Artefact reduction in fast Bayesian inversion in electrical tomography

Abstract

Purpose-The purpose of this paper is to reduce the artifacts in fast Bayesian reconstruction images in electrical tomography. This is in particular important with respect to object detection in electrical tomography applications. Design/methodology/approach-The authors suggest to apply the Box-Cox transformation in Bayesian linear minimum mean square error (BMMSE) reconstruction to better accommodate the non-linear relation between the capacitance matrix and the permittivity distribution. The authors compare the results of the original algorithm with the modified algorithm and with the ground truth in both, simulation and experiments. Findings-The results show a reduction of 50 percent of the mean square error caused by artifacts in low permittivity regions. Furthermore, the algorithm does not increase the computational complexity significantly such that the hard real time constraints can still be met. The authors demonstrate that the algorithm also works with limited observations angles. This allows for object detection in real time, e.g., in robot collision avoidance. Originality/value-This paper shows that the extension of BMMSE by applying the Box-Cox transformation leads to a significant improvement of the quality of the reconstruction image while hard real time constraints are still met.

A Review on Electrical Impedance Tomography Spectroscopy

“…This method has been used for EIT, but not yet used for EITS other than in simulations [23,53]. It is well described in [7] and used in [68] for robotic applications. It is essentially an implementation of a Bayesian Linear Minimum Mean Square Estimation (BLMMSE) approach.…”

Low-power electrical impedance tomography spectroscopy

“…In this paper, we perform EITS by using a non-iterative method described in Zangl and Mühlbacher-Karrer (2015), called optimal first order approximation (OFOA), which is essentially an implementation of a Bayesian linear minimum mean square estimation approach. In addition to that, a planar electrode geometry is used instead of the traditional circular electrode geometry.…”

Bayesian statistical inference using a regression in electrical impedance tomography

“…OA has already been tested on real experimental dataset with positive results [25,26,46] and a number of extensions have been made since its first appearance such as (i) dimension reduce x ∈ R n for further computational efficiency [28,29] (ii) reconstruct x with a variable electrode position on the surface [25] and (iii) estimate parameters related to permittivity in ECT such as volume fraction [41] and mass concentration [43], to name a few.…”

“…In OA, it was shown that a linear and quadratic operator B(y) worked well to obtain point estimates in ECT inverse problem [28,46,47]. Due to the close relation between ECT and EIT, we also investigate the fitting of linear, quadratic and cubic operators B(y) here.…”