tomography of human lungs. A test bench consisting of modern medical equipment, simulating pre-and postoperative environments to create conditions that are most similar to clinical ones, under which it is planned to operate multi-frequency electrical impedance tomography systems, was proposed and manufactured. This makes it possible to reduce significantly the time for approbation, testing, and elimination of practical inaccuracies and problems of clinical application of the developed medical and technical facilities. A positive result is achieved due to the possibility of forming new test plans with specified conditions and different levels of complexity. This enables enhancing the effectiveness of subsequent clinical tests on patients who are treated in a resuscitation unit or an intensive care unit. The operability of the bench is proved by the repeatability of obtained results of monitoring ventilation and perfusion for each examined person, the continuity of dynamic visualization of the breathing process, as well as a high degree of correlation of obtained values of differences of potentials with the readings of a bedside monitor of a patient. An information and measuring system of multi-frequency electric impedance tomography of human lungs, developed by the author earlier, was used as the EIT device. The EIT tests were performed for the frequency range of 50 kHz – 400 kHz at a current of 5 mA. All experimental studies involved volunteers who gave written information consent to participate in the tests. The results of the research show that the proposed bench can be used in practice to solve a wide range of scientific and applied problems in the field of electrical impedance tomography