Computational framework for applying electrical impedance tomography to head imaging

Valentina Candiani; Antti Hannukainen; Nuutti Hyvonen

doi:10.1137/19M1245098

Computational framework for applying electrical impedance tomography to head imaging

Valentina Candiani
, Antti Hannukainen
, Nuutti Hyvonen

Research output: Contribution to journal › Article › Scientific › peer-review

13 Citations (Scopus)

222 Downloads (Pure)

Abstract

This work introduces a computational framework for applying absolute electrical impedance tomography to head imaging without accurate information on the head shape or the electrode positions. A library of 50 heads is employed to build a principal component model for the typical variations in the shape of the human head, which leads to a relatively accurate parametrization for head shapes with only a few free parameters. The estimation of these shape parameters and the electrode positions is incorporated in a regularized Newton-type output least squares reconstruction algorithm. The presented numerical experiments demonstrate that strong enough variations in the internal conductivity of a human head can be detected by absolute electrical impedance tomography even if the geometric information on the measurement configuration is incomplete to an extent that is to be expected in practice.

Original language	English
Pages (from-to)	B1034-B1060
Number of pages	27
Journal	SIAM Journal on Scientific Computing
Volume	41
Issue number	5
DOIs	https://doi.org/10.1137/19M1245098
Publication status	Published - 1 Jan 2019
MoE publication type	A1 Journal article-refereed

Funding

\ast Submitted to the journal's Computational Methods in Science and Engineering section February 15, 2019; accepted for publication (in revised form) July 15, 2019; published electronically October 1, 2019. https://doi.org/10.1137/19M1245098 Funding: This work was supported by the Academy of Finland through grants 312124 and 312340. \dagger Department of Mathematics and Systems Analysis, Aalto University, FI-00076 Aalto, Finland ([email protected], [email protected], [email protected]).

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 3 Good Health and Well-being

Keywords

Computational head model
Detection of stroke
Electrical impedance tomography
Inaccurate measurement model
Principal components
Shape derivatives

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KW - Inaccurate measurement model

KW - Principal components

KW - Shape derivatives

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Computational framework for applying electrical impedance tomography to head imaging

Abstract

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UN SDGs

Keywords

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