Multi-frequency Electrical Impedance Tomography Research Articles

DESIGN OF MULIFREQUENCY ELECTRICAL IMPEDANCE TOMOGRAPHY (MfEIT) BASED ON ANALOG DISCOVERY TO DETECT BREAST CANCER

This study aims to design Multifrequency Electrical Impedance Tomography (MfEIT) based on Analog Discovery to detect breast cancer. The MfEIT was built from Analog Discovery which can be used as a signal generator, power supply, voltage control and measuring voltage and phase difference. Other complementary modules are Multiplexer, Instrument amplifier, Voltage Constant Current Source (VCCS), and High Pass Filter (HPF). The MfEIT performance test was carried out on a phantom object made of agar with a certain conductivity that represents breast and cancer tissue. The MfEIT performance testing at a frequency of 10 kHz, 40 kHz, and 80 kHz. The reconstruction from both potential and phase difference data, it shown that are in accordance with the phantom condition, both in number, size and position of anomalies. The reconstruction from the voltage data shown fine at all frequencies, but the phase data produces a good reconstruction image when frequencies was less than 80 kHz.

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.

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.

Cross modality generative learning framework for anatomical transitive Magnetic Resonance Imaging (MRI) from Electrical Impedance Tomography (EIT) image.

This paper presents a cross-modality generative learning framework for transitive magnetic resonance imaging (MRI) from electrical impedance tomography (EIT). The proposed framework is aimed at converting low-resolution EIT images to high-resolution wrist MRI images using a cascaded cycle generative adversarial network (CycleGAN) model. This model comprises three main components: the collection of initial EIT from the medical device, the generation of a high-resolution transitive EIT image from the corresponding MRI image for domain adaptation, and the coalescence of two CycleGAN models for cross-modality generation. The initial EIT image was generated at three different frequencies (70 kHz, 140 kHz, and 200 kHz) using a 16-electrode belt. Wrist T1-weighted images were acquired on a 1.5T MRI. A total of 19 normal volunteers were imaged using both EIT and MRI, which resulted in 713 paired EIT and MRI images. The cascaded CycleGAN, end-to-end CycleGAN, and Pix2Pix models were trained and tested on the same cohort. The proposed method achieved the highest accuracy in bone detection, with 0.97 for the proposed cascaded CycleGAN, 0.68 for end-to-end CycleGAN, and 0.70 for the Pix2Pix model. Visual inspection showed that the proposed method reduced bone-related errors in the MRI-style anatomical reference compared with end-to-end CycleGAN and Pix2Pix. Multifrequency EIT inputs reduced the testing normalized root mean squared error of MRI-style anatomical reference from 67.9% ± 12.7% to 61.4% ± 8.8% compared with that of single-frequency EIT. The mean conductivity values of fat and bone from regularized EIT were 0.0435 ± 0.0379 S/m and 0.0183 ± 0.0154 S/m, respectively, when the anatomical prior was employed. These results demonstrate that the proposed framework is able to generate MRI-style anatomical references from EIT images with a good degree of accuracy.

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.

Towards continuous EIT monitoring for hemorrhagic stroke patients.

The practical implementation of continuous monitoring of stroke patients by Electrical Impedance Tomography (EIT) is addressed. In a previous paper, we have demonstrated EIT sensitivity to cerebral hemodynamics, using scalp-mounted electrodes, very low-noise measurements, and a novel image reconstruction method. In the present paper, we investigate the potential to adapt that system for clinical application, by using 50% fewer electrodes and by incorporating into the measurement protocol an additional high-frequency measurement to provide an effective reference. Previously published image reconstruction methods for multi-frequency EIT are substantially improved by exploiting the forward calculations enabled by the detailed head model, particularly to make the referencing method more robust and to attempt to remove the effects of modelling error. Images are presented from simulation of a typical hemorrhagic stroke and its growth. These results are encouraging for exploration of the potential clinical benefit of the methodology in long-term monitoring of hemorrhagic stroke.

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.

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

Feasibility of Temperature Control by Electrical Impedance Tomography in Hyperthermia.

Simple SummaryOnline treatment monitoring is an important tool to ensure the safety and effectiveness of hyperthermia cancer therapy. However, current solutions provide only sparse/inaccurate data, demand extensive access to complex and expensive infrastructure, or are associated with increased toxicity. In this study, we present a simulation-based evaluation of the feasibility of electrical impedance tomography (EIT) for hyperthermia treatment monitoring. EIT is a low cost, information-rich, non-invasive technique that could potentially be adapted and employed to reconstruct conductivity changes and translate them to temperature- and perfusion-change maps. Using an innovative reconstruction methodology that leverages (ideally personalized) treatment simulations, physics-motivated constraints, multiple frequencies, measurement-derived compensation, and novel numerical approaches, we investigated the impact of factors such as noise and reference model accuracy on the temperature- and perfusion-reconstruction accuracy. Results suggest that EIT can provide valuable real-time monitoring capabilities. As a next step, experimental confirmation under real-world conditions is needed to validate our results.We present a simulation study investigating the feasibility of electrical impedance tomography (EIT) as a low cost, noninvasive technique for hyperthermia (HT) treatment monitoring and adaptation. Temperature rise in tissues leads to perfusion and tissue conductivity changes that can be reconstructed in 3D by EIT to noninvasively map temperature and perfusion. In this study, we developed reconstruction methods and investigated the achievable accuracy of EIT by simulating HT treatmentlike scenarios, using detailed anatomical models with heterogeneous conductivity distributions. The impact of the size and location of the heated region, the voltage measurement signal-to-noise ratio, and the reference model personalization and accuracy were studied. Results showed that by introducing an iterative reconstruction approach, combined with adaptive prior regions and tissue-dependent penalties, planning-based reference models, measurement-based reweighting, and physics-based constraints, it is possible to map conductivity-changes throughout the heated domain, with an accuracy of around 5% and cm-scale spatial resolution. An initial exploration of the use of multifrequency EIT to separate temperature and perfusion effects yielded promising results, indicating that temperature reconstruction accuracy can be in the order of 1 °C. Our results suggest that EIT can provide valuable real-time HT monitoring capabilities. Experimental confirmation in real-world conditions is the next step.

Development of an algorithm for selecting the required frequency of injected current for multi-frequency electrical impedance tomography for tasksrelated to preoperative monitoring of human lung function

This paper proposes an algorithm for selecting the required frequency of injected current for problems of personalized multi-frequency electricalimpedance tomography. The essence of the algorithm is to calculate the rate of change in the recorded difference of potentials for the assigned range of frequencies of injected current, followed by determining the frequency after which the rate of a change in potentials is minimal. Subsequently, the injection parameters are readjusted to the chosen frequency and the complete process of electricalimpedance tomography is started. The proposed solutions were studied on four subjects with different fat mass, defined by bioimpedance analysis. Thus, it seems possible to track the dynamics of a change in the lungs of a certain patient by visualizing the reconstructed conductivity field, taking into consideration its internal features. It was established that in the course of studying lungsby using the method of electricalimpedance tomography, it is necessary to take into account the frequency of injected current at an increase in percentage of fat mass. The results of the studies showing a change in the quality of imaging the breathing process at different frequencies of injected current (from 50 kHz to 400 kHz, with a pitch of 50 kHz) are presented. For the test participants with a fat weight of 7.6 kg, 23.3 kg, 15.2 kg and 37.3 kg, the injection frequency was determined as 150 kHz, 200 kHz, 200 kHz, and 350 kHz, respectively. The proposed algorithm enables visual monitoring of lung function and can be used in the problems of pre- and postoperative monitoring of respiratory function of patients. Its use is particularly relevant for patients connected to an apparatus of artificial lung ventilation.