Influence of changing peripheral geometry on electrical impedance tomography measurements

Gersing, E; Hofmann, Bernd; Osypka, M.

doi:10.1007/bf02520005

Cited by 31 publications

(21 citation statements)

References 2 publications

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“…These two regions appear as one in these images, although other reconstruction algorithms have resolved two lungs under similar test conditions, as did the D-bar algorithm on the experimental tank data in Isaacson et al 2004. The probable cause for this distortion is modeling the chest by a disc. It is well known that such an approximation can cause artifacts; see Adler et al 1996, Gersing et al 1996, Kolehmainen et al 1997, Kolehmainen et al 2005 …”

Section: Resultsmentioning

confidence: 99%

Imaging cardiac activity by the D-bar method for electrical impedance tomography

et al. 2006

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A practical D-bar algorithm for reconstructing conductivity changes from EIT data taken on electrodes in a 2D geometry is described. The algorithm is based on the global uniqueness proof of Nachman (1996 Ann. Math. 143 71-96) for the 2D inverse conductivity problem. Results are shown for reconstructions from data collected on electrodes placed around the circumference of a human chest to reconstruct a 2D cross-section of the torso. The images show changes in conductivity during a cardiac cycle.

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Section: Resultsmentioning

confidence: 99%

Imaging cardiac activity by the D-bar method for electrical impedance tomography

et al. 2006

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“…Thus a direct inversion of (3.6) is unsuitable. This explains the observation that a slightly incorrect boundary can lead to erroneous recoveries [1,24]. This issue can be resolved by mfEIT.…”

Section: Imperfectly Known Boundarymentioning

confidence: 99%

The Linearized Inverse Problem in Multifrequency Electrical Impedance Tomography

Alberti¹,

Ammari²,

Jin³

et al. 2016

SIAM J. Imaging Sci.

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This paper provides an analysis of the linearized inverse problem in multifrequency electrical impedance tomography. We consider an isotropic conductivity distribution with a finite number of unknown inclusions with different frequency dependence, as is often seen in biological tissues. We discuss reconstruction methods for both fully known and partially known spectral profiles, and demonstrate in the latter case the successful employment of difference imaging. We also study the reconstruction with an imperfectly known boundary, and show that the multifrequency approach can eliminate modeling errors and recover almost all inclusions. In addition, we develop an efficient group sparse recovery algorithm for the robust solution of related linear inverse problems. Several numerical simulations are presented to illustrate and validate the approach.

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“…For demonstrations of the domain model related reconstruction artifacts with practical EIT measurements, see e.g. [12,24,28].…”

Section: Shape-deforming Reconstruction: Theorymentioning

confidence: 99%

“…Further, the shape of the chest may change slightly because of breathing and changes in the patient position, and therefore, the knowledge on the shape of the chest would be approximate even at the most optimal scenario. This situation is problematic since even small errors in the domain model can origin large artifacts in the reconstructed conductivity images [1,12,28].…”

Section: Introductionmentioning

confidence: 99%

Simultaneous reconstruction of conductivity, boundary shape and contact impedances in electrical impedance tomography

Agnelli,

Kolehmainen,

Lassas

et al. 2021

Preprint

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The objective of electrical impedance tomography (EIT) is to reconstruct the internal conductivity of a physical body based on current and voltage measurements at the boundary of the body. In many medical applications the exact shape of the domain boundary and contact impedances are not available. This is problematic as even small errors in the boundary shape of the computation domain or in the contact impedance values can produce large artifacts in the reconstructed images which results in a loss of relevant information. A method is proposed that simultaneously reconstructs the conductivity, the contact impedances and the boundary shape from EIT data. The approach consists of three steps: first, the unknown contact impedances and an anisotropic conductivity reproducing the measured EIT data in a model domain are computed. Second, using isothermal coordinates, a deformation is constructed that makes the conductivity isotropic. The final step minimizes the error of true and reconstructed known geometric properties (like the electrode lengths) using conformal deformations. The feasibility of the method is illustrated with experimental EIT data, with robust and accurate reconstructions of both conductivity and boundary shape.

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Influence of changing peripheral geometry on electrical impedance tomography measurements

Cited by 31 publications

References 2 publications

Imaging cardiac activity by the D-bar method for electrical impedance tomography

Imaging cardiac activity by the D-bar method for electrical impedance tomography

The Linearized Inverse Problem in Multifrequency Electrical Impedance Tomography

Simultaneous reconstruction of conductivity, boundary shape and contact impedances in electrical impedance tomography

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