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Biomedical Optics Express

Biomedical Optics Express

  • Editor: Joseph A. Izatt
  • Vol. 2, Iss. 12 — Dec. 1, 2011
  • pp: 3334–3348

Improvement of image quality of time-domain diffuse optical tomography with lp sparsity regularization

Shinpei Okawa, Yoko Hoshi, and Yukio Yamada  »View Author Affiliations

Biomedical Optics Express, Vol. 2, Issue 12, pp. 3334-3348 (2011)

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An lp (0 < p ≤ 1) sparsity regularization is applied to time-domain diffuse optical tomography with a gradient-based nonlinear optimization scheme to improve the spatial resolution and the robustness to noise. The expression of the lp sparsity regularization is reformulated as a differentiable function of a parameter to avoid the difficulty in calculating its gradient in the optimization process. The regularization parameter is selected by the L-curve method. Numerical experiments show that the lp sparsity regularization improves the spatial resolution and recovers the difference in the absorption coefficients between two targets, although a target with a small absorption coefficient may disappear due to the strong effect of the lp sparsity regularization when the value of p is too small. The lp sparsity regularization with small p values strongly localizes the target, and the reconstructed region of the target becomes smaller as the value of p decreases. A phantom experiment validates the numerical simulations.

© 2011 OSA

OCIS Codes
(100.3190) Image processing : Inverse problems
(170.3880) Medical optics and biotechnology : Medical and biological imaging

ToC Category:
Image Reconstruction and Inverse Problems

Original Manuscript: September 23, 2011
Revised Manuscript: November 14, 2011
Manuscript Accepted: November 14, 2011
Published: November 21, 2011

Shinpei Okawa, Yoko Hoshi, and Yukio Yamada, "Improvement of image quality of time-domain diffuse optical tomography with lp sparsity regularization," Biomed. Opt. Express 2, 3334-3348 (2011)

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  1. S. R. Arridge, “Optical tomography in medical imaging,” Inverse Prob.15, R41–R93 (1999). [CrossRef]
  2. A. P. Gibson, J. C. Hebden, and S. R. Arridge, “Recent advances in diffuse optical imaging,” Phys. Med. Biol.50, R1–R43 (2005). [CrossRef] [PubMed]
  3. D. Grosenick, H. Wabnitz, H. H. Rinneberg, T. Moesta, and P. M. Schlag, “Development of a time-domain optical mammography and first in vivo applications,” Appl. Opt.38(13), 2927–2943 (1999). [CrossRef]
  4. D. Grosenick, K. T. Moesta, M. Möller, J. Mucke, H. Wabnitz, B. Gebauer, C. Stroszczynski, B. Wassermann, P. M. Schlag, and H. Rinneberg, “Time-domain scanning optical mammography: I. Recording and assessment of mammograms of 154 patients,” Phys. Med. Biol.50, 2429–2449 (2005). [CrossRef] [PubMed]
  5. D. Grosenick, H. Wabnitz, K. T. Moesta, J. Mucke, P. M. Schlag, and H. Rinneberg, “Time-domain scanning optical mammography: II. Optical properties and tissue parameters of 87 carcinomas,” Phys. Med. Biol.50, 2451–2468 (2005). [CrossRef] [PubMed]
  6. J. C. Hebden, H. Veenstra, H. Dehghani, E. M. C. Hillman, M. Schweiger, S. R. Arridge, and D. T. Delpy, “Three-dimensional time-resolved optical tomography of a conical breast phantom,” Appl. Opt.40 (19), 3278–32887 (2001). [CrossRef]
  7. T. Yates, C. Hebdan, A. Gibson, N. Everdell, S. R. Arridge, and M. Douek, “Optical tomography of the breast using a multi-channel time-resolved imager,” Phys. Med. Biol.50, 2503–2517 (2005). [CrossRef] [PubMed]
  8. A. P. Gibson, T. Austin, N. L. Everdell, M. Schweiger, S.R. Arridge, J. H. Meek, J. S. Wyatt, D. T. Delpy, and J. C. Hebden, “Three-dimensional whole-head optical tomography for passive motor evoked responses in the neonate,” NueroImage30, 521–528 (2006). [CrossRef]
  9. J. C. Hebden, A. Gibson, R. M. Yusof, N. Everdell, E. M. C. Hillman, D. T. Delpy, S. R. Arridge, T. Austin, J. H. Meek, and J. S. Wyatt, “Three-dimensional optical tomography of the premature infant brain,” Phys. Med. Biol.47, 4155–4166 (2002) [CrossRef] [PubMed]
  10. B. W. Pogue, T. O. McBride, J. Prewitt, U. Lösterberg, and K. D. Paulsen, “Spatially variant regularization improves diffuse optical tomography,” Appl. Opt.38(13), 2950–2961, (1999). [CrossRef]
  11. G. Boverman, E. L. Miller, A. Li, Q. Zhang, T. Chaves, D. H. Brooks, and D. A. Boas, “Quantitative spectroscopic optical tomography of the breast guided by imperfect a priori structural information,” Phys. Med. Biol.50, 3941–3956 (2005). [CrossRef] [PubMed]
  12. P. K. Yalavarthy, B. W. Pogue, H. Dehghani, C. M. Carpenter, S. Jiang, and K. D. Paulsen, “Structural information within regularization matrices improves near infrared diffuse optical tomography,” Opt. Express15(13), 8043–8058, (2007). [CrossRef] [PubMed]
  13. A. Douiri, M. Schweiger, J. Riley, and S. R. Arridge, “Anisotropic diffusion regularization methods for diffuse optical tomography using edge prior information,” Meas. Sci. Technol.18, 87–95 (2007). [CrossRef]
  14. P. Hiltunen, D. Calvetti, and E. Somersalo, “An adaptive smoothness regularization algorithm for optical tomography,” Opt, Express16(24), 19957–19977, (2008). [CrossRef]
  15. C. Panagiotou, S. Somayajula, A. P. Gibson, M. Schweiger, R. M. Leahy, and S. R. Arridge, “Information theoretic regularization in diffuse optical tomography,” J. Opt. Soc. Am. A26(5), 1277–1290 (2009). [CrossRef]
  16. N. Cao, A. Nehorai, and M. Jacob, “Image reconstruction for diffuse optical tomography using sparsity regularization and expectation-maximization algorithm,” Opt. Express, 15(21), 13695–13708 (2007). [CrossRef] [PubMed]
  17. P. M. Shankar and M. A. Neifeld, “Sparsity constrained regularization for multiframe image restoration,” J. Opt. Soc. Am. A25(5), 1199–1214 (2008). [CrossRef]
  18. P. Mohajerani, A. A. Eftekhar, J. Huang, and A. Adibi, “Optimal sparse solution for fluorescent diffuse optical tomography: theory and phantom experimental results,” Appl. Opt.46(10), 1679–1685 (2007). [CrossRef] [PubMed]
  19. Y. Lu, X. Zhang, A. Douraghy, D. Stout, J. Tian, T. F. Chan, and A. F. Chatziioannou, “Source reconstruction for spectrally-resolved bioluminescence tomography with sparse a priori information,” Opt. Express17(10), 8062–8088 (2009). [CrossRef] [PubMed]
  20. S. Okawa and Y. Yamada, “Reconstruction of fluorescence/bioluminescence sources in biological medium with spatial filter,” Opt. Express18(12), 13151–13172 (2010). [CrossRef] [PubMed]
  21. S. Baillet, J. C. Mosher, and R. M. Leahy, “Electromagnetic brain mapping,” IEEE Signal Process. Mag.18, 14–30 (2001). [CrossRef]
  22. P. Xu, Y. Tian, H. Chen, and D. Yao, “Lp Norm iterative sparse solution for EEG source localization,” IEEE Trans. Biomed. Eng.54 (3), 400–409 (2007). [CrossRef] [PubMed]
  23. Z. He, A. Cichocki, R. Zdunek, and S. Xie, “Improved FOCCUS Method With conjugate gradient iterations,” IEEE Tras. Signal Process.57 (1), 399–404 (2009). [CrossRef]
  24. M. Schweiger, S. R. Arridge, and D. T. Delpy, “Application of the finite-element method for the forward and inverse model in optical tomography,” J. Math. Imaging Vis.3, 263–283 (1993). [CrossRef]
  25. C. R. Vogel, Computational Methods for Inverse Problems, Frontiers in Applied Mathematics (SIAM, Philadelphia, 2002). [CrossRef]
  26. S. R. Arridge, “A gradient-based optimization scheme for optical tomography,” Opt. Express12(6), 213–226 (1998). [CrossRef]
  27. P. C. Hansen and D. P. O’Leary, “The use of the L-curve in the regularization of discrete ill-posed problems,” SIAM J. Sci. Compt.14(6), 1487–1503 (1993). [CrossRef]
  28. S. Holder, Electrical Impedance Tomography: Methods, History and Applications (Institute of Physics Publishing, Bristol, 2005).
  29. H. Eda, I. Oda, Y. Ito, Y. Wada, Y. Oikawa, Y. Tsunazawa, Y. Tuchiya, Y. Yamashita, M. Oda, A. Sassaroli, Y. Yamada, and N. Tamura, “Multichannel time-resolved optical tomographic imaging system,” Rev. Sci. Instrum.70(9), 3595–3602 (1999). [CrossRef]

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