A multi-frequency electrical impedance tomography system for real-time 2D and 3D imaging

High-resolution conductivity reconstruction by electrical impedance tomography using structure-aware hybrid-fusion learning

BACKGROUND

Electrical impedance tomography (EIT) has gained considerable attention in the medical field for the diagnosis of lung-related diseases, owing to its non-invasive and real-time characteristics. However, due to the ill-posedness and underdetermined nature of the inverse problem in EIT, suboptimal reconstruction performance and reduced robustness against the measurement noise and modeling errors are common issues.

OBJECTIVES

This study aims to mine the deep feature information from measurement voltages, acquired from the EIT sensor, to reconstruct the high-resolution conductivity distribution and enhance the robustness against the measurement noise and modeling errors using the deep learning method.

METHODS

A novel data-driven method named the structure-aware hybrid-fusion learning (SA-HFL) is proposed. SA-HFL is composed of three main components: a segmentation branch, a conductivity reconstruction branch, and a feature fusion module. These branches work in tandem to extract different feature information from the measurement voltage, which is then fused to reconstruct the conductivity distribution. The unique aspect of this network is its ability to utilize different features extracted from various branches to accomplish reconstruction objectives. To supervise the training of the network, we generated regular-shaped and lung-shaped EIT datasets through numerical calculations.

RESULTS

The simulations and three experiments demonstrate that the proposed SA-HFL exhibits superior performance in qualitative and quantitative analyses, compared with five cutting-edge deep learning networks and the optical image-guided group sparsity (IGGS) method. The evaluation metrics, relative error (RE), mean structural similarity index (MSSIM), and peak signal-to-noise ratio (PSNR), are improved by implementing the SA-HFL method. For the regular-shaped dataset, the values are 0.119 (RE), 0.9882 (MSSIM), and 31.03 (PSNR). For the lung-shaped dataset, the values are 0.257 (RE), 0.9151 (MSSIM), and 18.67 (PSNR). Furthermore, the proposed network can be executed with appropriate parameters and efficient floating-point operations per second (FLOPs), concerning network complexity and inference speed.

CONCLUSIONS

The reconstruction results indicate that fusing feature information from different branches enhances the accuracy of conductivity reconstruction in the EIT inverse problem. Moreover, the study shows that fusing different modalities of information to reconstruct the EIT conductivity distribution may be a future development direction.

A low-cost, portable 32-channel EIT system with four rings based on AFE4300 for body composition analysis

A proposed low-cost, portable, 32-channel (4 rings of 8-channel) Electrical Impedance Tomography (EIT) system based on the AFE4300 analog front-end for body composition measurement. Each ring allows obtaining the conductivity distribution of 4 cross sections, 4 cm apart; to analyze the behavior of conductivity in a volume. The switching of the 4 rings and the current injection and voltage measurement patterns are done with three Texas Instruments 74HC4067 multiplexers, which are managed by an ESP32 board. The proposed system has an average signal-to-noise ratio of 74.71 dB and a frame rate of 50 fps. The sensitivity tests to impedance and volume changes consisted of introducing 4 tubes of different diameters (2 steel and 2 polyvinyl chloride) into a tank with saline solution; then conductivity distribution images were generated in 4 cross-sections of the tank, using the algorithms Gauss-Newton and Noser. Finally, the global impedance index (GI) is calculated to estimate the volume of each tube inside the tank. The results show that the proposed system is highly sensitive to impedance and volume changes, being a promising system for monitoring tissues, and fluids biological.

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.

Robust electrical impedance tomography for biological application: A mini review

Electrical impedance tomography (EIT) has been used by researchers across several areas because of its low-cost and no-radiation properties. Researchers use complex conductivity in bioimpedance experiments to evaluate changes in various indicators within the image target. The diverse volumes and edges of biological tissues and the large impedance range impose dedicated demands on hardware design. The EIT hardware with a high signal-to-noise ratio (SNR), fast scanning and suitable for the impedance range of the image target is a fundamental foundation that EIT research needs to be equipped with. Understanding the characteristics of this technique and state-of-the-art design will accelerate the development of the robust system and provide a guidance for the superior performance of next-generation EIT. This review explores the hardware strategies for EIT proposed in the literature.

Real-Time Monitoring Using Multiplexed Multi-Electrode Bioelectrical Impedance Spectroscopy for the Stratification of Vascularized Composite Allografts: A Perspective on Predictive Analytics

Vascularized composite allotransplantation addresses injuries to complex anatomical structures such as the face, hand, and abdominal wall. Prolonged static cold storage of vascularized composite allografts (VCA) incurs damage and imposes transportation limits to their viability and availability. Tissue ischemia, the major clinical indication, is strongly correlated with negative transplantation outcomes. Machine perfusion and normothermia can extend preservation times. This perspective introduces multiplexed multi-electrode bioimpedance spectroscopy (MMBIS), an established bioanalytical method to quantify the interaction of the electrical current with tissue components, capable of measuring tissue edema, as a quantitative, noninvasive, real-time, continuous monitoring technique to provide crucially needed assessment of graft preservation efficacy and viability. MMBIS must be developed, and appropriate models explored to address the highly complex multi-tissue structures and time-temperature changes of VCA. Combined with artificial intelligence (AI), MMBIS can serve to stratify allografts for improvement in transplantation outcomes.

Impedance-Optical Dual-Modal Cell Culture Imaging With Learning-Based Information Fusion

While Electrical Impedance Tomography (EIT) has found many biomedicine applications, better image quality is needed to provide quantitative analysis for tissue engineering and regenerative medicine. This paper reports an impedance-optical dual-modal imaging framework that primarily targets at high-quality 3D cell culture imaging and can be extended to other tissue engineering applications. The framework comprises three components, i.e., an impedance-optical dual-modal sensor, the guidance image processing algorithm, and a deep learning model named multi-scale feature cross fusion network (MSFCF-Net) for information fusion. The MSFCF-Net has two inputs, i.e., the EIT measurement and a binary mask image generated by the guidance image processing algorithm, whose input is an RGB microscopic image. The network then effectively fuses the information from the two different imaging modalities and generates the final conductivity image. We assess the performance of the proposed dual-modal framework by numerical simulation and MCF-7 cell imaging experiments. The results show that the proposed method could improve the image quality notably, indicating that impedance-optical joint imaging has the potential to reveal the structural and functional information of tissue-level targets simultaneously.

Hardware for cell culture electrical impedance tomography: A critical review

Human cell cultures are powerful laboratory tools for biological models of diseases, drug development, and tissue engineering. However, the success of biological experiments often depends on real-time monitoring of the culture state. Conventional culture evaluation methods consist of end-point laborious techniques, not capable of real-time operation and not suitable for three-dimensional cultures. Electrical Impedance Tomography (EIT) is a non-invasive imaging technique with high potential to be used in cell culture monitoring due to its biocompatibility, non-invasiveness, high temporal resolution, compact hardware, automatic operation, and high throughput. This review approaches the different hardware strategies for cell culture EIT that are presented in the literature, discussing the main components of the measurement system: excitation circuit, voltage/current sensing, switching stage, signal specifications, electrode configurations, measurement protocols, and calibration strategies. The different approaches are qualitatively discussed and compared, and design guidelines are proposed.

A FPGA-based adaptive differential current source for electrical impedance tomography

A high output impedance current source with a wide bandwidth is needed in electrical impedance tomography systems. Limitations appear mainly at higher frequencies and non-simple loads. In order to adjust the output current, the amplitude and phase are made to achieve the expected value automatically. A current source based on the field programmable gate array is designed. In this paper, we proposed a double DAC differential current source structure. By measuring the voltage of the sampling resistor in series with the load and using the proposed dynamic reference point demodulation algorithm, the actual current amplitude and phase on the load can be quickly obtained. Through the adaptive compensation module, the output current is adjusted to the expected value. The experimental results show that the output resistance of the current source can reach 10 MΩ and the output capacitance can be less than 0.8 pF in the frequency range of 10 kHz-1.28 MHz. At the same time, the current amplitude attenuation is less than 0.016%, and the phase error is less than 0.0025° after compensation. Therefore, the proposed current source achieves widebands, biocompatibility, and high precision.

An Imaged Based Method for Universal Performance Evaluation of Electrical Impedance Tomography Systems

This paper describes a simple and reproducible method for universal evaluation of the performance of electrical impedance tomography (EIT) systems using reconstructed images. To address the issues where common electrical parameters are not directly related to the quality of EIT images, based on objective full reference (FR) image quality assessment, the method provides a visually distinguishable hot colormap and two new FR metrics, the global and the more specific 'region of interest'. A passive 16 electrode EIT system using an application specific integrated circuit front-end was used to evaluate the proposed method. The measured results show, both visually and in terms of the proposed FR metrics, the impact on recorded EIT images with different design parameters and non-idealities. The paper also compares the image results of a passive electrode system with a matched 'single variable' active electrode system and demonstrates the merit of an active electrode system for noise interference. A figure of merit based on the FR metrics is proposed.

Simultaneous Imaging of Bio- and Non-Conductive Targets by Combining Frequency and Time Difference Imaging Methods in Electrical Impedance Tomography

As a promising medical imaging modality, electrical impedance tomography (EIT) can image the electrical properties within a region of interest using electrical measurements applied at electrodes on the region boundary. This paper proposes to combine frequency and time difference imaging methods in EIT to simultaneously image bio- and non-conductive targets, where the image fusion is accomplished by applying a wavelet-based technique. To enable image fusion, both time and frequency difference imaging methods are investigated regarding the reconstruction of bio- or non-conductive inclusions in the target region at varied excitation frequencies, indicating that none of those two methods can tackle with the scenarios where both bio- and non-conductive inclusions exist. This dilemma can be resolved by fusing the time difference (td) and appropriate frequency difference (fd) EIT images since they are complementary to each other. Through simulation and in vitro experiment, it is demonstrated that the proposed fusion method can reasonably reconstruct both the bio- and non-conductive inclusions within the lung models established to simulate the ventilation process, which is expected to be beneficial for the diagnosis of lung-tissue related diseases by EIT.

A Three-Dimensional Sensor to Recognize Amyloid-β in Blood Plasma of Patients

Detecting amyloid beta (Aβ) in unpurified blood to diagnose Alzheimer's disease (AD) is challenging owing to low concentrations of Aβ and the presence of many other substances in the blood. Here, we propose a 3D sensor for AD diagnosis using blood plasma, with pairs of 3D silicon micropillar electrodes with a comprehensive circuit configuration. The sensor is developed with synthesized artificial peptide and impedance analysis based on a maximum signal-to-noise ratio. Its sensitivity and selectivity were verified using an in vitro test based on samples of human blood serum, which showed its feasibility for application in diagnosis of AD by testing blood plasma of the AD patient. The 3D sensor is designed to improve reliability by checking the impedance of each pair multiple times via constructing a reference pair and a working pair on the same sensor. Therefore, we demonstrate the ability of the 3D sensor to recognize cases of AD using blood plasma and introduce its potential as a self-health care sensor for AD patients.

Data preprocessing methods for electrical impedance tomography: a review

OBJECTIVE

Electrical impedance tomography (EIT) is a promising measurement technique in applications, especially in industrial monitoring and clinical diagnosis. However, two major drawbacks exist that limit the spatial resolution of reconstructed EIT images, i.e. the 'soft field' effect and the ill-posed problem. In recent years, apart from the development of reconstruction algorithms, some preprocessing methods for measured data or sensitivity maps have also been proposed to reduce these negative effects. It is necessary to find the optimal preprocessing method for various EIT reconstruction algorithms.

APPROACH

In this paper, seven typical data preprocessing methods for EIT are reviewed. The image qualities obtained using these methods are evaluated and compared in simulations, and their applicable ranges and combination effects are summarized.

MAIN RESULTS

The results show that all the reviewed methods can enhance the quality of EIT reconstructed images to different extents, and there is an optimal one under any given reconstruction algorithm. In addition, most of the reviewed methods do not work well when using the Tikhonov regularization algorithm.

SIGNIFICANCE

This paper introduces the preprocessing method to EIT, and the quality of reconstructed images obtained using these methods is evaluated through simulations. The results can provide a reference for practical applications.

A Review on Electrical Impedance Tomography Spectroscopy

Electrical Impedance Tomography Spectroscopy (EITS) enables the reconstruction of material distributions inside an object based on the frequency-dependent characteristics of different substances. In this paper, we present a review of EITS focusing on physical principles of the technology, sensor geometries, existing measurement systems, reconstruction algorithms, and image representation methods. In addition, a novel imaging method is proposed which could fill some of the gaps found in the literature. As an example of an application, EITS of ice and water mixtures is used.

A 1 MHz Miniaturized Electrical Impedance Tomography System for Prostate Imaging

An ASIC for a high frequency electrical impedance tomography (EIT) imaging system for prostate cancer screening is presented. The ASIC enables a small form-factor architecture, which ensures high signal-to-noise ratio (SNR) at MHz frequencies. The 4-channel ASIC was designed and fabricated in a standard CMOS 0.18- μm technology and integrates a novel current driver for current stimulus, instrumentation amplifier to interface with the tissue, VGA to provide variable gain and ADC with SPI interface for digitization. A prototype miniaturized EIT system was built and it was evaluated using a model transrectal imaging probe immersed into a tank filled with saline and a metal inclusion that demonstrated the open-domain problem of imaging prostate cancer lesion. The system maintained an SNR between 66 and 76 dB over the frequency range of 500 Hz to 1 MHz. Also, it produced reconstructed EIT images that depicted the presence of the small metal inclusion that modeled a prostate cancer imaging application.

Bladder Volume Monitoring Using Electrical Impedance Tomography With Simultaneous Multi-Tone Tissue Stimulation and DFT-Based Impedance Calculation Inside an FPGA

In this article, a novel method for measuring the volume of the urinary bladder non-invasively is presented that relies on the principles dictated by Electrical Impedance Tomography (EIT). The electronic prototype responsible for injecting innocuous electrical currents to the lower abdominal region and measuring the developed voltage levels is fully described, as well as the computational models for resolution of the so-called Forward and Inverse Problems in Imaging. The simultaneous multi-tone injection of current provided by a high performance Field Programmable Gate Array (FPGA), combined with impedance estimation by the Discrete Fourier Transform (DFT) constitutes a novelty in Urodynamics with potential to monitor continuously the intravesical volume of patients in a much faster and comfortable way than traditional transurethral catheterization methods. The resolution of the Inverse Problem is performed by the Gauss-Newton method with Laplacian regularization, allowing to obtain a sectional representation of the volume of urine encompassed by the bladder and surrounding body tissues. Experimentation has been carried out with synthetic phantoms and human subjects with results showing a good correlation between the levels of abdominal admittivity acquired by the EIT system and the volume of ingested water.

Process Monitoring of Antisolvent Based Crystallization in Low Conductivity Solutions Using Electrical Impedance Spectroscopy and 2-D Electrical Resistance Tomography

Industrial process monitoring is an important field of research where different chemical processes are monitored and controlled. In this work, electrical impedance spectroscopy (EIS) was used to analyze antisolvent based crystallization of sucrose solutions. The impedance and phase spectra were recorded for four known sucrose concentrations in water, and for each case, four predetermined amounts of ethanol were added. As a result, sixteen different solutions involving sucrose solutions of different concentrations and ethanol to water ratios were analyzed. Significant differences were observed in the magnitude and phase spectra of the solutions in the frequency range of 50 kHz to 300 kHz. The experimentally obtained data from the EIS were converted into frequency response models. Three continuous-time transfer function models of the first-order, second-order, and a second-order with a zero were estimated and compared. In addition, a 2-D electrical resistance tomography (ERT) system with a low conductivity sensor unit was designed and tested with demineralized water, tap water and industrial food grade saturated sucrose solution. Non-conducting phantom and sugar crystals were observed within the saturated sucrose solution using the Bayesian reconstruction algorithm. These demonstrations have the potential to be developed into a multi-frequency ERT systems for monitoring the distribution of the crystals in the reactor. The EIS modality can be a complementary process analytical technology (PAT) tool indicating supersaturation status and provide quality assurance.

Optimal similarity norm for electrical tomography based on Bregman divergence

Electrical Tomography (ET) is an advanced visualization technique, which can reconstruct all targets in an investigated field based on boundary measurements. Since the spatial resolution in the ET process can be greatly affected by the selected similarity norm, different norms may result in different ET time and spatial resolutions. In the tomographic applications nowadays, Bregman divergence (BD) has attracted increasing attention. BDs are a family of generalized similarity norm, and they can measure the similarity/difference between any two targets more accurately. Specifically, the mostly used similarity norm in the ET process (e.g., L₂-norm) is only a special case of the BD family. As the key step of applying BD to the ET process, an execution method is proposed in this paper, together with the selection criteria for the optimal norm in the BD family. Simulations and experiments were conducted, and the results show that the use of an optimal BD can effectively improve the spatial resolution of an ET image.

A 122 fps, 1 MHz Bandwidth Multi-Frequency Wearable EIT Belt Featuring Novel Active Electrode Architecture for Neonatal Thorax Vital Sign Monitoring

A highly integrated, wearable electrical impedance tomography (EIT) belt for neonatal thorax vital multiple sign monitoring is presented. The belt has 16 active electrodes. Each electrode has an application-specific integrated circuit (ASIC) connected to it. The ASIC contains a fully differential current driver, a high-performance instrumentation amplifier, a digital controller, and multiplexors. The belt features a new active electrode architecture that allows programmable flexible electrode current drive and voltage sense patterns under simple digital control. It provides intimate connections to the electrodes for the current drive and to the IA for direct differential voltage measurement, providing superior common-mode rejection ratio. The ASIC was designed in a CMOS 0.35-μm high-voltage technology. The high-specification EIT belt has an image frame rate of 122 fps, a wide operating bandwidth of 1 MHz, and multi-frequency operation. It measures impedance with 98% accuracy and has less than 0.5 Ω and 1° variation across all possible channels. The image results confirmed the advantage of the new active electrode architecture and the benefit of wideband, multi-frequency EIT operation. The system successfully captured high-quality lung-respiration EIT images, breathing cycle, and heart rate. It can also provide boundary-shape information by using an array of MEMS sensors interfaced to the ASICs.

Capacitively Coupled Electrical Impedance Tomography for Brain Imaging

Electrical impedance tomography (EIT) is considered as a potential candidate for brain stroke imaging due to its compactness and potential use in bedside and emergency settings. The electrode-skin contact impedance and low conductivity of skull pose some practical challenges to the EIT head imaging. This paper studies the application of capacitively coupled electrical impedance tomography (CCEIT) in brain imaging for the first time. CCEIT is a new contactless EIT technique which uses voltage excitation without direct contact with the skin, as oppose to directly injecting the current to the skin in EIT. Because the safety issue of a new technique should be strictly treated, simulation work based on a simplified head model was carried out to investigate the safety aspects of CCEIT. By comparing with the standard EIT excited by a typical safe current level used in brain imaging, the safe excitation reference of CCEIT is obtained. This is done by comparing the maximum level of internal electrical field (internal current density) of EIT and that of CCEIT. Simulation results provide useful knowledge of excitation signal level of CCEIT and also show a critical comparison with traditional EIT. Practical experiments were carried out with a 12-electrode CCEIT phantom, saline, and carrot samples. Experimental results show the feasibility and potential of CCEIT for stroke imaging. In this paper, the anomaly diameter resolution is 10 mm (1/18 of the phantom diameter), which indicates that small-volume stroke could be detected. This is achieved by a low excitation voltage of 1 V, showing the possibility of even better performance when higher but yet safe level of excitation voltages is used.

A floating wide-band current source for electrical impedance tomography

The quality of reconstructed images in Electrical Impedance Tomography (EIT) depends on two essential factors: first, precision of the EIT hardware in current injection and voltage measurement and second, efficiency of its image reconstruction algorithm. Therefore the current source plays an important and a vital role in EIT instruments. Floating-load current sources constructed using sink and source drivers have better performance and higher output impedance than grounded-load (single-ended) current sources. In addition, a main feature of this kind is that the current source is not connected to the ground potential directly but via a large impedance. In this paper, we first focus on recent studies on designed EIT current sources, and after that, a practical design of a floating-load high output impedance current source-operating over a wide frequency band-will be proposed in detail. Simulation results of the proposed voltage-controlled current source (VCCS), along with some other models, will be shown and compared. At the end, the results of practical tests on the VCCS and a few EIT images, taken using our prototype EIT system coupled with the mentioned VCCS, will be illustrated which proves the quality of the proposed current source.

Singular value decomposition based impulsive noise reduction in multi-frequency phase-sensitive demodulation of electrical impedance tomography

As an important means in electrical impedance tomography (EIT), multi-frequency phase-sensitive demodulation (PSD) can be viewed as a matched filter for measurement signals and as an optimal linear filter in the case of Gaussian-type noise. However, the additive noise usually possesses impulsive noise characteristics, so it is a challenging task to reduce the impulsive noise in multi-frequency PSD effectively. In this paper, an approach for impulsive noise reduction in multi-frequency PSD of EIT is presented. Instead of linear filters, a singular value decomposition filter is employed as the pre-stage filtering module prior to PSD, which has advantages of zero phase shift, little distortion, and a high signal-to-noise ratio (SNR) in digital signal processing. Simulation and experimental results demonstrated that the proposed method can effectively eliminate the influence of impulsive noise in multi-frequency PSD, and it was capable of achieving a higher SNR and smaller demodulation error.

Exploring the Potential of Electrical Impedance Tomography for Tissue Engineering Applications

In tissue engineering, cells are generally cultured in biomaterials to generate three-dimensional artificial tissues to repair or replace damaged parts and re-establish normal functions of the body. Characterizing cell growth and viability in these bioscaffolds is challenging, and is currently achieved by destructive end-point biological assays. In this study, we explore the potential to use electrical impedance tomography (EIT) as a label-free and non-destructive technology to assess cell growth and viability. The key challenge in the tissue engineering application is to detect the small change of conductivity associated with sparse cell distributions in regards to the size of the hosting scaffold, i.e., low volume fraction, until they assemble into a larger tissue-like structure. We show proof-of-principle data, measure cells within both a hydrogel and a microporous scaffold with an ad-hoc EIT equipment, and introduce the frequency difference technique to improve the reconstruction.

Electrical impedance tomography for real-time and label-free cellular viability assays of 3D tumour spheroids

Real-time and label-free screening of the cell viability of 3D tumour spheroids by electrical impedance tomography.