OSA's Digital Library

Journal of the Optical Society of America A

Journal of the Optical Society of America A


  • Vol. 19, Iss. 10 — Oct. 1, 2002
  • pp: 1983–1993

Source-detector calibration in three-dimensional Bayesian optical diffusion tomography

Seungseok Oh, Adam B. Milstein, R. P. Millane, Charles A. Bouman, and Kevin J. Webb  »View Author Affiliations

JOSA A, Vol. 19, Issue 10, pp. 1983-1993 (2002)

View Full Text Article

Acrobat PDF (778 KB)

Browse Journals / Lookup Meetings

Browse by Journal and Year


Lookup Conference Papers

Close Browse Journals / Lookup Meetings

Article Tools



Optical diffusion tomography is a method for reconstructing three-dimensional optical properties from light that passes through a highly scattering medium. Computing reconstructions from such data requires the solution of a nonlinear inverse problem. The situation is further complicated by the fact that while reconstruction algorithms typically assume exact knowledge of the optical source and detector coupling coefficients, these coupling coefficients are generally not available in practical measurement systems. A new method for estimating these unknown coupling coefficients in the three-dimensional reconstruction process is described. The joint problem of coefficient estimation and three-dimensional reconstruction is formulated in a Bayesian framework, and the resulting estimates are computed by using a variation of iterative coordinate descent optimization that is adapted for this problem. Simulations show that this approach is an accurate and efficient method for simultaneous reconstruction of absorption and diffusion coefficients as well as the coupling coefficients. A simple experimental result validates the approach.

© 2002 Optical Society of America

OCIS Codes
(100.3010) Image processing : Image reconstruction techniques
(100.3190) Image processing : Inverse problems
(100.6890) Image processing : Three-dimensional image processing
(170.5280) Medical optics and biotechnology : Photon migration

Seungseok Oh, Adam B. Milstein, R. P. Millane, Charles A. Bouman, and Kevin J. Webb, "Source-detector calibration in three-dimensional Bayesian optical diffusion tomography," J. Opt. Soc. Am. A 19, 1983-1993 (2002)

Sort:  Author  |  Year  |  Journal  |  Reset


  1. S. R. Arridge, “Optical tomography in medical imaging,” Inverse Probl. 15, R41–R93 (1999).
  2. J. C. Ye, K. J. Webb, C. A. Bouman, and R. P. Millane, “Optical diffusion tomography using iterative coordinate descent optimization in a Bayesian framework,” J. Opt. Soc. Am. A 16, 2400–2412 (1999).
  3. J. C. Ye, C. A. Bouman, K. J. Webb, and R. P. Millane, “Nonlinear multigrid algorithms for Bayesian optical diffusion tomography,” IEEE Trans. Image Process. 10, 909–922 (2001).
  4. S. S. Saquib, K. M. Hanson, and G. S. Cunningham, “Model-based image reconstruction from time-resolved diffusion data,” in Medical Imaging 1997: Image Processing, K. M. Hanson, ed., Proc. SPIE 3034, 369–380 (1997).
  5. S. R. Arridge and M. Schweiger, “A gradient-based optimisation scheme for optical tomography,” Opt. Express 2, 213–226 (1998), www.opticsexpress.org.
  6. A. H. Hielscher, A. D. Klose, and K. M. Hanson, “Gradient-based iterative image reconstruction scheme for time-resolved optical tomography,” IEEE Trans. Med. Imaging 18, 262–271 (1999).
  7. D. Boas, T. Gaudette, and S. Arridge, “Simultaneous imaging and optode calibration with diffuse optical tomography,” Opt. Express 8, 263–270 (2001), www.opticsexpress.org.
  8. H. Jiang, K. Paulsen, and U. Osterberg, “Optical image reconstruction using dc data: simulations and experiments,” Phys. Med. Biol. 41, 1483–1498 (1996).
  9. H. Jiang, K. Paulsen, U. Osterberg, and M. Patterson, “Improved continuous light diffusion imaging in single- and multi-target tissue-like phantoms,” Phys. Med. Biol. 43, 675–693 (1998).
  10. B. W. Pogue, S. P. Poplack, T. O. McBride, W. A. Wells, K. S. Osterman, U. L. Osterberg, and K. D. Paulsen, “Quantitative hemoglobin tomography with diffuse near-infrared spectroscopy: pilot results in the breast,” Radiology 218, 261–266 (2001).
  11. B. W. Pogue, C. Willscher, T. O. McBride, U. L. Osterberg, and K. D. Paulsen, “Contrast-detail analysis for detection and characterization with near-infrared diffuse tomography,” Med. Phys. 27, 2693–2700 (2000).
  12. T. O. McBride, B. W. Pogue, S. Poplack, S. Soho, W. A. Wells, S. Jiang, U. Osterberg, and K. D. Paulsen, “Multispectral near-infrared tomography: a case study in compensating for water and lipid content in hemoglobin imaging of the breast,” J. Biomed. Opt. 7, 72–79 (2002).
  13. N. Iftimia and H. Jiang, “Quantitative optical image reconstructions of turbid media by use of direct-current measurements,” Appl. Opt. 39, 5256–5261 (2000).
  14. A. B. Milstein, S. Oh, J. S. Reynolds, K. J. Webb, C. A. Bouman, and R. P. Millane, “Three-dimensional Bayesian optical diffusion tomography using experimental data,” Opt. Lett. 27, 95–97 (2002).
  15. J. J. Duderstadt and L. J. Hamilton, Nuclear Reactor Analysis (Wiley, New York, 1976).
  16. A. Ishimaru, Wave Propagation and Scattering in Random Media (Academic, New York, 1978), Vol. 1.
  17. J. C. Ye, K. J. Webb, R. P. Millane, and T. J. Downar, “Modified distorted Born iterative method with an approximate Fréchet derivative for optical diffusion tomography,” J. Opt. Soc. Am. A 16, 1814–1826 (1999).
  18. L. E. Baum and T. Petrie, “Statistical inference for probabilistic functions of finite state Markov chains,” Ann. Math. Stat. 37, 1554–1563 (1966).
  19. S. Geman and D. McClure, “Statistical methods for tomographic image reconstruction,” Bull. Int. Stat. Inst. LII-4, 5–21 (1987).
  20. S. S. Saquib, C. A. Bouman, and K. Sauer, “ML parameter estimation for Markov random fields with applications to Bayesian tomography,” IEEE Trans. Image Process. 7, 1029–1044 (1998).
  21. A. Mohammad-Djafari, “On the estimation of hyperparameters in Bayesian approach of solving inverse problems,” in Proceedings of IEEE International Conference on Acoustics, Speech, and Signal Processing (Institute of Electrical and Electronics Engineers, New York, 1993), pp. 495–498.
  22. A. Mohammad-Djafari, “Joint estimation of parameters and hyperparameters in a Bayesian approach of solving inverse problems,” in Proceedings of IEEE International Conference on Image Processing (Institute of Electrical and Electronics Engineers, New York, 1996), Vol. II, pp. 473–476.
  23. K. Lange, “An overview of Bayesian methods in image reconstruction,” in Digital Image Synthesis and Inverse Optics, A. F. Gmitro, P. S. Idell, and I. J. LaHaie, eds., Proc. SPIE 1351, 270–287 (1990).
  24. C. A. Bouman and K. Sauer, “A generalized Gaussian image model for edge-preserving MAP estimation,” IEEE Trans. Image Process. 2, 296–310 (1993).
  25. S. R. Arridge, “Photon-measurement density functions. Part 1. Analytical forms,” Appl. Opt. 34, 7395–7409 (1995).
  26. J. S. Reynolds, A. Przadka, S. Yeung, and K. J. Webb, “Optical diffusion imaging: a comparative numerical and experimental study,” Appl. Opt. 35, 3671–3679 (1996).

Cited By

Alert me when this paper is cited

OSA is able to provide readers links to articles that cite this paper by participating in CrossRef's Cited-By Linking service. CrossRef includes content from more than 3000 publishers and societies. In addition to listing OSA journal articles that cite this paper, citing articles from other participating publishers will also be listed.

« Previous Article  |  Next Article »

OSA is a member of CrossRef.

CrossCheck Deposited