Chest electrical impedance tomography examination, data analysis, terminology, clinical use and recommendations: consensus statement of the TRanslational EIT developmeNt stuDy group.

Electrical impedance tomography (EIT) has undergone 30 years of development. Functional chest examinations with this technology are considered clinically relevant, especially for monitoring regional lung ventilation in mechanically ventilated patients and for regional pulmonary function testing in patients with chronic lung diseases. As EIT becomes an established medical technology, it requires consensus examination, nomenclature, data analysis and interpretation schemes. Such consensus is needed to compare, understand and reproduce study findings from and among different research groups, to enable large clinical trials and, ultimately, routine clinical use. Recommendations of how EIT findings can be applied to generate diagnoses and impact clinical decision-making and therapy planning are required. This consensus paper was prepared by an international working group, collaborating on the clinical promotion of EIT called TRanslational EIT developmeNt stuDy group. It addresses the stated needs by providing (1) a new classification of core processes involved in chest EIT examinations and data analysis, (2) focus on clinical applications with structured reviews and outlooks (separately for adult and neonatal/paediatric patients), (3) a structured framework to categorise and understand the relationships among analysis approaches and their clinical roles, (4) consensus, unified terminology with clinical user-friendly definitions and explanations, (5) a review of all major work in thoracic EIT and (6) recommendations for future development (193 pages of online supplements systematically linked with the chief sections of the main document). We expect this information to be useful for clinicians and researchers working with EIT, as well as for industry producers of this technology.

A comparison framework for temporal image reconstructions in electrical impedance tomography.

Electrical impedance tomography (EIT) provides low-resolution images of internal conductivity distributions, but is able to achieve relatively high temporal resolutions. Most EIT image reconstruction algorithms do not explicitly account for the temporal constraints on the measurements or physiological processes under investigation. Instead, algorithms typically assume both that the conductivity distribution does not change during the acquisition of each EIT data frame, and that frames can be reconstructed independently, without consideration of the correlation between images. A failure to account for these temporal effects will result in aliasing-related artefacts in images. Several methods have been proposed to compensate for these effects, including interpolation of raw data, and reconstruction algorithms using Kalman and temporal filtering. However, no systematic work has been performed to understand the severity of the temporal artefacts nor the extent to which algorithms can account for them. We seek to address this need by developing a temporal comparison framework and figures of merit to assess the ability of reconstruction algorithms to account for temporal effects. Using this approach, we compare combinations of three reconstruction algorithms using three EIT data frame types: perfect, realistic and interpolated. The results show that, without accounting for temporal effects, artefacts are present in images for dynamic conductivity contrasts at frequencies 10-20 times slower than the frame rate. The proposed methods show some improvements in reducing these artefacts.

A resistive mesh phantom for assessing the performance of EIT systems.

Assessing the performance of electrical impedance tomography (EIT) systems usually requires a phantom for validation, calibration, or comparison purposes. This paper describes a resistive mesh phantom to assess the performance of EIT systems while taking into account cabling stray effects similar to in vivo conditions. This phantom is built with 340 precision resistors on a printed circuit board representing a 2-D circular homogeneous medium. It also integrates equivalent electrical models of the Ag/AgCl electrode impedances. The parameters of the electrode models were fitted from impedance curves measured with an impedance analyzer. The technique used to build the phantom is general and applicable to phantoms of arbitrary shape and conductivity distribution. We describe three performance indicators that can be measured with our phantom for every measurement of an EIT data frame: SNR, accuracy, and modeling accuracy. These performance indicators were evaluated on our EIT system under different frame rates and applied current intensities. The performance indicators are dependent on frame rate, operating frequency, applied current intensity, measurement strategy, and intermodulation distortion when performing simultaneous measurements at several frequencies. These parameter values should, therefore, always be specified when reporting performance indicators to better appreciate their significance.

A 3-D hybrid finite element model to characterize the electrical behavior of cutaneous tissues.

Finite element modeling of the skin is useful to study the electrical properties of cutaneous tissues and gain a better understanding of the current distribution within the skin. Such an epithelial finite element model comprises extremely thin structures like cellular membranes, nuclear membranes, and the extracellular fluid. Meshing such narrow spaces considerably increases the number of elements leading to longer computing time. This also greatly reduces the number of epithelial cells that can be assembled before reaching computing limitations. To avoid the problem of meshing extremely narrow spaces while unnecessarily increasing the number of elements, we present a new hybrid modeling approach to develop a 3-D finite element model of the skin. This skin model comprises all skin layers, different lesion types, and a complete electrode model. It is used to analyze the complex electrical behavior of normal and malignant skin tissues. The current distribution within this model is also simulated to assess the depth of field achievable by an electrical impedance tomography system at different operating frequencies.

GREIT: a unified approach to 2D linear EIT reconstruction of lung images.

Electrical impedance tomography (EIT) is an attractive method for clinically monitoring patients during mechanical ventilation, because it can provide a non-invasive continuous image of pulmonary impedance which indicates the distribution of ventilation. However, most clinical and physiological research in lung EIT is done using older and proprietary algorithms; this is an obstacle to interpretation of EIT images because the reconstructed images are not well characterized. To address this issue, we develop a consensus linear reconstruction algorithm for lung EIT, called GREIT (Graz consensus Reconstruction algorithm for EIT). This paper describes the unified approach to linear image reconstruction developed for GREIT. The framework for the linear reconstruction algorithm consists of (1) detailed finite element models of a representative adult and neonatal thorax, (2) consensus on the performance figures of merit for EIT image reconstruction and (3) a systematic approach to optimize a linear reconstruction matrix to desired performance measures. Consensus figures of merit, in order of importance, are (a) uniform amplitude response, (b) small and uniform position error, (c) small ringing artefacts, (d) uniform resolution, (e) limited shape deformation and (f) high resolution. Such figures of merit must be attained while maintaining small noise amplification and small sensitivity to electrode and boundary movement. This approach represents the consensus of a large and representative group of experts in EIT algorithm design and clinical applications for pulmonary monitoring. All software and data to implement and test the algorithm have been made available under an open source license which allows free research and commercial use.

A multi-frequency EIT system design based on telecommunication signal processors.

A multi-frequency electrical impedance tomography system for cardiopulmonary monitoring has been designed with specialized digital signal processors developed primarily for the telecommunications sector. The system consists of two modules: a scan-head and a base-station. The scan-head, located close to the patient's torso, contains front-end circuits for measuring transfer impedance with a 16-electrode array. The base-station, placed at the bedside, comprises 16 direct digital synthesizers, 32 digital down-converters, digital circuits to control the data acquisition sequence and a USB-2.0 microcontroller. At every step of the scan sequence, the system simultaneously measures four complex variables at eight frequencies. These variables are the potential difference between the selected pair of sense electrodes, the currents applied by the source and sink electrodes, and the current flowing through the ground electrode. Frequencies are programmable from 10 kHz to 2 MHz with a resolution of 2 mHz. Characterization tests were performed with a precision mesh phantom connected to the scan-head. For a 5 Hz frame rate, the mean signal-to-noise ratio and accuracy are, respectively, 43 dB and 95.4% for eight frequencies logarithmically spaced from 70 to 950 kHz. In vitro and in vivo time-difference images have been reconstructed.

Real-time management of faulty electrodes in electrical impedance tomography.

Completely or partially disconnected electrodes are a fairly common occurrence in many electrical impedance tomography (EIT) clinical applications. Several factors can contribute to electrode disconnection: patient movement, perspiration, manipulations by clinical staff, and defective electrode leads or electronics. By corrupting several measurements, faulty electrodes introduce significant image artifacts. In order to properly manage faulty electrodes, it is necessary to: 1) account for invalid data in image reconstruction algorithms and 2) automatically detect faulty electrodes. This paper presents a two-part approach for real-time management of faulty electrodes based on the principle of voltage-current reciprocity. The first part allows accounting for faulty electrodes in EIT image reconstruction without a priori knowledge of which electrodes are at fault. The method properly weights each measurement according to its compliance with the principle of voltage-current reciprocity. Results show that the algorithm is able to automatically determine the valid portion of the data and use it to calculate high-quality images. The second part of the approach allows automatic real-time detection of at least one faulty electrode with 100% sensitivity and two faulty electrodes with 80% sensitivity enabling the clinical staff to fix the problem as soon as possible to minimize data loss.

Comparison of applied and induced current electrical impedance tomography.

Several papers on induced current electrical impedance tomography (IC-EIT) have dwelt on potential advantages of this technique over conventional EIT which uses applied current (AC-EIT). Experimental evidence that IC-EIT could surpass AC-EIT in similar imaging conditions is lacking. In this paper, we describe a system that can switch rapidly between both AC-EIT and IC-EIT. The system makes it possible to image objects in a saline-filled tank, providing data acquired in identical test conditions for comparing the performance of the two modes. The system uses eight circular coils and 16 electrodes to acquire 120 linearly independent measurements in IC-EIT and 104 in AC-EIT. Difference images were reconstructed from data acquired with both modes using the maximum a posteriori method. Spatial resolution was lower in IC-EIT images than in AC-EIT, especially in the radial direction. IC-EIT also exhibits a bias toward the center for positioning a conductivity perturbation. These results were obtained for a typical coil configuration widely used in the literature and may not be representative of alternate coil configurations. The system described in this paper provides stable experimental conditions for comparing the performance of the two EIT imaging modes and would be a valuable tool for validating new coil configurations.

Accounting for hardware imperfections in EIT image reconstruction algorithms.

Electrical impedance tomography (EIT) is a non-invasive technique for imaging the conductivity distribution of a body section. Different types of EIT images can be reconstructed: absolute, time difference and frequency difference. Reconstruction algorithms are sensitive to many errors which translate into image artefacts. These errors generally result from incorrect modelling or inaccurate measurements. Every reconstruction algorithm incorporates a model of the physical set-up which must be as accurate as possible since any discrepancy with the actual set-up will cause image artefacts. Several methods have been proposed in the literature to improve the model realism, such as creating anatomical-shaped meshes, adding a complete electrode model and tracking changes in electrode contact impedances and positions. Absolute and frequency difference reconstruction algorithms are particularly sensitive to measurement errors and generally assume that measurements are made with an ideal EIT system. Real EIT systems have hardware imperfections that cause measurement errors. These errors translate into image artefacts since the reconstruction algorithm cannot properly discriminate genuine measurement variations produced by the medium under study from those caused by hardware imperfections. We therefore propose a method for eliminating these artefacts by integrating a model of the system hardware imperfections into the reconstruction algorithms. The effectiveness of the method has been evaluated by reconstructing absolute, time difference and frequency difference images with and without the hardware model from data acquired on a resistor mesh phantom. Results have shown that artefacts are smaller for images reconstructed with the model, especially for frequency difference imaging.

Electrical impedance tomography's correlation to lung volume is not influenced by anthropometric parameters.

STUDY OBJECTIVES: Electrical impedance tomography (EIT) is able to reflect physiological parameters such as real-time changes in global and regional lung volume. EIT can aid in the assessment of lung recruitment, and its use has been validated in preliminary studies monitoring mechanical ventilation at the bedside. ICU patients vary widely in their body habitus, and obesity is becoming more prevalent. Our primary research purpose was to establish whether anthropometric parameters influence EIT's reliability. Our secondary question was whether body position alters its correlation to spirometric measurements. SUBJECTS: 22 healthy adult volunteers (12 male, 10 female) with broadly variable anthropometric parameters. INTERVENTIONS: Simultaneous measurements of changes in lung volume using EIT imaging and a pneumotachograph were obtained with two breathing patterns (quiet and deep breathing) and in four body positions (standing, sitting, semi-reclining and supine). MEASUREMENTS AND RESULTS: Correlation between measurements of changes in lung volume using EIT imaging and a pneumotachograph was excellent. Variations attributable to anthropometric measurements accounted for at most a 1.3% difference. CONCLUSIONS: Anthropometric variability and body position do not adversely influence the EIT estimation of changes in lung volume. These data suggest EIT could be used to monitor critically ill mechanically ventilated adults with variable body habitus regardless of position.

A method for modelling and optimizing an electrical impedance tomography system.

Electrical impedance tomography (EIT) image reconstruction is an ill-posed problem requiring maximum measurement precision. Recent EIT systems claim 60 to 80 dB precision. Achieving higher values is hard in practice since measurements must be performed at relatively high frequency, on a living subject, while using components whose tolerance is usually higher than 0.1%. To circumvent this difficulty, a method for modelling the electronic circuits of an EIT system was developed in order to optimize the circuits and incorporate the model in the reconstruction algorithms. The proposed approach is based on a matrix method for solving electrical circuits and has been applied to the scan-head which contains the front-end electronic circuits of our system. The method is used to simulate the system characteristic curves which are then optimized with the Levenberg-Marquardt method to find optimal component values. A scan-head was built with the new component values and its simulated performance curves were compared with network analyser measurements. As a result of the optimization, the impedance at the operating frequency was increased to minimize the effects of variations in skin/electrode contact impedance. The transconductance and gain frequency responses were also reshaped to reduce noise sensitivity and unintended signal modulation. Integrating the model in the reconstruction algorithms should further improve overall performance of an EIT system.

A parametric model of the relationship between EIT and total lung volume.

Spirometry and electrical impedance tomography (EIT) data from 26 healthy subjects (14 males, 12 females) were used to develop a model linking contrast variations in EIT difference images to lung volume changes. Eight recordings, each 64 s long, were made for each subject in four postures (standing, sitting, reclining at 45 degrees, supine) and two breathing modes (quiet tidal and deep breathing). Age, gender and five anthropometric variables were recorded. The database was divided into four subsets. The first subset, data from 22 subjects (12 males, 10 females) recorded in deep breathing mode, was used to create the model. Validation was done with the other subsets: data recorded during quiet tidal breathing in the same 22 subjects, and data recorded in both breathing modes for the other four subjects. A quadratic equation in DeltaV(P) (lung volume changes recorded by the spirometer) provided a very good fit to total contrast changes in the EIT images. The model coefficients were found to depend on posture, gender, thoracic circumference and scapular skin fold. To validate the model, the quadratic equation was inverted to estimate lung volume changes from the EIT images. The estimated changes were then compared to the measured volume changes. Validations with each data subset yielded mean standard errors ranging from 9.3% to 12.4%. The proposed model is a first step in enabling inter individual comparisons of EIT images since: (1) it provides a framework for incorporating the effects of anthropometric variables, gender and posture, and (2) it references the images to a physical quantity (volume) verifiable by spirometry.