Noise-based correction for electrical impedance tomography.

Objective.Noisy measurements frequently cause noisy and inaccurate images in impedance imaging. No post-processing technique exists to calculate the propagation of measurement noise and use this to suppress noise in the image. The objectives of this work were (1) to develop a post-processing method for noise-based correction (NBC) in impedance tomography, (2) to test whether NBC improves image quality in electrical impedance tomography (EIT), (3) to determine whether it is preferable to use correlated or uncorrelated noise for NBC, (4) to test whether NBC works within vivodata and (5) to test whether NBC is stable across model and perturbation geometries.Approach.EIT was performedin silicoin a 2D homogeneous circular domain and an anatomically realistic, heterogeneous 3D human head domain for four perturbations and 25 noise levels in each case. This was validated by performing EIT for four perturbations in a circular, saline tank in 2D as well as a human head-shaped saline tank with a realistic skull-like layer in 3D. Images were assessed on the error in the weighted spatial variance (WSV) with respect to the true, target image. The effect of NBC was also tested forin vivoEIT data of lung ventilation in a human thorax and cortical activity in a rat brain.Main results.On visual inspection, NBC maintained or increased image quality for all perturbations and noise levels in 2D and 3D, both experimentally andin silico. Analysis of the WSV showed that NBC significantly improved the WSV in nearly all cases. When the WSV was inferior with NBC, this was either visually imperceptible or a transformation between noisy reconstructions. Forin vivodata, NBC improved image quality in all cases and preserved the expected shape of the reconstructed perturbation.Significance.In practice, uncorrelated NBC performed better than correlated NBC and is recommended as a general-use post-processing technique in EIT.

Non-invasive imaging of neural activity with magnetic detection electrical impedance tomography (MDEIT): a modelling study.

Objectives.(1) Develop a computational pipeline for three-dimensional fast neural magnetic detection electrical impedance tomography (MDEIT), (2) determine whether constant current or constant voltage is preferable for MDEIT, (3) perform reconstructions of simulated neural activity in a human head model with realistic noise and compare MDEIT to EIT and (4) perform a two-dimensional study in a saline tank for MDEIT with optically pumped magnetometers (OPMs) and compare reconstruction algorithms.Approach.Forward modelling and image reconstruction were performed with a realistic model of a human head in three dimensions and at three noise levels for four perturbations representing neural activity. Images were compared using the error in the position and size of the reconstructed perturbations. Two-dimensional MDEIT was performed in a saline tank with a resistive perturbation and one OPM. Six reconstruction algorithms were compared using the error in the position and size of the reconstructed perturbations.Main results.A computational pipeline was developed in COMSOL Multiphysics, reducing the Jacobian calculation time from months to days. MDEIT reconstructed images with a lower reconstruction error than EIT with a mean difference of 7.0%, 5.5% and 11% for three noise cases representing current noise, reduced current source noise and reduced current source and magnetometer noise. A rank analysis concluded that the MDEIT Jacobian was less rank-deficient than the EIT Jacobian. Reconstructions of a phantom in a saline tank had a best reconstruction error of 13%, achieved using 0th-order Tikhonov regularisation with simulated noise-based correction.Significance.This study demonstrated that three-dimensional MDEIT for neural imaging is feasible and that MDEIT reconstructed superior images to EIT, which can be explained by the lesser rank deficiency of the MDEIT Jacobian. Reconstructions of a perturbation in a saline tank demonstrated a proof of principle for two-dimensional MDEIT with OPMs and identified the best reconstruction algorithm.