A Parametric Level Set-based Approach to Difference Imaging in Electrical Impedance Tomography

Tutkimustuotos: Lehtiartikkelivertaisarvioitu

Standard

A Parametric Level Set-based Approach to Difference Imaging in Electrical Impedance Tomography. / Liu, Dong; Smyl, Danny; Du, Jianfeng.

julkaisussa: IEEE Transactions on Medical Imaging, Vuosikerta 38, Nro 1, 8416750, 01.01.2019, s. 145-155.

Tutkimustuotos: Lehtiartikkelivertaisarvioitu

Harvard

APA

Vancouver

Author

Liu, Dong ; Smyl, Danny ; Du, Jianfeng. / A Parametric Level Set-based Approach to Difference Imaging in Electrical Impedance Tomography. Julkaisussa: IEEE Transactions on Medical Imaging. 2019 ; Vuosikerta 38, Nro 1. Sivut 145-155.

Bibtex - Lataa

@article{ad3484b632ef4dabb89145a85426247f,
title = "A Parametric Level Set-based Approach to Difference Imaging in Electrical Impedance Tomography",
abstract = "This paper presents a novel difference imaging approach based on the recently developed parametric level set (PLS) method for estimating the change in a target conductivity from electrical impedance tomography measurements. As in conventional difference imaging, the reconstruction of conductivity change is based on data sets measured from the surface of a body before and after the change. The key feature of the proposed approach is that the conductivity change to be reconstructed is assumed to be piecewise constant, while the geometry of the anomaly is represented by a shape-based PLS function employing Gaussian radial basis functions (GRBFs). The representation of the PLS function by using GRBF provides flexibility in describing a large class of shapes with fewer unknowns. This feature is advantageous, as it may significantly reduce the overall number of unknowns, improve the condition number of the inverse problem, and enhance the computational efficiency of the technique. To evaluate the proposed PLS-based difference imaging approach, results obtained via simulation, phantom study, and in vivo pig data are studied. We find that the proposed approach tolerates more modeling errors and leads to a significant improvement in image quality compared with the conventional linear approach.",
keywords = "Electrical impedance tomography, parametric level set method, difference imaging, lung imaging, inverse problems, lung imaging, Electrical impedance tomography, difference imaging, parametric level set method, PERMITTIVITY, ERRORS, D-BAR METHOD, RECONSTRUCTION ALGORITHM, ABSOLUTE, SHAPE, SPATIAL PRIOR, CONDUCTIVITY, EIT RECONSTRUCTION, BOUNDARY",
author = "Dong Liu and Danny Smyl and Jianfeng Du",
year = "2019",
month = "1",
day = "1",
doi = "10.1109/TMI.2018.2857839",
language = "English",
volume = "38",
pages = "145--155",
journal = "IEEE Transactions on Medical Imaging",
issn = "0278-0062",
publisher = "IEEE",
number = "1",

}

RIS - Lataa

TY - JOUR

T1 - A Parametric Level Set-based Approach to Difference Imaging in Electrical Impedance Tomography

AU - Liu, Dong

AU - Smyl, Danny

AU - Du, Jianfeng

PY - 2019/1/1

Y1 - 2019/1/1

N2 - This paper presents a novel difference imaging approach based on the recently developed parametric level set (PLS) method for estimating the change in a target conductivity from electrical impedance tomography measurements. As in conventional difference imaging, the reconstruction of conductivity change is based on data sets measured from the surface of a body before and after the change. The key feature of the proposed approach is that the conductivity change to be reconstructed is assumed to be piecewise constant, while the geometry of the anomaly is represented by a shape-based PLS function employing Gaussian radial basis functions (GRBFs). The representation of the PLS function by using GRBF provides flexibility in describing a large class of shapes with fewer unknowns. This feature is advantageous, as it may significantly reduce the overall number of unknowns, improve the condition number of the inverse problem, and enhance the computational efficiency of the technique. To evaluate the proposed PLS-based difference imaging approach, results obtained via simulation, phantom study, and in vivo pig data are studied. We find that the proposed approach tolerates more modeling errors and leads to a significant improvement in image quality compared with the conventional linear approach.

AB - This paper presents a novel difference imaging approach based on the recently developed parametric level set (PLS) method for estimating the change in a target conductivity from electrical impedance tomography measurements. As in conventional difference imaging, the reconstruction of conductivity change is based on data sets measured from the surface of a body before and after the change. The key feature of the proposed approach is that the conductivity change to be reconstructed is assumed to be piecewise constant, while the geometry of the anomaly is represented by a shape-based PLS function employing Gaussian radial basis functions (GRBFs). The representation of the PLS function by using GRBF provides flexibility in describing a large class of shapes with fewer unknowns. This feature is advantageous, as it may significantly reduce the overall number of unknowns, improve the condition number of the inverse problem, and enhance the computational efficiency of the technique. To evaluate the proposed PLS-based difference imaging approach, results obtained via simulation, phantom study, and in vivo pig data are studied. We find that the proposed approach tolerates more modeling errors and leads to a significant improvement in image quality compared with the conventional linear approach.

KW - Electrical impedance tomography

KW - parametric level set method

KW - difference imaging

KW - lung imaging

KW - inverse problems

KW - lung imaging

KW - Electrical impedance tomography

KW - difference imaging

KW - parametric level set method

KW - PERMITTIVITY

KW - ERRORS

KW - D-BAR METHOD

KW - RECONSTRUCTION ALGORITHM

KW - ABSOLUTE

KW - SHAPE

KW - SPATIAL PRIOR

KW - CONDUCTIVITY

KW - EIT RECONSTRUCTION

KW - BOUNDARY

UR - http://www.scopus.com/inward/record.url?scp=85050398567&partnerID=8YFLogxK

U2 - 10.1109/TMI.2018.2857839

DO - 10.1109/TMI.2018.2857839

M3 - Article

VL - 38

SP - 145

EP - 155

JO - IEEE Transactions on Medical Imaging

JF - IEEE Transactions on Medical Imaging

SN - 0278-0062

IS - 1

M1 - 8416750

ER -

ID: 26544473