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Multi-Energy X-Ray Tomography of an Optical Fiber: The Role of Spatial Averaging

Published online by Cambridge University Press:  14 March 2019

Zachary H. Levine
Affiliation:
Sensor Science Division, National Institute of Standards and Technology, Gaithersburg, Maryland 20899-8441, USA
Adele P. Peskin*
Affiliation:
Software and Systems Division, National Institute of Standards and Technology, Boulder, Colorado 80305-3337, USA
Edward J. Garboczi
Affiliation:
Applied Chemicals and Materials Division, National Institute of Standards and Technology, Boulder, Colorado 80305, USA
Andrew D. Holmgren
Affiliation:
Holmgren Professional Research Experience Program, University of Colorado, Boulder, Colorado 80309, USA Applied Chemicals and Materials Division, National Institute of Standards and Technology, Boulder, Colorado 80305, USA
*
*Author for correspondence: Adele P. Peskin, E-mail: adele.peskin@nist.gov
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Abstract

Using a commercial X-ray tomography instrument, we have obtained reconstructions of a graded-index optical fiber with voxels of edge length 1.05 µm at 12 tube voltages. The fiber manufacturer created a graded index in the central region by varying the germanium concentration from a peak value in the center of the core to a very small value at the core-cladding boundary. Operating on 12 tube voltages, we show by a singular value decomposition that there are only two singular vectors with significant weight. Physically, this means scans beyond two tube voltages contain largely redundant information. We concentrate on an analysis of the images associated with these two singular vectors. The first singular vector is dominant and images of the coefficients of the first singular vector at each voxel look are similar to any of the single-energy reconstructions. Images of the coefficients of the second singular vector by itself appear to be noise. However, by averaging the reconstructed voxels in each of several narrow bands of radii, we can obtain values of the second singular vector at each radius. In the core region, where we expect the germanium doping to go from a peak value at the fiber center to zero at the core-cladding boundary, we find that a plot of the two coefficients of the singular vectors forms a line in the two-dimensional space consistent with the dopant decreasing linearly with radial distance from the core center. The coating, made of a polymer rather than silica, is not on this line indicating that the two-dimensional results are sensitive not only to the density but also to the elemental composition.

Type
Materials Science Applications
Copyright
Copyright © Microscopy Society of America 2019 

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Footnotes

This contribution of NIST, an agency of the US government, is not subject to copyright.

References

Alvarez, RE (2013). Dimensionality and noise in energy selective x-ray imaging, Med Phys 40, 111909.Google Scholar
Alvarez, RE & Macovski, A (1976). Energy-selective reconstructions in x-ray computerized tomography, Phys Med Biol 21, 733744.Google Scholar
Berger, MJ, Hubbell, JH, Seltzer, SM, Chang, J, Coursey, JS, Sukumar, R, Zucker, DS & Olsen, K (2016). Xcom: Photon cross sections database. Available at http://www.nist.gov/pml/data/xcom/index.cfm (retrieved October 5, 2016).Google Scholar
Bidola, PM, Zanette, I, Achterhold, K, Holzner, C & Pfeiffer, F (2015). Optimization of propagation-based phase-contrast imaging at a laboratory setup, Opt Express 23, 3000030013.Google Scholar
Chiesura, G, Luyckx, G, Veot, E, Lammens, N, Paepegem, WV, Degrieck, J, Dierick, M, Hoorebeke, LV, Van-derniepen, P, Sulejmani, S, Sonnenfeld, C, Geernaert, T & Berghmans, F (2015). A micro-computed tomography technique to study the quality of fibre optics embedded in composite materials, Sensors 15, 1085210871.Google Scholar
Clark, DP & Badea, CT (2015). Spectral deblurring: an algorithm for high-resolution, hybrid spectral CT, Proc SPIE 9412, 9412.Google Scholar
Clark, DP & Badea, CT (2017). Hybrid spectral CT reconstruction, Plos One 12(7), e0180324. Available at https://doi.org/10.1371/journal.pone.0180324.Google Scholar
Dichiro, G, Brooks, RA, Kessler, RM, Johnston, GS, Jones, AE, Herdt, JR & Sheridan, WT (1979). Tissue signatures with dual-energy computed tomography, Radiology 131, 521523.Google Scholar
Genant, HK & Boyd, D (1977). Quantitative bone mineral analysis using dual energy computed tomography, Invest Radiol 12, 545555.Google Scholar
Ghassemian, H (2016). A review of remote sensing image fusion methods, Inf Fusion 32, 7589.Google Scholar
Golub, GH & Van Loan, CF (1983). Matrix Computations. Baltimore, Maryland: Johns Hopkins University Press, pp. 16ff.Google Scholar
Gureyev, TE, Mayo, SC, Myers, DE, Nesterets, Y, Paganin, DM, Pogany, A, Stevenson, AW & Wilkins, SW (2009). Refracting Röntgen's rays: Propagation-based x-ray phase contrast for biomedical imaging, J Appl Phys 105, 102005.Google Scholar
Hansen, PC (1990). Truncated singular value decomposition solutions to discrete ill-posed problems with ill-determined numerical rank, SIAM. J Sci Stat Comput 11, 503518.Google Scholar
Harms, J, Wang, T, Petrongolo, M, Niu, T & Zhu, L (2016). Noise suppression for dual-energy CT via penalized weighted least-square optimization with similarity-based regularization, Med Phys 43, 26762686.Google Scholar
Heismann, BJ, Leppert, J & Stierstorfer, K (2003). Density and atomic number measurements with spectral x-ray attenuation method, J Appl Phys 94, 20732079.Google Scholar
Hernandez, AM & Boone, JM (2014). Tungsten anode spectral model using interpolating cubic splines: Unfiltered x-ray spectra from 20 kV to 640 kV, Med Phys 41, 042101.Google Scholar
Hounsfield, GN (1980). Computed medical imaging, Med Phys 7, 283290.Google Scholar
Koike, S, Yanagi, S, Ueno, Y, Suzuki, K, Takahashi, T, Uesugi, K, Takeuchi, A, Hoshino, M, Suzuki, Y & Watanabe, Y (2013). Nondestructive three-dimensional observation of the influence of zirconium inclusions in laser-irradiated fusion-spliced optical fiber on core structure changes using synchrotron radiation x-ray micro-computed tomography, Jap J Appl Phys 52, 111.Google Scholar
Li, HT, Kingston, AM, Myers, GR, Beeching, L & Sheppard, AP (2018). Linear iterative near-field phase retrieval (LIPR) for dual-energy x-ray imaging and material discrimination, J Optical Soc Amer. A-Optics Image Science and Vision 35, A30A39.Google Scholar
Maier, DS, Schock, J & Pfeiffer, F (2017). Dual-energy micro-CT with a dual-layer, dual-color, single-crystal scintillator, Opt Express 25, 69246935.Google Scholar
Mayo, S, Stevenson, A & Wilkins, S (2012). In-line phase-contrast x-ray imaging and tomography for materials science, Materials (Basel) 5, 937965.Google Scholar
McCullough, EC (1975). Photon attenuation in computed tomography, Med Phys 2, 307320.Google Scholar
McDavid, WD, Waggener, RG & Payne, WH, and Dennis, MJ (1975). Spectral effects on three-dimensional reconstruction from x rays, Med Phys 2, 321324.Google Scholar
Paziresh, M, Kingston, AM, Latham, SJ, Fullagar, WK & Myers, GM (2016). Tomography of atomic number and density of materials using dual-energy imaging and the Alvarez and Macovski attenuation model, J Applied Phys 119, 214901.Google Scholar
Petrongolo, M, Dong, X & Zhu, L (2015). A general framework of noise suppression in material decomposition for dual-energy CT, Med Phys 42, 48484862.Google Scholar
Sandoghchi, SR, Jasion, GT, Wheeler, NV, Jain, S, Lian, Z, Wooler, JP, Boardman, RP, Baddela, N, Chen, Y & Hayes, J (2014). X-ray tomography for structural analysis of microstructured and multimaterial optical fibers and preforms, Opt Express, 22, 2618126192.Google Scholar
Teles, AP, Lima, I & Lopes, RT (2016). Rock porosity quantification by dual-energy X-ray computed microtomography, Micron 83, 7278.Google Scholar
Thomas, C, Ranchin, T, Wald, L & Chanussot, J (2008). Synthesis of multispectral images to high spatial resolution: A critical review of fusion methods based on remote sensing physics, IEEE Trans Geosci Remote Sens 46, 13011312.Google Scholar
ThorLabs (2016). Thorlabs gif625. Available at https://www.thorlabs.com/search/thorsearch.cfm?search=gif625 (retrieved September 30, 2016).Google Scholar
Tsuchiyama, A, Nakano, T, Uesugi, K, Uesugi, M, Takeuchi, A, Suzuki, Y, Noguchi, R, Matsumoto, T, Matsuno, J, Nagano, T, Imai, Y, Nakamura, T, Ogami, T, Noguchi, T, Abe, M, Yada, T & Fujimura, A (2013). Analytical dual-energy microtomography: A new method for obtaining three-dimensional mineral phase images and its application to Hayabusa samples, Geochim Cosmochim Acta 116, 516.Google Scholar
Van Geet, M, Swennen, R & Wevers, M (2000). Quantitative analysis of reservoir rocks by microfocus X-ray computerised tomography, Sedimentary Geol 132, 2536.Google Scholar
Victor, RA, Prodanovic, M & Torres-Verdin, JC (2017). Monte Carlo approach for estimating density and atomic number from dual-energy computed tomography images of carbonate rocks, Geophys Res-Solid Earth 122, 98049824.Google Scholar
Weaver, JE & Huddleston, AL (1985). Attenuation coefficients of body tissues using principal-components analysis, Med Phys 12, 4045.Google Scholar
Wilkins, SW, Gureyev, TE, Gao, D, Pogany, A & Stevenson, AW (1996). Phase-contrast imaging using polychromatic hard X-rays, Nature 384, 335338.Google Scholar
Williams, GP (2001). Electron Binding Energies, in X-ray Data Booklet, 2nd ed. Thompson, AC & Vaughan, D (Eds.), Berkeley, CA: Center for X-Ray Optics, Advanced Light Source. Chap. 1.1, p. 3.Google Scholar
Xiaoyu, J, Page, RL, Chaudhuri, S, Liu, W, Yu, SY, Mohney, SE, Badding, JV, and Gopalan, V (2017). Single-Crystal Germanium Core Optoelectronic Fibers, Advanced Optical Materials 5, 1600592.Google Scholar
Xue, Y, Ruan, R, Hu, X, Kuang, Y, Wang, J, Long, Y & Niu, T (2017). Statistical image-domain multimaterial decomposition for dual-energy CT, Med Phys 44, 886901.Google Scholar
Yu, B, Bradley, RS, Soutis, C & Withers, PJ (2016). A comparison of different approaches for imaging cracks in composites by X-ray microtomography. Phil Trans R Soc A 374, 20160037.Google Scholar
Zeiss (2016). Zeiss Xradia 520 versa your x-ray microscope for submicron x-ray imaging. Available at http://www.zeiss.com/microscopy/us/products/x-ray-microscopy/zeiss-xradia-520-versa.html (retrieved October 14, 2016).Google Scholar