Flexible Generation of Prior Images for Metal Artifact Reduction in Industrial Computed Tomography

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26620%2F22%3APU143631" target="_blank" >RIV/00216305:26620/22:PU143631 - isvavai.cz</a>

Výsledek na webu

<a href="https://www.ndt.net/article/ctc2022/papers/ICT2022_paper_id255.pdf" target="_blank" >https://www.ndt.net/article/ctc2022/papers/ICT2022_paper_id255.pdf</a>

DOI - Digital Object Identifier

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Jazyk výsledku

angličtina

Název v původním jazyce

Flexible Generation of Prior Images for Metal Artifact Reduction in Industrial Computed Tomography

Popis výsledku v původním jazyce

In computed tomography, artifacts caused by metal objects pose a major challenge, as they severely degrade the final image. Reduction of these artifacts is a topic of ongoing research, and many possible approaches have been developed so far. Among these, projection completion methods are effective and reasonably simple to implement compared to others. The most effective of these methods require a prior image, a synthetic, artifact-free representation of the data. This prior is used to recover data obscured by the metals and thus reduce the presence of potential secondary artifacts, but it may not be trivial to obtain especially in industrial computed tomography, where sample variability tends to be high. Traditional methods of prior generation are often based on multi-segmentation of a preliminary tomographic slice, which requires substantial a-priori knowledge about the scanned sample, its morphology, and the materials it is made of. This is a major limit for applications where such information is no

Název v anglickém jazyce

Flexible Generation of Prior Images for Metal Artifact Reduction in Industrial Computed Tomography

Popis výsledku anglicky

In computed tomography, artifacts caused by metal objects pose a major challenge, as they severely degrade the final image. Reduction of these artifacts is a topic of ongoing research, and many possible approaches have been developed so far. Among these, projection completion methods are effective and reasonably simple to implement compared to others. The most effective of these methods require a prior image, a synthetic, artifact-free representation of the data. This prior is used to recover data obscured by the metals and thus reduce the presence of potential secondary artifacts, but it may not be trivial to obtain especially in industrial computed tomography, where sample variability tends to be high. Traditional methods of prior generation are often based on multi-segmentation of a preliminary tomographic slice, which requires substantial a-priori knowledge about the scanned sample, its morphology, and the materials it is made of. This is a major limit for applications where such information is no

Druh

O - Ostatní výsledky

CEP obor

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OECD FORD obor

20501 - Materials engineering

Projekt

Výsledek vznikl pri realizaci vícero projektů. Více informací v záložce Projekty.

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2022

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů