Literature DB >> 24111213

FEM electrode refinement for electrical impedance tomography.

Bartlomiej Grychtol, Andy Adler.   

Abstract

Electrical Impedance Tomography (EIT) reconstructs images of electrical tissue properties within a body from electrical transfer impedance measurements at surface electrodes. Reconstruction of EIT images requires the solution of an inverse problem in soft field tomography, where a sensitivity matrix, J, of the relationship between internal changes and measurements is calculated, and then a pseudo-inverse of J is used to update the image estimate. It is therefore clear that a precise calculation of J is required for solution accuracy. Since it is generally not possible to use analytic solutions, the finite element method (FEM) is typically used. It has generally been recommended in the EIT literature that FEMs be refined near electrodes, since the electric field and sensitivity is largest there. In this paper we analyze the accuracy requirement for FEM refinement near electrodes in EIT and describe a technique to refine arbitrary FEMs.

Mesh:

Year:  2013        PMID: 24111213     DOI: 10.1109/EMBC.2013.6611026

Source DB:  PubMed          Journal:  Conf Proc IEEE Eng Med Biol Soc        ISSN: 1557-170X


  4 in total

1.  Comparing D-bar and common regularization-based methods for electrical impedance tomography.

Authors:  S J Hamilton; W R B Lionheart; A Adler
Journal:  Physiol Meas       Date:  2019-04-26       Impact factor: 2.833

2.  Methods for specific electrode resistance measurement during transcranial direct current stimulation.

Authors:  Niranjan Khadka; Asif Rahman; Chris Sarantos; Dennis Q Truong; Marom Bikson
Journal:  Brain Stimul       Date:  2014-10-17       Impact factor: 8.955

3.  Accuracy and reliability of noninvasive stroke volume monitoring via ECG-gated 3D electrical impedance tomography in healthy volunteers.

Authors:  Fabian Braun; Martin Proença; Andy Adler; Thomas Riedel; Jean-Philippe Thiran; Josep Solà
Journal:  PLoS One       Date:  2018-01-26       Impact factor: 3.240

4.  Brain haemorrhage detection using a SVM classifier with electrical impedance tomography measurement frames.

Authors:  Barry McDermott; Martin O'Halloran; Emily Porter; Adam Santorelli
Journal:  PLoS One       Date:  2018-07-12       Impact factor: 3.240

  4 in total

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