Effects of incompatible boundary information in EIT on the convergence behavior of an iterative algorithm.
In electrical impedance tomography, currents are applied to the body through electrodes that are attached to the surface and the corresponding surface voltages are measured. Based on these boundary measurements, the internal admittivity distribution of the body can be reconstructed. In order to improve the image quality it is necessary and useful to apply physiologically meaningful prior information into the image reconstruction. Such prior information usually can be obtained from other sources. For example, information on the object's boundary shape and internal structure can be obtained from computed tomography and magnetic resonance imaging scan. However, this type of prior information may change from time to time and from person to person. As these changes are limited anatomically and physiologically, the prior information including the possible changes can be presented in a number of variational forms. The aim of this paper is to find which form of prior information is more compatible for a specific imaged object at the time of imaging. This paper proposes a new method for selecting the most appropriate form of prior information, through the procedure of iterative image reconstruction by using the information obtained from boundary measurements. The method is based on the principle that incompatible prior information causes errors which are able to affect the image reconstruction's convergence behavior. In this method, according to the various forms of prior information available, several image reconstruction configurations are designed. Then, through monitoring the convergence behavior in an iterative image reconstruction, the configuration with compatible prior information can be found among those different configurations. As an example, the prior information regarding the imaged object's boundary shape and internal structure was studied by computer simulation. Results were shown and discussed.
Tang
Mengxing
M
3D Imaging/Biomedical Engineering, Faculty of Computing Science and Engineering, De Montfort University, Leicester, UK. mtang@dmu.ac.uk
Wang
Wei
W
Wheeler
James
J
McCormick
Malcolm
M
Dong
Xiuzhen
X
D000465:Algorithms; D016477:Artifacts; D003198:Computer Simulation; D017097:Electric Impedance; D020342:Finite Element Analysis; D007089:Image Enhancement / Q000379:methods*; D011786:Quality Control; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D013269:Stochastic Processes; D014054:Tomography / Q000379:methods*