Image Reconstruction In Electrical Tomography Research Articles

A virtual platform of electrical tomography for multiphase flow imaging

This paper reports a new approach to quantitatively evaluate the performance of Electrical Tomography (ET) in measuring dynamic multiphase flows. A virtual multiphase flow imaging platform based on ET is constructed and demonstrated on two typical gas–liquid flows, i.e., water–gas flow and oil–gas flow. Two coupling simulation cases, i.e., water–gas flow field and electric currents field coupling simulation and oil–gas flow field and electrostatics field coupling simulation, are performed to simulate multiphase flow sensing of Electrical Impedance Tomography (EIT) and Electrical Capacitance Tomography (ECT). We quantitatively evaluated the representative EIT and ECT image reconstruction algorithms on the virtual evaluation platform bringing evidence of the improved capability to capture the key flow features of the fluid mixture with respect to traditional static phantoms. Ad-hoc treatment of the signal noise enables one to better capture dynamic responses of the fluid phase volume fractions and their spatial gradients throughout their mixing along the conduit, ultimately demonstrating unprecedented potential in the quantitative characterization of complex, unsteady multi-phase systems. The proposed image reconstruction constitutes a highly effective platform for quantitative performance evaluation of ET, parameter optimization of model-based ET image reconstruction algorithms, and for the development of data-driven ET algorithms in multiphase flow imaging.

Inverse Radon Method Based on Electrical Field Lines for Dual-Modality Electrical Tomography

Usually, the sensitivity matrix-based algorithms are used for image reconstruction in electrical tomography. These algorithms assume that the inverse problem is like that in the well-established medical CT and find successful applications. In the medical CT, rather than using the matrix-based algorithms, the inverse Radon transform and its derivatives dominate. However, these inverse transforms have not been introduced to electrical tomography due to a lack of the link between the parallel projections and electrical distributions. This article presents a map from electrical field lines to parallel lines in the inverse Radon transform and a novel image reconstruction algorithm by using the transform for dual-modality electrical tomography. The transform is realized through mapping the coordinates in electric field to the assumed parallel projections. The points of intersections between electrical field lines from different exciting electrodes are mapped to those of parallel lines from the related projections in a typical inverse Radon transform. Unlike the medical CT, different excitation schemes are related to distinct equivalent projections, and iterative scanning is used to apply all available projected data. Simulated and experimental data are implemented to validate the feasibility and effectiveness of the proposed algorithm, and performance comparisons are made with three algorithms, such as Calderon’s method, iterative method based on electrical field lines, and typical Landweber method. The proposed method yields good quality images in the least time and dynamic flames are monitored. Dual-modality images of material distributions are obtained to capture the ignition and blowout of the flame evolution in a Bunsen burner.

Application of Gaussian Kernel with Regard to Correlations for Image Reconstruction in Electrical Tomography

Application of Gaussian Kernel with Regard to Correlations for Image Reconstruction in Electrical Tomography

A fast sparse reconstruction algorithm for electrical tomography

Electrical tomography (ET) has been widely investigated due to its advantages of being non-radiative, low-cost and high-speed. However, the image reconstruction of ET is a nonlinear and ill-posed inverse problem and the imaging results are easily affected by measurement noise. A sparse reconstruction algorithm based on L1 regularization is robust to noise and consequently provides a high quality of reconstructed images. In this paper, a sparse reconstruction by separable approximation algorithm (SpaRSA) is extended to solve the ET inverse problem. The algorithm is competitive with the fastest state-of-the-art algorithms in solving the standard L2−L1 problem. However, it is computationally expensive when the dimension of the matrix is large. To further improve the calculation speed of solving inverse problems, a projection method based on the Krylov subspace is employed and combined with the SpaRSA algorithm. The proposed algorithm is tested with image reconstruction of electrical resistance tomography (ERT). Both simulation and experimental results demonstrate that the proposed method can reduce the computational time and improve the noise robustness for the image reconstruction.