Significant Parameters of Image Reconstruction Convergence

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26220%2F10%3APU87043" target="_blank" >RIV/00216305:26220/10:PU87043 - isvavai.cz</a>
Result on the web
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DOI - Digital Object Identifier
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Result language
angličtina
Original language name
Significant Parameters of Image Reconstruction Convergence
Original language description
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 isan 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
Czech name
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Czech description
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Publication year
2010
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

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