EIT image reconstruction with four dimensional regularization

Abstract

Electrical impedance tomography (EIT) reconstructs internal impedance images of the body from electrical measurements on body surface. The temporal resolution of EIT data can be very high, although the spatial resolution of the images is relatively low. Most EIT reconstruction algorithms calculate images from data frames independently, although data are actually highly correlated especially in high speed EIT systems. This paper proposes a 4-D EIT image reconstruction for functional EIT. The new approach is developed to directly use prior models of the temporal correlations among images and 3-D spatial correlations among image elements. A fast algorithm is also developed to reconstruct the regularized images. Image reconstruction is posed in terms of an augmented image and measurement vector which are concatenated from a specific number of previous and future frames. The reconstruction is then based on an augmented regularization matrix which reflects the a priori constraints on temporal and 3-D spatial correlations of image elements. A temporal factor reflecting the relative strength of the image correlation is objectively calculated from measurement data. Results show that image reconstruction models which account for inter-element correlations, in both space and time, show improved resolution and noise performance, in comparison to simpler image models.

Frequency-domain reconstruction of signals in electrical bioimpedance spectroscopy

“…An interval of a values in [0. 6,1] can also be obtained where |Z frac (x)| is retrieved from its phase sequence with errors below a 5% bound. In Fig.…”

“…In general, the algorithm may be employed in the retrieval of the phase from the estimated difference conductivities in EIT in each reconstructed frame. Given that the proposed algorithm is computationally optimized being based on Fast-Fourier transform algorithms, and the use of the algorithm in the data set is permissible, the first potential use of the algorithm in difference EIT reconstruction techniques, as, for example, in the technique described in [6], could be the application of phase-retrieval in the conductivity change data.…”

Beneficial techniques for spatio-temporal imaging in electrical impedance tomography

“…x = R have inverse covariances Υ and Γ respectively across frames. By expansion from (2), the spatio-temporal Gauss-Newton solution (Adler et al 2007, Dai et al 2008 takes the form…”

A modified L1/2 regularization algorithm for electrical impedance tomography

“…Among the traditional methods of EIT, the Tik with the L 2 -norm penalty term and L 1 regularization algorithms are commonly used. Although the Tik algorithm is simple and convenient [17,18], it has too smooth an edge and many artifacts [19]. L 1 regularization and its adaptive algorithms have been extensively studied, such as lasso regression [20,21], L 1 -norm penalized (logistic) regression [22,23] and some methods integrating the L 1 algorithm with other algorithms [24][25][26], because the convex functions can be easily solved.…”