scholarly journals Investigating Machine Learning Based X-Ray Computed Tomography Reconstruction Methods to Enhance the Accuracy of CT Scans.

2019 ◽  
Author(s):  
Carianne Martinez ◽  
John P. Korbin ◽  
Kevin Matthew Potter ◽  
Emily Donahue ◽  
Jeremy David Gamet ◽  
...  
PLoS ONE ◽  
2018 ◽  
Vol 13 (11) ◽  
pp. e0207430 ◽  
Author(s):  
Ashkan Pakzad ◽  
Francesco Iacoviello ◽  
Andrew Ramsey ◽  
Robert Speller ◽  
Jennifer Griffiths ◽  
...  

2014 ◽  
Vol 27 ◽  
pp. 1460135
Author(s):  
CARMEN PAVEL ◽  
FLORIN CONSTANTIN ◽  
COSMIN IOAN SUCIU ◽  
ROXANA BUGOI

X-ray Computed Tomography (CT) is a powerful non-destructive technique that can yield interesting structural information not discernible through visual examination only. This paper presents the results of the CT scans of four objects belonging to the Romanian cultural heritage attributed to the Vinča, Cucuteni and Cruceni-Belegiš cultures. The study was performed with an X-ray tomographic device developed at the Department for Applied Nuclear Physics from Horia Hulubei National Institute for Nuclear Physics and Engineering in Măgurele, Romania. This apparatus was specially designed for archaeometric studies of low-Z artifacts: ceramic, wood, bone. The tomographic investigations revealed the internal configuration of the objects and provided information about the degree to which the previous manipulations affected the archaeological items. Based on the X-ray images resulting from the CT scans, hints about the techniques used in the manufacturing of the artifacts were obtained, as well as some indications useful for conservation/restoration purposes.


2020 ◽  
pp. 002199832096255
Author(s):  
Jennifer M Sietins ◽  
Jessica C Sun ◽  
Daniel B Knorr Jr

It is well known that the mechanical performance of composite materials is highly dependent on the fiber orientation. Several techniques have historically been used to quantify fiber orientation experimentally. Newer methods have involved 3 D X-ray computed tomography (CT) scans due to the high resolution that is now achievable within a laboratory setting. The accuracy of the analysis, however, is a function of the resulting scan image quality and the specific parameters influencing the resulting orientation analysis. This report summarizes a methodology to quantify fiber orientation from 3 D CT scans. Optimal scanning parameters are presented taking into account both the necessary resolution, geometric unsharpness, and the scan volume size. The influence of varied software analysis parameters and their effects on the resulting orientation data is discussed. The selection of software analysis parameters was independently validated with optical microscopy on a sample with only two fibers. Lastly, the orientation analysis was applied to a 0/+45/−45/90 composite to demonstrate this technique on a larger scale.


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