Electrical Impedance Tomography Algorithm Research Articles

Iterative Decomposition Algorithm for Electrical Impedance Tomography to Image Large Resistivity Changes

Iterative Decomposition Algorithm for Electrical Impedance Tomography to Image Large Resistivity Changes

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.

A Graph Neural Network Approach with Improved Levenberg–Marquardt for Electrical Impedance Tomography

Electrical impedance tomography (EIT) is a non-invasive imaging method that allows for the acquisition of resistivity distribution information within an object without the use of radiation. EIT is widely used in various fields, such as medical imaging, industrial imaging, geological exploration, etc. Presently, most electrical impedance imaging methods are restricted to uniform domains, such as pixelated pictures. These algorithms rely on model learning-based image reconstruction techniques, which often necessitate interpolation and embedding if the fundamental imaging model is solved on a non-uniform grid. EIT technology still confronts several obstacles today, such as insufficient prior information, severe pathological conditions, numerous imaging artifacts, etc. In this paper, we propose a new electrical impedance tomography algorithm based on the graph convolutional neural network model. Our algorithm transforms the finite-element model (FEM) grid data from the ill-posed problem of EIT into a network graph within the graph convolutional neural network model. Subsequently, the parameters in the non-linear inverse problem of the EIT process are updated by using the improved Levenberg—Marquardt (ILM) method. This method generates an image that reflects the electrical impedance. The experimental results demonstrate the robust generalizability of our proposed algorithm, showcasing its effectiveness across different domain shapes, grids, and non-distributed data.

Using Redistribution Index to Identify an Outdated Detail Prior in the DCT-based 3D EIT Algorithm

The integration of detailed prior information obtained from morphological images into the discrete cosine transformation (DCT) based 3D electrical impedance tomography (EIT) algorithm significantly enhances the interpretability of the obtained results. However, the presence of outdated detail priors may introduce inaccuracies, thereby compromising the accuracy of clinical decisions. Consequently, detecting outdated detail priors in an EIT algorithm is essential for ensuring the reliability of the reconstruction results. To address this concern, we propose the redistribution index to assess and quantify potential errors associated with detailed priors. Simulations covering various atelectasis scales were conducted to evaluate the redistribution index behavior. An increase is observed in the redistribution index, escalating from 0.02 at 64% atelectasis to 0.50 at 0% atelectasis when the detail prior is outdated. The redistribution index from the accurate detail prior remains small and stable. This finding demonstrates that the redistribution index effectively detects outdated detail priors, but consideration of specific application requirements and tolerances in the clinical setting is necessary.

Development of an Electrical Impedance Tomography Coupled Surgical Stapler for Tissue Characterization.

This study explores the feasibility of coupling Electrical Impedance Tomography (EIT) to a standard-of-care laparoscopic surgical stapler, stapler+EIT, with the long-term goal of enabling intraoperative tissue differentiation for tumor margin detection. Two custom printed-circuit-board-based electrode arrays with 60 and 8 electrodes, respectively, matching the stapler geometry, served as the jaws of an electrode-integrated surrogate stapler+EIT device. The device was evaluated through a series of simulations and bench-top imaging experiments of agar-gel phantoms and bovine tissue samples to evaluate the technique and determine optimal imaging parameters. Electrodes localized to only one jaw (the 60-electrode side) of the stapler outperformed a dual-jaw distribution of electrodes. Using this one-sided electrode array, reconstructions achieved an Area-Under-the-Curve (AUC) ≥ 0.94 for inclusions with conductivity contrasts of ≥30% for any size considered on measured agar-gel tests, and an AUC of 0.80 for bovine tissue samples. A stapler+EIT algorithm has been tuned and evaluated on challenging phantom tests and demonstrated to produce accurate reconstructions. This work is an important step in the development of a surgical stapler+EIT technique for margin assessment.

Research Trends and Hotspots of Medical Electrical Impedance Tomography Algorithms: A Bibliometric Analysis From 1987 to 2021.

Electrical impedance tomography (EIT) is a gradually maturing medical imaging technique that relies on computational algorithms for reconstructing and visualizing internal conductivity distributions within the human body.To provide a comprehensive and objective understanding of the current state and trendsin theEIT algorithm research, we conducted bibliometric analysis on a 25-year EIT algorithm research dataset sourced from Web of Science Core Collections. We visualized publication characteristics, collaboration patterns, keywords, and co-cited references. The results indicate a steady increase in annual publications over recent decades. The United States, United Kingdom, China, and South Korea contributed60% of the articles collaboratively. Keyword analysis unveiled three distinct stages in the evolution of EIT algorithm research: the establishment of fundamental algorithm frameworks, optimization for improved imaging performance, and the development of algorithms for clinical applications. Additionally, there has been a shift in research focus from traditional theories to the incorporation of new methods, such as artificial intelligence. Co-cited references suggest that integrating EIT with other established imaging techniques may emerge as a new trend in EIT algorithm research. In summary, EIT algorithms have been a consistent research focus, with current efforts centered on optimizing algorithms to enhance imaging performance. The emerging research trend involves utilizing more diverse and intersecting algorithms.

Influence of a Structural Prior Mask on EIT Image Reconstruction.

Electrical Impedance Tomography (EIT) is a low-cost imaging method with promising results in visualizing ventilation distribution within the lungs. However, in clinical settings, the interpretability of EIT images is often limited by blurred anatomical alignment and reconstruction artifacts. Integrating structural priors into the EIT reconstruction process can enhance the interpretability of EIT images. In this contribution, we introduced a patient-specific structural prior mask into the EIT reconstruction process. Such prior mask ensures that only conductivity changes within the lung regions are reconstructed. With the aim to investigate the influence of the structural prior mask on the EIT images, we conducted numerical simulations in terms of four different ventilation status. EIT images were reconstructed with Gauss-Newton algorithm and discrete cosine transform-based EIT algorithm. We carried out quantitative analysis including the reconstruction error and figures of merit for the evaluation. The results show that the morphological structures of the lungs introduced by the prior mask are preserved in the EIT images, and the reconstruction artefacts are also limited. In conclusion, the incorporation of the structural prior mask enhances the interpretability of EIT images in clinical settings.Clinical relevance-The correct interpretation of an EIT image is crucial for a clinical diagnosis. This research demonstrates that a structural prior mask might have the potential to improve the interpretability of an EIT image, which facilitates the clinicians with a better understanding of EIT results.

Effect of a Patient-Specific Structural Prior Mask on Electrical Impedance Tomography Image Reconstructions.

Electrical Impedance Tomography (EIT) is a low-cost imaging method which reconstructs two-dimensional cross-sectional images, visualising the impedance change within the thorax. However, the reconstruction of an EIT image is an ill-posed inverse problem. In addition, blurring, anatomical alignment, and reconstruction artefacts can hinder the interpretation of EIT images. In this contribution, we introduce a patient-specific structural prior mask into the EIT reconstruction process, with the aim of improving image interpretability. Such a prior mask ensures that only conductivity changes within the lung regions are reconstructed. To evaluate the influence of the introduced structural prior mask, we conducted numerical simulations with two scopes in terms of their different ventilation statuses and varying atelectasis scales. Quantitative analysis, including the reconstruction error and figures of merit, was applied in the evaluation procedure. The results show that the morphological structures of the lungs introduced by the mask are preserved in the EIT reconstructions and the reconstruction artefacts are decreased, reducing the reconstruction error by 25.9% and 17.7%, respectively, in the two EIT algorithms included in this contribution. The use of the structural prior mask conclusively improves the interpretability of the EIT images, which could facilitate better diagnosis and decision-making in clinical settings.

A multithreaded real-time solution for 2D EIT reconstruction with the D-bar algorithm

Electrical impedance tomography (EIT) is a non-ionizing real-time imaging tool for bedside pulmonary imaging. Dynamic cross-sectional images of ventilation and perfusion can be produced in real time with fast algorithms from measured voltage data arising from current applied on electrodes placed around the circumference of the patient’s chest. The D-bar method is a direct (non-iterative) reconstruction algorithm for EIT that uses equations of inverse scattering to independently reconstruct the conductivity at each point in the region of interest. However, this nonlinear reconstruction method has a computational complexity that makes real-time imaging a challenge. At the same time, it has the attributes that it does not require successive solutions of the forward problem, as is the case with iterative methods, and it is trivially parallelizable in the spatial variable. Here, we present a novel multithreaded implementation of the D-bar method with a front-end MATLAB/Octave program interfaced with C code that uses the pthreads library. The implementation is analyzed on several different platforms with concurrent threads ranging from 2 to 32 for spatial grids of several sizes. We demonstrate that on a CPU with many cores, very high frame rates (50 frames/s) with high resolution (6000 grid points) are achievable, and on a single AMD CPU real-time reconstructions (faster than 30 frames/s) are achieved with this implementation using 10 or more threads on a grid of 1969 points.

Image reconstruction based on a modified bird swarm optimization algorithm for electrical impedance tomography

Electrical impedance tomography (EIT) is a potential and promising tomographic technique. It is known that image reconstruction of EIT is mathematically an ill-posed inverse problem that leads to poor reconstruction quality. To improve the quality of reconstructed images, a novel image reconstruction method based on modified bird swarm optimization algorithm is proposed in this paper. An image of conductivity distribution acquired by Newton's one-step error reconstruction algorithm performs as initial population. Then a modified bird swarm optimization algorithm is proposed to optimize the initial conductivity distribution. By introducing a dynamic inertia weight parameter, flight interval of the bird swarm varies with the number of iterations. With the proposed method, a strong global search capability in the initial stage and a strong local search capability in the later stage are ensured during the optimization. To verify the effectiveness of the proposed method, simulation work and experimental validation are carried out. Also, reconstructed images are compared with the results obtained by iterative Landweber method and Newton–Raphson method. Meanwhile, the impact of noise on the reconstruction is investigated. The simulation and experimental results have demonstrated the superior performance of the proposed method in visualizing conductivity distribution.

Influence of the patient-specific structural prior mask on image reconstruction using the discrete cosine transform-based EIT algorithm

Electrical impedance tomography (EIT) is an imaging technology but suffers greatly from the ill-posed inverse problem when reconstructing an image, which is mainly caused by the high degrees of freedom and the relatively large measurement noise. The use of discrete cosine transform (DCT) to cluster finite elements has been proposed to reduce the degrees of freedom in inverse computations. However, blurred anatomical alignment and artifacts still present challenges to the interpretation of EIT images. Incorporating prior information into the reconstruction process has been reported to enhance the quality of EIT images. In this contribution, we propose the use of a patient-specific structural prior mask for the DCT-based EIT algorithm. We evaluate the influence of this mask on simulation models with varying ventilation statuses. Our results demonstrate that the structural prior mask preserves the morphological structures of the lungs and avoids blurring of the solution, thereby facilitating EIT image interpretation for clinicians.

Detection of Carbon-Fiber-Reinforced Polymer Damage Based on L1/L2 Regularization Electrical Impedance Tomography Algorithm

Detection of Carbon-Fiber-Reinforced Polymer Damage Based on L1/L2 Regularization Electrical Impedance Tomography Algorithm

Advances of deep learning in electrical impedance tomography image reconstruction.

Electrical impedance tomography (EIT) has been widely used in biomedical research because of its advantages of real-time imaging and nature of being non-invasive and radiation-free. Additionally, it can reconstruct the distribution or changes in electrical properties in the sensing area. Recently, with the significant advancements in the use of deep learning in intelligent medical imaging, EIT image reconstruction based on deep learning has received considerable attention. This study introduces the basic principles of EIT and summarizes the application progress of deep learning in EIT image reconstruction with regards to three aspects: a single network reconstruction, deep learning combined with traditional algorithm reconstruction, and multiple network hybrid reconstruction. In future, optimizing the datasets may be the main challenge in applying deep learning for EIT image reconstruction. Adopting a better network structure, focusing on the joint reconstruction of EIT and traditional algorithms, and using multimodal deep learning-based EIT may be the solution to existing problems. In general, deep learning offers a fresh approach for improving the performance of EIT image reconstruction and could be the foundation for building an intelligent integrated EIT diagnostic system in the future.

Application of constrained coefficient fuzzy linear programming in medical electrical impedance tomography

ABSTRACT Electrical impedance tomography (EIT) is an imaging technique that realizes the image reconstruction of conductivity distribution in the field. The existing EIT algorithms ignore the hidden fuzzy features during imaging, making the EIT technique exhibit a high degree of uncertainty, imprecision, incompleteness, and inconsistency in the actual use process, resulting in a low spatial resolution of the reconstructed images. In order to solve this problem, we introduce fuzzy linear programming into EIT imaging. On the basis of analyzing the fuzzy features of EIT in detail, a new model of fuzzy optimization is built up, whose optimal solution is obtained by using constrained coefficient fuzzy linear programming. To this end, we devise two types of simulation experiments to verify the performance of the optimization algorithm. Experimental results prove that compared with the traditional Tikhonov regularization algorithm, the correlation coefficient of the reconstructed image of the proposed algorithm is higher, the relative error value is smaller.

Influence of hyperparameter on the Untrue Prior Detection in Discrete Transformation-based EIT Algorithm.

Incorporated with a structural prior, discrete cosine transformation (DCT) based electrical impedance tomog-raphy (EIT) algorithm can improve the interpretability of EIT images in clinical settings. However, this benefit comes with a risk of the untrue prior which yields a misleading result compromising clinical decision. The redistribution index is able to detect an untrue prior by analysing EIT reconstructions. In addition to structural priors, EIT reconstruction is also affected by the choice of hyperparameter A in DCT-based EIT algorithm. In this research, influence of hyperparameter on untrue prior detection is investigated in terms of simulation experiment. A series of simulation settings consisting of 30 different atelectasis scales was conducted, then reconstructed with 20 different hyperparameters, to investigate the behavior of redistribution index. The result shows, despite the fact that redistribution index is indeed influenced by the choice of the hyperparameter A, the detection of an untrue prior is not significantly affected. The untrue prior detection is rather stable regardless of the optimal hyperparameter. Clinical Relevance - Optimal hyperparameter is not always guaranteed in clinical settings. This research confirms that the untrue prior detection is not strongly influenced by the hyperparameter. An update of untrue priors incorporated into EIT approach will facilitate a better interpretation of EIT results and an accurate clinical decision.

Damage detection of carbon fiber reinforced polymer composite materials based on one-dimensional multi-scale residual convolution neural network

Carbon fiber reinforced polymers (CFRPs) have been widely applied in the aerospace industry, and the health conditions of CFRPs largely affect aerospace safety. Due to the limitations of traditional detection methods, electrical impedance tomography (EIT) has been gradually applied in the damage detection of CFRP composite materials. Aiming at the problems of poor imaging quality and low identification rate in the traditional EIT reconstruction algorithm, an EIT algorithm based on the one-dimensional multi-scale residual convolution neural network (1D-MSK-ResNet) is proposed in this paper. A "voltage vector-conductivity media distribution" dataset is first established, and the training results of the testing dataset are used to verify and evaluate the algorithm. Simulation and experimental results indicated that the 1D-MSK-ResNet EIT algorithm could enhance the ability of damage identification and significantly improve the imaging quality.

A Point-Matching Method of Moment with Sparse Bayesian Learning Applied and Evaluated in Dynamic Lung Electrical Impedance Tomography.

Dynamic lung imaging is a major application of Electrical Impedance Tomography (EIT) due to EIT’s exceptional temporal resolution, low cost and absence of radiation. EIT however lacks in spatial resolution and the image reconstruction is very sensitive to mismatches between the actual object’s and the reconstruction domain’s geometries, as well as to the signal noise. The non-linear nature of the reconstruction problem may also be a concern, since the lungs’ significant conductivity changes due to inhalation and exhalation. In this paper, a recently introduced method of moment is combined with a sparse Bayesian learning approach to address the non-linearity issue, provide robustness to the reconstruction problem and reduce image artefacts. To evaluate the proposed methodology, we construct three CT-based time-variant 3D thoracic structures including the basic thoracic tissues and considering 5 different breath states from end-expiration to end-inspiration. The Graz consensus reconstruction algorithm for EIT (GREIT), the correlation coefficient (), the root mean square error () and the full-reference () metrics are applied for the image quality assessment. Qualitative and quantitative comparison with traditional and more advanced reconstruction techniques reveals that the proposed method shows improved performance in the majority of cases and metrics. Finally, the approach is applied to single-breath online in-vivo data to qualitatively verify its applicability.

Comparison of Electrical Impedance Tomography and Ultrasonography for Determination of Solid and Cystic Lesion Resembling Breast Tumor Embedded in Chicken Phantom.

Ultrasonography (US) and Electrical Impedance Tomography (EIT) can be used to detect breast cancer. Ultrasonography is based on non-ionizing radiations without adverse biological effects. A set of electrodes was placed around the torso and a small alternating current (AC) was injected via two of the electrodes into the object. This study aimed to acquire preliminary data to evaluate the EIT method for differentiation of artificial solid and cystic tumors in comparison to standard US.This study used a phantom made from chicken meat. In order to obtain the image of the solid tumor, an olive with carrot insertion was done, and the cystic tumor was created by filling a small balloon with water. GE Logic C5 ultrasound was performed with a 12 MHz linear transducer. For EIT measurement, 16 ECG electrodes and 32 ECG electrodes were placed. Data processing was done using the Graz consensus Reconstruction algorithm for EIT (GREIT) and Newton's One Step Error Reconstructor (NOSER) methods.The artificial solid tumor produced an ultrasound image of an oval, inhomogeneous lesions. The GREIT method with 16 electrodes of artificial solid tumor did not show a match between the reconstructed image and the original object containing 2 anomalies, but a match was found with 32 electrodes. In the NOSER method, both 16 and 32 electrodes showed a match. Ultrasound of the artificial cystic tumor showed an oval, circumscribed, anechoic with posterior enhancement. Both the GREIT and NOSER methods using the artificial cystic tumor showed a match between the reconstructed image and the original object containing two anomalies.EIT has a lower imaging resolution in comparison to ultrasonography, but is progressively maturing as a tool for monitoring and imaging. The solid and cystic anomalies on the phantoms were visualized by the GREIT and NOSER methods except for the solid anomaly with the GREIT 16 electrodes.

Redistribution Index - Detection of an Outdated Prior Information in the Discrete Cosine Transformation-based EIT Algorithm.

The morphological prior information incorporated with the discrete cosine transformation (DCT) based electrical impedance tomography (EIT) algorithm can improve the interpretability of the EIT results in clinical settings. However, an outdated prior information can yield a misleading result compromising the accuracy of the clinical decisions. Detection of the outdated prior information is critical in the DCT-based EIT algorithm. In this contribution, a redistribution index calculated from the DCT approach result was proposed to quantify the possible error induced by the morphological prior information. Two simulations in terms of different atelectasis and collapse scales were conducted to evaluate the plausibility of the redistribution index. Thus, an experiential threshold for redistribution index was adopt as an indicator to the outdated prior in DCT-based EIT approach. A retrospective research was conducted with the seven-day patient monitor data as a proof-of-concept to verify plausibility and comparability of the redistribution index. From the evaluation, the redistribution index qualifies the function as an indicator for the outdated prior in the DCT-based EIT approach.Clinical relevance- This establishes an indicator to advice an update to the morphological prior information embedded in EIT approach, which lower the risk misleading interpretation of EIT results in mechanical ventilation monitoring.

Detection of Outdated Structural Priors in the Discrete Cosine Transformation-based Electrical Impedance Tomography Algorithm

Abstract Morphological prior information incorporated with the discrete cosine transformation (DCT) based electrical impedance tomography (EIT) algorithm can improve the interpretability of EIT reconstructions in clinical applications. However, an outdated structural prior can yield a misleading reconstruction compromising the accuracy of the clinical diagnosis and the appropriate treatment decision. In this contribution, we propose a redistribution index scaled between 0 and 1 to quantify the possible error in a DCT-based EIT reconstruction influenced by structural prior information. Two simulation models of different tissue atelectasis and collapsed ratios were investigated. Outdated and updated structural prior information were applied to obtain different EIT reconstructions using this simulated data, with which the redistribution index was calculated and compared. When the difference between prior and reality (the redistribution index) became larger and exceeded a threshold, this was considered as an indicator of an outdated prior information. The evaluation result shows the potential of the redistribution index to detect outdated prior information in a DCT-based EIT algorithm.