  Introduction. 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.
Objectives. 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 with in vivo data and (5) to test
whether NBC is stable across model and perturbation geometries. Methods. EIT
was performed in silico in 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 weighted spatial variance (WSV) with respect to the target image. The
effect of NBC was also tested for example in vivo EIT data of lung ventilation
in a human thorax and cortical activity in a rat brain. Results. On visual
inspection, NBC maintained or increased image quality for all perturbations and
noise levels in 2D and 3D 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 images. 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.
