Electrical Impedance Tomography Images Research Articles

Electrode module for EIT with a robust howland current source

Electrical Impedance Tomography (EIT) is a non-invasive imaging technique that reconstructs internal conductivity distributions of a body from electrical measurements taken on its boundary. This study contributes to the field by focusing on the technological intricacies of absolute EIT imaging, which is challenged by limitations such as the resolution capacity of the hardware and the complexities introduced by imaging capacitive bodies. The novel EIT system architecture proposed enhances the accuracy of measurement by integrating current sources and Analog-to-Digital Converters (ADCs) closer to the electrodes, employing alternating current excitations to accurately capture phase information. This system uses a dynamic arrangement of surface electrodes that continuously alter their roles between current injection and voltage measurement, in a synchronized sequence, to ensure the accuracy of the measurements. The paper describes the design and implementation of both the excitation and measurement subsystems, highlighting the use of digital signal demodulation near the electrode to reduce data transfer issues. Experimental results confirm the system’s capability for real-time image reconstruction at 50 frames per second with precision in phase delay measurements, suggesting significant potential for clinical and industrial applications. Future work will aim to further refine signal generation with higher-speed DACs and expand to image reconstruction with more channels.

Electrical impedance tomography image reconstruction for lung monitoring based on ensemble learning algorithms1

AbstractElectrical impedance tomography (EIT) is a promising non‐invasive imaging technique that visualizes the electrical conductivity of an anatomic structure to form based on measured boundary voltages. However, the EIT inverse problem for the image reconstruction is nonlinear and highly ill‐posed. Therefore, in this work, a simulated dataset that mimics the human thorax was generated with boundary voltages based on given conductivity distributions. To overcome the challenges of image reconstruction, an ensemble learning method was proposed. The ensemble method combines several convolutional neural network models, which are the simple Convolutional Neural Network (CNN) model, AlexNet, AlexNet with residual block, and the modified AlexNet model. The ensemble models’ weights selection was based on average technique giving the best coefficient of determination (R2 score). The reconstruction quality is quantitatively evaluated by calculating the root mean square error (RMSE), the coefficient of determination (R2 score), and the image correlation coefficient (ICC). The proposed method's best performance is an RMSE of 0.09404, an R2 score of 0.926186, and an ICC of 0.95783 using an ensemble model. The proposed method is promising as it can construct valuable images for clinical EIT applications and measurements compared to previous studies.

TSS-ConvNet for electrical impedance tomography image reconstruction

Objective. The objective of this study was to propose a novel data-driven method for solving ill-posed inverse problems, particularly in certain conditions such as time-difference electrical impedance tomography for detecting the location and size of bubbles inside a pipe. Approach. We introduced a new layer architecture composed of three paths: spatial, spectral, and truncated spectral paths. The spatial path processes information locally, whereas the spectral and truncated spectral paths provide the network with a global receptive field. This unique architecture helps eliminate the ill-posedness and nonlinearity inherent in the inverse problem. The three paths were designed to be interconnected, allowing for an exchange of information on different receptive fields with varied learning abilities. Our network has a bottleneck architecture that enables it to recover signal information from noisy redundant measurements. We named our proposed model truncated spatial-spectral convolutional neural network (TSS-ConvNet). Main results. Our model demonstrated superior accuracy with relatively high resolution on both simulation and experimental data. This indicates that our approach offers significant potential for addressing ill-posed inverse problems in complex conditions effectively and accurately. Significance. The TSS-ConvNet overcomes the receptive field limitation found in most existing models that only utilize local information in Euclidean space. We trained the network on a large dataset covering various configurations with random parameters to ensure generalization over the training samples.

Electrical impedance tomography in anaesthetised chickens (Gallus domesticus).

The applicability of electrical impedance tomography (EIT) in birds is unknown. This study aimed to evaluate the use of EIT in anaesthetised chickens in four recumbency positions. Four adult Hyline chickens were anaesthetised with isoflurane in oxygen, and intubated endotracheally for computed tomography (CT). A rubber belt was placed around the coelom caudal to the shoulder joint. A chicken-specific finite element (FE) model, which is essential to generate anatomically accurate functional EIT images for analysis, was constructed based on the CT images obtained at the belt level. Ten additional chickens were anaesthetised with the same protocol. An EIT electrode belt was placed at the same location. The chickens were breathing spontaneously and positioned in dorsal, ventral, right and left lateral recumbency in a randomised order. For each recumbency, raw EIT data were collected over 2 min after 13 min of stabilisation. The data were reconstructed into functional EIT images. EIT variables including tidal impedance variation (TIV), centre of ventilation right to left (CoVRL) and ventral to dorsal (CoVVD), right to left (RL) ratio, impedance change (ΔZ) and eight regional impedance changes including the dorsal, central-dorsal, central-ventral and ventral regions of the right and left regions were analysed. Four breathing patterns (BrP) were observed and categorised based on the expiratory curve. A linear mixed model was used to compare EIT variables between recumbencies. Fisher's exact test was used to compare the frequencies of breathing patterns for each recumbency. The ΔZ observed was synchronous to ventilation, and represented tidal volume of the cranial air sacs as confirmed by CT. Significant differences were found in CoVVD and regional impedance changes between dorsal and ventral recumbencies (P < 0.05), and in CoVRL, RL ratio and regional impedance changes between right and left recumbencies (P < 0.05), which suggested a tendency for the distribution of ventilation to shift towards non-dependent air sacs. No differences were found for TIV and respiratory rate between recumbencies. Recumbency had a significant effect on the frequencies of each of the four BrPs (P = 0.001). EIT can monitor the magnitude and distribution of ventilation of the cranial air sacs in different recumbencies in anaesthetised chickens.

Frequency-Dependent Contrast Enhancement for Conductive and Non-Conductive Materials in Electrical Impedance Tomography

This research investigates the critical role of frequency selection in Electrical Impedance Tomography (EIT), a non-invasive imaging technique that reconstructs internal conductivity distributions through injected electrical currents. Empirical frequency selection is paramount to maximizing the fidelity and specificity of EIT images. The study explores the impact of distinct frequency ranges—low, medium, and high—on image contrast and clarity, particularly focusing on differentiating conductive materials from non-conductive materials. The findings reveal distinct empirical frequency bands for enhancing the respective contrasts: 15–38 kHz for conductive materials (copper) and 45–75 kHz for non-conductive materials (acrylic resin). These insights shed light on the frequency-dependent nature of material contrast in EIT images, guiding the selection of empirical operating ranges for various target materials. This research paves the way for improved sensitivity and broader applicability of EIT in diverse areas.

A multi-scale attention residual-based U-Net network for stroke electrical impedance tomography.

Electrical impedance tomography (EIT), a non-invasive, radiation-free, and convenient imaging technique, has been widely used in the diagnosis of stroke. However, due to soft-field nonlinearity and the ill-posed inverse problem, EIT images always suffer from low spatial resolution. Therefore, a multi-scale convolutional attention residual-based U-Net (MARU-Net) network is proposed for stroke reconstruction. Based on the U-Net network, a residual module and a multi-scale convolutional attention module are added to the concatenation layer. The multi-scale module extracts feature information of different sizes, the attention module strengthens the useful information, and the residual module improves the performance of the network. Based on the above advantages, the network is used in the EIT system for stroke imaging. Compared with convolutional neural networks and one-dimensional convolutional neural networks, the MARU-Net network has fewer artifacts, and the reconstructed image is clear. At the same time, the reduction of noisy artifacts in the MARU-Net network is verified. The results show that the image correlation coefficient of the reconstructed image with noise is greater than 0.87. Finally, the practicability of the network is verified by a model physics experiment.

Application of electrical impedance tomography imaging technology combined with generative adversarial network in pulmonary ventilation monitoring

Electrical impedance tomography (EIT) plays a crucial role in the monitoring of pulmonary ventilation and regional pulmonary function test. However, the inherent ill-posed nature of EIT algorithms results in significant deviations in the reconstructed conductivity obtained from voltage data contaminated with noise, making it challenging to obtain accurate distribution images of conductivity change as well as clear boundary contours. In order to enhance the image quality of EIT in lung ventilation monitoring, a novel approach integrating the EIT with deep learning algorithm was proposed. Firstly, an optimized operator was introduced to enhance the Kalman filter algorithm, and Tikhonov regularization was incorporated into the state-space expression of the algorithm to obtain the initial lung image reconstructed. Following that, the imaging outcomes were fed into a generative adversarial network model in order to reconstruct accurate lung contours. The simulation experiment results indicate that the proposed method produces pulmonary images with clear boundaries, demonstrating increased robustness against noise interference. This methodology effectively achieves a satisfactory level of visualization and holds potential significance as a reference for the diagnostic purposes of imaging modalities such as computed tomography.

Voltage-based separation of respiration and cardiac activity by harmonic analysis in electrical impedance tomography

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

Evaluation of adjacent and opposite current injection patterns for a wearable chest electrical impedance tomography system

Objective. Wearable electrical impedance tomography (EIT) can be used to monitor regional lung ventilation and perfusion at the bedside. Due to its special system architecture, the amplitude of the injected current is usually limited compared to stationary EIT system. This study aims to evaluate the performance of current injection patterns with various low-amplitude currents in healthy volunteers. Approach. A total of 96 test sets of EIT measurement was recorded in 12 healthy subjects by employing adjacent and opposite current injection patterns with four amplitudes of small current (i.e. 1 mA, 500 uA, 250 uA and 125 uA). The performance of the two injection patterns with various currents was evaluated in terms of signal-to-noise ratio (SNR) of thorax impedance, EIT image metrics and EIT-based clinical parameters. Main results. Compared with adjacent injection, opposite injection had higher SNR (p < 0.01), less inverse artifacts (p < 0.01), and less boundary artifacts (p < 0.01) with the same current amplitude. In addition, opposite injection exhibited more stable EIT-based clinical parameters (p < 0.01) across the current range. For adjacent injection, significant differences were found for three EIT image metrics (p < 0.05) and four EIT-based clinical parameters (p < 0.01) between the group of 125 uA and the other groups. Significance. For better performance of wearable pulmonary EIT, currents greater than 250 uA should be used in opposite injection, 500 uA in adjacent one, to ensure a high level of SNR, a high quality of reconstructed image as well as a high reliability of clinical parameters.

Rapid patient-specific FEM meshes from 3D smart-phone based scans

Objective. The objective of this study was to describe and evaluate a smart-phone based method to rapidly generate subject-specific finite element method (FEM) meshes. More accurate FEM meshes should lead to more accurate thoracic electrical impedance tomography (EIT) images. Approach. The method was evaluated on an iPhone® that utilized an app called Heges, to obtain 3D scans (colored, surface triangulations), a custom belt, and custom open-source software developed to produce the subject-specific meshes. The approach was quantitatively validated via mannequin and volunteer tests using an infrared tracker as the gold standard, and qualitatively assessed in a series of tidal-breathing EIT images recorded from 9 subjects. Main results. The subject-specific meshes can be generated in as little as 6.3 min, which requires on average 3.4 min of user interaction. The mannequin tests yielded high levels of precision and accuracy at 3.2 ± 0.4 mm and 4.0 ± 0.3 mm root mean square error (RMSE), respectively. Errors on volunteers were only slightly larger (5.2 ± 2.1 mm RMSE precision and 7.7 ± 2.9 mm RMSE accuracy), illustrating the practical RMSE of the method. Significance. Easy-to-generate, subject-specific meshes could be utilized in the thoracic EIT community, potentially reducing geometric-based artifacts and improving the clinical utility of EIT.

Flexi-EIT: A Flexible and Reconfigurable Active Electrode Electrical Impedance Tomography System.

Electrical Impedance Tomography (EIT) systems have shown great promise in many fields such as real-time wearable healthcare imaging, but their fixed number of electrodes and placement locations limit the system's flexibility and adaptability for further advancement. In this article, we propose a flexible and reconfigurable EIT system (Flexi-EIT) based on digital active electrode (DAE) architecture to address these limitations. By integrating a reconfigurable number of up to 32 replaceable DAEs into the flexible printed circuit (FPC) based wearable electrode belt, we can enable rapid, reliable, and easy placement while maintaining high device flexibility and reliability. We also explore hardware-software co-optimization image reconstruction solutions to balance the size and accuracy of the model, the power consumption, and the real-time latency. Each DAE is designed using commercial chips and fabricated on a printed circuit board (PCB) measuring 13.1 mm × 24.4 mm and weighing 2 grams. In current excitation mode, it can provide programmable sinusoidal current signal output with frequencies up to 100 kHz and amplitudes up to 1 mA p-p that meets IEC 60601-1 standard. In voltage acquisition mode, it can pre-amplify, filter, and digitize the external response voltage signal, improving the robustness of the system while avoiding the need for subsequent analog signal processing circuits. Measured results on a mesh phantom demonstrate that the Flexi-EIT system can be easily configured with different numbers of DAEs and scan patterns to provide EIT measurement frames at 38 fps and real-time EIT images with at least 5 fps, showing the potential to be deployed in a variety of application scenarios and providing the optimal balance of system performance and hardware resource usage solutions.

ACT5 Electrical Impedance Tomography System.

This article introduces the Adaptive Current Tomograph 5 (ACT5) Electrical Impedance Tomography (EIT) system. ACT5 is a 32 electrode applied-current multiple-source EIT system that can display real-time images of conductivity and susceptivity at 27 frames per second. The adaptive current sources in ACT5 can apply fully programmable current patterns with frequencies varying from 5 kHz to 500 kHz. The system also displays real-time ECG readings during the EIT imaging process. The hardware and software design and specifications are presented, including the current source design, FPGA hardware, safety features, calibration, and shunt impedance measurement. Images of conductivity and susceptivity are presented from ACT5 data collected on tank phantoms and a human subject illustrating the system's ability to provide real-time images of pulsatile perfusion and ECG traces. The portability, high signal-to-noise ratio, and flexibility of applied currents over a wide range of frequencies enable this instrument to be used to obtain useful human subject data with relative clinical ease.

Data-driven approaches for electrical impedance tomography image segmentation from partial boundary data

Data-driven approaches for electrical impedance tomography image segmentation from partial boundary data

Damage Detection of CFRP by Electrical Impedance Tomography Imaging Based on RM-DeeplabV3

Damage Detection of CFRP by Electrical Impedance Tomography Imaging Based on RM-DeeplabV3

Applied machine learning for stroke differentiation by electrical impedance tomography with realistic numerical models

Electrical impedance tomography (EIT) may have potential to overcome existing limitations in stroke differentiation, enabling low-cost, rapid, and mobile data collection. Combining bioimpedance measurement technologies such as EIT with machine learning classifiers to support decision-making can avoid commonly faced reconstruction challenges due to the nonlinear and ill-posed nature of EIT imaging. Therefore, in this work, we advance this field through a study integrating realistic head models with clinically relevant test scenarios, and a robust architecture consisting of nested cross-validation and principal component analysis. Specifically, realistic head models are designed which incorporate the highly conductive layers of cerebrospinal fluid in the subarachnoid space and ventricles. In total, 135 unique models are created to represent a large patient population, with normal, haemorrhagic, and ischemic brains. Simulated EIT voltage data generated from these models are used to assess the classification performance of support vector machines. Parameters explored include driving frequency, signal-to-noise ratio, kernel function, and composition of binary classes. Classifier accuracy at 60 dB signal-to-noise ratio, reported as mean and standard deviation, are (79.92% ± 10.82%) for lesion differentiation, (74.78% ± 3.79%) for lesion detection, (77.49% ± 15.90%) for bleed detection, and (60.31% ± 3.98%) for ischemia detection (after ruling out bleed). The results for each method were obtained with statistics from 3 independent runs with 17,280 observations, polynomial kernel functions, and feature reduction of 76% by PCA (from 208 to 50 features). While results of this study show promise for stroke differentiation using EIT data, our findings indicate that the achievable accuracy is highly dependent on the classification scenario and application-specific classifiers may be necessary to achieve acceptable accuracy.

Damage detection of CFRP laminates based on Adamax-INN model for EIT

Carbon fiber reinforced polymer (CFRP) is electrically conductive, making it possible to detect structural damage of CFRP laminates with electrical impedance tomography (EIT). However, the inverse problem of EIT is severely non-linear, ill-posed, and underdetermined, thus limiting the resolution and accuracy of images reconstructed with EIT. This paper solves the inverse problem of EIT based on invertible neural networks (INN) and optimizes INN with Adamax gradient descent algorithm. Simulation and experimental results demonstrated that, compared with traditional EIT image reconstruction algorithms and radial basis function (RBF) neural network algorithm, the Adamax-INN model largely reduced the artifacts in reconstructed images, improved the damage recognition accuracy and edge clarity, and displayed good noise immunity.

Comparison of Machine Learning Classifiers for the Detection of Breast Cancer in an Electrical Impedance Tomography Setup

Breast cancer is the leading cause of cancer-related death among women. Early prediction is crucial as it severely increases the survival rate. Although classical X-ray mammography is an established technique for screening, many eligible women do not consider this due to concerns about pain from breast compression. Electrical Impedance Tomography (EIT) is a technique that aims to visualize the conductivity distribution within the human body. As cancer has a greater conductivity than surrounding fatty tissue, it provides a contrast for image reconstruction. However, the interpretation of EIT images is still hard, due to the low spatial resolution. In this paper, we investigated three different classification models for the detection of breast cancer. This is important as EIT is a highly non-linear inverse problem and tends to produce reconstruction artifacts, which can be misinterpreted as, e.g., tumors. To aid in the interpretation of breast cancer EIT images, we compare three different classification models for breast cancer. We found that random forests and support vector machines performed best for this task.

Image Reconstruction Using Supervised Learning in Wearable Electrical Impedance Tomography of the Thorax.

Electrical impedance tomography (EIT) is a non-invasive technique for visualizing the internal structure of a human body. Capacitively coupled electrical impedance tomography (CCEIT) is a new contactless EIT technique that can potentially be used as a wearable device. Recent studies have shown that a machine learning-based approach is very promising for EIT image reconstruction. Most of the studies concern models containing up to 22 electrodes and focus on using different artificial neural network models, from simple shallow networks to complex convolutional networks. However, the use of convolutional networks in image reconstruction with a higher number of electrodes requires further investigation. In this work, two different architectures of artificial networks were used for CCEIT image reconstruction: a fully connected deep neural network and a conditional generative adversarial network (cGAN). The training dataset was generated by the numerical simulation of a thorax phantom with healthy and illness-affected lungs. Three kinds of illnesses, pneumothorax, pleural effusion, and hydropneumothorax, were modeled using the electrical properties of the tissues. The thorax phantom included the heart, aorta, spine, and lungs. The sensor with 32 area electrodes was used in the numerical model. The ECTsim custom-designed toolbox for Matlab was used to solve the forward problem and measurement simulation. Two artificial neural networks were trained with supervision for image reconstruction. Reconstruction quality was compared between those networks and one-step algebraic reconstruction methods such as linear back projection and pseudoinverse with Tikhonov regularization. This evaluation was based on pixel-to-pixel metrics such as root-mean-square error, structural similarity index, 2D correlation coefficient, and peak signal-to-noise ratio. Additionally, the diagnostic value measured by the ROC AUC metric was used to assess the image quality. The results showed that obtaining information about regional lung function (regions affected by pneumothorax or pleural effusion) is possible using image reconstruction based on supervised learning and deep neural networks in EIT. The results obtained using cGAN are strongly better than those obtained using a fully connected network, especially in the case of noisy measurement data. However, diagnostic value estimation showed that even algebraic methods allow us to obtain satisfactory results.

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.

Electrical impedance tomography with deep Calderón method

Electrical impedance tomography (EIT) is a noninvasive medical imaging modality utilizing the current-density/voltage data measured on the surface of the subject. Calderón's method is a relatively recent EIT imaging algorithm that is non-iterative, fast, and capable of reconstructing complex-valued electric impedances. However, due to the regularization via low-pass filtering and linearization, the reconstructed images suffer from severe blurring and under-estimation of the exact conductivity values. In this work, we develop an enhanced version of Calderón's method, using deep convolution neural networks (i.e., U-net) as an effective targeted post-processing step, and term the resulting method by deep Calderón's method. Specifically, we learn a U-net to postprocess the EIT images generated by Calderón's method so as to have better resolutions and more accurate estimates of conductivity values. We simulate chest configurations with which we generate the current-density/voltage boundary measurements and the corresponding reconstructed images by Calderón's method. With the paired training data, we learn the deep neural network and evaluate its performance on real tank measurement data. The experimental results indicate that the proposed approach indeed provides a fast and direct (complex-valued) impedance tomography imaging technique, and substantially improves the capability of the standard Calderón's method.