Imaging of conductivity changes and electrode movement in EIT

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.

Unsupervised sequence-to-sequence learning for automatic signal quality assessment in multi-channel electrical impedance-based hemodynamic monitoring

BACKGROUND AND OBJECTIVE

This study proposes an unsupervised sequence-to-sequence learning approach that automatically assesses the motion-induced reliability degradation of the cardiac volume signal (CVS) in multi-channel electrical impedance-based hemodynamic monitoring. The proposed method attempts to tackle shortcomings in existing learning-based assessment approaches, such as the requirement of manual annotation for motion influence and the lack of explicit mechanisms for realizing motion-induced abnormalities under contextual variations in CVS over time.

METHOD

By utilizing long-short term memory and variational auto-encoder structures, an encoder-decoder model is trained not only to self-reproduce an input sequence of the CVS but also to extrapolate the future in a parallel fashion. By doing so, the model can capture contextual knowledge lying in a temporal CVS sequence while being regularized to explore a general relationship over the entire time-series. A motion-influenced CVS of low-quality is detected, based on the residual between the input sequence and its neural representation with a cut-off value determined from the two-sigma rule of thumb over the training set.

RESULT

Our experimental observations validated two claims: (i) in the learning environment of label-absence, assessment performance is achievable at a competitive level to the supervised setting, and (ii) the contextual information across a time series of CVS is advantageous for effectively realizing motion-induced unrealistic distortions in signal amplitude and morphology. We also investigated the capability as a pseudo-labeling tool to minimize human-craft annotation by preemptively providing strong candidates for motion-induced anomalies. Empirical evidence has shown that machine-guided annotation can reduce inevitable human-errors during manual assessment while minimizing cumbersome and time-consuming processes.

CONCLUSION

The proposed method has a particular significance in the industrial field, where it is unavoidable to gather and utilize a large amount of CVS data to achieve high accuracy and robustness in real-world applications.

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.

Machine learning-based signal quality assessment for cardiac volume monitoring in electrical impedance tomography

Owing to recent advances in thoracic electrical impedance tomography (EIT), a patient’s hemodynamic function can be noninvasively and continuously estimated in real-time by surveilling a cardiac volume signal (CVS) associated with stroke volume and cardiac output. In clinical applications, however, a CVS is often of low quality, mainly because of the patient’s deliberate movements or inevitable motions during clinical interventions. This study aims to develop a signal quality indexing method that assesses the influence of motion artifacts on transient CVSs. The assessment is performed on each cardiac cycle to take advantage of the periodicity and regularity in cardiac volume changes. Time intervals are identified using the synchronized electrocardiography system. We apply divergent machine-learning methods, which can be sorted into discriminative-model and manifold-learning approaches. The use of machine-learning could be suitable for our real-time monitoring application that requires fast inference and automation as well as high accuracy. In the clinical environment, the proposed method can be utilized to provide immediate warnings so that clinicians can minimize confusion regarding patients’ conditions, reduce clinical resource utilization, and improve the confidence level of the monitoring system. Numerous experiments using actual EIT data validate the capability of CVSs degraded by motion artifacts to be accurately and automatically assessed in real-time by machine learning. The best model achieved an accuracy of 0.95, positive and negative predictive values of 0.96 and 0.86, sensitivity of 0.98, specificity of 0.77, and AUC of 0.96.

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.

Automated 3D thorax model generation using handheld video-footage

PURPOSE

For the visualization of pulmonary ventilation with Electrical Impedance Tomography (EIT) most devices use standard reconstruction models, featuring common thorax dimensions and predetermined electrode locations. Any discrepancies between the available model and the patient in terms of body shape and electrode position lead to incorrectly displayed impedance distributions. This work addresses that problem by presenting and evaluating a method for 3D model generation of the thorax and any affixed electrodes based on handheld video-footage.

METHODS

Therefore, a process was developed, providing users with the ability to capture a patient's chest and the attached electrodes via smartphone. Once data is collected, extracted images are used to generate a 3D model with a structure from motion approach and locate electrodes with ArUco markers. For the evaluation of the developed method, multiple tests were performed in laboratory environments, which were compared with manually created reference models and differences quantified based on mean distance, standard deviation, and maximum distance.

RESULTS

The implemented workflow allows for automated model reconstruction based on videos or selected images captured with a handheld device. It generates sparse point clouds from which a surface mesh is reconstructed and returns relative coordinates of any identified ArUco marker. The average value for the mean distance error of two model generations was 5.4 mm while the mean standard deviation was 6.0 mm. The average runtime of twelve reconstructions was 5:17 min, with a minimal runtime of 3:22 min and a maximal runtime of 7:29 min.

CONCLUSION

The presented methods and results show that model reconstruction of a patient's thorax and applied electrodes at an emergency site is feasible with already available devices. This is a first step toward the automated generation of patient-specific reconstruction models for Electrical Impedance Tomography based on images recorded with handheld devices.

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.

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 (CC), the root mean square error (RMSE) and the full-reference (FR) 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.

Evaluation of Thoracic Equivalent Multiport Circuits Using an Electrical Impedance Tomography Hardware Simulation Interface

Electrical impedance tomography is a low-cost, safe, and high temporal resolution medical imaging modality which finds extensive application in real-time thoracic impedance imaging. Thoracic impedance changes can reveal important information about the physiological condition of patients’ lungs. In this way, electrical impedance tomography can be a valuable tool for monitoring patients. However, this technique is very sensitive to measurement noise or possible minor signal errors, coming from either the hardware, the electrodes, or even particular biological signals. Thus, the design of a good performance electrical impedance tomography hardware setup which properly interacts with the tissue examined is both an essential and a challenging concept. In this paper, we adopt an extensive simulation approach, which combines the system’s analogue and digital hardware, along with equivalent circuits of 3D finite element models that represent thoracic cavities. Each thoracic finite element model is created in MATLAB based on existing CT images, while the tissues’ conductivity and permittivity values for a selected frequency are acquired from a database using Python. The model is transferred to a multiport RLC network, embedded in the system’s hardware which is simulated at LT SPICE. The voltage output data are transferred to MATLAB where the electrical impedance tomography signal sampling and digital processing is also simulated. Finally, image reconstructions are performed in MATLAB, using the EIDORS library tool and considering the signal noise levels and different electrode and signal sampling configurations (ADC bits, sampling frequency, number of taps).

Feasibility of Temperature Control by Electrical Impedance Tomography in Hyperthermia

We present a simulation study investigating the feasibility of electrical impedance tomography (EIT) as a low cost, noninvasive technique for hyperthermia (HT) treatment monitoring and adaptation. Temperature rise in tissues leads to perfusion and tissue conductivity changes that can be reconstructed in 3D by EIT to noninvasively map temperature and perfusion. In this study, we developed reconstruction methods and investigated the achievable accuracy of EIT by simulating HT treatmentlike scenarios, using detailed anatomical models with heterogeneous conductivity distributions. The impact of the size and location of the heated region, the voltage measurement signal-to-noise ratio, and the reference model personalization and accuracy were studied. Results showed that by introducing an iterative reconstruction approach, combined with adaptive prior regions and tissue-dependent penalties, planning-based reference models, measurement-based reweighting, and physics-based constraints, it is possible to map conductivity-changes throughout the heated domain, with an accuracy of around 5% and cm-scale spatial resolution. An initial exploration of the use of multifrequency EIT to separate temperature and perfusion effects yielded promising results, indicating that temperature reconstruction accuracy can be in the order of 1 ∘C. Our results suggest that EIT can provide valuable real-time HT monitoring capabilities. Experimental confirmation in real-world conditions is the next step.

Technologies for magnetic induction tomography sensors and image reconstruction in medical assisted diagnosis: A review

Magnetic induction tomography (MIT) is a non-invasive and non-contact imaging technology, which can be used in medical diagnosis by reconstructing the electrical distribution of biological tissues. Unlike other large medical imaging equipment, the device of MIT is with small size and low cost. The theoretical basis of MIT is by measuring the phase difference of magnetic flux density generated around the imaging objects, analyzing the eddy current distribution, and then using the reconstruction algorithms to obtain the electrical characteristic distribution of the object. This review introduces the development of imaging systems and the reconstruction algorithms of MIT as a medical assisted diagnostic technology, including the optimal design of the sensors, the excitation methods of the system, the calculation methods of the eddy current, and the improved methods of different reconstruction algorithms.

Electrical Impedance Tomography for monitoring cardiac radiofrequency ablation: a scoping review of an emerging technology

Arrhythmias are common cardiac diseases which can be treated effectively by the cardiac radiofrequency ablation (CRFA). However, information regarding the lesion growth within the myocardium is critical to the procedure's safety and efficacy but still unavailable in the current catheterisation lab (CathLab). Over the last 20 years, many efforts have been made in order to track the lesion size during the procedure. Unfortunately, all the approaches have their own limitations preventing them from the clinical translation and hence making the lesion size monitoring during a CRFA still an open issue. Electrical Impedance Tomography (EIT) is an impedance imaging modality that might be able to image the thermal-related impedance changes from which the lesion size can be measured. With the availability of the patient's CT scans, for a detailed model, and the catheter-based electrodes for the internal electrodes, EIT accuracy and sensitivity to the ablated sites can be significantly improved and is worth being explored for this application. Though EIT is still new to CRFA with no in-vivo experiments being done according to our up-to-date searching, many related EIT studies and its extensive research in Hyperthermia and other ablations can reveal many hints for a possibility of the CRFA-EIT application. In this paper, we present a review on multiple aspects of EIT in CRFA. First, the expected CRFA-EIT signal range and frequency are discussed based on various measured impedance results obtained from lesions in the past. Second, the possible noise sources that can happen in a clinical CRFA procedure, along with their signal range and frequency compared to the CRFA-EIT signal, and, third, the available current solutions to separate such noises from the CRFA-EIT signal. Finally, we review the progress of EIT in thermal applications over the last two decades in order to identify the developments that EIT can take advantage of and the current drawbacks that need to be solved for a potential CRFA-EIT application.

Real-Time Detection of Hemothorax and Monitoring its Progression in a Piglet Model by Electrical Impedance Tomography: A Feasibility Study

Hemothorax is a serious medical condition that can be life-threatening if left untreated. Early diagnosis and timely treatment are of great importance to produce favorable outcome. Although currently available diagnostic techniques, e.g., chest radiography, ultrasonography, and CT, can accurately detect hemothorax, delayed hemothorax cannot be identified early because these examinations are often performed on patients until noticeable symptoms manifest. Therefore, for early detection of delayed hemothorax, real-time monitoring by means of a portable and noninvasive imaging technique is needed. In this study, we employed electrical impedance tomography (EIT) to detect the onset of hemothorax in real time on eight piglet hemothorax models. The models were established by injection of 60 ml fresh autologous blood into the pleural cavity, and the subsequent development of hemothorax was monitored continuously. The results showed that EIT was able to sensitively detect hemothorax as small as 10 ml in volume, as well as its location. Also, the development of hemothorax over a range of 10 ml up to 60 ml was well monitored in real time, with a favorable linear relationship between the impedance change in EIT images and the volume of blood injected. These findings demonstrated that EIT has a unique potential for early diagnosis and continuous monitoring of hemothorax in clinical practice, providing medical staff valuable information for prompt identification and treatment of delayed hemothorax.

A Fidelity-Embedded Regularization Method for Robust Electrical Impedance Tomography

Electrical impedance tomography (EIT) provides functional images of an electrical conductivity distribution inside the human body. Since the 1980s, many potential clinical applications have arisen using inexpensive portable EIT devices. EIT acquires multiple trans-impedance measurements across the body from an array of surface electrodes around a chosen imaging slice. The conductivity image reconstruction from the measured data is a fundamentally ill-posed inverse problem notoriously vulnerable to measurement noise and artifacts. Most available methods invert the ill-conditioned sensitivity or the Jacobian matrix using a regularized least-squares data-fitting technique. Their performances rely on the regularization parameter, which controls the trade-off between fidelity and robustness. For clinical applications of EIT, it would be desirable to develop a method achieving consistent performance over various uncertain data, regardless of the choice of the regularization parameter. Based on the analysis of the structure of the Jacobian matrix, we propose a fidelity-embedded regularization (FER) method and a motion artifact reduction filter. Incorporating the Jacobian matrix in the regularization process, the new FER method with the motion artifact reduction filter offers stable reconstructions of high-fidelity images from noisy data by taking a very large regularization parameter value. The proposed method showed practical merits in experimental studies of chest EIT imaging.

Evaluation of electrical impedance tomography for determination of urinary bladder volume: comparison with standard ultrasound methods in healthy volunteers

Background

Continuous non-invasive urinary bladder volume measurement (cystovolumetry) would allow better management of urinary tract disease. Electrical impedance tomography (EIT) represents a promising method to overcome the limitations of non-continuous ultrasound measurements. The aim of this study was to compare the measurement accuracy of EIT to standard ultrasound in healthy volunteers.

Methods

For EIT of the bladder a commercial device (Goe MF II) was used with 4 different configurations of 16 standard ECG electrodes attached to the lower abdomen of healthy participants. To estimate maximum bladder capacity (BCmax) and residual urine (RU) two ultrasound methods (US-Ellipsoid and US-L × W × H) and a bedside bladder scanner (BS), were performed at the point of urgency and after voiding. For volume reference, BCmax and RU were validated by urine collection in a weight measuring pitcher. The global impedance method was used offline to estimate BCmax and RU from EIT.

Results

The mean error of US-Ellipsoid (37 ± 17%) and US-L × W × H (36 ± 15%) and EIT (32 ± 18%) showed no significant differences in the estimation of BCmax (mean 743 ± 200 ml) normalized to pitcher volumetry. BS showed significantly worse accuracy (55 ± 9%). Volumetry of RU (mean 152.1 ± 64 ml) revealed comparable higher errors for both EIT (72 ± 58%) and BS (63 ± 24%) compared to US-Ellipsoid (54 ± 25%). In case of RU, EIT accuracy is dependent on electrode configuration, as the Stripes (41 ± 25%) and Matrix (38 ± 27%) configurations revealed significantly superior accuracy to the 1 × 16 (116 ± 62%) configuration.

Conclusions

EIT-cystovolumetry compares well with ultrasound techniques. For estimation of RU, the selection of the EIT electrode configuration is important. Also, the development of an algorithm should consider the impact of movement artefacts. Finally, the accuracy of non-invasive ultrasound accepted as gold standard of cystovolumetry should be reconsidered.

Lobe based image reconstruction in Electrical Impedance Tomography

PURPOSE

Electrical Impedance Tomography (EIT) is an imaging modality used to generate two-dimensional cross-sectional images representing impedance change in the thorax. The impedance of lung tissue changes with change in air content of the lungs; hence, EIT can be used to examine regional lung ventilation in patients with abnormal lungs. In lung EIT, electrodes are attached around the circumference of the thorax to inject small alternating currents and measure resulting voltages. In contrast to X-ray computed tomography (CT), EIT images do not depict a thorax slice of well defined thickness, but instead visualize a lens-shaped region around the electrode plane, which results from diffuse current propagation in the thorax. Usually, this is considered a drawback, since image interpretation is impeded if 'off-plane' conductivity changes are projected onto the reconstructed two-dimensional image. In this paper we describe an approach that takes advantage of current propagation below and above the electrode plane. The approach enables estimation of the individual conductivity change in each lung lobe from boundary voltage measurements. This could be used to monitor disease progression in patients with obstructive lung diseases, such as chronic obstructive pulmonary disease (COPD) or cystic fibrosis (CF) and to obtain a more comprehensive insight into the pathophysiology of the lung.

METHODS

Electrode voltages resulting from different conductivities in each lung lobe were simulated utilizing a realistic 3D finite element model (FEM) of the human thorax and the lungs. Overall 200 different patterns of conductivity change were simulated. A 'lobe reconstruction' algorithm was developed, applying patient-specific anatomical information in the reconstruction process. A standard EIT image reconstruction algorithm and the proposed 'lobe reconstruction' algorithm were used to estimate conductivity change in the lobes. The agreement between simulated and reconstructed conductivity change in particular lobes were compared using Bland-Altman plots, correlation plots and linear regression. To test the applicability of the approach in a realistic scenario, EIT measurements of a patient suffering from cystic fibrosis (CF) were carried out.

RESULTS

Conductivity changes in each lobe generate specific patterns of voltage change. These can be used to estimate the conductivity change in lobes from measured boundary voltage. The correlation coefficient between simulated and reconstructed conductivity change in particular lobes is r > 0.89 for all lobes. Unknown position of the electrode plane leads to over- or underestimation of reconstructed conductivity change. Slight mismatches (± 5% of the forward model height) between the actual position of the electrode plane and the position used in the reconstruction model lead to regression coefficients of 0.7 to 1.3 between simulated and reconstructed conductivity change in the lobes.

CONCLUSION

The presented approach enhances common reconstruction methods by providing information about anatomically assignable units and thus facilitates image interpretation, since impedance change and thus ventilation of each lobe is directly determined in the reconstructions.

Compensation for large thorax excursions in EIT imaging

Besides the application of EIT in the intensive care unit it has recently also been used in spontaneously breathing patients suffering from asthma bronchiole, cystic fibrosis (CF) or chronic obstructive pulmonary disease (COPD). In these cases large thorax excursions during deep inspiration, e.g. during lung function testing, lead to artifacts in the reconstructed images. In this paper we introduce a new approach to compensate for image artifacts resulting from excursion induced changes in boundary voltages. It is shown in a simulation study that boundary voltage change due to thorax excursion on a homogeneous model can be used to modify the measured voltages and thus reduce the impact of thorax excursion on the reconstructed images. The applicability of the method on human subjects is demonstrated utilizing a motion-tracking-system. The proposed technique leads to fewer artifacts in the reconstructed images and improves image quality without substantial increase in computational effort, making the approach suitable for real-time imaging of lung ventilation. This might help to establish EIT as a supplemental tool for lung function tests in spontaneously breathing patients to support clinicians in diagnosis and monitoring of disease progression.

Interface and permittivity simultaneous reconstruction in electrical capacitance tomography based on boundary and finite-elements coupling method

Electrical capacitance tomography (ECT) is a non-destructive detection technique for imaging the permittivity distributions inside an observed domain from the capacitances measurements on its boundary. Owing to its advantages of non-contact, non-radiation, high speed and low cost, ECT is promising in the measurements of many industrial or biological processes. However, in the practical industrial or biological systems, a deposit is normally seen in the inner wall of its pipe or vessel. As the actual region of interest (ROI) of ECT is surrounded by the deposit layer, the capacitance measurements become weakly sensitive to the permittivity perturbation occurring at the ROI. When there is a major permittivity difference between the deposit and the ROI, this kind of shielding effect is significant, and the permittivity reconstruction becomes challenging. To deal with the issue, an interface and permittivity simultaneous reconstruction approach is proposed. Both the permittivity at the ROI and the geometry of the deposit layer are recovered using the block coordinate descent method. The boundary and finite-elements coupling method is employed to improve the computational efficiency. The performance of the proposed method is evaluated with the simulation tests. This article is part of the themed issue 'Supersensing through industrial process tomography'.

Correction of electrode modelling errors in multi-frequency EIT imaging

The differentiation of haemorrhagic from ischaemic stroke using electrical impedance tomography (EIT) requires measurements at multiple frequencies, since the general lack of healthy measurements on the same patient excludes time-difference imaging methods. It has previously been shown that the inaccurate modelling of electrodes constitutes one of the largest sources of image artefacts in non-linear multi-frequency EIT applications. To address this issue, we augmented the conductivity Jacobian matrix with a Jacobian matrix with respect to electrode movement. Using this new algorithm, simulated ischaemic and haemorrhagic strokes in a realistic head model were reconstructed for varying degrees of electrode position errors. The simultaneous recovery of conductivity spectra and electrode positions removed most artefacts caused by inaccurately modelled electrodes. Reconstructions were stable for electrode position errors of up to 1.5 mm standard deviation along both surface dimensions. We conclude that this method can be used for electrode model correction in multi-frequency EIT.

Structural-functional lung imaging using a combined CT-EIT and a Discrete Cosine Transformation reconstruction method

Lung EIT is a functional imaging method that utilizes electrical currents to reconstruct images of conductivity changes inside the thorax. This technique is radiation free and applicable at the bedside, but lacks of spatial resolution compared to morphological imaging methods such as X-ray computed tomography (CT). In this article we describe an approach for EIT image reconstruction using morphologic information obtained from other structural imaging modalities. This leads to recon- structed images of lung ventilation that can easily be superimposed with structural CT or MRI images, which facilitates image interpretation. The approach is based on a Discrete Cosine Transformation (DCT) of an image of the considered transversal thorax slice. The use of DCT enables reduction of the dimensionality of the reconstruction and ensures that only conductivity changes of the lungs are reconstructed and displayed. The DCT based approach is well suited to fuse morphological image information with functional lung imaging at low computational costs. Results on simulated data indicate that this approach preserves the morphological structures of the lungs and avoids blurring of the solution. Images from patient measurements reveal the capabilities of the method and demonstrate benefits in possible applications.

A fast time-difference inverse solver for 3D EIT with application to lung imaging

A class of sparse optimization techniques that require solely matrix-vector products, rather than an explicit access to the forward matrix and its transpose, has been paid much attention in the recent decade for dealing with large-scale inverse problems. This study tailors application of the so-called Gradient Projection for Sparse Reconstruction (GPSR) to large-scale time-difference three-dimensional electrical impedance tomography (3D EIT). 3D EIT typically suffers from the need for a large number of voxels to cover the whole domain, so its application to real-time imaging, for example monitoring of lung function, remains scarce since the large number of degrees of freedom of the problem extremely increases storage space and reconstruction time. This study shows the great potential of the GPSR for large-size time-difference 3D EIT. Further studies are needed to improve its accuracy for imaging small-size anomalies.

Correcting electrode modelling errors in EIT on realistic 3D head models

Electrical impedance tomography (EIT) is a promising medical imaging technique which could aid differentiation of haemorrhagic from ischaemic stroke in an ambulance. One challenge in EIT is the ill-posed nature of the image reconstruction, i.e., that small measurement or modelling errors can result in large image artefacts. It is therefore important that reconstruction algorithms are improved with regard to stability to modelling errors. We identify that wrongly modelled electrode positions constitute one of the biggest sources of image artefacts in head EIT. Therefore, the use of the Fréchet derivative on the electrode boundaries in a realistic three-dimensional head model is investigated, in order to reconstruct electrode movements simultaneously to conductivity changes. We show a fast implementation and analyse the performance of electrode position reconstructions in time-difference and absolute imaging for simulated and experimental voltages. Reconstructing the electrode positions and conductivities simultaneously increased the image quality significantly in the presence of electrode movement.

Steps towards 3D Electrical Impedance Tomography

Electrical Impedance Tomography (EIT) is a promising imaging technique to visualize the dynamics of regional lung ventilation. 2D EIT has shown promise in monitoring ventilation therapy, with the drawback of only displaying a single horizontal slice of the lungs. Until now there are no generally accepted approaches available to generate meaningful 3D images in real-time. This paper describes general problems and first attempts to overcome those that may extend to a hierarchical scheme for 3D EIT imaging.

Functional validation and comparison framework for EIT lung imaging

INTRODUCTION

Electrical impedance tomography (EIT) is an emerging clinical tool for monitoring ventilation distribution in mechanically ventilated patients, for which many image reconstruction algorithms have been suggested. We propose an experimental framework to assess such algorithms with respect to their ability to correctly represent well-defined physiological changes. We defined a set of clinically relevant ventilation conditions and induced them experimentally in 8 pigs by controlling three ventilator settings (tidal volume, positive end-expiratory pressure and the fraction of inspired oxygen). In this way, large and discrete shifts in global and regional lung air content were elicited.

METHODS

We use the framework to compare twelve 2D EIT reconstruction algorithms, including backprojection (the original and still most frequently used algorithm), GREIT (a more recent consensus algorithm for lung imaging), truncated singular value decomposition (TSVD), several variants of the one-step Gauss-Newton approach and two iterative algorithms. We consider the effects of using a 3D finite element model, assuming non-uniform background conductivity, noise modeling, reconstructing for electrode movement, total variation (TV) reconstruction, robust error norms, smoothing priors, and using difference vs. normalized difference data.

RESULTS AND CONCLUSIONS

Our results indicate that, while variation in appearance of images reconstructed from the same data is not negligible, clinically relevant parameters do not vary considerably among the advanced algorithms. Among the analysed algorithms, several advanced algorithms perform well, while some others are significantly worse. Given its vintage and ad-hoc formulation backprojection works surprisingly well, supporting the validity of previous studies in lung EIT.

Mutual information as a measure of image quality for 3D dynamic lung imaging with EIT

We report on a pilot study of dynamic lung electrical impedance tomography (EIT) at the University of Manchester. Low-noise EIT data at 100 frames per second were obtained from healthy male subjects during controlled breathing, followed by magnetic resonance imaging (MRI) subsequently used for spatial validation of the EIT reconstruction. The torso surface in the MR image and electrode positions obtained using MRI fiducial markers informed the construction of a 3D finite element model extruded along the caudal-distal axis of the subject. Small changes in the boundary that occur during respiration were accounted for by incorporating the sensitivity with respect to boundary shape into a robust temporal difference reconstruction algorithm. EIT and MRI images were co-registered using the open source medical imaging software, 3D Slicer. A quantitative comparison of quality of different EIT reconstructions was achieved through calculation of the mutual information with a lung-segmented MR image. EIT reconstructions using a linear shape correction algorithm reduced boundary image artefacts, yielding better contrast of the lungs, and had 10% greater mutual information compared with a standard linear EIT reconstruction.

Real-time imaging of cerebral infarction in rabbits using electrical impedance tomography

Objective

To investigate the possible use of electrical impedance tomography (EIT) in monitoring focal cerebral infarction in a rabbit model.

Methods

A model of focal cerebral infarction was established in eight New Zealand rabbits using a photochemical method without craniectomy. Focal cerebral infarction was confirmed by histopathological examination. Intracranial impedance variation was measured using 16 electrodes placed in a circle on the scalp. EIT images were obtained using a damped least-squares reconstruction algorithm. The average resistivity value (ARV) of the infarct region on EIT images was calculated to quantify relative resistivity changes. A symmetry index was calculated to evaluate the relative difference in resistivity between the two sides of the cerebrum.

Results

EIT images and ARV curves showed that impedance changes caused by cerebral infarction increased linearly with irradiation time. A difference in ARV was found between measurements taken before and after infarct induction.

Conclusions

Focal cerebral infarction can be monitored by EIT in the proposed animal model. The results are sufficiently encouraging that the authors plan to extend this study to humans, after further technical improvements.

Interaction of dependent and non-dependent regions of the acutely injured lung during a stepwise recruitment manoeuvre

The benefit of treating acute lung injury with recruitment manoeuvres is controversial. An impediment to settling this debate is the difficulty in visualizing how distinct lung regions respond to the manoeuvre. Here, regional lung mechanics were studied by electrical impedance tomography (EIT) during a stepwise recruitment manoeuvre in a porcine model with acute lung injury. The following interaction between dependent and non-dependent regions consistently occurred: atelectasis in the most dependent region was reversed only after the non-dependent region became overdistended. EIT estimates of overdistension and atelectasis were validated by histological examination of lung tissue, confirming that the dependent region was primarily atelectatic and the non-dependent region was primarily overdistended. The pulmonary pressure-volume equation, originally designed for modelling measurements at the airway opening, was adapted for EIT-based regional estimates of overdistension and atelectasis. The adaptation accurately modelled the regional EIT data from dependent and non-dependent regions (R(2) > 0.93, P < 0.0001) and predicted their interaction during recruitment. In conclusion, EIT imaging of regional lung mechanics reveals that overdistension in the non-dependent region precedes atelectasis reversal in the dependent region during a stepwise recruitment manoeuvre.

Shape deformation in two-dimensional electrical impedance tomography

Electrical impedance tomography (EIT) uses measurements from surface electrodes to reconstruct an image of the conductivity of the contained medium. However, changes in measurements result from both changes in internal conductivity and changes in the shape of the medium relative to the electrode positions. Failure to account for shape changes results in a conductivity image with significant artifacts. Previous work to address shape changes in EIT has shown that in some cases boundary shape and electrode location can be uniquely determined for isotropic conductivities; however, for geometrically conformal changes, this is not possible. This prior work has shown that the shape change problem can be partially addressed. In this paper, we explore the limits of compensation for boundary movement in EIT using three approaches. First, a theoretical model was developed to separate a deformation vector field into conformal and nonconformal components, from which the reconstruction limits may be determined. Next, finite element models were used to simulate EIT measurements from a domain whose boundary has been deformed. Finally, an experimental phantom was constructed from which boundary deformation measurements were acquired. Results, both in simulation and with experimental data, suggest that some electrode movement and boundary distortions can be reconstructed based on conductivity changes alone while reducing image artifacts in the process.

Interpretation of the modality of touch on an artificial arm covered with an EIT-based sensitive skin

During social interaction humans extract important information from tactile stimuli that can improve their understanding of the interaction. The development of a similar capability in a robot will contribute to the future success of intuitive human–robot interaction. This paper presents a thin, flexible and stretchable artificial skin for robotics based on the principle of electrical impedance tomography. This skin, which can be used to extract information such as location, duration and intensity of touch, was used to cover the forearm and upper arm of a full-size mannequin. A classifier based on the ‘LogitBoost’ algorithm was used to classify the modality of eight different types of touch applied by humans to the mannequin arm. Experiments showed that the modality of touch was correctly classified in approximately 71% of the trials. This was shown to be comparable to the accuracy of humans when identifying touch. The classification accuracies obtained represent significant improvements over previous classification algorithms applied to artificial sensitive skins. It is shown that features based on touch duration and intensity are sufficient to provide a good classification of touch modality. Gender and cultural background were examined and found to have no statistically significant effect on the classification results.

Electrical impedance tomography system based on active electrodes

Electrical impedance tomography (EIT) can image the distribution of ventilated lung tissue, and is thus a promising technology to help monitor patient breathing to help selection of mechanical ventilation parameters. Two key difficulties in EIT instrumentation make such monitoring difficult: (1) EIT data quality depends on good electrode contact and is sensitive to changes in contact quality, and (2) EIT electrodes are difficult and time consuming to place on patients. This paper presents the design and initial tests of an active electrode-based system to address these difficulties. Our active electrode EIT system incorporates an active electrode belt, a central voltage-driven current source, central analog to digital converters and digital to analog converters, a central FPGA-based demodulator and controller. The electrode belt is designed incorporating 32 active electrodes, each of which contains the electronic amplifiers, switches and associated logic. Tests show stable device performance with a convenient ease of use and good imaging ability in volunteer tests.

A unified approach for EIT imaging of regional overdistension and atelectasis in acute lung injury

Patients with acute lung injury or acute respiratory distress syndrome (ALI/ARDS) are vulnerable to ventilator-induced lung injury. Although this syndrome affects the lung heterogeneously, mechanical ventilation is not guided by regional indicators of potential lung injury. We used electrical impedance tomography (EIT) to estimate the extent of regional lung overdistension and atelectasis during mechanical ventilation. Techniques for tidal breath detection, lung identification, and regional compliance estimation were combined with the Graz consensus on EIT lung imaging (GREIT) algorithm. Nine ALI/ARDS patients were monitored during stepwise increases and decreases in airway pressure. Our method detected individual breaths with 96.0% sensitivity and 97.6% specificity. The duration and volume of tidal breaths erred on average by 0.2 s and 5%, respectively. Respiratory system compliance from EIT and ventilator measurements had a correlation coefficient of 0.80. Stepwise increases in pressure could reverse atelectasis in 17% of the lung. At the highest pressures, 73% of the lung became overdistended. During stepwise decreases in pressure, previously-atelectatic regions remained open at sub-baseline pressures. We recommend that the proposed approach be used in collaborative research of EIT-guided ventilation strategies for ALI/ARDS.

Patient examinations using electrical impedance tomography--sources of interference in the intensive care unit

Electrical impedance tomography (EIT) is expected to become a valuable tool for monitoring mechanically ventilated patients due to its ability to continuously assess regional lung ventilation and aeration. Several sources of interference with EIT examinations exist in intensive care units (ICU). Our objectives are to demonstrate how some medical nursing and monitoring devices interfere with EIT measurements and modify the EIT scans and waveforms, which approaches can be applied to minimize these effects and how possible misinterpretation can be avoided. We present four cases of EIT examinations of adult ICU patients. Two of the patients were subjected to pulsation therapy using a pulsating air suspension mattress while being ventilated by high-frequency oscillatory or conventional pressure-controlled ventilation, respectively. The EIT signal modulation synchronous with the occurrence of the pulsating wave was 2.3 times larger than the periodic modulation synchronous with heart rate and high-frequency oscillations. During conventional ventilation, the pulsating mattress induced an EIT signal fluctuation with a magnitude corresponding to about 20% of the patient's tidal volume. In the third patient, interference with EIT examination was caused by continuous cardiac output monitoring. The last patient's examination was disturbed by impedance pneumography when excitation currents of similar frequency to EIT were used. In all subjects, the generation of functional EIT scans was compromised and interpretation of regional ventilation impossible. Discontinuation of pulsation therapy and of continuous cardiac output and impedance respiration monitoring immediately improved the EIT signal and scan quality. Offline processing of the disturbed data using frequency filtering enabled partial retrieval of relevant information. We conclude that thoracic EIT examinations in the ICU require cautious interpretation because of possible mechanical and electromagnetic interference.

Compensation of modelling errors due to unknown domain boundary in electrical impedance tomography

Electrical impedance tomography is a highly unstable problem with respect to measurement and modeling errors. This instability is especially severe when absolute imaging is considered. With clinical measurements, accurate knowledge about the body shape is usually not available, and therefore an approximate model domain has to be used in the computational model. It has earlier been shown that large reconstruction artefacts result if the geometry of the model domain is incorrect. In this paper, we adapt the so-called approximation error approach to compensate for the modeling errors caused by inaccurately known body shape. This approach has previously been shown to be applicable to a variety of modeling errors, such as coarse discretization in the numerical approximation of the forward model and domain truncation. We evaluate the approach with a simulated example of thorax imaging, and also with experimental data from a laboratory setting, with absolute imaging considered in both cases. We show that the related modeling errors can be efficiently compensated for by the approximation error approach. We also show that recovery from simultaneous discretization related errors is feasible, allowing the use of computationally efficient reduced order models.

Real-time imaging of subarachnoid hemorrhage in piglets with electrical impedance tomography

Subarachnoid hemorrhage (SAH) is one of the most severe medical emergencies in neurosurgery. Early detection or diagnosis would significantly reduce the rate of disability and mortality, and improve the prognosis of the patients. Although the present medical imaging techniques generally have high sensitivity to identify bleeding, the use of an additional, non-invasive imaging technique capable of continuously monitoring SAH is required to prevent contingent bleeding or re-bleeding. In this study, electrical impedance tomography (EIT) was applied to detect the onset of SAH modeled on eight piglets in real time, with the subsequent process being monitored continuously. The experimental SAH model was introduced by one-time injection of 5 ml fresh autologous arterial blood into the cisterna magna. Results showed that resistivity variations within the brain caused by the added blood could be detected using the EIT method and may be associated not only with the resistivity difference among brain tissues, but also with variations of cerebrospinal fluid dynamics. In conclusion, EIT has unique potential for use in clinical practice to provide invaluable real-time neuroimaging data for SAH after the improvement of electrode design, anisotropic realistic modeling and instrumentation.

Electrical impedance tomography problem with inaccurately known boundary and contact impedances

In electrical impedance tomography (EIT) electric currents are injected into a body with unknown electromagnetic properties through a set of contact electrodes at the boundary of the body. The resulting voltages are measured on the same electrodes and the objective is to reconstruct the unknown conductivity function inside the body based on these data. All the traditional approaches to the reconstruction problem assume that the boundary of the body and the electrode-skin contact impedances are known a priori. However, in clinical experiments one usually lacks the exact knowledge of the boundary and contact impedances, and therefore, approximate model domain and contact impedances have to be used in the image reconstruction. However, it has been noticed that even small errors in the shape of the computation domain or contact impedances can cause large systematic artefacts in the reconstructed images, leading to loss of diagnostically relevant information. In a recent paper (Kolehmainen , 2006), we showed how in the 2-D case the errors induced by the inaccurately known boundary can be eliminated as part of the image reconstruction and introduced a novel method for finding a deformed image of the original isotropic conductivity using the theory of TeichmUller mappings. In this paper, the theory and reconstruction method are extended to include the estimation of unknown contact impedances. The method is implemented numerically and tested with experimental EIT data. The results show that the systematic errors caused by inaccurately known boundary and contact impedances can efficiently be eliminated by the reconstruction method.

Reconstruction of conductivity changes and electrode movements based on EIT temporal sequences

Electrical impedance tomography (EIT) reconstructs a conductivity change image within a body from electrical measurements on the body surface; while it has relatively low spatial resolution, it has a high temporal resolution. One key difficulty with EIT measurements is due to the movement and position uncertainty of the electrodes, especially due to breathing and posture change. In this paper, we develop an approach to reconstruct both the conductivity change image and the electrode movements from the temporal sequence of EIT measurements. Since both the conductivity change and electrode movement are slow with respect to the data frame rate, there are significant temporal correlations which we formulate as priors for the regularized image reconstruction model. Image reconstruction is posed in terms of a regularization matrix and a Jacobian matrix which are augmented for the conductivity change and electrode movement, and then further augmented to concatenate the d previous and future frames. Results are shown for simulation, phantom and human data, and show that the proposed algorithm yields improved resolution and noise performance in comparison to a conventional one-step reconstruction method.