Abstract
X-ray computed tomography (XCT) is increasingly used for dimensional metrology, where it can offer accurate measurements of internal features that are not accessible with other techniques. However, XCT scanning can be relatively slow, which often prevents routine uptake for many applications. This paper explores the feasibility of improving the speed of XCT measurements while maintaining the quality of the dimensional measurements derived from reconstructed volumes. In particular, we compare two approaches to fast XCT acquisition, the use of fewer XCT projections as well as the use of shortened x-ray exposure times for each projection. The study shows that the additional Poisson noise produced by reducing the exposure for each projection has significantly less impact on dimensional measurements compared to the artefacts associated with strategies that take fewer projection images, leading to about half the measurement error variability. Advanced reconstruction algorithms such as the conjugate gradient least squares method or total variation constrained approaches, are shown to allow further improvements in measurement speed, though this can come at the cost of increased measurement bias (e.g. 2.8% increase in relative error in one example) and variance (e.g. 25% in the same example).
Original language | English |
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Article number | 044003 |
Journal | Metrologia |
Volume | 59 |
Issue number | 4 |
Early online date | 12 Jul 2022 |
DOIs | |
Publication status | Published - 1 Aug 2022 |
Bibliographical note
Funding Information:The authors gratefully acknowledge the support of NVIDIA Corporation with the donation of the Titan Xp GPU used for this research. This research was supported by EPSRC Grant EP/R002495/1 and the EMPIR Project 17IND08, which has received funding from the EMPIR programme confirmed by the Participating States and from the European Union Horizon 2020 Research and Innovation Programme. The authors acknowledge the µ-VIS X-Ray Imaging Centre at the University of Southampton for the provision of the x-ray tomographic imaging facilities.
Funding
The authors gratefully acknowledge the support of NVIDIA Corporation with the donation of the Titan Xp GPU used for this research. This research was supported by EPSRC Grant EP/R002495/1 and the EMPIR Project 17IND08, which has received funding from the EMPIR programme confirmed by the Participating States and from the European Union Horizon 2020 Research and Innovation Programme. The authors acknowledge the µ-VIS X-Ray Imaging Centre at the University of Southampton for the provision of the x-ray tomographic imaging facilities.
Keywords
- conjugate gradient least squares
- dimensional metrology
- iterative reconstruction
- total variation constraints
- x-ray tomography
ASJC Scopus subject areas
- General Engineering
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