In electrical impedance tomography (EIT), the spatially distributed electrical conductivity is reconstructed based on surface potential measurements. Due to the ill-posedness of the image reconstruction problem, the quality of reconstructions depends heavily on available prior information on the target. This talk considers especially total variation (TV) priors, which favor sharp edges in the conductivity distribution. We discuss differences between two TV models, isotropic and anisotropic TV, and show examples of applications to non-destructive material testing.

This presentation is part of Minisymposium “MS62 - Imaging models with non-linear constraints (2 parts)”
organized by: Tuomo Valkonen (University of Liverpool) , Juan Carlos De Los Reyes (Escuela Politécnica Nacional) .

Authors:

Aku Seppänen (University of Eastern Finland)

Gerardo González (University of Eastern Finland)

Ville Kolehmainen (University of Eastern Finland)

Mohammad Pour-Ghaz (North Carolina State University)

Keywords:

bayesian methods, image reconstruction, inverse problems, nonlinear optimization, partial differential equation models