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

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 ch...

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Bibliographic Details
Published in:IEEE transactions on medical imaging 2019-01, Vol.38 (1), p.145-155
Main Authors: Liu, Dong, Smyl, Danny, Du, Jiangfeng
Format: Article
Language:English
Subjects:
Algorithms
Animals
Basis functions
Computational modeling
Computer applications
Computer simulation
Computing time
Conductivity
Conductivity measurement
difference imaging
Electric Impedance
Electrical impedance
Electrical impedance tomography
Electrical resistivity
Humans
Image Processing, Computer-Assisted - methods
Image quality
Image reconstruction
Impedance
In vivo methods and tests
Inverse problems
Level set
lung imaging
Medical imaging
parametric level set method
Phantoms, Imaging
Radial basis function
Swine
Thorax - diagnostic imaging
Tomography
Tomography - methods
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Summary: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.
ISSN:0278-0062
1558-254X
DOI:10.1109/TMI.2018.2857839