A Review of Electrical Impedance Tomography in Lung Applications: Theory and Algorithms for Absolute Images

Technical Principles and Clinical Applications of Electrical Impedance Tomography in Pulmonary Monitoring

Pulmonary monitoring is crucial for the diagnosis and management of respiratory conditions, especially after the epidemic of coronavirus disease. Electrical impedance tomography (EIT) is an alternative non-radioactive tomographic imaging tool for monitoring pulmonary conditions. This review proffers the current EIT technical principles and applications on pulmonary monitoring, which gives a comprehensive summary of EIT applied on the chest and encourages its extensive usage to clinical physicians. The technical principles involving EIT instrumentations and image reconstruction algorithms are explained in detail, and the conditional selection is recommended based on clinical application scenarios. For applications, specifically, the monitoring of ventilation/perfusion (V/Q) is one of the most developed EIT applications. The matching correlation of V/Q could indicate many pulmonary diseases, e.g., the acute respiratory distress syndrome, pneumothorax, pulmonary embolism, and pulmonary edema. Several recently emerging applications like lung transplantation are also briefly introduced as supplementary applications that have potential and are about to be developed in the future. In addition, the limitations, disadvantages, and developing trends of EIT are discussed, indicating that EIT will still be in a long-term development stage before large-scale clinical applications.

Sensitivity volume as figure-of-merit for maximizing data importance in electrical impedance tomography

Objective.Electrical impedance tomography (EIT) is a noninvasive imaging method whereby electrical measurements on the periphery of a heterogeneous conductor are inverted to map its internal conductivity. The EIT method proposed here aims to improve computational speed and noise tolerance by introducing sensitivity volume as a figure-of-merit for comparing EIT measurement protocols.Approach.Each measurement is shown to correspond to a sensitivity vector in model space, such that the set of measurements, in turn, corresponds to a set of vectors that subtend a sensitivity volume in model space. A maximal sensitivity volume identifies the measurement protocol with the greatest sensitivity and greatest mutual orthogonality. A distinguishability criterion is generalized to quantify the increased noise tolerance of high sensitivity measurements.Main result.The sensitivity volume method allows the model space dimension to be minimized to match that of the data space, and the data importance to be increased within an expanded space of measurements defined by an increased number of contacts.Significance.The reduction in model space dimension is shown to increasecomputational efficiency, accelerating tomographic inversion by several orders of magnitude, while the enhanced sensitivitytolerates higher noiselevels up to several orders of magnitude larger than standard methods.

A novel framework for three-dimensional electrical impedance tomography reconstruction of maize ear via feature reconfiguration and residual networks

Electrical impedance tomography (EIT) provides an indirect measure of the physiological state and growth of the maize ear by reconstructing the distribution of electrical impedance. However, the two-dimensional (2D) EIT within the electrode plane finds it challenging to comprehensively represent the spatial distribution of conductivity of the intact maize ear, including the husk, kernels, and cob. Therefore, an effective method for 3D conductivity reconstruction is necessary. In practical applications, fluctuations in the contact impedance of the maize ear occur, particularly with the increase in the number of grids and computational workload during the reconstruction of 3D spatial conductivity. These fluctuations may accentuate the ill-conditioning and nonlinearity of the EIT. To address these challenges, we introduce RFNetEIT, a novel computational framework specifically tailored for the absolute imaging of the three-dimensional electrical impedance of maize ear. This strategy transforms the reconstruction of 3D electrical conductivity into a regression process. Initially, a feature map is extracted from measured boundary voltage via a data reconstruction module, thereby enhancing the correlation among different dimensions. Subsequently, a nonlinear mapping model of the 3D spatial distribution of the boundary voltage and conductivity is established, utilizing the residual network. The performance of the proposed framework is assessed through numerical simulation experiments, acrylic model experiments, and maize ear experiments. Our experimental results indicate that our method yields superior reconstruction performance in terms of root-mean-square error (RMSE), correlation coefficient (CC), structural similarity index (SSIM), and inverse problem-solving time (IPST). Furthermore, the reconstruction experiments on maize ears demonstrate that the method can effectively reconstruct the 3D conductivity distribution.

Electrical impedance tomography as a bedside assessment tool for COPD treatment during hospitalization

For patients with chronic obstructive pulmonary disease (COPD), the assessment of the treatment efficacy during hospitalization is of importance to the optimization of clinical treatments. Conventional spirometry might not be sensitive enough to capture the regional lung function development. The study aimed to evaluate the feasibility of using electrical impedance tomography (EIT) as an objective bedside evaluation tool for the treatment of acute exacerbation of COPD (AECOPD). Consecutive patients who required hospitalization due to AECOPD were included prospectively. EIT measurements were conducted at the time of admission and before the discharge simultaneously when a forced vital capacity maneuver was conducted. EIT-based heterogeneity measures of regional lung function were calculated based on the impedance changes over time. Surveys for attending doctors and patients were designed to evaluate the ease of use, feasibility, and overall satisfaction level to understand the acceptability of EIT measurements. Patient-reported outcome assessments were conducted. User's acceptance of EIT technology was investigated with a five-dimension survey. A total of 32 patients were included, and 8 patients were excluded due to the FVC maneuver not meeting the ATS criteria. Spirometry-based lung function was improved during hospitalization but not significantly different (FEV1 %pred.: 35.8% ± 6.7% vs. 45.3% ± 8.8% at admission vs. discharge; p = 0.11. FVC %pred.: 67.8% ± 0.4% vs. 82.6% ± 5.0%; p = 0.15. FEV1/FVC: 0.41 ± 0.09 vs. 0.42 ± 0.07, p = 0.71). The symptoms of COPD were significantly improved, but the correlations between the improvement of symptoms and spirometry FEV1 and FEV1/FVC were low (R = 0.1 and -0.01, respectively). The differences in blood gasses and blood tests were insignificant. All but one EIT-based regional lung function parameter were significantly improved after hospitalization. The results highly correlated with the patient-reported outcome assessment (R > 0.6, p < 0.001). The overall acceptability score of EIT measurement for both attending physicians and patients was high (4.1 ± 0.8 for physicians, 4.5 ± 0.5 for patients out of 5). These results demonstrated that it was feasible and acceptable to use EIT as an objective bedside evaluation tool for COPD treatment efficacy.

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.

Automated evaluation of typical patient-ventilator asynchronies based on lung hysteretic responses

BACKGROUND

Patient-ventilator asynchrony is common during mechanical ventilation (MV) in intensive care unit (ICU), leading to worse MV care outcome. Identification of asynchrony is critical for optimizing MV settings to reduce or eliminate asynchrony, whilst current clinical visual inspection of all typical types of asynchronous breaths is difficult and inefficient. Patient asynchronies create a unique pattern of distortions in hysteresis respiratory behaviours presented in pressure-volume (PV) loop.

METHODS

Identification method based on hysteretic lung mechanics and hysteresis loop analysis is proposed to delineate the resulted changes of lung mechanics in PV loop during asynchronous breathing, offering detection of both its incidence and 7 major types. Performance is tested against clinical patient data with comparison to visual inspection conducted by clinical doctors.

RESULTS

The identification sensitivity and specificity of 11 patients with 500 breaths for each patient are above 89.5% and 96.8% for all 7 types, respectively. The average sensitivity and specificity across all cases are 94.6% and 99.3%, indicating a very good accuracy. The comparison of statistical analysis between identification and human inspection yields the essential same clinical judgement on patient asynchrony status for each patient, potentially leading to the same clinical decision for setting adjustment.

CONCLUSIONS

The overall results validate the accuracy and robustness of the identification method for a bedside monitoring, as well as its ability to provide a quantified metric for clinical decision of ventilator setting. Hence, the method shows its potential to assist a more consistent and objective assessment of asynchrony without undermining the efficacy of the current clinical practice.

CycleGAN with mutual information loss constraint generates structurally aligned CT images from functional EIT images

Electrical impedance tomography (EIT) has been employed in the field of medical imaging due to its cost effectiveness, safety profile and portability, but the images generated are relatively low resolution. To address these limitations, we create a novel method using EIT images to generate high resolution structurally aligned images of lungs like those from CT scans. A way to achieve this transformation is via Cycle generative adversarial networks (CycleGAN), which have demonstrated image-to-image translation capabilities across different modalities. However, a generic implementation yields images which may not be aligned with their input image. To solve this issue, we construct and incorporate a Mutual Information (MI) constraint in CycleGAN to translate functional lung EIT images to structural high resolution CT images. The CycleGAN is first trained on unpaired EIT and CT lung images. Afterwards, we generate CT image pairs from EIT images via CycleGANs constrained with MI loss and without this loss. Finally, through generating these 1560 CT image pairs and then comparing the visual results and quantitative metrics, we show that MI constrained CycleGAN produces more structurally aligned CT images, where Normalised Mutual Information (NMI) is increased to 0.2621+/- 0.0052 versus 0.2600 +/- 0.0066, p<0.0001 for non-MI constrained images. By this process, we simultaneously provide functional and structural information, and potentially enable more detailed assessment of lungs.Clinical Relevance- By establishing a structurally aligning generative process via MI Loss in CycleGAN, this study enables EIT-CT conversion, thereby providing functional and structural images for enhanced lung assessment, from just EIT images.

Global and regional lung function assessment using portable electrical impedance tomography (EIT) system: clinical study

Recent development of affordable, portable and self-administrable electrical impedance tomography (EIT) system demonstrated the feasibility of using standalone EIT and subject's anthropometrics to predict the gold standard spirometry indicators for lung-function assessment. Compared to spirometry, the system showed the advantage of providing spatial mapping of the spirometry indicators. Nevertheless, the previous study was limited to healthy subjects. Here, we recruited (N=88): 47 lung disease patients and 41 healthy controls to perform simultaneous EIT and spirometry measurements to validate the capabilities of the system. Lung disease patients include 13 interstitial lung disease (ILD), 10 asthma, 8 chronic obstructive pulmonary disease (COPD), 8 bronchiectasis, and 8 with other diseases including left pneumonectomy, lung cancer, lung tumor, lymphangioleiomyomatosis, motor neuron disease, heart failure and bronchiolitis obliterans syndrome. The results showed significant correlation of the predicted global spirometry indicators (p<0.0001) and significant distinguishability between most disease groups and healthy subjects demonstrating the capability of the EIT system in diagnostic screening. Furthermore, the regional mapping of the spirometry indicators is evaluated and shown to be distinct for each disease group, providing an additional dimension for medical professionals to diagnose and monitor lung disease patients.Clinical Relevance- This establishes the significance of EIT-based global and regional indicators for assessing lung function on lung disease patients.

Structural priors represented by discrete cosine transform improve EIT functional imaging

Structural prior information can improve electrical impedance tomography (EIT) reconstruction. In this contribution, we introduce a discrete cosine transformation-based (DCT-based) EIT reconstruction algorithm to demonstrate a way to incorporate the structural prior with the EIT reconstruction process. Structural prior information is obtained from other available imaging methods, e.g., thorax-CT. The DCT-based approach creates a functional EIT image of regional lung ventilation while preserving the introduced structural information. This leads to an easier interpretation in clinical settings while maintaining the advantages of EIT in terms of bedside monitoring during mechanical ventilation. Structural priors introduced in the DCT-based approach are of two categories in terms of different levels of information included: a contour prior only differentiates lung and non-lung region, while a detail prior includes information, such as atelectasis, within the lung area. To demonstrate the increased interpretability of the EIT image through structural prior in the DCT-based approach, the DCT-based reconstructions were compared with reconstructions from a widely applied one-step Gauss-Newton solver with background prior and from the advanced GREIT algorithm. The comparisons were conducted both on simulation data and retrospective patient data. In the simulation, we used two sets of forward models to simulate different lung conditions. A contour prior and a detail prior were derived from simulation ground truth. With these two structural priors, the reconstructions from the DCT-based approach were compared with the reconstructions from both the one-step Gauss-Newton solver and the GREIT. The difference between the reconstructions and the simulation ground truth is calculated by the ℓ2-norm image difference. In retrospective patient data analysis, datasets from six lung disease patients were included. For each patient, a detail prior was derived from the patient's CT, respectively. The detail prior was used for the reconstructions using the DCT-based approach, which was compared with the reconstructions from the GREIT. The reconstructions from the DCT-based approach are more comprehensive and interpretable in terms of preserving the structure specified by the priors, both in simulation and retrospective patient data analysis. In simulation analysis, the ℓ2-norm image difference of the DCT-based approach with a contour prior decreased on average by 34% from GREIT and 49% from the Gauss-Newton solver with background prior; for reconstructions of the DCT-based approach with detail prior, on average the ℓ2-norm image difference is 53% less than GREIT and 63% less than the reconstruction with background prior. In retrospective patient data analysis, the reconstructions from both the DCT-based approach and GREIT can indicate the current patient status, but the DCT-based approach yields more interpretable results. However, it is worth noting that the preserved structure in the DCT-based approach is derived from another imaging method, not from the EIT measurement. If the structural prior is outdated or wrong, the result might be misleadingly interpreted, which induces false clinical conclusions. Further research in terms of evaluating the validity of the structural prior and detecting the outdated prior is necessary.

Deep feature-domain matching for cardiac-related component separation from a chest electrical impedance tomography image series: proof-of-concept study

Objectives.The cardiac-related component in chest electrical impedance tomography (EIT) measurement is of potential value to pulmonary perfusion monitoring and cardiac function measurement. In a spontaneous breathing case, cardiac-related signals experience serious interference from ventilation-related signals. Traditional cardiac-related signal-separation methods are usually based on certain features of signals. To further improve the separation accuracy, more comprehensive features of the signals should be exploited.Approach.We propose an unsupervised deep-learning method called deep feature-domain matching (DFDM), which exploits the feature-domain similarity of the desired signals and the breath-holding signals. This method is characterized by two sub-steps. In the first step, a novel Siamese network is designed and trained to learn common features of breath-holding signals; in the second step, the Siamese network is used as a feature-matching constraint between the separated signals and the breath-holding signals.Main results.The method is first tested using synthetic data, and the results show satisfactory separation accuracy. The method is then tested using the data of three patients with pulmonary embolism, and the consistency between the separated images and the radionuclide perfusion scanning images is checked qualitatively.Significance.The method uses a lightweight convolutional neural network for fast network training and inference. It is a potential method for dynamic cardiac-related signal separation in clinical settings.

Emerging trends and hot spots on electrical impedance tomography extrapulmonary applications

Objective

Electrical impedance tomography (EIT) develops rapidly in technology and applications. Nowadays EIT is used in multiple clinical and experimental scenarios including pulmonary, brain, and tissue monitoring, etc. The present study explores the research trends and hotspots on EIT extrapulmonary application research by bibliometrics analysis.

Approach

Publications on EIT extrapulmonary applications between 1987 and 2021 were retrieved from the Web of Science Core Collection database. For precise screening, search strategy "electrical impedance tomography" plus "hemodynamic" or "brain" or "nerve" or "cancer" or "venous" or "vessel" or "tumor" or "veterinary" or "tissue" or "cell" or "wearable" or "application" and excluding "lung", "ventilation" "respiratory", "pulmonary", "algorithm", "current", "voltage" or "electrode" were used. CiteSpace and VOSviewer were used to analyze the publication features, collaboration, keywords co-occurrence, and co-cited reference.

Main results

A total of 506 articles were finally identified. The global publication numbers on extrapulmonary applications gradually increased yearly in the past 30 years. The US, UK, and China contributed most three publications concerning EIT extrapulmonary applications. "tissues", "conductivity", "model" were research hotspots, and "cutaneous melanoma", "microstructure", "diagnosis" were recent topics (Portions of this research have previously been presented in poster form).

Significance

Overall, EIT extrapulmonary applications bibliometrics analysis provides a unique insight into research focus, current trends, and future directions.

Fast absolute 3D CGO-based electrical impedance tomography on experimental tank data

Objective.To present the first 3D CGO-based absolute EIT reconstructions from experimental tank data.Approach.CGO-based methods for absolute EIT imaging are compared to traditional TV regularized non-linear least squares reconstruction methods. Additional robustness testing is performed by considering incorrect modeling of domain shape.Main Results.The CGO-based methods are fast, and show strong robustness to incorrect domain modeling comparable to classic difference EIT imaging and fewer boundary artefacts than the TV regularized non-linear least squares reference reconstructions.Significance.This work is the first to demonstrate fully 3D CGO-based absolute EIT reconstruction on experimental data and also compares to TV-regularized absolute reconstruction. The speed (1-5 s) and quality of the reconstructions is encouraging for future work in absolute EIT.

Study and comparison of different Machine Learning-based approaches to solve the inverse problem in Electrical Impedance Tomographies

Electrical Impedance Tomography (EIT) is a non-invasive technique used to obtain the electrical internal conductivity distribution from the interior of bodies. This is a promising method from the manufacturing viewpoint, since it could be used to estimate different physical inner body properties during the production of goods. Nevertheless, this technique requires dealing with an inverse problem that makes its usage in real-time processes challenging. Recently, Machine Learning techniques have been proposed to solve the inverse problem accurately. However, the majority of prior research is focused on qualitative results, and they typically lack a systematic methodology to determine the optimal hyperparameters appropriately. This work presents a systematic comparison of six popular Machine Learning algorithms: Artificial Neural Network, Random Forest, K-Nearest Neighbors, Elastic Net, Ada Boost, and Gradient Boosting. Particularly, the last two algorithms were based on decision tree learners. Furthermore, we studied the relationship between model performance and different EIT configurations. Specifically, we analyzed whether the measurement pattern and the number of used electrodes could increase the model performance. Experiments revealed that tree-based models present high performance, even better than Neural Networks, the most widely-used Machine Learning model to deal with EIT. Experiments also showed a model performance improvement when the EIT configuration was optimized. Most favorable metrics were attained using the tree-based Gradient Boosting model with a combination of both adjacent and mono measurement patterns as well as with 32 electrodes deployed during the tomographic process. With this particular setting, we achieved an accuracy of 99.14% detecting internal artifacts and a Root Mean Square Error of 4.75 predicting internal conductivity distributions.

Electrical Impedance Tomography Based on Grey Wolf Optimized Radial Basis Function Neural Network

Electrical impedance tomography (EIT) is a non-invasive, radiation-free imaging technique with a lot of promise in clinical monitoring. However, since EIT image reconstruction is a non-linear, pathological, and ill-posed issue, the quality of the reconstructed images needs constant improvement. To increase image reconstruction accuracy, a grey wolf optimized radial basis function neural network (GWO-RBFNN) is proposed in this paper. The grey wolf algorithm is used to optimize the weights in the radial base neural network, determine the mapping between the weights and the initial position of the grey wolf, and calculate the optimal position of the grey wolf to find the optimal solution for the weights, thus improving the image resolution of EIT imaging. COMSOL and MATLAB were used to numerically simulate the EIT system with 16 electrodes, producing 1700 simulation samples. The standard Landweber, RBFNN, and GWO-RBFNN approaches were used to train the sets separately. The obtained image correlation coefficient (ICC) of the test set after training with GWO-RBFNN is 0.9551. After adding 30, 40, and 50 dB of Gaussian white noise to the test set, the attained ICCs with GWO-RBFNN are 0.8966, 0.9197, and 0.9319, respectively. The findings reveal that the proposed GWO-RBFNN approach outperforms the existing methods when it comes to image reconstruction.

Robust imaging using electrical impedance tomography: review of current tools

Electrical impedance tomography (EIT) is a medical imaging technique with many advantages and great potential for development in the coming years. Currently, some limitations of EIT are related to the ill-posed nature of the problem. These limitations are translated on a practical level by a lack of genericity of the developed tools. In this paper, the main robust data acquisition and processing tools for EIT proposed in the scientific literature are presented. Their relevance and potential to improve the robustness of EIT are analysed, in order to conclude on the feasibility of a robust EIT tool capable of providing resistivity or difference of resistivity mapping in a wide range of applications. In particular, it is shown that certain measurement acquisition tools and algorithms, such as faulty electrode detection algorithm or particular electrode designs, can ensure the quality of the acquisition in many circumstances. Many algorithms, aiming at processing acquired data, are also described and allow to overcome certain difficulties such as an error in the knowledge of the position of the boundaries or the poor conditioning of the inverse problem. They have a strong potential to faithfully reconstruct a quality image in the presence of disturbances such as noise or boundary modelling error.

Anatomical atlas of the upper part of the human head for electroencephalography and bioimpedance applications

Objective.The objective of this work is to develop a 4D (3D+T) statistical anatomical atlas of the electrical properties of the upper part of the human head for cerebral electrophysiology and bioimpedance applications.Approach.The atlas was constructed based on 3D magnetic resonance images (MRI) of 107 human individuals and comprises the electrical properties of the main internal structures and can be adjusted for specific electrical frequencies. T1w+T2w MRI images were used to segment the main structures of the head while angiography MRI was used to segment the main arteries. The proposed atlas also comprises a time-varying model of arterial brain circulation, based on the solution of the Navier-Stokes equation in the main arteries and their vascular territories.Main results.High-resolution, multi-frequency and time-varying anatomical atlases of resistivity, conductivity and relative permittivity were created and evaluated using a forward problem solver for EIT. The atlas was successfully used to simulate electrical impedance tomography measurements indicating the necessity of signal-to-noise between 100 and 125 dB to identify vascular changes due to the cardiac cycle, corroborating previous studies. The source code of the atlas and solver are freely available to download.Significance.Volume conductor problems in cerebral electrophysiology and bioimpedance do not have analytical solutions for nontrivial geometries and require a 3D model of the head and its electrical properties for solving the associated PDEs numerically. Ideally, the model should be made with patient-specific information. In clinical practice, this is not always the case and an average head model is often used. Also, the electrical properties of the tissues might not be completely known due to natural variability. Anatomical atlases are important tools forin silicostudies on cerebral circulation and electrophysiology that require statistically consistent data, e.g. machine learning, sensitivity analyses, and as a benchmark to test inverse problem solvers.

Electrical impedance tomography as a tool for monitoring mechanical ventilation. An introduction to the technique

Electrical impedance tomography (EIT) is a non-invasive, radiation-free method of diagnostics imaging, allowing for a bedside, real-time dynamic assessment of lung function. It stands as an alternative for other imagining methods, such as computed tomography (CT) or ultrasound. Even though the technique is rather novel, it has a wide variety of possible applications. In the era of modern mechanical ventilation, a dynamic assessment of patient's respiratory condition appears to fulfil the idea of personalized treatment. Additionally, an increasing frequency of respiratory failure among intensive care populations raises demand for improved monitoring tools. This review aims to raise awareness and presents possible implications for the use of EIT in the intensive care setting.

Electrical impedance tomography detects changes in ventilation after airway clearance in spinal muscular atrophy type I

The effect of mechanical insufflation-exsufflation (MIE) for airway clearance in patients with spinal muscular atrophy type I (SMA-I) on the distribution of ventilation in the lung is unknown, as is the duration of its beneficial effects. A pilot study to investigate the feasibility of using three dimensional (3-D) electrical impedance tomography (EIT) images to estimate lung volumes pre- and post-MIE for assessing the effectiveness of mechanical insufflation-exsufflation (MIE) was conducted in 6 pediatric patients with SMA-I in the neuromuscular clinic at Children's Hospital Colorado. EIT data were collected before, during, and after the MIE procedure on two rows of 16 electrodes placed around the chest. Lung volumes were computed from the images and compared before, during, and after the MIE procedure to assess the ability of EIT to estimate changes in lung volume during insufflation and exsufflation. Images of pulsatile pulmonary perfusion were computed in subjects able to perform breath-holding. In four of the six subjects, lung volumes during tidal breathing increased after MIE (average change from pre to post MIE was 58.8±55.1 mL). The time-dependent plots of lung volume computed from the EIT data clearly show when the MIE device insufflates and exsufflates air and the rest periods between mechanical coughs. Images of pulmonary pulsatile perfusion were computed from data collected during breathing pauses. The results suggest that EIT holds promise for estimating lung volumes and ventilation/perfusion mismatch, both of which are useful for assessing the effectiveness of MIE in clearing mucus plugs.

Identification and analysis of stable breathing periods in electrical impedance tomography recordings

Objective. In this paper, an automated stable tidal breathing period (STBP) identification method based on processing electrical impedance tomography (EIT) waveforms is proposed and the possibility of detecting and identifying such periods using EIT waveforms is analyzed. In wearable chest EIT, patients breathe spontaneously, and therefore, their breathing pattern might not be stable. Since most of the EIT feature extraction methods are applied to STBPs, this renders their automatic identification of central importance.Approach. The EIT frame sequence is reconstructed from the raw EIT recordings and the raw global impedance waveform (GIW) is computed. Next, the respiratory component of the raw GIW is extracted and processed for the automatic respiratory cycle (breath) extraction and their subsequent grouping into STBPs.Main results. We suggest three criteria for the identification of STBPs, namely, the coefficient of variation of (i) breath tidal volume, (ii) breath duration and (iii) end-expiratory impedance. The total number of true STBPs identified by the proposed method was 294 out of 318 identified by the expert corresponding to accuracy over 90%. Specific activities such as speaking, eating and arm elevation are identified as sources of false positives and their discrimination is discussed.Significance. Simple and computationally efficient STBP detection and identification is a highly desirable component in the EIT processing pipeline. Our study implies that it is feasible, however, the determination of its limits is necessary in order to consider the implementation of more advanced and computationally demanding approaches such as deep learning and fusion with data from other wearable sensors such as accelerometers and microphones.

Real-time assessment of global and regional lung ventilation in the anti-gravity straining maneuver using electrical impedance tomography

OBJECTIVE

Anti-gravity straining maneuver (AGSM) helps to reduce the occurrence of gravity-induced visual disturbances and loss of consciousness. An objective assessment of the AGSM is still missing during ground training. This study evaluated the feasibility of using electrical impedance tomography (EIT) to assess the performance of AGSM.

METHODS

Eight undergraduates and eight teachers majoring in aerospace medicine were included in the study. An experienced professor from the department of aerospace medicine reviewed the key points of AGSM with each subject. EIT measurement was performed during AGSM. The global and regional ventilation were used to investigate the characteristics of AGSM. The professor and the subjects rated the performance of AGSM according to the maneuver requirements of AGSM (maximum 16 points) before and after reviewing the ventilations from EIT.

RESULTS

For global ventilation, the relative depth of gas exchange and duration of exhalation of the teachers were larger than those of the students (p < 0.01), and stability of the teachers was better as well (p < 0.001). No difference in the duration of gas exchange and leakage during exhalation between the teachers and the students was found. For regional ventilation, the teachers had significantly increased ventral ventilation during AGSM implementation (p < 0.001) whereas students did otherwise. Additionally, the differences of rating scores with and without EIT were also significant. Significant reductions were found in rating scores with EIT assessed by the professor (4.5 ± 2.0, p < 0.001) and by the students themselves (3.9 ± 2.2, p < 0.001). The scores were systematically higher when the students rated themselves compared with the professor's rating (p < 0.001 for both with and without EIT).

CONCLUSION

These findings demonstrated that EIT could objectively characterize the maneuver details of AGSM, which might provide a potential tool for real-time assessment of AGSM quality in an objective manner.

A Three Dimensional Calderon-Based Method for EIT on the Cylindrical Geometry

OBJECTIVE

Electrical impedance tomography (EIT) is an imaging modality in which voltage data arising from currents applied on the boundary are used to reconstruct the conductivity distribution in the interior. This paper provides a novel direct (noniterative) 3-D reconstruction algorithm for EIT in the cylindrical geometry.

METHODS

The algorithm is based on Calderón's method [Calderón, 1980], and is implemented for data collected on two or four rows of electrodes on the boundary of a cylinder.

RESULTS

The effectiveness of the method to localize inhomogeneities in the plane of the electrodes and in the z-direction is demonstrated on simulated and experimental data.

CONCLUSIONS AND SIGNIFICANCE

The results from simulated and experimental data show that the method is effective for distinguishing in-plane and nearby out-of-plane inhomogeneities with good spatial resolution in the vertical z direction with computational efficiency.

Shape-Driven EIT Reconstruction Using Fourier Representations

Shape-driven approaches have been proposed as an effective strategy for the electrical impedance tomography (EIT) reconstruction problem in recent years. In order to augment the shape-driven approaches, we propose a new method that transforms the shape to be reconstructed as basic primitives directly modeled by using Fourier representations. To allow automatic topological changes between the basic primitives and surrounding objects simultaneously, Boolean operations are employed. The Boolean operations with direct representation of primitives can be utilized for dimensionality and ill-posedness reduction, enabling feasible shape and topology optimization with shape-driven approaches. As a proof of principle, we leverage the proposed method for two dimensional shape reconstruction in EIT with various conductivity distributions. We demonstrate that our method is able to improve EIT reconstructions by enabling accurate shape and topology optimization.

A DIRECT RECONSTRUCTION ALGORITHM FOR THE ANISOTROPIC INVERSE CONDUCTIVITY PROBLEM BASED ON CALDERÓN'S METHOD IN THE PLANE

A direct reconstruction algorithm based on Calderón's linearization method for the reconstruction of isotropic conductivities is proposed for anisotropic conductivities in two-dimensions. To overcome the non-uniqueness of the anisotropic inverse conductivity problem, the entries of the unperturbed anisotropic tensors are assumed known a priori, and it remains to reconstruct the multiplicative scalar field. The quasi-conformal map in the plane facilitates the Calderón-based approach for anisotropic conductivities. The method is demonstrated on discontinuous radially symmetric conductivities of high and low contrast.

Introduction of Sample Based Prior into the D-Bar Method Through a Schur Complement Property

Electrical impedance tomography (EIT) is a non-invasive medical imaging technique in which images of the conductivity in a region of interest in the body are computed from measurements of voltages on electrodes arising from low-frequency, low-amplitude applied currents. Mathematically, the inverse conductivity problem is nonlinear and ill-posed, and the reconstructions have characteristically low spatial resolution. One approach to improve the spatial resolution of EIT images is to include anatomically and physiologically-based prior information in the reconstruction algorithm. Statistical inversion theory provides a means of including prior information from a representative sample population. In this paper, a method is proposed to introduce statistical prior information into the D-bar method based on Schur complement properties. The method presents an improvement of the image obtained by the D-bar method by maximizing the conditional probability density function of an image that is consistent with a prior information and the model, given a D-bar image computed from the voltage measurements. Experimental phantoms show an improved spatial resolution by the use of the proposed method for the D-bar image reconstructions.

Electrical conductivity spectra of hepatic tumors reflect hepatocellular carcinoma progression in mice

Background:Electrical impedance spectroscopy is a technique which evaluates differences in dielectric properties of tissues for cancer identification.Methods:Murine hepatic cancer model was developed by intraperitoneal administration of N-nitrosodiethylamine to male BALB/c mice. Tumors obtained were evaluated for their conductivity in frequency range of (4 Hz-5 MHz). All tumors were subjected to histopathological grading and parameters such as free spacing, necrosis, and cell density were estimated on histological slides. The status of gap junctions and gap junction intercellular communication (GJIC) were studied using enzyme-linked immunosorbent assay, immunohistochemistry, dye transfer assay, and electron microscopy.Results:Histopathological investigation revealed the presence of moderately to poorly-differentiated hepatocellular carcinoma (HCC) in mice. All types of tumors showed higher electrical conductivity than normal liver tissue in frequency range (4 Hz-1 kHz). However, in frequency range (10 kHz-5 MHz) only poorly-differentiated tumors showed higher conductivity compared to normal tissue. The most prominent findings in moderately-differentiated and poorly-differentiated HCC were increased visible free spaces and necrosis respectively. The status of cell gap junctions were significantly deteriorated in tumors and a corresponding significant reduction in GJIC was also observed. These biological indicators were correlated with electrical conductivity of hepatic tumors.Conclusion:Variations in electrical conductivity spectra of hepatic tumors reflect progression of HCC.General significance:Future studies can be planned to perform hierarchical clustering of dielectric parameters with more number of tumor samples to establish dielectric spectroscopy-based classification or staging of hepatic tumors.

Shape Reconstruction Using Boolean Operations in Electrical Impedance Tomography

In this work, we propose a new shape reconstruction framework rooted in the concept of Boolean operations for electrical impedance tomography (EIT). Within the framework, the evolution of inclusion shapes and topologies are simultaneously estimated through an explicit boundary description. For this, we use B-spline curves as basic shape primitives for shape reconstruction and topology optimization. The effectiveness of the proposed approach is demonstrated using simulated and experimentally-obtained data (testing EIT lung imaging). In the study, improved preservation of sharp features is observed when employing the proposed approach relative to the recently developed moving morphable components-based approach. In addition, robustness studies of the proposed approach considering background inhomogeneity and differing numbers of B-spline curve control points are performed. It is found that the proposed approach is tolerant to modeling errors caused by background inhomogeneity and is also quite robust to the selection of control points.

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.

Minimally Invasive Real-Time Electrical Impedance Spectroscopy Diagnostic Tool for Lung Parenchyma Pathologies. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2020;2020:5077-5080. [PMID: 33019128 DOI: 10.1109/embc44109.2020.9175860]

Electrical Impedance Spectroscopy has already demonstrated the ability to distinguish different tissues types, or tumors from normal tissue, or tissues displaying diverse degrees of pathology. When applying the technique, however, the necessity to make contact with the tissue often constitutes a practical limitation. Electrical Impedance Imaging (EIT), or in a broader sense, regional impedance assessment, struggle to assess different tissue conditions out of measurements from the surface of the body. But sensitivity is very small even for tissue a few centimeters under the skin, and in-vivo measurements are often not viable.The lung offer a third approximation by introducing a catheter though a bronchoscope, which is a routine clinical procedure. Measurements have been obtained by using 3 or 4-electrode techniques and allow us to distinguish, at least, fibrotic, emphysema or neoplastic regions from normal parenchyma. New instrumental developments, clinical measurements and preliminary results are presented and discussed.

The Levenberg–Marquardt Method for Acousto-Electric Tomography on Different Conductivity Contrast

The stability and convergence performance of Levenberg–Marquardt method for acousto-electric tomography (AET) applied to different levels of conductivity contrast is studied in this paper. As a multi-physical imaging modality, acousto-electric tomography (AET) provides high spatial imaging resolution while also conserving the high contrast property of electrical impedance tomography. The Levenberg–Marquardt method is a well known iteration scheme which can be applied for the nonlinear problem of AET. However, the influence of the conductivity contrast on the stability and convergence performances of this conductivity reconstruction method is rarely discussed in the literature. In this paper, the performance of the Tikhonov regularization-based Levenberg–Marquardt method is applied to reconstruct conductivity map with different conductivity contrast between different regions of the domain of interest (DOI). Numerical investigations are carried out for phantoms with different conductivity contrast. Reconstructed results with different levels of noise are presented and discussed in detail.