Marcel Ullrich

Bastian Harrach, Marcel Ullrich

Current-voltage measurements in electrical impedance tomography can be partially ordered with respect to definiteness of the associated self-adjoint Neumann-to-Dirichlet operators (NtD). With this ordering, a point-wise larger conductivity leads to smaller current-voltage measurements, and smaller conductivities lead to larger measurements. We present a converse of this simple monotonicity relation and use it to solve the shape reconstruction (aka inclusion detection) problem in EIT. The outer shape of a region where the conductivity differs from a known background conductivity can be found by simply comparing the measurements to that of smaller or larger test regions.

Bastian Harrach, Marcel Ullrich

Electrical impedance tomography (EIT) uses current-voltage measurements on the surface of an imaging subject to detect conductivity changes or anomalies. EIT is a promising new technique with great potential in medical imaging and non-destructive testing. However, in many applications, EIT suffers from inconsistent reliability due to its enormous sensitivity to modeling and measurement errors. In this work we show that rigorous resolution guarantees are possible within a realistic EIT measurement setting including systematic and random errors. We derive a constructive criterion to decide whether a desired resolution can be achieved in a given measurement setup. Our result covers the detection of anomalies of a known minimal contrast using noisy measurements on a number of electrodes attached to a subject with imprecisely known background conductivity.

Bastian Harrach, Eunjung Lee, Marcel Ullrich

Electrical impedance tomography (EIT) is highly affected by modeling errors regarding electrode positions and the shape of the imaging domain. In this work, we propose a new inclusion detection technique that is completely independent of such errors. Our new approach is based on a combination of frequency-difference and ultrasound modulated EIT measurements.