A Local Region of Interest Imaging Method for Electrical Impedance Tomography with Internal Electrodes

Abstract

Electrical Impedance Tomography (EIT) is a very attractive functional imaging method despite the low sensitivity and resolution. The use of internal electrodes with the conventional reconstruction algorithms was not enough to enhance image resolution and accuracy in the region of interest (ROI). We propose a local ROI imaging method with internal electrodes developed from careful analysis of the sensitivity matrix that is designed to reduce the sensitivity of the voxels outside the local region and optimize the sensitivity of the voxel inside the local region. We perform numerical simulations and physical measurements to demonstrate the localized EIT imaging method. In preliminary results with multiple objects we show the benefits of using an internal electrode and the improved resolution due to the local ROI image reconstruction method. The sensitivity is further increased by allowing the surface electrodes to be unevenly spaced with a higher density of surface electrodes near the ROI. Also, we analyse how much the image quality is improved using several performance parameters for comparison. While these have not yet been studied in depth, it convincingly shows an improvement in local sensitivity in images obtained with an internal electrode in comparison to a standard reconstruction method.

The Research Progress of Electrical Impedance Tomography for Lung Monitoring

“…The difference algorithm is to make the difference between the boundary voltage values of the same field obtained at two different times under the same excitation and use the difference to reconstruct the image so as to get the change of the impedance distribution in two different fields at different times. The typical difference algorithms include the back projection algorithm (Barber et al, 1983;Santos and Vogelius, 1990), the one-step Newton-Gauss method (Cheney et al, 2010), the sensitivity matrix method (Murai and Kagawa, 1985;Hyeuknam et al, 2013) and so on. Table 2 summarizes the above three differential algorithms.…”

“…After repeated calculation, the conductivity distribution problem of the whole circular object is transformed into the conductivity distribution problem of multiple single layers, which can simplify the calculation difficulty, but there will be instability. On the basis of previous research, Siltanen proposed a D-bar algorithm (Siltanen et al, 2001;Isaacson et al, 2006;Knudsen et al, 2007), which transforms the conductivity problem into a Schrodinger equation and then combines with the sigma method in inverse scattering to solve the problem (Cornean et al, 2006); Mueller and Siltanen (2020) At present, several common regularization algorithms can be divided into two categories according to different constraints. The regularization algorithms based on the L-2 norm include the Tikhonov regularization algorithm (Peng et al, 2007) and the noser regularization algorithm; the regularization algorithms based on the L-1 norm include the sparse regularization algorithm (Varanasi et al, 2019) and the TV (Total Variation) regularization algorithm (Fan, 2012).…”

Applications of bioimpedance measurement techniques in tissue engineering

“…Biological tissues can also demonstrate inductive properties, but when compared to their resistance and reactance, inductance is very low at frequencies below 10MHz, therefore it can often be neglected ( 15 ). Thus, the complex electrical impedance produced by biological tissues which can also be called bioimpedance, is the result of contribution of both capacitance and conductance of the tissues which are both frequency-dependent ( 9 , 16 , 17 , 18 , 19 , 20 ).…”

Tiled-Block Image Reconstruction by Wavelet- Based, Parallel-Filtered Back-Projection

“…Different approaches to local or limited data reconstruction have been reviewed in [19]- [21]. Work related to local Tomography has been generalized to different geometries [23], [24], as well as to emission [25], [26] and/or soft-field modalities [27].…”