Our overarching goal is the development and analysis of EIT reconstruction algorithms and hardware systems for
A direct reconstruction algorithm for imaging complex conductivity
Direct reconstruction algorithms based on D-bar methods hold promise for computing absolute images with accurate values
- a property that is important in diagnostic medical applications. As part of a recent NIH grant, we have developed
the first D-bar method for computing complex conductivities, also known as admittivities. The complex conductivity
provides reconstructions of two distinct tissue properties: the
through the real part and the
through the imaginary part. Some physiological conditions are much easier to distinguish in the permittivity component
than the conductivity component.
Here are reconstructions of a simulated pleural effusion:
Reconstructions from experimental data can be found here.
Nonlinear regularization of the D-bar method
In collaboration with
and Kim Knudsen,
it was rigorously proved that truncation of the range of the complex frequencies in the scattering transform constitutes
regularization scheme for the D-bar method. It serves to stabilize the solution of the inverse conductivity problem by the
D-bar method and an estimate of the suggested truncation radius in terms of the noise level is provided. For details, see
Design of EIT hardware for clinical lung imaging
In close collaboration with the EIT group at the University of Sao Paulo, Brazil, and as part of a recent NIH grant, a new
EIT system for measuring phasic voltage data has been developed.
If you are interested in working in EIT and want to know more about our current projects, please contact Jennifer Mueller.