Abstract

We examine the impact of background lumiphore on image quality in luminescence optical tomography. A modification of a previously described algorithm [ J. Chang, H. L. Graber, and R. L. Barbour, J. Opt. Soc. Am. A 14, 288–299 (1997); J. Chang, H. L. Graber, and R. L. Barbour, IEEE Trans. Biomed. Eng. 44, 810–822 (1997)] that estimates the background luminescence directly from the detector readings is developed. Numerical simulations were performed to calculate the diffusion-regime limiting form of forward-problem solutions for a specific test medium. We performed image reconstructions with and without white noise added to the detector readings, using both the original and the improved versions of the algorithm. The results indicate that the original version produces unsatisfactory reconstructions when background lumiphore is present, whereas the improved algorithm yields qualitatively better images, especially for small target-to-background luminescence yield ratios.

© 1998 Optical Society of America

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References

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  1. J. Chang, H. L. Graber, R. L. Barbour, “Luminescence optical tomography of dense scattering media,” J. Opt. Soc. Am. A 14, 288–299 (1997).
    [CrossRef]
  2. J. Chang, H. L. Graber, R. L. Barbour, “Imaging of fluorescence in highly scattering media,” IEEE Trans. Biomed. Eng. 44, 810–822 (1997).
    [CrossRef] [PubMed]
  3. D. Y. Paithankar, A. U. Chen, B. W. Pogue, M. S. Patterson, E. M. Sevick-Muraca, “Fluorescence lifetime imaging with frequency-domain photon migration measurement,” Appl. Opt. 36, 2260–2272 (1997).
    [CrossRef] [PubMed]
  4. M. A. O’Leary, D. A. Boas, X. D. Li, B. Chance, A. G. Yodh, “Fluorescence lifetime imaging in turbid media,” Opt. Lett. 21, 158–160 (1996).
    [CrossRef] [PubMed]
  5. J. Wu, Y. Wang, L. Perelman, I. Itzkan, R. R. Dasari, M. S. Feld, “Analytical model for extracting intrinsic fluorescence in turbid media,” Appl. Opt. 32, 3585–3595 (1993).
    [CrossRef] [PubMed]
  6. D. C. Youla, “Mathematical theory of image reconstruction by the method of convex projections,” in Image Recovery: Theory and Application, H. Stark, ed. (Academic, New York, 1987).
  7. J. Chang, H. Graber, R. L. Barbour, R. Aronson, “Recovery of optical cross-section perturbations in dense scattering media by transport-theory-based imaging operators and steady-state simulated data,” Appl. Opt. 35, 3963–3978 (1996).
    [CrossRef] [PubMed]
  8. S. R. Arridge, “Forward and inverse problems in time-resolved infrared imaging,” in Medical Optical Tomography: Functional Imaging and Monitoring, G. J. Mueller, B. Chance, R. R. Alfano, S. R. Arridge, J. Beuthan, E. Gratton, M. Kaschke, B. Masters, S. Svanberg, P. van der Zee, eds., Vol. IS11 of SPIE Institute Series (SPIE Press, Bellingham, Wash., 1993), pp. 35–64.
  9. J. Chang, W. Zhu, Y. Wang, H. L. Graber, R. L. Barbour, “A regularized progressive expansion algorithm for recovery of scattering media from time-resolved data,” J. Opt. Soc. Am. A 14, 306–312 (1997).
    [CrossRef]
  10. J. Chang, H. L. Graber, R. L. Barbour, “Concentration, size, mean lifetime, and noise effects on image quality in luminescence optical tomography,” in Optical Tomography and Spectroscopy of Tissue: Theory, Instrumentation, Model, and Human Studies, B. Chance, R. R. Alfano, eds., Proc. SPIE2979, 750–758 (1997).
    [CrossRef]
  11. J. Chang, H. L. Graber, R. L. Barbour, “Dependence of optical diffusion tomography image quality on image operator and noise,” in Proceedings of 1995 IEEE Medical Imaging Conference, (Institute of Electrical and Electronics Engineers, New York, 1995), pp. 1524–1528.
  12. Y. Yao, Y. Wang, Y. Pei, W. Zhu, R. L. Barbour, “Frequency-domain optical imaging of absorption and scattering distributions by a Born iterative method,” J. Opt. Soc. Am. A 14, 325–342 (1997).
    [CrossRef]
  13. B. W. Bogue, M. S. Patterson, H. Jiang, K. D. Paulsen, “Initial assessment of a simple system for frequency domain diffuse optical tomography,” Phys. Med. Biol. 40, 1709–1729 (1995).
    [CrossRef]
  14. M. Schweiger, S. R. Arridge, D. T. Delpy, “Application of the finite-element method for the forward and inverse models in optical tomography,” J. Math. Imaging Vision 13, 263–283 (1996).

1997 (5)

1996 (3)

1995 (1)

B. W. Bogue, M. S. Patterson, H. Jiang, K. D. Paulsen, “Initial assessment of a simple system for frequency domain diffuse optical tomography,” Phys. Med. Biol. 40, 1709–1729 (1995).
[CrossRef]

1993 (1)

Aronson, R.

Arridge, S. R.

M. Schweiger, S. R. Arridge, D. T. Delpy, “Application of the finite-element method for the forward and inverse models in optical tomography,” J. Math. Imaging Vision 13, 263–283 (1996).

S. R. Arridge, “Forward and inverse problems in time-resolved infrared imaging,” in Medical Optical Tomography: Functional Imaging and Monitoring, G. J. Mueller, B. Chance, R. R. Alfano, S. R. Arridge, J. Beuthan, E. Gratton, M. Kaschke, B. Masters, S. Svanberg, P. van der Zee, eds., Vol. IS11 of SPIE Institute Series (SPIE Press, Bellingham, Wash., 1993), pp. 35–64.

Barbour, R. L.

J. Chang, W. Zhu, Y. Wang, H. L. Graber, R. L. Barbour, “A regularized progressive expansion algorithm for recovery of scattering media from time-resolved data,” J. Opt. Soc. Am. A 14, 306–312 (1997).
[CrossRef]

Y. Yao, Y. Wang, Y. Pei, W. Zhu, R. L. Barbour, “Frequency-domain optical imaging of absorption and scattering distributions by a Born iterative method,” J. Opt. Soc. Am. A 14, 325–342 (1997).
[CrossRef]

J. Chang, H. L. Graber, R. L. Barbour, “Imaging of fluorescence in highly scattering media,” IEEE Trans. Biomed. Eng. 44, 810–822 (1997).
[CrossRef] [PubMed]

J. Chang, H. L. Graber, R. L. Barbour, “Luminescence optical tomography of dense scattering media,” J. Opt. Soc. Am. A 14, 288–299 (1997).
[CrossRef]

J. Chang, H. Graber, R. L. Barbour, R. Aronson, “Recovery of optical cross-section perturbations in dense scattering media by transport-theory-based imaging operators and steady-state simulated data,” Appl. Opt. 35, 3963–3978 (1996).
[CrossRef] [PubMed]

J. Chang, H. L. Graber, R. L. Barbour, “Concentration, size, mean lifetime, and noise effects on image quality in luminescence optical tomography,” in Optical Tomography and Spectroscopy of Tissue: Theory, Instrumentation, Model, and Human Studies, B. Chance, R. R. Alfano, eds., Proc. SPIE2979, 750–758 (1997).
[CrossRef]

J. Chang, H. L. Graber, R. L. Barbour, “Dependence of optical diffusion tomography image quality on image operator and noise,” in Proceedings of 1995 IEEE Medical Imaging Conference, (Institute of Electrical and Electronics Engineers, New York, 1995), pp. 1524–1528.

Boas, D. A.

Bogue, B. W.

B. W. Bogue, M. S. Patterson, H. Jiang, K. D. Paulsen, “Initial assessment of a simple system for frequency domain diffuse optical tomography,” Phys. Med. Biol. 40, 1709–1729 (1995).
[CrossRef]

Chance, B.

Chang, J.

J. Chang, H. L. Graber, R. L. Barbour, “Imaging of fluorescence in highly scattering media,” IEEE Trans. Biomed. Eng. 44, 810–822 (1997).
[CrossRef] [PubMed]

J. Chang, W. Zhu, Y. Wang, H. L. Graber, R. L. Barbour, “A regularized progressive expansion algorithm for recovery of scattering media from time-resolved data,” J. Opt. Soc. Am. A 14, 306–312 (1997).
[CrossRef]

J. Chang, H. L. Graber, R. L. Barbour, “Luminescence optical tomography of dense scattering media,” J. Opt. Soc. Am. A 14, 288–299 (1997).
[CrossRef]

J. Chang, H. Graber, R. L. Barbour, R. Aronson, “Recovery of optical cross-section perturbations in dense scattering media by transport-theory-based imaging operators and steady-state simulated data,” Appl. Opt. 35, 3963–3978 (1996).
[CrossRef] [PubMed]

J. Chang, H. L. Graber, R. L. Barbour, “Concentration, size, mean lifetime, and noise effects on image quality in luminescence optical tomography,” in Optical Tomography and Spectroscopy of Tissue: Theory, Instrumentation, Model, and Human Studies, B. Chance, R. R. Alfano, eds., Proc. SPIE2979, 750–758 (1997).
[CrossRef]

J. Chang, H. L. Graber, R. L. Barbour, “Dependence of optical diffusion tomography image quality on image operator and noise,” in Proceedings of 1995 IEEE Medical Imaging Conference, (Institute of Electrical and Electronics Engineers, New York, 1995), pp. 1524–1528.

Chen, A. U.

Dasari, R. R.

Delpy, D. T.

M. Schweiger, S. R. Arridge, D. T. Delpy, “Application of the finite-element method for the forward and inverse models in optical tomography,” J. Math. Imaging Vision 13, 263–283 (1996).

Feld, M. S.

Graber, H.

Graber, H. L.

J. Chang, H. L. Graber, R. L. Barbour, “Luminescence optical tomography of dense scattering media,” J. Opt. Soc. Am. A 14, 288–299 (1997).
[CrossRef]

J. Chang, W. Zhu, Y. Wang, H. L. Graber, R. L. Barbour, “A regularized progressive expansion algorithm for recovery of scattering media from time-resolved data,” J. Opt. Soc. Am. A 14, 306–312 (1997).
[CrossRef]

J. Chang, H. L. Graber, R. L. Barbour, “Imaging of fluorescence in highly scattering media,” IEEE Trans. Biomed. Eng. 44, 810–822 (1997).
[CrossRef] [PubMed]

J. Chang, H. L. Graber, R. L. Barbour, “Dependence of optical diffusion tomography image quality on image operator and noise,” in Proceedings of 1995 IEEE Medical Imaging Conference, (Institute of Electrical and Electronics Engineers, New York, 1995), pp. 1524–1528.

J. Chang, H. L. Graber, R. L. Barbour, “Concentration, size, mean lifetime, and noise effects on image quality in luminescence optical tomography,” in Optical Tomography and Spectroscopy of Tissue: Theory, Instrumentation, Model, and Human Studies, B. Chance, R. R. Alfano, eds., Proc. SPIE2979, 750–758 (1997).
[CrossRef]

Itzkan, I.

Jiang, H.

B. W. Bogue, M. S. Patterson, H. Jiang, K. D. Paulsen, “Initial assessment of a simple system for frequency domain diffuse optical tomography,” Phys. Med. Biol. 40, 1709–1729 (1995).
[CrossRef]

Li, X. D.

O’Leary, M. A.

Paithankar, D. Y.

Patterson, M. S.

D. Y. Paithankar, A. U. Chen, B. W. Pogue, M. S. Patterson, E. M. Sevick-Muraca, “Fluorescence lifetime imaging with frequency-domain photon migration measurement,” Appl. Opt. 36, 2260–2272 (1997).
[CrossRef] [PubMed]

B. W. Bogue, M. S. Patterson, H. Jiang, K. D. Paulsen, “Initial assessment of a simple system for frequency domain diffuse optical tomography,” Phys. Med. Biol. 40, 1709–1729 (1995).
[CrossRef]

Paulsen, K. D.

B. W. Bogue, M. S. Patterson, H. Jiang, K. D. Paulsen, “Initial assessment of a simple system for frequency domain diffuse optical tomography,” Phys. Med. Biol. 40, 1709–1729 (1995).
[CrossRef]

Pei, Y.

Perelman, L.

Pogue, B. W.

Schweiger, M.

M. Schweiger, S. R. Arridge, D. T. Delpy, “Application of the finite-element method for the forward and inverse models in optical tomography,” J. Math. Imaging Vision 13, 263–283 (1996).

Sevick-Muraca, E. M.

Wang, Y.

Wu, J.

Yao, Y.

Yodh, A. G.

Youla, D. C.

D. C. Youla, “Mathematical theory of image reconstruction by the method of convex projections,” in Image Recovery: Theory and Application, H. Stark, ed. (Academic, New York, 1987).

Zhu, W.

Appl. Opt. (3)

IEEE Trans. Biomed. Eng. (1)

J. Chang, H. L. Graber, R. L. Barbour, “Imaging of fluorescence in highly scattering media,” IEEE Trans. Biomed. Eng. 44, 810–822 (1997).
[CrossRef] [PubMed]

J. Math. Imaging Vision (1)

M. Schweiger, S. R. Arridge, D. T. Delpy, “Application of the finite-element method for the forward and inverse models in optical tomography,” J. Math. Imaging Vision 13, 263–283 (1996).

J. Opt. Soc. Am. A (3)

Opt. Lett. (1)

Phys. Med. Biol. (1)

B. W. Bogue, M. S. Patterson, H. Jiang, K. D. Paulsen, “Initial assessment of a simple system for frequency domain diffuse optical tomography,” Phys. Med. Biol. 40, 1709–1729 (1995).
[CrossRef]

Other (4)

D. C. Youla, “Mathematical theory of image reconstruction by the method of convex projections,” in Image Recovery: Theory and Application, H. Stark, ed. (Academic, New York, 1987).

S. R. Arridge, “Forward and inverse problems in time-resolved infrared imaging,” in Medical Optical Tomography: Functional Imaging and Monitoring, G. J. Mueller, B. Chance, R. R. Alfano, S. R. Arridge, J. Beuthan, E. Gratton, M. Kaschke, B. Masters, S. Svanberg, P. van der Zee, eds., Vol. IS11 of SPIE Institute Series (SPIE Press, Bellingham, Wash., 1993), pp. 35–64.

J. Chang, H. L. Graber, R. L. Barbour, “Concentration, size, mean lifetime, and noise effects on image quality in luminescence optical tomography,” in Optical Tomography and Spectroscopy of Tissue: Theory, Instrumentation, Model, and Human Studies, B. Chance, R. R. Alfano, eds., Proc. SPIE2979, 750–758 (1997).
[CrossRef]

J. Chang, H. L. Graber, R. L. Barbour, “Dependence of optical diffusion tomography image quality on image operator and noise,” in Proceedings of 1995 IEEE Medical Imaging Conference, (Institute of Electrical and Electronics Engineers, New York, 1995), pp. 1524–1528.

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Figures (6)

Fig. 1
Fig. 1

(A) Source–detector ring and phantom structure used for diffusion computations. Two target positions were adopted in this study. They are (B) the center of the ROI, and (C) the midway point between the center of ROI and its bottom edge. Four different target sizes were studies for each position, ranging from a 0.5 cm × 0.5 cm square to a 2.0 cm × 2.0 cm square. The thickness of the ROI is 1 mm. Thus it is essentially a 2D region, although 3D diffusion model is used for the computations.

Fig. 2
Fig. 2

Reconstructions of luminescence yield γσ a,l N 0 and mean lifetime τ with background-to-target γσ a,l N 0 ratio of 0.01 and different target sizes, (A) 0.5 cm × 0.5 cm × 0.1 cm, (B) 1.0 cm × 1.0 cm × 0.1 cm, (C) 1.5 cm × 1.5 cm × 0.1 cm, and (D) 2.0 cm × 2.0 cm × 0.1 cm, and with the previously described algorithm1,2 with positivity constraints.

Fig. 3
Fig. 3

Reconstruction results with background-to-target γσ a,l N 0 ratio of 0.2 and different target sizes, when the improved algorithm with range constraints is used. Target locations and sizes are the same as for Fig. 2, but background-to-target γσ a,l N 0 ratio is 20 times larger in these cases. Note that the gray scale of the τ is reversed for display purposes. That is, the darker region corresponds to lower values.

Fig. 4
Fig. 4

Reconstruction results of the off-center case with background-to-target γσ a,l N 0 ratio of 0.2 and different target sizes when the improved algorithm with range constraints is used. Centers of targets are located halfway between the center and the bottom edge of the ROI. Sizes are the same as for Fig. 2, but the background-to-target γσ a,l N 0 ratio is 20 times larger in these cases. Notice that the numerical values of τ can exceed the maximum value of the constraint range because they are the ratios of the reconstructed real and imaginary parts of the unknown quantities in Eq. (4). If range constraints are applied directly to the above reconstructed τ, the numerical values of the target and the background mean lifetimes are within 2.0% of the correct values.

Fig. 5
Fig. 5

Comparison of images reconstructed by use of the previous and the improved algorithms, with background-to-target γσ a,l N 0 ratio of 0.01 after (A) 10 iterations, (B) 100 iterations, (C) 1000 iterations, and (D) 10,000 iterations. A four-lobed pattern was observed in both cases after 10 iterations. As the number of iterations increases, the improved algorithm accurately reconstructs the target’s size and position, whereas the results from the previous algorithm still contain unacceptable artifacts.

Fig. 6
Fig. 6

Reconstruction results of the center case with a background-to-target γσ a,l N 0 ratio of 0.01 and different levels of added noise, with target size 2.0 cm × 2.0 cm × 0.1 cm, and when the improved algorithm with range constraints is used. (A) 1.0%, (B) 3.0%, (C) 5.0%, and (D) 10.0% noise.

Equations (4)

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- j ω ϕ ˜ 1 c + Ω · ϕ ˜ 1 + μ T , 1 + μ T , 1 2 ϕ ˜ 1 = S ˜ 1 + 4 π μ s , 1 Ω · Ω ϕ ˜ 1 d Ω ,
- j ω ϕ ˜ 2 c + Ω · ϕ ˜ 2 + μ T , 2 ϕ ˜ 2 = S ˜ 2 + 4 π μ s , 2 Ω · Ω ϕ ˜ 2 d Ω ,
R ˜ = V   w γ σ a , l N 0 d 3 r ,
R ˜ = V   w   1 - j ω τ 1 + ω 2 τ 2 d 3 r ,

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