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Electrical Impedance Tomography (EIT) is a medical imaging technique which can be used to monitor the regional ventilation in patients utilizing voltage measurements made at the thorax. Several reconstruction algorithms have been developed during the last few years. In this manuscript we compare a well-established algorithm and a re-cently developed method for image reconstruction regarding EIT indices derived from the differently reconstructed images.

Electrical Impedance Tomography (EIT) is a novel medical imaging technique which can be applied to visualize changes of impedance within in the body. Small alternating currents are injected into the body and resulting voltages are measured on the skin surface. In a clinical context EIT is used to trace changes in impedance of the lungs, caused by ventilation [

Compared to well-established imaging technologies, such as X-ray computed tomography (CT) or magnetic resonance imaging (MRI), EIT has several advantages. No radiation is needed for image acquisition, which makes EIT suitable for frequent examinations or long term monitoring. Additionally, the necessary technical equipment is portable and inexpensive, which enables ventilation monitoring at the bedside.

Usually, in commercially available EIT-systems for lung imaging, an array of 16 to 32 electrodes is attached around the circumference of the chest. A pair of electrodes is used for current injection and the resulting voltage between the remaining electrodes is measured. Subsequently, the pair of electrodes used for current injection is changed in a rotating manner. The measured voltages are used to reconstruct images of conductivity change. The voltage measurements can be done relatively fast and enables frame rates in commercially available system of up to 50 frames/second. Thus EIT is capable of to monitoring rapid processes in the lungs, which are currently not detectable with CT or MRI.

However, a drawback of EIT is that the reconstructed image of conductivity change does not depict a thorax slice of well defined thickness, as e.g. in CT imaging. The diffuse current propagation in the thorax results in a lens-shaped volume, whose impedance changes are projected onto a two-dimensional image. As a result, impedance changes above or below the electrode plane are also reflected in the reconstructed image.

The challenge in EIT imaging is to reconstruct changes of conductivity inside a domain based on voltage measurements on the boundary of the domain. This problem is ill-posed, meaning that arbitrarily small changes in measured voltages may result in arbitrarily large values of reconstructed conductivity. The ill-posedness is usually addressed with regularization, forcing the solution, i.e. the reconstructed change in conductivity, to be either small, smooth or slowly changing.

Recently, we have developed an approach for image reconstruction including patient specific structural information (obtained e.g. from CT or MRI data) into the reconstruction process [

In this paper we compare two different approaches for images reconstruction. Two EIT derived parameters, the “Center of Ventilation” (CoV) and the “ventilation shift” (vShift) are evaluated.

Calculations in this work have been carried out using Matlab 2015a (Mathworks, Natick, USA) and the EIDORS toolbox [

In this paper the “adjacent current stimulation pattern” was used, where currents are injected and voltages are measured between neighboring electrodes. For the considered 16 electrode system this results in 208 voltages for every frame, of which 104 are independent.

Boundary voltages for end-expiration and end-inspiration were simulated on a 3D FEM model, generated from a CT dataset. The contour of the thorax at the 5^{th} intercostal space was used for the outline of the model. FEM elements not-corresponding to lung tissue were assigned to a conductivity of

a) Conductivity of right lung systematically varying between

b) Conductivity of dorsal lung systematically varying between

In this manuscript we use unit-less values for conductivity. The values for conductivity are based on the values published by Witsoe and Kinnen, whereas a collapsed lung has a conductivity of

The EIT problem is usually formulated as shown in Equation (1)

with z being the relative change in voltage, where

where R forces the solution

In linearized EIT imaging the forward model is linearized around a conductivity distribution

and each element

Thus, for linearized EIT Equation (1) can be solved in a closed form:

The solution of the EIT problem according to Equation (3) can be regarded as classical approach with one-step Gauss-Newton solver (one-step GN).

This reconstruction method is compared with the above mentioned approach, where patient specific morphological prior information is used in the reconstruction process. In this case the Jacobian J is replaced with

Images were reconstructed with the one-step GN solver and with the DCT approach. For both approaches the “ventilation shift” (vShift) and the “Center of Ventilation” (CoV) are calculated, where

with

The “Center of Ventilation” (CoV) is defined as:

with

Exemplary reconstructions with the GN solver and the DCT approach are depicted in

Several EIT reconstruction methods have been developed during the past years. It has already been demonstrated that indices for EIT image analysis, such as “CoV” or “vShift” are not influenced from the reconstruction method [

study we used an algorithm including patient specific morphological information in the reconstruction process in comparison with a standard approach. Results demonstrate that both EIT indices show only slight differences for the different reconstruction methods. Reconstruction methods including morphological information might be used if the structural information is available. In patients where an actual CT or MRI dataset is not available standard EIT reconstruction algorithms, as the used one-step GN with Laplace prior still gives valuable information regarding the examined EIT indices.

This work is partially supported by the Federal Ministry of Education and Research (BMBF) under grant no. 03FH038I3 (MOSES).

Schullcke, B., Krueger-Ziolek, S., Gong, B., Mueller-Lisse, U. and Moelle, K. (2016) Comparison of Image Reconstruction Algorithms in EIT Imaging. J. Biomedical Science and Engineering, 9, 137-142. http://dx.doi.org/10.4236/jbise.2016.910B018