The increasing use of EIT in clinical research on severely ill lung patients requires a clarification of the influence of pathologic impedance distributions on the validity of the resulting tomograms. Significant accumulation of low-conducting air (e.g. pneumothorax or emphysema) or well-conducting liquid (e.g. haematothorax or atelectases) may conflict with treating the imaging problem as purely linear. First, we investigated the influence of stepwise inflation and deflation by up to 300 ml of air and 300 ml of Ringer solution into the pleural space of five pigs on the resulting tomograms during ventilation at constant tidal volume. Series of EIT images representing relative impedance changes were generated on the basis of a modified Sheffield back projection algorithm and ventilation distribution was displayed as functional (f-EIT) tomograms. In addition, a modified simultaneous iterative reconstruction technique (SIRT) was applied to quantify the resistivity distribution on an absolute level scaled in Omega m (a-EIT). Second, we applied these two EIT techniques on four intensive care patients with inhomogeneous air and fluid distribution and compared the EIT results to computed tomography (CT) and to a reference set of intrathoracic resistivity data of 20 healthy volunteers calculated by SIRT. The results of the animal model show that f-EIT based on back projection is not disturbed by the artificial pneumo- or haematothorax. Application of SIRT allows reliable discrimination and detection of the location and amplitude of pneumo- or haematothorax. These results were supported by the good agreement between the electrical impedance tomograms and CT scans on patients and by the significant differences of regional resistivity data between patients and healthy volunteers.

We investigated five different methods which can be applied to quantitatively construct functional tomograms of the lungs. The focus was on the sensitivity of functional tomograms to errors in acquired data. To quantify this sensitivity, theoretical, error-free data sets of well-known properties were artificially generated based on a 'living thorax model'. Physiological time courses and a typical distribution of errors caused by a typical Goe-MF II EIT system were used for the calculations which encompassed a range up to 50 times greater than the initial error level (4 microV(rms max)-400 microV(rms max)). Additionally, low-pass filtering and principal component analysis (PCA) were used to quantify the effect of preprocessing the raw data. The results demonstrate that all methods based on fitting the local to the global time course were superior to the common functional tomograms utilizing standard deviation or maximum and minimum detection. Ventilation distribution was best quantified by the so-called VT methods. Filling capacity--a lung tissue property--was least dependent on increasing error levels. The errors introduced by filtering are significant with respect to a quantitative analysis of ventilation distribution. A preprocessing of raw data by applying a PCA performed well on the data sets which had been constructed but were, nonetheless, realistic. This approach appears to be highly promising for application on real data which is known to be erroneous.

Between the years 2008 and 2013, approximately 67 kilotons of CO2 have been injected at the Ketzin site, Germany. As part of the geophysical monitoring programme, time‐lapse electrical resistivity tomography has been applied using crosshole and surface‐downhole measurements of electrical resistivity tomography. The data collection of electrical resistivity tomography is partly based on electrodes that are permanently installed in three wells at the site (one injection well and two observation wells). Both types of ERT measurements consistently show the build‐up of a CO2‐related resistivity signature near the injection point. Based on the imaged resistivity changes and a petrophysical model, CO2 saturation levels are estimated. These CO2 saturations are interpreted in conjunction with CO2 saturations inferred from neutron‐gamma loggings. Apart from the CO2–brine substitution response in the observed resistivity changes, significant imprints from the dynamic behaviour of the CO2 in the reservoir are observed.

We present a simple method to determine systematic errors that will occur in the measurements by EIT systems. The approach is based on very simple scalable resistive phantoms for EIT systems using a 16 electrode adjacent drive pattern. The output voltage of the phantoms is constant for all combinations of current injection and voltage measurements and the trans-impedance of each phantom is determined by only one component. It can be chosen independently from the input and output impedance, which can be set in order to simulate measurements on the human thorax. Additional serial adapters allow investigation of the influence of the contact impedance at the electrodes on resulting errors. Since real errors depend on the dynamic properties of an EIT system, the following parameters are accessible: crosstalk, the absolute error of each driving/sensing channel and the signal to noise ratio in each channel. Measurements were performed on a Goe-MF II EIT system under four different simulated operational conditions. We found that systematic measurement errors always exceeded the error level of stochastic noise since the Goe-MF II system had been optimized for a sufficient signal to noise ratio but not for accuracy. In time difference imaging and functional EIT (f-EIT) systematic errors are reduced to a minimum by dividing the raw data by reference data. This is not the case in absolute EIT (a-EIT) where the resistivity of the examined object is determined on an absolute scale. We conclude that a reduction of systematic errors has to be one major goal in future system design.

We present an improved approach to image ventilation in functional electrical impedance tomography (f-EIT). It combines the advantages of the two established procedures of calculating standard deviation as a functional parameter of ventilation (SD method) and the so-called filling capacity (FC method). The SD method quantifies the local impedance variation over a series of tomograms for each pixel; the FC method is based on the slope of a linear fit of regional versus the global impedance change. Tidal volume V(T) is displayed linearly by the SD method in f-EIT; it is, however, sensitive to noisy data. The FC method is much more robust with respect to noise but does not display the tidal volume V(T). We combined the advantages of both techniques in a new VT method which is based on raw data. It saves computing time and is suitable for both f-EIT and absolute EIT (a-EIT). We separated the raw data into two representative sets: end expiratory and end inspiratory. This was accomplished by calculating the global time course of the relative impedance changes from the raw data. In this time course, we determined all frame numbers (indices) of end expiration and end inspiration. These frame numbers were used to calculate one mean expiratory and one mean inspiratory raw data frame. Reconstruction by difference imaging directly reflects the mean tidal volume V(T) during the acquired frame series. The effect of the improvement by the VT method was investigated at different noise levels by adding artificial noise from 0 to 100 microV(rms) to a real raw dataset. The robustness with regard to noise of the VT method was similar to that of the FC method. The practical value of suppression of non-ventilatory impedance changes, artefacts and noise was tested by studying ten healthy subjects (four females, six males) during normal breathing. We found a highly significant improvement in the image quality (p < 0.001) of ventilation for this group of volunteers.