Multi-phase flow monitoring with electrical impedance tomography using level set based method

Smyl

2019

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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 (GRBF). 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 significant improvement in image quality compared with the conventional linear approach.

Section: Introductionmentioning

confidence: 99%

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

Smyl

2019

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“…shape-based solution framework. For example, in the wellestablished level set approaches [37], [38], a shape and topology description function (STDF), also called a level set function (LSF), is used to implicitly represent the boundary of the targets as the zero level set (with respect to the space variables) with one higher dimension, and it easily handles topological changes. In traditional level set (TLS) methods, during the reconstruction, the shape boundary evolution is described by a Hamilton-Jacobi partial differential equation (PDE) and is driven by the extension velocity field derived from shape sensitivity analysis.…”

Section: Introductionmentioning

confidence: 99%

A Moving Morphable Components Based Shape Reconstruction Framework for Electrical Impedance Tomography

2019

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This paper presents a new computational framework in electrical impedance tomography (EIT) for shape reconstruction based on the concept of moving morphable components (MMC).In the proposed framework, the shape reconstruction problem is solved in an explicit and geometrical way. Compared with the traditional pixel or shape-based solution framework, the proposed framework can incorporate more geometry and prior information into shape and topology optimization directly and therefore render the solution process more flexibility. It also has the afford potential to substantially reduce the computational burden associated with shape and topology optimization. The effectiveness of the proposed approach is tested with noisy synthetic data and experimental data, which demonstrates the most popular biomedical application of EIT: lung imaging. In addition, robustness studies of the proposed approach considering modeling errors caused by nonhomogeneous background, varying initial guesses, differing numbers of candidate shape components, and differing exponent in the shape and topology description function are performed. The simulation and experimental results show that the proposed approach is tolerant to modeling errors and is fairly robust to these parameter choices, offering significant improvements in image quality in comparison to the conventional absolute reconstructions using smoothness prior regularization and total variation regularization.

“…Traditional level set methods [40], [42] Flexibility in handling topological changes when phases merges or splits Need to solve the Hamilton-Jacobi PDE; Need reinitialization Parametric level set methods [43]- [45] Inherit the pros from TLS methods; solve ODEs rather than PDEs;No need for reinitialization; Dimension reduction;…”

Section: Stdfs-based Methodsmentioning

confidence: 99%

Shape Reconstruction Using Boolean Operations in Electrical Impedance Tomography

Smyl

et al. 2020

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