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contributor authorLi, Xin
contributor authorDentinger, Aaron
contributor authorBrault, Michelle
contributor authorRoss, William R.
contributor authorOsterlitz, Mark
contributor authorFu, Lin
contributor authorWu, Mingye
contributor authorPrice, J. Scott
contributor authorDe Man, Bruno
contributor authorBueno, Clifford
contributor authorFitzgerald, Paul
date accessioned2022-02-04T14:33:40Z
date available2022-02-04T14:33:40Z
date copyright2020/04/08/
date issued2020
identifier issn2572-3901
identifier othernde_3_3_031101.pdf
identifier urihttp://yetl.yabesh.ir/yetl1/handle/yetl/4273913
description abstractWhen comparing the performance of different industrial X-ray computed tomography (CT) systems, reconstruction algorithms, or scan protocols, it is important to assess how well the required inspection and measurement tasks can be performed. Furthermore, it can be very informative to quantify image quality (IQ) metrics that can provide insight into the IQ characteristics that lead to the resulting inspection or measurement task performance. Inspection and measurement task performance is determined by basic characteristics such as spatial resolution; feature contrast, size, and shape; random noise (noise due to statistical uncertainty in measurements); and image artifacts. In this report, we describe a modular phantom set that enables robustly quantifying these characteristics and also enables assessing the performance of the inspection or measurement tasks themselves. The phantom set includes two phantom bodies and several insert types that can be optionally installed in the bodies. Phantom body extensions can be optionally included to increase scatter. The phantom bodies combined with the available insert types can comprehensively evaluate all important IQ metrics and inspection or measurement tasks. The precisely-known phantom body geometry and insert location, geometry, and orientation supports automatic analysis of large, complex experiments of multiple variables. This phantom set, with the associated image analysis software, could potentially serve as a general evaluation method for non-destructive testing (NDT) CT.
publisherThe American Society of Mechanical Engineers (ASME)
titleToward Comprehensive Industrial Computed Tomography Image Quality Assessment: I. Phantom Design
typeJournal Paper
journal volume3
journal issue3
journal titleJournal of Nondestructive Evaluation, Diagnostics and Prognostics of Engineering Systems
identifier doi10.1115/1.4046717
page31101
treeJournal of Nondestructive Evaluation, Diagnostics and Prognostics of Engineering Systems:;2020:;volume( 003 ):;issue: 003
contenttypeFulltext


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