Recovery of organ boundaries in electrical impedance tomography images using a priori data, optimization, and deep learning - Capps, Michael, author; Mueller, Jennifer, advisor; Cheney, Margaret, committee member; Pinaud, Olivier, committee member; Bartels, Randy, committee member (Colorado State University. Libraries, 2019)
In this thesis we explore electrical impedance tomography (EIT) and new aspects of the solutions to the inverse conductivity problem. Specifically we will focus on new methods for obtaining additional information from direct reconstructions on 2D domains using the D-bar method based on work by Nachmann in 1996 and Mueller and Siltanen in 2000. We cover the history of EIT as well as performing a review of relevant literature. Original work presented covers (1) an application of signal separation of cardiac and ventilation signals to the recovery of pulmonary measures and detection of air trapping in children with cystic fibrosis, (2) recovery of the boundaries of internal structures in EIT data sets using optimization of a priori data in the D-bar method, (3) recovery of the boundaries of internal structures in EIT data sets using deep neural networks applied to the scattering transform in the D-bar method. Results using both numerically simulated data and data collected on a tank with simulated organs made of agar are presented.
Keywords: electrical impedance tomography; deep learning; inverse problems