Detail publikace

Significant Parameters of Image Reconstruction Convergence

Originální název

Significant Parameters of Image Reconstruction Convergence

Anglický název

Significant Parameters of Image Reconstruction Convergence

Jazyk

en

Originální abstrakt

Classical electrical impedance tomography (EIT) is an imaging modality in which the internal volume impedivity distribution is reconstructed based on the known injected currents and measured voltages on the surface of the object. Image reconstruction is an ill-posed inverse problem of finding such internal impedivity distribution that minimizes certain optimization criteria. The optimization necessitates algorithms that impose regularization and some prior information constraint. The regularization techniques vary in their complexity. This paper proposes the specification of significant parameters of regularization techniques such as the Tikhonov regularization method. We intend to show in the proposed paper the influence of these parameters on the stability, accuracy and convergence of an optimization process. The optimal parameters were found and applied during the image reconstruction process for a two-dimensional (2D) example. The obtained results were presented in related research reports.

Anglický abstrakt

Classical electrical impedance tomography (EIT) is an imaging modality in which the internal volume impedivity distribution is reconstructed based on the known injected currents and measured voltages on the surface of the object. Image reconstruction is an ill-posed inverse problem of finding such internal impedivity distribution that minimizes certain optimization criteria. The optimization necessitates algorithms that impose regularization and some prior information constraint. The regularization techniques vary in their complexity. This paper proposes the specification of significant parameters of regularization techniques such as the Tikhonov regularization method. We intend to show in the proposed paper the influence of these parameters on the stability, accuracy and convergence of an optimization process. The optimal parameters were found and applied during the image reconstruction process for a two-dimensional (2D) example. The obtained results were presented in related research reports.

BibTex


@inproceedings{BUT29160,
  author="Ksenia {Kořínková} and Jarmila {Dědková}",
  title="Significant Parameters of Image Reconstruction Convergence",
  annote="Classical electrical impedance tomography (EIT) is an imaging modality in which the internal volume impedivity distribution is reconstructed based on the known injected currents and measured voltages on the surface of the object. Image reconstruction is an ill-posed inverse problem of finding such internal impedivity distribution that minimizes certain optimization criteria. The optimization necessitates algorithms that impose regularization and some prior information constraint. The regularization techniques vary in their complexity. This paper proposes the specification of significant 
parameters of regularization techniques such as the Tikhonov regularization method. We intend to show in the proposed paper the influence of these parameters on the stability, accuracy and convergence of an optimization process. The optimal parameters were found and applied during the image reconstruction process for a two-dimensional (2D) example. The obtained results were presented in related research reports.",
  booktitle="ELEKTRO 2010 proceedings",
  chapter="29160",
  howpublished="print",
  year="2010",
  month="may",
  pages="41--44",
  type="conference paper"
}