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Electrical Impedance Tomography (EIT) aims to recover the internalconductivity and permittivity distributions of a body from electricalmeasurements taken on electrodes on the surface of the body. The reconstructiontask is a severely ill-posed nonlinear inverse problem that is highly sensitiveto measurement noise and modeling errors. Regularized D-bar methods have showngreat promise in producing noise-robust algorithms by employing a low-passfiltering of nonlinear (nonphysical) Fourier transform data specific to the EITproblem. Including prior data with the approximate locations of major organboundaries in the scattering transform provides a means of extending the radiusof the low-pass filter to include higher frequency components in thereconstruction, in particular, features that are known with high confidence.This information is additionally included in the system of D-bar equations withan independent regularization parameter from that of the extended scatteringtransform. In this paper, this approach is used in the 2-D D-bar method foradmittivity (conductivity as well as permittivity) EIT imaging. Noise-robustreconstructions are presented for simulated EIT data on chest-shaped phantomswith a simulated pneumothorax and pleural effusion. No assumption of thepathology is used in the construction of the prior, yet the method stillproduces significant enhancements of the underlying pathology (pneumothorax orpleural effusion) even in the presence of strong noise.

Incorporating a Spatial Prior into Nonlinear D-Bar EIT imaging for Complex Admittivities