Abstract

Combined use of the algebraic reconstruction technique (ART) and the genetic algorithm (GA) shows highly accurate and efficient tomographic reconstruction of line-of-sight projection images of two-phase flows compared with reconstructions obtained by separate use of these methods. A modified GA-based tomography uses the ART reconstruction result as preliminary information on the number, shapes, and sizes of bubbles to be reconstructed. This combined use of the two methods exploits faster convergence of the ART to the approximate solution space and more robust and accurate optimization of the GA to the ultimate solution space. In the investigation a computer-synthesized phantom field that consisted of five elliptical gas bubbles in liquid or solid surroundings was used.

© 1998 Optical Society of America

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References

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  1. J. Radon, Math. Phys. Klin. 29, 262 (1917).
  2. S. R. Deans, The Radon Transform and Its Applications (Wiley, New York, 1983).
  3. A. C. Kak and M. Slaney, Principles of Computerized Tomographic Imaging (Institute of Electrical and Electronics Engineers, Piscataway, N.J., 1987), Chap. 3.
  4. R. Gordon, IEEE Trans. Nucl. Sci. 21, 78 (1974).
    [CrossRef]
  5. D. E. Goldberg, Genetic Algorithms in Search, Optimization, and Machine Learning (Addison-Wesley, New York, 1989).
  6. D. Tsuru, K. Okamoto, H. Madarame, and M. Fumizawa, Fourth Fluid Control, Measurement and Visualization Conference, Toulouse, France, August 30–September 1.
  7. K. D. Kihm and D. P. Lyons, Opt. Lett. 21, 1327 (1996).
    [CrossRef] [PubMed]
  8. D. P. Lyons and K. D. Kihm, Opt. Lett. 22, 847 (1997).
    [CrossRef] [PubMed]

1997 (1)

1996 (1)

1974 (1)

R. Gordon, IEEE Trans. Nucl. Sci. 21, 78 (1974).
[CrossRef]

1917 (1)

J. Radon, Math. Phys. Klin. 29, 262 (1917).

Deans, S. R.

S. R. Deans, The Radon Transform and Its Applications (Wiley, New York, 1983).

Fumizawa, M.

D. Tsuru, K. Okamoto, H. Madarame, and M. Fumizawa, Fourth Fluid Control, Measurement and Visualization Conference, Toulouse, France, August 30–September 1.

Goldberg, D. E.

D. E. Goldberg, Genetic Algorithms in Search, Optimization, and Machine Learning (Addison-Wesley, New York, 1989).

Gordon, R.

R. Gordon, IEEE Trans. Nucl. Sci. 21, 78 (1974).
[CrossRef]

Kak, A. C.

A. C. Kak and M. Slaney, Principles of Computerized Tomographic Imaging (Institute of Electrical and Electronics Engineers, Piscataway, N.J., 1987), Chap. 3.

Kihm, K. D.

Lyons, D. P.

Madarame, H.

D. Tsuru, K. Okamoto, H. Madarame, and M. Fumizawa, Fourth Fluid Control, Measurement and Visualization Conference, Toulouse, France, August 30–September 1.

Okamoto, K.

D. Tsuru, K. Okamoto, H. Madarame, and M. Fumizawa, Fourth Fluid Control, Measurement and Visualization Conference, Toulouse, France, August 30–September 1.

Radon, J.

J. Radon, Math. Phys. Klin. 29, 262 (1917).

Slaney, M.

A. C. Kak and M. Slaney, Principles of Computerized Tomographic Imaging (Institute of Electrical and Electronics Engineers, Piscataway, N.J., 1987), Chap. 3.

Tsuru, D.

D. Tsuru, K. Okamoto, H. Madarame, and M. Fumizawa, Fourth Fluid Control, Measurement and Visualization Conference, Toulouse, France, August 30–September 1.

IEEE Trans. Nucl. Sci. (1)

R. Gordon, IEEE Trans. Nucl. Sci. 21, 78 (1974).
[CrossRef]

Math. Phys. Klin. (1)

J. Radon, Math. Phys. Klin. 29, 262 (1917).

Opt. Lett. (2)

Other (4)

S. R. Deans, The Radon Transform and Its Applications (Wiley, New York, 1983).

A. C. Kak and M. Slaney, Principles of Computerized Tomographic Imaging (Institute of Electrical and Electronics Engineers, Piscataway, N.J., 1987), Chap. 3.

D. E. Goldberg, Genetic Algorithms in Search, Optimization, and Machine Learning (Addison-Wesley, New York, 1989).

D. Tsuru, K. Okamoto, H. Madarame, and M. Fumizawa, Fourth Fluid Control, Measurement and Visualization Conference, Toulouse, France, August 30–September 1.

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Figures (3)

Fig. 1
Fig. 1

Line-of-sight optical projection of two-phase impedance field.

Fig. 2
Fig. 2

(a) Computer-synthesized two-phase field, (b) its line-of-sight (Radon transform) projection, (c) image reconstructed with ART, and (d) image reconstructed by the combined use of the ART and the GA.

Fig. 3
Fig. 3

Schematic illustration of the genetic operators by biased random selection, crossover, mutation, and creation of offspring.

Tables (2)

Tables Icon

Table 1 Examples of Line-of-Sight Projection Ψ and Impedance I for Different Optical Techniques

Tables Icon

Table 2 Comparison of Features of the ART and GA-Based Reconstruction

Equations (3)

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Ψr, ϕ=Ix, yδr-x cos ϕ-y sin ϕdxdy,
gjx, y; Aj=[1x-xjaj2+y-yjbj210otherwise],
Aiq+1=Aiq+Wiqpψp-ψˆpNP,

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