Tissue resistivity estimation in the presence of positional and geometrical uncertainties

Abstract

Geometrical uncertainties (organ boundary variation and electrode position uncertainties) are the biggest sources of error in estimating electrical resistivity of tissues from body surface measurements. In this study, in order to decrease estimation errors, the statistically constrained minimum mean squared error estimation algorithm (MiMSEE) is constrained with a priori knowledge of the geometrical uncertainties in addition to the constraints based on geometry, resistivity range, linearization and instrumentation errors. The MiMSEE calculates an optimum inverse matrix, which maps the surface measurements to the unknown resistivity distribution. The required data are obtained from four-electrode impedance measurements, similar to injected-current electrical impedance tomography (EIT). In this study, the surface measurements are simulated by using a numerical thorax model. The data are perturbed with additive instrumentation noise. Simulated surface measurements are then used to estimate the tissue resistivities by using the proposed algorithm. The results are compared with the results of conventional least squares error estimator (LSEE). Depending on the region, the MiMSEE yields an estimation error between 0.42% and 31.3% compared with 7.12% to 2010% for the LSEE. It is shown that the MiMSEE is quite robust even in the case of geometrical uncertainties.

Principles of electrical impedance tomography and its clinical application

“…There are no hard data on validation especially with modern EIT tomographs published yet. However, problems of data errors and interferences are known and numerous methodologic studies exist about issues on technical limitations, back projection algorithms, or data analysis (57)(58)(59)(60). The authors of this review have the impression that modern hard-and software reduced most problems of the early days.…”

Determination of Cardiac Ejection Fraction by Electrical Impedance Tomography

“…However, biological tissues are difficult to characterize, and the reported values vary substantially in the literature. Blood 150 (Yang & Patterson, 2007) 100 (Schwan & Kay, 1956) 400 (Patterson & Zhang, 2003) Heart 250 (Yang & Patterson, 2007) 400 -800 (Baysal & Eyuboglu, 2000) 727 -2363 (Barber & Brown, 1984) Lung 1400 (Patterson & Zhang, 2003) 600 -2000 (Baysal & Eyuboglu, 2000) Table 1. Resistivity values of biological tissues that are found in the literature.…”

Determination of Cardiac Ejection Fraction by Electrical Impedance Tomography - Numerical Experiments and Viability Analysis

“…In addition, the value of the conductivity of each tissue depends on the frequency of the electrical current, on the temperature, on the presence of water, among other issues. Baysal and Eyuboglu [20] In this work, we assume the conductivity of each tissue is taken as known, constant and isotropic. These are all simplified assumption, since biological tissues are usually heterogeneous and anisotropic.…”

“…The conductivity of the air is 10 20 Ωcm, but the conductivity of the lung filled of air is difficult to determine. Rush et al [21] presents a very simplified resistivity distribution model characterized by the presence of cavities filled of blood, surrounded by homogeneous material with resistivity ten times greater.…”