Conductivity reconstructions using real data from a new planar electrical impedance tomography device

We present a 3D reconstruction algorithm with sparsity constraints for electrical impedance tomography (EIT). EIT is the inverse problem of determining the distribution of conductivity in the interior of an object from simultaneous measurements of currents and voltages on its boundary. The feasibility of the sparsity reconstruction approach is tested with real data obtained from a new planar EIT device developed at the The complete electrode model is adapted for the given device to handle incomplete measurements and the inhomogeneities of the conductivity are a priori assumed to be sparse with respect to a certain basis. This prior information is incorporated into a Tikhonov-type functional by including a sparsity-promoting 1 -regularization term. The functional is minimized with an iterative soft shrinkage-type algorithm.

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“…More technical details of the Mainz EIT system can be found in [29]. The sensing head has a fixed geometry.…”

Section: Experimental Set-up and Numerical Reconstructions From Real mentioning

confidence: 99%

Sparse 3D reconstructions in electrical impedance tomography using real data

Gehre

Kluth

Sebu

et al. 2013

Inverse Problems in Science and Engineering

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“…Figure 1(a), all the electrodes located on the boundary of the rectangular array are active, while the remaining ones are passive. At this point, it is important to note that this electrode configuration not only provides a simpler geometry than the hexagonal pattern of the earlier prototypes in [22,23], but for some electrical impedance imaging problems in geophysics, archaeology, medical diagnosis and industrial plant control, an appropriate electrode geometry may be that of a rectangular array of electrodes placed on a surface plane. For example, rectangular electrode configurations have been employed before for medical applications such as: breast cancer detection [6,7,21,32], respiratory monitoring, functional imaging of the digestive system and peripheral venography [33], or for engineering and environmental studies (e.g.…”

Section: σ(X Y Z)mentioning

confidence: 99%

“…In order to find the values of the simulated voltages U k 0,l given by equation (23), we need to compute first u k 0 (x, y) = u k 0 (x, y, z = 0) by evaluating the expression in equation (27) at z = 0. To this end, we use the same change of variables as in subsection 3.2 and equation (32).…”

Section: Construction Of Matrix Bmentioning

confidence: 99%

“…In contrast to most previous EIT instruments designed for breast cancer detection [9], but similar to devices studied by [6,7,16,18,20,21], these mammographic sensors are planar. Detailed descriptions of earlier prototypes can be found in [22,23]. The latest design consists of a planar sensing head with 36 disk electrodes of equal size arranged in a rectangular array of 20 outer (active) electrodes where the external currents are injected, and 16 inner (passive) electrodes where the induced voltages are measured, see Figure 1(a).…”

Section: Introductionmentioning

confidence: 99%

“…In two-dimensions, two different non-iterative algorithms for imaging the conductivity at the surface using the tomographs designed at the University of Mainz were described in [22,23]. In both cases numerical reconstructions had very good spatial resolution, and the algorithms were robust with respect to errors in the data.…”

Section: Introductionmentioning

confidence: 99%

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A three-dimensional image reconstruction algorithm for electrical impedance tomography using planar electrode arrays

Perez

Pidcock

Sebu

2016

Inverse Problems in Science and Engineering

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We present a three-dimensional non-iterative reconstruction algorithm developed for conductivity imaging with real data collected on a planar rectangular array of electrodes. Such an electrode configuration as well as the proposed imaging technique are intended to be used for breast cancer detection. The algorithm is based on linearizing the conductivity about a constant value and allows real-time reconstructions. The performance of the algorithm was tested on numerically simulated data and we successfully detected small inclusions with conductivities three or four times the background lying beneath the data collection surface. The results were fairly stable with respect to the noise level in the data and displayed very good spatial resolution in the plane of electrodes.

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