The ACE1 Electrical Impedance Tomography System for Thoracic Imaging

The design and performance of the active complex electrode (ACE1) electrical impedance tomography system for single-ended phasic voltage measurements are presented. The design of the hardware and calibration procedures allows for reconstruction of conductivity and permittivity images. Phase measurement is achieved with the ACE1 active electrode circuit which measures the amplitude and phase of the voltage and the applied current at the location at which current is injected into the body. An evaluation of the system performance under typical operating conditions includes details of demodulation and calibration and an in-depth look at insightful metrics, such as signal-to-noise ratio variations during a single current pattern. Static and dynamic images of conductivity and permittivity are presented from ACE1 data collected on tank phantoms and human subjects to illustrate the system\u27s utility

Active complex electrode (ACE1) electrical impedance tomography system & anatomically inspired modeling of electrode-skin contact impedance, The

Includes bibliographical references.2016 Summer.Electrical Impedance Tomography (EIT) is a technique used to image the varying electrical properties of biological tissues or tissue conductivity and permittivity. There are many clinical uses of EIT, but as a newer imaging modality, there is interest in improving hardware to acquire EIT data, creating models of the system and generating high quality images. The two main contributions of this work include: (1) EIT hardware advancements and (2) software modeling to simulate measured human subject data. Specifically, this dissertation includes the design and testing of Colorado State University's first EIT system, the pairwise current injection active complex electrode (ACE1) system for phasic voltage measurement. The ACE1 system was primarily designed for thoracic EIT applications, and its performance and limitations were tested through a variety of experiments. Additionally, the EIT forward problem was used to investigate electrode-skin contact impedance

Computational advancements in the D-bar reconstruction method for 2-D electrical impedance tomography

2016 Spring.Includes bibliographical references.We study the problem of reconstructing 2-D conductivities from boundary voltage and current density measurements, also known as the electrical impedance tomography (EIT) problem, using the D-bar inversion method, based on the 1996 global uniqueness proof by Adrian Nachman. We focus on the computational implementation and efficiency of the D-bar algorithm, its application to finite-precision practical data in human thoracic imaging, and the quality and spatial resolution of the resulting reconstructions. The main contributions of this work are (1) a parallelized computational implementation of the algorithm which has been shown to run in real-time, thus demonstrating the feasibility of the D-bar method for use in real-time bedside imaging, and (2) a modification of the algorithm to include \emph{a priori} data in the form of approximate organ boundaries and (optionally) conductivity estimates, which we show to be effective in improving spatial resolution in the resulting reconstructions. These computational advancements are tested using both numerically simulated data as well as experimental human and tank data collected using the ACE1 EIT machine at CSU. In this work, we provide details regarding the theoretical background and practical implementation for each advancement, we demonstrate the effectiveness of the algorithm modifications through multiple experiments, and we provide discussion and conclusions based on the results

Robust Computation in 2D Absolute EIT (A-EIT) Using D-Bar Methods with the “EXP” Approximation

Objective Absolute images have important applications in medical Electrical Impedance Tomography (EIT) imaging, but the traditional minimization and statistical based computations are very sensitive to modeling errors and noise. In this paper, it is demonstrated that D-bar reconstruction methods for absolute EIT are robust to such errors. Approach The effects of errors in domain shape and electrode placement on absolute images computed with 2-D D-bar reconstruction algorithms are studied on experimental data. Main Results It is demonstrated with tank data from several EIT systems that these methods are quite robust to such modeling errors, and furthermore the artefacts arising from such modeling errors are similar to those occurring in classic time-difference EIT imaging. Significance This study is promising for clinical applications where absolute EIT images are desirable, but previously thought impossible

Electrical Impedance Tomography with Deep Calder\'on Method

Electrical impedance tomography (EIT) is a noninvasive medical imaging modality utilizing the current-density/voltage data measured on the surface of the subject. Calder\'on's method is a relatively recent EIT imaging algorithm that is non-iterative, fast, and capable of reconstructing complex-valued electric impedances. However, due to the regularization via low-pass filtering and linearization, the reconstructed images suffer from severe blurring and underestimation of the exact conductivity values. In this work, we develop an enhanced version of Calder\'on's method, using convolution neural networks (i.e., U-net) via a postprocessing step. Specifically, we learn a U-net to postprocess the EIT images generated by Calder\'on's method so as to have better resolutions and more accurate estimates of conductivity values. We simulate chest configurations with which we generate the current-density/voltage boundary measurements and the corresponding reconstructed images by Calder\'on's method. With the paired training data, we learn the neural network and evaluate its performance on real tank measurement data. The experimental results indicate that the proposed approach indeed provides a fast and direct (complex-valued) impedance tomography imaging technique, and substantially improves the capability of the standard Calder\'on's method.Comment: 20 page

Pacing with restoration of respiratory sinus arrhythmia improved cardiac contractility and the left ventricular output: a translational study

Introduction: Respiratory sinus arrhythmia (RSA) is a prognostic value for patients with heart failure and is defined as a beat-to-beat variation of the timing between the heart beats. Patients with heart failure or patients with permanent cardiac pacing might benefit from restoration of RSA. The aim of this translational, proof-of-principle study was to evaluate the effect of pacing with or without restored RSAon parameters of LV cardiac contractility and the cardiac output