Pankhurst, MJ, Fowler, R, Courtois, L et al. (5 more authors) (2018) Enabling three-dimensional densitometric measurements using laboratory source X-ray micro-computed tomography. SoftwareX, 7. pp. 115-121. ISSN 2352-7110
Abstract
We present new software allowing significantly improved quantitative mapping of the three-dimensional density distribution of objects using laboratory source polychromatic X-rays via a beam characterisation approach (c.f. filtering or comparison to phantoms). One key advantage is that a precise representation of the specimen material is not required. The method exploits well-established, widely available, non-destructive and increasingly accessible laboratory-source X-ray tomography. Beam characterisation is performed in two stages: (1) projection data are collected through a range of known materials utilising a novel hardware design integrated into the rotation stage; and (2) a Python code optimises a spectral response model of the system. We provide hardware designs for use with a rotation stage able to be tilted, yet the concept is easily adaptable to virtually any laboratory system and sample, and implicitly corrects the image artefact known as beam hardening.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | (c) 2018 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/). |
Keywords: | Laboratory X-ray micro-computed tomography; Beam characterisation; Python; Beam hardening; Three-dimensional densitometry |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Environment (Leeds) > School of Earth and Environment (Leeds) > Earth Surface Science Institute (ESSI) (Leeds) |
Funding Information: | Funder Grant number AXA Research Fund No External Ref |
Depositing User: | Symplectic Publications |
Date Deposited: | 22 May 2018 10:28 |
Last Modified: | 22 May 2018 10:28 |
Status: | Published |
Publisher: | Elsevier |
Identification Number: | 10.1016/j.softx.2018.03.004 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:131093 |
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