  Electrical Impedance Tomography (EIT) aims to recover the internal
conductivity and permittivity distributions of a body from electrical
measurements taken on electrodes on the surface of the body. The reconstruction
task is a severely ill-posed nonlinear inverse problem that is highly sensitive
to measurement noise and modeling errors. Regularized D-bar methods have shown
great promise in producing noise-robust algorithms by employing a low-pass
filtering of nonlinear (nonphysical) Fourier transform data specific to the EIT
problem. Including prior data with the approximate locations of major organ
boundaries in the scattering transform provides a means of extending the radius
of the low-pass filter to include higher frequency components in the
reconstruction, in particular, features that are known with high confidence.
This information is additionally included in the system of D-bar equations with
an independent regularization parameter from that of the extended scattering
transform. In this paper, this approach is used in the 2-D D-bar method for
admittivity (conductivity as well as permittivity) EIT imaging. Noise-robust
reconstructions are presented for simulated EIT data on chest-shaped phantoms
with a simulated pneumothorax and pleural effusion. No assumption of the
pathology is used in the construction of the prior, yet the method still
produces significant enhancements of the underlying pathology (pneumothorax or
pleural effusion) even in the presence of strong noise.
