A major drawback of electrical impedance tomography is the poor quality of the conductivity images, i.e., the low spatial resolution as well as large errors in the reconstructed conductivity values. The main reason is the necessity for regularization of the ill-conditioned inverse problem which results in excessive spatial low-pass filtering. A novel regularization method (SMORR (spectral modelling regularized reconstructor)) is proposed, which is based on the inclusion of spectral a priori information in the form of appropriate tissue models (e.g. Cole models). This approach reduces the ill-posedness of the inverse problem, when multifrequency data are available. An additional advantage is the direct reconstruction of the (physiological) tissue parameters of interest instead of the conductivities. SMORR was compared with posterior fitting of a Cole model to the conductivity spectra obtained with a classical iterative reconstruction scheme at various frequencies. SMORR performed significantly better than the reference method concerning robustness against noise in the data.

In a previous publication, it was demonstrated that the abdominal subcutaneous fat layer thickness (SFL) is strongly correlated with the abdominal electrical impedance when measured with a transversal tetrapolar electrode arrangement. This article addresses the following questions: 1) To which extent do different abdominal compartments contribute to the impedance? 2) How does the hydration state of tissues affect the data? 3) Can hydration and fat content be assessed independently? For simulating the measured data a hierarchical electrical model was built. The abdomen was subdivided into three compartments (subcutaneous fat, muscle, mesentery). The true anatomical structure of the compartment boundaries was modeled using finite-element modeling (FEM). Each compartment is described by an electrical tissue model parameterized in physiological terms. Assuming the same percent change of the fat fraction in the mesentery and the SFL the model predicts a change of 1,24 omega/mm change of the SFL compared to 1,1 omega/mm measured. 42% of the change stem from the SFL, 56% from the mesentery and 2% from changes of fat within the muscle compartment. A 1% increase of the extracellular water in the muscle is not discernible from a 1% decrease of the SFL. The measured data reflect not only the SFL but also the visceral fat. The tetrapolar electrode arrangement allows the measurement of the abdominal fat content only if the hydration remains constant.

The basic purpose of electrical impedance tomography (EIT) is the reconstruction of conductivity distributions. While multifrequency measurements are of common use, the majority of reconstructed images are still conductivity distributions from one single frequency. More interesting than conductivities at each frequency are electrical tissue parameters, which describe the frequency-dependent conductivity changes of tissue. These parameters give information about physiological or electrical properties of tissues. By using this spectral information, a classification of different tissue types is possible. To get a distribution of tissue parameters, usually a posterior fitting of a tissue model to the conductivity spectra obtained with classical reconstruction algorithms at various frequencies is used. In this work, a single-step reconstruction algorithm for differential imaging was developed for the direct estimation of Cole parameters. This method is termed differential parametric reconstruction. The Cole model was used as the underlying tissue model, where only the relative changes of the two conductivity parameters sigma(0) and sigma(infinity) were reconstructed and the other two parameters of the model which are less identifiable were set to constant values. The reconstruction algorithm was tested with simulated noisy datasets and real measurement data from EIT measurements on the human thorax. These measurements were taken from healthy subjects and from patients with a serious lung injury. The new method yields a good image quality and higher robustness against noise compared to conventional reconstruction methods.

Ein wesentliches Element der Erfolgsgeschichte des Stahlbetonbaus ist die Beherrschung der Rissbildung, sodass die Rissbreiten im Gebrauchszustand auf ein tolerierbares Maß begrenzt werden können. Mehr als 60 Jahre nach den ersten gezielten Forschungsarbeiten zur Rissbreitenentwicklung in Stahlbeton mit Rippenbewehrung besteht aber noch immer kein Konsens darüber, wie die Rissbreite rechnerisch prognostiziert werden sollte. Dieser Beitrag greift die mechanische Modellvorstellung aus DAfStb‐Heft 466 nach König/Tue zur Berechnung der maximalen Rissbreite [1] auf und vergleicht die Ergebnisse mit modernen numerischen Simulationsmethoden. Besonderes Augenmerk wird hierbei auf den Einfluss der Betondeckung auf die Oberflächenrissbreite und die Bedeutung einer rissbreitenabhängigen Verbundspannung in der Berechnung gelegt. Dabei zeigt sich, dass das mechanische Modell zur maximalen Rissbreite durch die numerische Simulation sehr gut bestätigt werden kann.

Currently only ionizing or invasive methods are used in clinical applications for the monitoring of extracellular lung water. Alternatively a method called focused conductivity spectroscopy (FCS) is suggested, which aims at reconstructing a pulmonary edema index (PEIX) by measuring the electrical conductivity of the region of interest (ROI) at several frequencies. In contrast to electrical impedance tomography (EIT) a minimum number of strategically placed electrodes is used. The goals of this study were the analysis of the sensitivity for the PEIX, an estimate of the optimal electrode configuration and the determination of the required frequencies. In order to calculate the solution of the FCS forward problem a realistic 3D model of a human torso was developed containing both lungs, the heart, the liver and the thorax musculature. The bioelectrical properties for each compartment were described with appropriate tissue models which relate the conductivity spectra to physiological parameters. The PEIX was defined as the interstitial volume fraction of the alveolar septa. Furthermore the model includes 48 electrodes subdivided into three layers. The optimal electrode configuration was selected by minimizing the number of electrodes, among certain subsets of these electrodes. The analysis shows that eight to ten electrodes and six frequencies are theoretically sufficient to obtain a coefficient of variation.