X-ray computed tomography (CT) can nondestructively inspect an object and can clearly, accurately, and intuitively display its internal structure, composition, texture, and damage. In industry this technology was initially used for material analysis and nondestructive testing and evaluation. Recently, as an alternative to optical and tactile measurement devices, CT has entered industrial use for dimensional metrology. Unfortunately, industrial-level accuracy is very difficult to attain with CT for various reasons. In this paper we analyze one of the most serious effects, the Feldkamp–Davis–Kress (FDK) effect, which can be observed in most of the common X-ray CT scanners with a cone beam. The FDK is the reconstruction algorithm widely accepted as a standard reconstruction method for cone-beam type of CT because of its computation efficiency. However, this algorithm merely provides an approximate result. An accurate measurement result can be obtained only in the case of small cone angle. We aim at analyzing the FDK effect independently from other kinds of artifacts. In a practical CT scanning situation, various kinds of artifacts appear in the reconstruction results; thus, we apply a simulation to obtain projection images without noise (scattering, beam hardening, etc.). Then, the FDK algorithm is applied to these projection images to reconstruct CT images so that only the FDK effect can be observed in the reconstructed CT images. Based on this approach, we conducted quantitative analysis on the FDK effect using numerical phantoms of the sphere and stepped cylinders that may be adopted as ISO reference standards for dimensional metrology using X-ray CT scanners. This paper describes the evaluation workflow and discusses the cause of the FDK effect on the measurement of the sphere and the stepped cylinders. Particular attention is given to the evaluation of the error distribution feature on different spatial positions. After discussing the error feature, a method for improving measurement accuracy is proposed.

References

1.
Kalender
,
W. A.
,
2006
, “
X-Ray Computed Tomography
,”
Phys. Med. Biol.
,
51
(
13
), pp.
R29
R43
.10.1088/0031-9155/51/13/R03
2.
Reimers
,
P.
, and
Goebbels
,
J.
,
1983
, “
New Possibilities of Non-Destructive Evaluation by X-Ray Computed Tomography
,”
Mater. Eval.
,
41
(6), pp.
732
737
.
3.
Hsieh
,
J.
,
2003
,
Computed Tomography: Principles, Design, Artifacts and Recent Advances
,
SPIE Press
,
Bellingham, WA
, p.
2
.
4.
Tan
,
Y.
,
2011
, “
Material Dependent Thresholding for Dimensional X-Ray Computed Tomography
,”
International Symposium on Digital Industrial Radiology and Computed Tomography
,
Berlin, Germany
, June 20–22.
5.
Kruth
,
J. P.
,
2011
, “
Computed Tomography for Dimensional Metrology
,”
CIRP Ann.-Manuf. Technol.
,
60
(
2
), pp.
821
842
.10.1016/j.cirp.2011.05.006
6.
Welkenhuyzen
,
F.
,
2009
, “
OPTIME. Industrial Computer Tomography for Dimensional Metrology: Overview of Influence Factors and Improvement Strategies
,” Antwerp, Belgium, May 25–26.
7.
Feldkamp
,
L. A.
,
1984
, “
Practical Cone-Beam Algorithm
,”
JOSA A
,
1
(
6
), pp.
612
619
.10.1364/JOSAA.1.000612
8.
Lorensen
,
W. E.
,
1987
, “
Marching Cubes: A High Resolution 3D Surface Construction Algorithm
,”
Proceedings of the 14th Annual Conference on Computer Graphics and Interactive Techniques
,
ACM Siggraph
,
New York
, July 27–31, pp.
163
169
.10.1145/37401.37422
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