Advances in Reconstruction Methods for Electrical Impedance TomographyMS20

Electrical Impedance Tomography (EIT) is an imaging modality wherein electrical current is applied to the surface of an object, surface voltages are measured, and this boundary data is used to reconstruct interior electrical properties. Numerous exciting applications for EIT are being developed by researchers in diverse fields. However, EIT reconstruction is an extremely ill-posed inverse problem, leading to significant challenges in the reconstruction process. Improved reconstruction methods, which seek to stabilize the inversion process, are therefore an active area of research. This minisymposium will bring together researchers from around the world to present the latest advances in EIT reconstruction methods.

Nonlinear D-bar Reconstructions of 2D Human EIT Data with an Optimized Spatial Prior
Melody Alsaker (Gonzaga University)
Bayesian approximation of continuous boundary data for EIT
Sumanth Reddy Nakkireddy (Case Western Reserve University)
D-bar methods applied to functional pulmonary imaging: methods and clinical results
Jennifer Mueller (Colorado State University)
Electrical impedance tomography imaging via the Radon transform
Matti Lassas (University of Helsinki)
Acousto-Electric Tomography with limited data
Kim Knudsen (Technical University of Denmark)
Robust Absolute EIT Imaging in 2D
Sarah Hamilton (Marquette University)
Improving direct reconstructions from partial-boundary data in electrical impedance tomography
Andreas Hauptmann (University College London)
Reconstruction of a piecewise constant conductivity on a polygonal partition via shape optimization in EIT
Matteo Santacesaria (University of Helsinki)
Contrast enhancement in EIT imaging of the brain
Aku Seppänen (University of Eastern Finland)
Generalized linearization techniques and smoothened complete electrode model
Nuutti Hyvönen (Aalto University)
Image reconstruction in rotational EIT with limited boundary access
Olli Koskela (Tampere University of Technology)
The Use of the Approximation Error Method and Bayesian Inference to Introduce Anatomical and Physiological Prior Information into D-bar Algorithms
Talles Santos (Polytechnic School of University of São Paulo)
Melody Alsaker (Gonzaga University)
Samuli Siltanen (University of Helsinki)
eit, electrical impedance tomography, image reconstruction