Abstract : Electrical impedance tomography is an imaging modality based on solving the inverse conductivity problem, in which known boundary voltages and currents are used to reconstruct information about an object's interior. The inversion process is known to be both highly nonlinear and highly ill-posed, and thus provides researchers with an ongoing wealth of interesting problems. The cutting edge of research in EIT includes both theoretical and computational developments, and is relevant to a wide variety of medical and industrial applications. This minisymposium will gather leading experts in EIT along with young researchers to share their new results and insights.
[04879] A Multithreaded Implementation of the D-bar Algorithm for 2D Functional EIT Imaging
Format : Talk at Waseda University
Author(s) :
Melody Alsaker (Gonzaga University)
Abstract : D-bar algorithms for Electrical Impedance Tomography have high computational complexity. Previous attempts at fast D-bar implementations had some limitations: these methods used a parallelization strategy which caused a time delay between data acquisition and reconstruction, and they used coarse spatial meshes with large numbers of CPU cores. Furthermore, data acquisition speed of modern EIT systems has increased, making previously published runtimes out-of-date. In this talk, we present a new multithreaded solution which addresses these problems.
[02729] New insight into EIT reconstruction using virtual X-rays
Format : Talk at Waseda University
Author(s) :
Siiri Rautio (University of Helsinki)
Abstract : We introduce a new reconstruction algorithm for EIT, which provides a connection between EIT and X-ray tomography. We divide the ill-posed and nonlinear inverse problem of EIT into separate steps. We start by calculating “virtual” X-ray projection data from the DN map. Then, we perform algebraic operations and integration, ending up with a blurry Radon sinogram. We use neural networks to deconvolve the sinogram and finally, we compute a reconstruction using the inverse Radon transform.
[04009] Combining electrical impedance tomography and machine learning for stroke classification
Format : Talk at Waseda University
Author(s) :
Juan Pablo Agnelli (National University of Córdoba)
Abstract : There are two main types of stroke: ischemic and hemorrhagic. In both cases the symptoms are the same, but treatments very different, so a cost-effective and portable classification device is needed.
In (Agnelli et al. 2020) a methodology for classifying stroke was proposed. The methodology combines the use of EIT data, the computation of VHED functions (Greenleaf et al. 2018) that have a geometric interpretation of the EIT data and finally machine learning applied to these VHED functions for the stroke classification. In this talk we continue this research line and extend the previous results to a more realistic scenario.
[03129] Recent developments on integral equation approaches for Electrical Impedance Tomography
Format : Talk at Waseda University
Author(s) :
Cristiana Sebu (University of Malta)
Abstract : The talk is focused on recent developments of reconstruction algorithms that can be used to approximate admittivity distributions in Electrical Impedance Tomography. The algorithms are non-iterative and are based on linearized integral equation formulations to allow reconstructions of the conductivity and/or permittivity distributions of two and three-dimensional domains from boundary measurements of both low and high-frequency alternating input currents and induced potentials. Reconstructions from noisy simulated data are obtained from single-time, time-difference and multiple-times data.
[04744] Monitoring of hemorrhagic stroke using Electrical Impedance Tomography
Format : Talk at Waseda University
Author(s) :
Ville Kolehmainen (Department of Technical Physics, University of Eastern Finland)
Abstract : In this talk, we present recent progress in development of electrical impedance tomography (EIT)
based bedside monitoring of hemorrhagic stroke. We present the practical setup and pipeline for this novel application of EIT and the image reconstruction method we have developed for it.
Feasibility of the approach is studied with simulated data from anatomically highly accurate simulation models and experimental phantom data from a laboratory setup
[04874] Exploration of deep generative modelling approaches to electrical impedance tomography
Format : Talk at Waseda University
Author(s) :
Valentina Candiani (University of Genoa)
Abstract : Reconstruction of conductivity images in electrical impedance tomography (EIT) requires the solution of a nonlinear inverse problem on noisy data. This problem is typically ill-conditioned and solution algorithms need either simplifying assumptions or regularization based on a priori knowledge.
In this work we study the applicability, the challenges and the limitations of some relatively new deep generative models such as score-based generative diffusion models and normalising flows, for both image reconstruction and medical anomaly detection. This talk will present some preliminary results obtained with such approaches in the application of EIT to the detection of stroke.
[04904] Fast CGO-based absolute reconstructions for 3D EIT
Format : Talk at Waseda University
Author(s) :
Peter Muller (Villanova University)
Sarah Hamilton (Marquette University)
Abstract : Complex geometrical optics (CGO)-based methods for 3-D electrical impedance tomography are presented. Calderón’s method and the $\mathbf{t}^{\mathrm{exp}}$ method are adapted for reconstructions from 3-D electrode data. These are the first absolute images to be produced from these methods for 3-D electrode data, both simulated and experimental. Some benefits of these CGO-based methods are that they provide real-time imaging and are shown to be robust to modelling errors such as electrode location and domain size.
[05054] Use of reference measurements in electrical tomography
Format : Talk at Waseda University
Author(s) :
Aku Seppänen (University of Eastern Finland )
Laura E. Dalton (Duke University)
Mikko Räsänen (University of Eastern Finland)
Moe Pourghaz (North Carolina State University)
Abstract : Reconstruction methods in electrical resistance/capacitance tomography are often divided into classes of absolute and difference methods. While absolute reconstructions are based on data from a single time instant, difference reconstructions use reference data, to image the change of conductivity/permittivity from the reference state qualitatively. In this talk, we demonstrate that absolute reconstructions can also benefit from the use of reference data, when available, especially because it improves their tolerance to modeling errors.
