Machine learning approach to EIT image reconstruction of the human forearm section for different hand signs

Electrical impedance tomography (EIT) is an imaging technique used to reconstruct the conductivity of a target object from boundary voltages. In this study, we investigate suitable image reconstruction algorithms for EIT to enable the reconstruction of the conductivity distribution in the forearm section inferring muscle contractions at different hand signs. As EIT image reconstruction is an ill-posed inverse problem, the Gauss-Newton algorithm needs many iterations for the determination of suitable values of the regularization parameter and corresponding calculations of the Jacobian matrix. To reduce computational effort, we propose to use machine learning algorithms to directly reconstruct the EIT image. We explore the Radial Basis Neural Network (RBNN) and a one-dimensional Convolutional Neural Network (1D-CNN), which has been trained based on the measured EIT data for eight subjects, ten hand signs with ten trials. Both methods reach a low deviation at 0.0017 for RBNN and 0.0109 for CNN

Review on electrical impedance tomography: Artificial intelligence methods and its applications

© 2019 by the authors. Electrical impedance tomography (EIT) has been a hot topic among researchers for the last 30 years. It is a new imaging method and has evolved over the last few decades. By injecting a small amount of current, the electrical properties of tissues are determined and measurements of the resulting voltages are taken. By using a reconstructing algorithm these voltages then transformed into a tomographic image. EIT contains no identified threats and as compared to magnetic resonance imaging (MRI) and computed tomography (CT) scans (imaging techniques), it is cheaper in cost as well. In this paper, a comprehensive review of efforts and advancements undertaken and achieved in recent work to improve this technology and the role of artificial intelligence to solve this non-linear, ill-posed problem are presented. In addition, a review of EIT clinical based applications has also been presented

Investigation of sine-wave inputs for an FDM EIT system

Includes bibliographical references.This thesis project report describes the research done by the author under the supervision of Prof. J. Tapson. The area of research is an investigation of sine-wave drive for a frequency division multiplexing (FDM) electrical impedance tomography (EIT) system. This thesis was commissioned by Prof. J. Tapson, on the 1 st of November 200 1. The goals were as follows: 1. Investigate the research done on this project by previous researchers. 2. Investigate the current applications in which capacitance, resistance and impedance tomography are used in research level and in industry. 3. Design and develop a working 8-electrode impedance tomography system. Also, make provisions for a possible upgrade of the 8-electrode system to a 16-electrode (16 capacitance and 16 resistance electrodes) system employing FDM and using sine-wave excitation. 4. VerifY and compare the performance of the 8-electode impedance tomography system to the previous research done by Teague [53], for static configurations of multi-phase air-gravel-seawater mixtures. 5. Evaluate the ability of the system to differentiate between air and gravel mass in static situations. 6. Draw conclusions regarding the performance, effectiveness and limitations of the system. 7. Make recommendations for future project developments. 8. Submit the thesis by the 28th of March 2003

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

Mass flow measurement of multi-phase mixtures by means of tomographic techniques

Includes bibliographical references.This thesis investigates the use of a dual-plane impedance tomography system to calculate the individual mass flow rates of the components in an air-gravel-seawater mixture. The long-term goal of this research is to develop a multi-phase flowmeter for the on-line monitoring of an airlift used in an offshore mining application. This requires the measurement of both the individual component volume fractions and their velocities. Tomography provides a convenient non-intrusive technique to obtain this information. Capacitance tomography is used to reconstruct the dielectric distribution of the material within a pipeline. It is based on the concept that the capacitance of a pair of electrodes depends on the dielectric distribution of the material between the electrodes. By mounting a number of electrodes around the periphery of the pipeline, and measuring the capacitances of the different electrode combinations, it is possible to reconstruct the distribution of the phases within the pipeline, provided the phases have different dielectric constants. Resistance tomography is used to reconstruct the resistivity distribution within the cross-section of the pipeline and operates in a similar way to capacitance tomography. Impedance tomography can be described as a dual-modal approach since both the capacitance and conductance of the different electrode combinations are measured to reconstruct the omplex impedance of the material distribution. Previous research has shown that impedance tomography can be used to reconstruct a three-phase air-gravelwater mixture [3,4]. In addition, it has been shown that neural networks can be used to perform this reconstruction task [3,4]. In particular, a single-layer feed-forward neural network with a 1-of-C output encoding can be trained to perform a three-phase image reconstruction. Further, a double-layer feed-forward neural network can be trained to predict the volume fractions of the three phases within the flow directly, based on the capacitance and conductance readings obtained from the data acquisition system. However, these tests were only for static configurations. This thesis will readdress this problem from the dynamic viewpoint. In addition, the individual component velocities will be calculated using the cross-correlation of the volume fraction predictions from two impedance tomography systems spaced a certain distance apart

Electrical Impedance Tomography: A Fair Comparative Study on Deep Learning and Analytic-based Approaches

Electrical Impedance Tomography (EIT) is a powerful imaging technique with diverse applications, e.g., medical diagnosis, industrial monitoring, and environmental studies. The EIT inverse problem is about inferring the internal conductivity distribution of an object from measurements taken on its boundary. It is severely ill-posed, necessitating advanced computational methods for accurate image reconstructions. Recent years have witnessed significant progress, driven by innovations in analytic-based approaches and deep learning. This review explores techniques for solving the EIT inverse problem, focusing on the interplay between contemporary deep learning-based strategies and classical analytic-based methods. Four state-of-the-art deep learning algorithms are rigorously examined, harnessing the representational capabilities of deep neural networks to reconstruct intricate conductivity distributions. In parallel, two analytic-based methods, rooted in mathematical formulations and regularisation techniques, are dissected for their strengths and limitations. These methodologies are evaluated through various numerical experiments, encompassing diverse scenarios that reflect real-world complexities. A suite of performance metrics is employed to assess the efficacy of these methods. These metrics collectively provide a nuanced understanding of the methods' ability to capture essential features and delineate complex conductivity patterns. One novel feature of the study is the incorporation of variable conductivity scenarios, introducing a level of heterogeneity that mimics textured inclusions. This departure from uniform conductivity assumptions mimics realistic scenarios where tissues or materials exhibit spatially varying electrical properties. Exploring how each method responds to such variable conductivity scenarios opens avenues for understanding their robustness and adaptability