Noise-based correction for electrical impedance tomography

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 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 improves image quality with in vivo data and (5) to test whether NBC is stable across model and perturbation geometries. EIT was performed in silico in a 2D homogeneous circular domain and a realistic, heterogeneous 3D human head domain for four perturbations and 25 noise levels. This was validated by performing EIT for four perturbations in a circular, saline tank in 2D and a human head-shaped saline tank with a realistic skull-like layer in 3D. Images were assessed on the weighted spatial variance (WSV) with respect to the true, target image. The effect of NBC was also tested for in vivo EIT data of lung ventilation in a human thorax and cortical activity in a rat brain. On visual inspection, NBC maintained or increased image quality for all perturbations and noise levels in 2D and 3D, both experimentally and in 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. For in vivo data, NBC improved image quality in all cases and preserved the expected shape of the reconstructed perturbation. In practice, uncorrelated NBC performed better than correlated NBC and is recommended as a general-use post-processing technique in EIT.