Refined cartesian mesh for modeling in electrical capacitance tomography

Author

Smolik, Waldemar T. ; Kryszyn, Jacek

Author_Institution

Inst. of Radioelectron., Warsaw Univ. of Technol., Warsaw, Poland

fYear

2013

fDate

22-23 Oct. 2013

Firstpage

372

Lastpage

376

Abstract

This paper presents modeling of electric potential distribution using a refined Cartesian mesh in electrical capacitance tomography. The application of the thickened Cartesian discretization mesh in a forward problem solver allows more accurate discrete description of cylindrical elements of tomographic sensor. The irregular structured grid enables possibility to increase precision of capacitance measurement simulations. A tree structure was used for description of refined mesh nodes to obtain fast computations. The .NET based software library was created. A set of MATLAB functions was created to provide an interface between the library and the MATLAB environment. The results of electrical field computation were presented.

Keywords

bioelectric potentials; capacitance measurement; electric impedance imaging; mathematics computing; medical image processing; mesh generation; .NET based software library; MATLAB environment; MATLAB functions; capacitance measurement simulation; cylindrical element; discrete description; electric potential distribution modeling; electrical capacitance tomography; electrical field computation; irregular structured grid; refined Cartesian mesh; refined cartesian mesh; refined mesh node description; thickened Cartesian discretization mesh; tomographic sensor; tree structure; Capacitance; Electric potential; Electrodes; Finite element analysis; Mathematical model; Permittivity; Sensitivity; Electrical capacitance tomography; Electromagnetic field; FEM; Sensitivity matrix;

fLanguage

English

Publisher

ieee

Conference_Titel

Imaging Systems and Techniques (IST), 2013 IEEE International Conference on

Conference_Location

Beijing

Print_ISBN

978-1-4673-5790-6

Type

conf

DOI

10.1109/IST.2013.6729724

Filename

6729724