Hostname: page-component-78c5997874-s2hrs Total loading time: 0 Render date: 2024-11-10T03:22:14.080Z Has data issue: false hasContentIssue false
  • English
  • Français

Statistical Comparison of Various Reconstruction Algorithms with respect to Missing Wedge Artifacts in Computed Tomography

Published online by Cambridge University Press:  03 February 2012

Sebastian Lueck ,
Andreas Kupsch ,
Axel Lange ,
Manfred P. Hentschel  and
Volker Schmidt
Sebastian Lueck
Affiliation:
Institute of Stochastics, Ulm University, Helmholtzstr. 18, 89081 Ulm, Germany
Andreas Kupsch
Affiliation:
BAM Federal Institute of Materials Research and Testing, Unter den Eichen 87, 12205 Berlin, Germany
Axel Lange
Affiliation:
BAM Federal Institute of Materials Research and Testing, Unter den Eichen 87, 12205 Berlin, Germany
Manfred P. Hentschel
Affiliation:
BAM Federal Institute of Materials Research and Testing, Unter den Eichen 87, 12205 Berlin, Germany
Volker Schmidt
Affiliation:
Institute of Stochastics, Ulm University, Helmholtzstr. 18, 89081 Ulm, Germany
Get access

Abstract

The presence of elongation, streak and blurring artifacts in tomograms recorded under a missing wedge of rotation angles presents a major challenge for the quantitative analysis of tomographic image data. We show that the missing wedge artifacts of standard reconstruction algorithms may be reduced by the innovative reconstruction technique DIRECTT. For the comparison of missing wedge artifacts we apply techniques from spatial statistics, which have been specifically designed to investigate the shape of phase boundaries in tomograms.

Keywords

x-ray tomographyscanning transmission electron microscopy (STEM)structural
Type
Research Article
Information
MRS Online Proceedings Library (OPL) , Volume 1421: Symposium PP – Three-Dimensional Tomography of Materials , 2012 , mrsf11-1421-pp01-06
Copyright
Copyright © Materials Research Society 2012

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

REFERENCES

1. Midgley, P. A. and Weyland, M., Ultramicroscopy 96, 413 (2003).Google Scholar
2. Buzug, T.M. “Computed Tomography”, Springer, Berlin, Heidelberg (2010).Google Scholar
3. Gilbert, P., J. Theor. Biol. 36, 105 (1972).Google Scholar
4. Tong, J., Arslan, I. and Midgley, P. A., J. Struct. Biol. 153, 55 (2006).Google Scholar
5. Bals, S., Batenburg, K. J., Verbeeck, J., Sijbers, J. and Van Tendeloo, G., Nano Lett. 7, 3669 (2007).Google Scholar
6. Lange, A., Hentschel, M.P. and Kupsch, A., MP Mater. Test. 50, 272 (2008).Google Scholar
7. Lange, A., Kupsch, A., Hentschel, M.P., Manke, I., Kardjilov, N., Arlt, T. and Grothausmann, R., J. Power Sources 196, 5293 (2011).Google Scholar
8. Lück, S., Kupsch, A., Lange, A., Hentschel, M. P. and Schmidt, V., Image Anal. Stereol. 29, 61 (2010)Google Scholar
9. Hanson, K. M., J. Opt. Soc. Am. A 7, 1294 (1990)Google Scholar
10. Illian, J., Penttinen, H., Stoyan, H. and Stoyan, D. “Statistical Analysis and Modelling of Spatial Point Patterns”, J. Wiley & Sons, Chichester (2008).Google Scholar
11. Rasband, W.S. “ImageJ”, U.S. National Institute of Health (1997–2011).Google Scholar
12. Heinrich, L., Lück, S., and Schmidt, V. (preprint submitted) (2011).Google Scholar