Literature DB >> 16398414

Finite element implementation of Maxwell's equations for image reconstruction in electrical impedance tomography.

Nirmal K Soni1, Keith D Paulsen, Hamid Dehghani, Alex Hartov.   

Abstract

Traditionally, image reconstruction in electrical impedance tomography (EIT) has been based on Laplace's equation. However, at high frequencies the coupling between electric and magnetic fields requires solution of the full Maxwell equations. In this paper, a formulation is presented in terms of the Maxwell equations expressed in scalar and vector potentials. The approach leads to boundary conditions that naturally align with the quantities measured by EIT instrumentation. A two-dimensional implementation for image reconstruction from EIT data is realized. The effect of frequency on the field distribution is illustrated using the high-frequency model and is compared with Laplace solutions. Numerical simulations and experimental results are also presented to illustrate image reconstruction over a range of frequencies using the new implementation. The results show that scalar/vector potential reconstruction produces images which are essentially indistinguishable from a Laplace algorithm for frequencies below 1 MHz but superior at frequencies reaching 10 MHz.

Mesh:

Year:  2006        PMID: 16398414     DOI: 10.1109/tmi.2005.861001

Source DB:  PubMed          Journal:  IEEE Trans Med Imaging        ISSN: 0278-0062            Impact factor:   10.048


  2 in total

1.  Intracranial electrical impedance tomography: a method of continuous monitoring in an animal model of head trauma.

Authors:  Preston K Manwaring; Karen L Moodie; Alexander Hartov; Kim H Manwaring; Ryan J Halter
Journal:  Anesth Analg       Date:  2013-07-10       Impact factor: 5.108

2.  Mapping the conductivity of graphene with Electrical Resistance Tomography.

Authors:  Alessandro Cultrera; Danilo Serazio; Amaia Zurutuza; Alba Centeno; Oihana Txoperena; David Etayo; Alvaro Cordon; Albert Redo-Sanchez; Israel Arnedo; Massimo Ortolano; Luca Callegaro
Journal:  Sci Rep       Date:  2019-07-23       Impact factor: 4.379

  2 in total

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