Hostname: page-component-669899f699-chc8l Total loading time: 0 Render date: 2025-04-27T06:01:01.006Z Has data issue: false hasContentIssue false
  • English
  • Français

X-ray microtomographic imaging and analysis for basic research

Published online by Cambridge University Press:  01 March 2012

J. H. Dunsmuir ,
S. Bennett ,
L. Fareria ,
A. Mingino  and
M. Sansone
J. H. Dunsmuir*
Affiliation:
ExxonMobil Research and Engineering Company, Corporate Strategic Research, Annandale, New Jersey 08801
S. Bennett
Affiliation:
ExxonMobil Research and Engineering Company, Corporate Strategic Research, Annandale, New Jersey 08801
L. Fareria
Affiliation:
ExxonMobil Research and Engineering Company, Corporate Strategic Research, Annandale, New Jersey 08801
A. Mingino
Affiliation:
ExxonMobil Research and Engineering Company, Corporate Strategic Research, Annandale, New Jersey 08801
M. Sansone
Affiliation:
ExxonMobil Research and Engineering Company, Corporate Strategic Research, Annandale, New Jersey 08801
*
a)Electronic mail: john.h.dunsmuir@exxonmobil.com
Get access Rights & Permissions [Opens in a new window]

Abstract

For research facilities with access to synchrotron X-ray sources, X-ray absorption microtomography (XMT) has evolved from an experimental imaging method to a specialized, if not yet routine, microscopy for imaging the three-dimensional (3D) distribution of linear attenuation coefficients and, in some cases, elemental concentration with micron spatial resolution. Recent advances in source and detector design have produced conventional X-ray source instruments with comparable spatial resolution but with lower throughput and without element specific imaging. Both classes of instrument produce 3D images for analysis. We discuss an integrated approach for the implementation of analytical XMT to support basic research into the structure-property relationships of a variety of materials. The essential components include instrumentation for collecting quantitative 3D images, a 3D image processing environment to address questions as to the quantity, composition, geometry, and relationships among the features in one or more images, and visualization to provide insight and communicate results. We give examples of image analysis of resolved and unresolved pore spaces of sandstones.

Type
X-Ray Fluorescence and Related Techniques
Information
Powder Diffraction , Volume 21 , Issue 2 , June 2006 , pp. 125 - 131
Copyright
Copyright © Cambridge University Press 2006

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.)

Article purchase

Temporarily unavailable

References

Arns, C. H., Averdunk, H., Sakellariou, A., Senden, T. J., Sheppard, A. P., Sok, R. M., Pinczewski, W. V., and Knackstedt, M. A. (2004). “Society of Petrophysicists & Well Log Analysts, 45th Annual Symposium, Noordwijk, The Netherlands, June 6–9, paper EEE.Google Scholar
Danielsson, P.-E. (1980). “Euclidean distance mapping,” Comput. Graph. Image Process. CGIPBG 14, 227–248.CrossRefGoogle Scholar
Davis, G. R. and Elliott, J. C. (2004). Proc. SPIE PSISDG 5535, 182–190.CrossRefGoogle Scholar
Delerue, J.-F., Perrier, E., Yu, Z. Y., and Velde, B. (1999). J. Phys. Chem. Earth 24, 639–644.CrossRefGoogle Scholar
Dunsmuir, J. H., Zhou, M., Flannery, B. P., Amabile, M. J., Lanzillotto, A. M., Leu, T., Samtaney, R., and Wildes, R. P. (1997). Proceedings of the SPIE Conference on Developments in X-ray Tomography, pp. 82–89.Google Scholar
Flannery, B. P., Deckman, H. W., Roberge, W. G., and D’Amico, K. L. (1987). Science SCIEAS 237, 1489.CrossRefGoogle Scholar
Frigo, M. and Johnson, S. G. (2005). Proc. IEEE IEEPAD 10.1109/JPROC.2004.840301 93(2), 216–231.CrossRefGoogle Scholar
Hilpert, M., and Miller, C. T. (2001). Adv. Water Resour. AWREDI 10.1016/S0309-1708(00)00056-7 24, 243–255.CrossRefGoogle Scholar
Lindquist, W. B., Venkatarangan, A., Dunsmuir, J., and Wong, T.-F. (2000).J. Geophys. Res. JGREA2 10.1029/2000JB900208 105B, 21508–21528.Google Scholar
Lu, D., Zhou, M., Dunsmuir, J. H., and Thomann, H. (2001). Magn. Reson. Imaging MRIMDQ 19(3–4), 443–448.CrossRefGoogle Scholar
Ma, S., Mason, G., and Morrow, N. R. (1996). Colloids Surf., A CPEAEH 10.1016/0927-7757(96)03702-8 117, 273–291.CrossRefGoogle Scholar
Nikolaidis, N. , and Pitas, I. (2000). 3-D Image Processing Algorithms (Wiley, New York).Google Scholar
Press, W. H., Flannery, B. P., Teukolsky, S. A., and Vetterling, W. T. (1992). Numerical Recipes in C: The Art of Scientific Computing, 2nd ed. (Cambridge University Press, New York).Google Scholar
Thévenaz, P., Ruttimann, U. E., and Unser, M. (1995). Proceedings of the 1995 IEEE International Conference on Image Processing (ICIP’95), Washington, DC 23–26, October 1995, Vol. III, pp. 228–231.Google Scholar
Thévenaz, P., Ruttimann, U. E., and Unser, M. (1998). IEEE Trans. Image Process. IIPRE4 7(1).CrossRefGoogle Scholar
Washburn, E. W. (1921). Proc. Natl. Acad. Sci. U.S.A. PNASA6 7(115).CrossRefGoogle Scholar