Computational framework for applying electrical impedance tomography to head imaging

Tutkimustuotos: Lehtiartikkeli

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Bibtex - Lataa

@article{7663f27d1cd54ab19c2002bf2adf9505,
title = "Computational framework for applying electrical impedance tomography to head imaging",
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.",
keywords = "Computational head model, Detection of stroke, Electrical impedance tomography, Inaccurate measurement model, Principal components, Shape derivatives",
author = "Valentina Candiani and Antti Hannukainen and Nuutti Hyvonen",
year = "2019",
month = "1",
day = "1",
doi = "10.1137/19M1245098",
language = "English",
volume = "41",
pages = "B1034--B1060",
journal = "SIAM Journal on Scientific Computing",
issn = "1064-8275",
number = "5",

}

RIS - Lataa

TY - JOUR

T1 - Computational framework for applying electrical impedance tomography to head imaging

AU - Candiani, Valentina

AU - Hannukainen, Antti

AU - Hyvonen, Nuutti

PY - 2019/1/1

Y1 - 2019/1/1

N2 - 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.

AB - 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.

KW - Computational head model

KW - Detection of stroke

KW - Electrical impedance tomography

KW - Inaccurate measurement model

KW - Principal components

KW - Shape derivatives

UR - http://www.scopus.com/inward/record.url?scp=85074651830&partnerID=8YFLogxK

U2 - 10.1137/19M1245098

DO - 10.1137/19M1245098

M3 - Article

VL - 41

SP - B1034-B1060

JO - SIAM Journal on Scientific Computing

JF - SIAM Journal on Scientific Computing

SN - 1064-8275

IS - 5

ER -

ID: 38832787