Electrical Capacitance Volume Tomography Static Imaging by Non-Optimized Compressive Sensing Framework

Electrical capacitance volume tomography is a volumetric tomography technique that utilizes capacitance and fringing to capture behavior or perturbation in the sensing domain. One of the crucial issues in developing ECVT technology is the reconstruction algorithm. In practice, ILBP is most used due to its simplicity. However, it still presents elongation errors for certain dielectric contrasts. The high undersampling measurement of the ECVT imaging system, which is mathematically defined as an undetermined linear system, is one of the most challenging issues. Compressive sensing (CS) is a framework that enables the recovery of a sparse signal or a signal that can be represented as sparse in a certain domain, by having a lower dimension of measurement data compared to the Shanon-Nyquist theorem. Thus, mathematically, this framework is promising for solving an undetermined linear system such as the ECVT imaging system. This paper discusses the possibility of developing an ECVT imaging technique for static objects based on a CS framework. Based on the simulation results, Non-optimized CS does not completely succeed in providing better ECVT imaging quality. However, it does provide more localized imaging compared to ILBP. In addition, by having fewer requirements for the measurement data dimension, the CS framework is promising for reducing the number of required electrodes

EIT Reconstruction Algorithms: Pitfalls, Challenges and Recent Developments

We review developments, issues and challenges in Electrical Impedance Tomography (EIT), for the 4th Workshop on Biomedical Applications of EIT, Manchester 2003. We focus on the necessity for three dimensional data collection and reconstruction, efficient solution of the forward problem and present and future reconstruction algorithms. We also suggest common pitfalls or ``inverse crimes'' to avoid.Comment: A review paper for the 4th Workshop on Biomedical Applications of EIT, Manchester, UK, 200

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