A Post-Processing Method for Three-Dimensional Electrical Impedance Tomography

Abstract

Electrical impedance tomography is a modern biomedical imaging method. Its goal is to image the electrical properties of human tissues. This approach is safe for the patient’s health, is non-invasive and has no known hazards. However, the approach suffers from low accuracy. Linear inverse solvers are commonly used in medical applications, as they are strongly robust to noise. However, linear methods can give only an approximation of the solution that corresponds to a linear perturbation from an initial estimate. This paper proposes a novel reconstruction process. After applying a linear solver, the conductivity distribution is post-processed with a nonlinear algorithm, with the aim of reproducing the abrupt change in conductivity at the boundaries between tissues or organs. The results are used to compare the proposed method with three other widely used methods. The proposed method offers higher quality images and a higher robustness to noise, and significantly reduces the error associated with image reconstruction.

Deep D-Bar: Real-Time Electrical Impedance Tomography Imaging With Deep Neural Networks

“…The basic recipe is to use a fast and simple reconstruction algorithm to obtain corrupted images and then train the network to remove those artefacts. A related study for electrical impedance tomography is [30], where the authors used artificial neural networks (ANNs) to post-process initial reconstructions from one step of a linear Gauss-Newton algorithm for 3D time-difference EIT imaging. Our approach is fundamentally different as it recovers absolute EIT images.…”

On the randomised stability constant for inverse problems

“…The first step consists in the derivation of an approximate conductivity σ by a stable, albeit blurry, regularised inversion method. For instance, in [23] the "Dbar" equation is used, while in [33] a one-step Gauss-Newton method is used. In both cases, the output of this step is a representation of the conductivity coefficient, which depends on the inversion method used.…”

Towards a Fast and Accurate EIT Inverse Problem Solver: A Machine Learning Approach

“…Concretely, they trained an ANN model adding noise to the voltage measurements as well as body shape deformations to get a more stable method. In the recent past, they improved their algorithm with a preprocessed step based on a linear solver that applied before the ANN reduced the noise effect simplifying the training phase [16]. Nevertheless, results were only evaluated from the qualitative perspective.…”