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Bernhard Brandstätter

Publications and source records attributed to Bernhard Brandstätter.

3 recordsLinked to original sources

Fat and hydration monitoring by abdominal bioimpedance analysis: data interpretation by hierarchical electrical modeling.

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.

Abdomen↗

Direct estimation of Cole parameters in multifrequency EIT using a regularized Gauss-Newton method.

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.

Algorithms↗

Numerical solution of the general 3D eddy current problem for magnetic induction tomography (spectroscopy).

Magnetic induction tomography (MIT) is used for reconstructing the changes of the conductivity in a target object using alternating magnetic fields. Applications include, for example, the non-invasive monitoring of oedema in the human brain. A powerful software package has been developed which makes it possible to generate a finite element (FE) model of complex structures and to calculate the eddy currents in the object under investigation. To validate our software a model of a previously published experimental arrangement was generated. The model consists of a coaxial coil system and a conducting sphere which is moved perpendicular to the coil axis (a) in an empty space and (b) in a saline-filled cylindrical tank. The agreement of the measured and simulated data is very good when taking into consideration the systematic measurement errors in case (b). Thus the applicability of the simulation algorithm for two-compartment systems has been demonstrated even in the case of low conductivities and weak contrast. This can be considered an important step towards the solution of the inverse problem of MIT.

Computer Simulation↗