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Journal of the Optical Society of America A

Journal of the Optical Society of America A

| OPTICS, IMAGE SCIENCE, AND VISION

  • Editor: Franco Gori
  • Vol. 31, Iss. 8 — Aug. 1, 2014
  • pp: 1847–1855

Compensation of modeling errors due to unknown domain boundary in diffuse optical tomography

Meghdoot Mozumder, Tanja Tarvainen, Jari P. Kaipio, Simon R. Arridge, and Ville Kolehmainen  »View Author Affiliations


JOSA A, Vol. 31, Issue 8, pp. 1847-1855 (2014)
http://dx.doi.org/10.1364/JOSAA.31.001847


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Abstract

Diffuse optical tomography is a highly unstable problem with respect to modeling and measurement errors. During clinical measurements, the body shape is not always known, and an approximate model domain has to be employed. The use of an incorrect model domain can, however, lead to significant artifacts in the reconstructed images. Recently, the Bayesian approximation error theory has been proposed to handle model-based errors. In this work, the feasibility of the Bayesian approximation error approach to compensate for modeling errors due to unknown body shape is investigated. The approach is tested with simulations. The results show that the Bayesian approximation error method can be used to reduce artifacts in reconstructed images due to unknown domain shape.

© 2014 Optical Society of America

OCIS Codes
(100.3190) Image processing : Inverse problems
(170.3010) Medical optics and biotechnology : Image reconstruction techniques
(170.6960) Medical optics and biotechnology : Tomography
(290.7050) Scattering : Turbid media

ToC Category:
Image Processing

History
Original Manuscript: March 3, 2014
Revised Manuscript: June 27, 2014
Manuscript Accepted: June 30, 2014
Published: July 29, 2014

Citation
Meghdoot Mozumder, Tanja Tarvainen, Jari P. Kaipio, Simon R. Arridge, and Ville Kolehmainen, "Compensation of modeling errors due to unknown domain boundary in diffuse optical tomography," J. Opt. Soc. Am. A 31, 1847-1855 (2014)
http://www.opticsinfobase.org/josaa/abstract.cfm?URI=josaa-31-8-1847


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References

  1. S. Arridge and J. Schotland, “Optical tomography: forward and inverse problems,” Inverse Probl. 25, 123010 (2009). [CrossRef]
  2. A. Gibson, J. Hebden, and S. Arridge, “Recent advances in diffuse optical imaging,” Phys. Med. Biol. 50, R1–R43 (2005). [CrossRef]
  3. B. J. Tromberg, B. W. Pogue, K. D. Paulsen, A. G. Yodh, D. A. Boas, and A. E. Cerussi, “Assessing the future of diffuse optical imaging technologies for breast cancer management,” Med. Phys. 35, 2443–2451 (2008). [CrossRef]
  4. D. Leff, O. Warren, L. Enfield, A. Gibson, T. Athanasiou, D. Patten, J. Hebden, G. Yang, and A. Darzi, “Diffuse optical imaging of the healthy and diseased breast: a systematic review,” Breast Cancer Res. Tr. 108, 9–22 (2008).
  5. J. P. Culver, A. M. Siegel, J. J. Stott, and D. A. Boas, “Volumetric diffuse optical tomography of brain activity,” Opt. Lett. 28, 2061–2063 (2003). [CrossRef]
  6. 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]
  7. T. Näsi, H. Mäki, P. Hiltunen, J. Heiskala, I. Nissilä, K. Kotilahti, and R. J. Ilmoniemi, “Effect of task-related extracerebral circulation on diffuse optical tomography: experimental data and simulations on the forehead,” Biomed. Opt. Express 4, 412–426 (2013). [CrossRef]
  8. G. Gulsen, O. Birgul, M. B. Unlu, R. Shafiiha, and O. Nalcioglu, “Combined diffuse optical tomography (DOT) and MRI system for cancer imaging in small animals,” Technol. Cancer Res. T. 5, 351–363 (2006).
  9. J. P. Culver, T. Durduran, D. Furuya, C. Cheung, J. H. Greenberg, and A. G. Yodh, “Diffuse optical tomography of cerebral blood flow, oxygenation and metabolism in rat during focal ischemia,” J. Cereb. Blood Flow Metab. 23, 911–924 (2003). [CrossRef]
  10. A. Gibson, R. M. Yusof, H. Dehghani, J. Riley, N. Everdell, R. Richards, J. C. Hebden, M. Schweiger, S. R. Arridge, and D. T. Delpy, “Optical tomography of a realistic neonatal head phantom,” Appl. Opt. 42, 3109–3116 (2003). [CrossRef]
  11. A. H. Barnett, J. P. Culver, A. G. Sorensen, A. Dale, and D. A. Boas, “Robust inference of baseline optical properties of the human head with three-dimensional segmentation from magnetic resonance imaging,” Appl. Opt. 42, 3095–3108 (2003). [CrossRef]
  12. B. W. Pogue and K. D. Paulsen, “High-resolution near-infrared tomographic imaging simulations of the rat cranium by use of a priori magnetic resonance imaging structural information,” Opt. Lett. 23, 1716–1718 (1998). [CrossRef]
  13. E. M. C. Hillman, J. C. Hebden, F. E. W. Schmidt, S. R. Arridge, M. Schweiger, H. Dehghani, and D. T. Delpy, “Calibration techniques and datatype extraction for time-resolved optical tomography,” Rev. Sci. Instrum. 71, 3415–3427 (2000). [CrossRef]
  14. A. P. Gibson, J. Riley, M. Schweiger, J. C. Hebden, S. R. Arridge, and D. T. Delpy, “A method for generating patient-specific finite element meshes for head modelling,” Phys. Med. Biol. 48, 481–495 (2003). [CrossRef]
  15. A. Bluestone, G. Abdoulaev, C. Schmitz, R. Barbour, and A. Hielscher, “Three-dimensional optical tomography of hemodynamics in the human head,” Opt. Express 9, 272–286 (2001). [CrossRef]
  16. J. Kaipio and E. Somersalo, Statistical and Computational Inverse Problems (Springer, 2005).
  17. J. Kaipio and E. Somersalo, “Discretization model reduction and inverse crimes,” J. Comput. Appl. Math. 198, 493–504 (2007). [CrossRef]
  18. V. Kolehmainen, T. Tarvainen, and S. R. Arridge, “Marginalization of uninteresting distributed parameters in inverse problems–application to diffuse optical tomography,” Int. J. Uncertainty Quantif. 1, 1–17 (2011). [CrossRef]
  19. S. R. Arridge, J. P. Kaipio, V. Kolehmainen, M. Schweiger, E. Somersalo, T. Tarvainen, and M. Vauhkonen, “Approximation errors and model reduction with an application in optical diffusion tomography,” Inverse Probl. 22, 175–195 (2006). [CrossRef]
  20. V. Kolehmainen, M. Schweiger, I. Nissilä, T. Tarvainen, S. R. Arridge, and J. P. Kaipio, “Approximation errors and model reduction in three-dimensional diffuse optical tomography,” J. Opt. Soc. Am. A 26, 2257–2268 (2009). [CrossRef]
  21. T. Tarvainen, V. Kolehmainen, A. Pulkkinen, M. Vauhkonen, M. Schweiger, S. R. Arridge, and J. P. Kaipio, “An approximation error approach for compensating for modelling errors between the radiative transfer equation and the diffusion approximation in diffuse optical tomography,” Inverse Probl. 26, 015005 (2010). [CrossRef]
  22. T. Tarvainen, V. Kolehmainen, J. P. Kaipio, and S. R. Arridge, “Corrections to linear methods for diffuse optical tomography using approximation error modelling,” Biomed. Opt. Express 1, 209–222 (2010). [CrossRef]
  23. J. Heiskala, V. Kolehmainen, T. Tarvainen, J. P. Kaipio, and S. R. Arridge, “Approximation error method can reduce artifacts due to scalp blood flow in optical brain activation imaging,” J. Biomed. Opt. 17, 0960121 (2012). [CrossRef]
  24. M. Mozumder, T. Tarvainen, S. R. Arridge, J. Kaipio, and V. Kolehmainen, “Compensation of optode sensitivity and position errors in diffuse optical tomography using the approximation error approach,” Biomed. Opt. Express 4, 2015–2031 (2013). [CrossRef]
  25. J. Heino, E. Somersalo, and J. Kaipio, “Compensation for geometric mismodelling by anisotropies in optical tomography,” Opt. Express 13, 296–308 (2005). [CrossRef]
  26. J. Sylvester, “An anisotropic inverse boundary value problem,” Comm. Pure Appl. Math. 43, 201–232 (1990). [CrossRef]
  27. V. Kolehmainen, M. Lassas, and P. Ola, “The inverse conductivity problem with an imperfectly known boundary,” SIAM J. Appl. Math. 66, 365–383 (2005). [CrossRef]
  28. A. Nissinen, V. P. Kolehmainen, and J. P. Kaipio, “Compensation of modelling errors due to unknown domain boundary in electrical impedance tomography,” IEEE Trans. Med. Imaging 30, 231–242 (2011). [CrossRef]
  29. A. Ishimaru, Wave Propagation and Scattering in Random Media (Academic, 1978).
  30. H. Rue and L. Held, Gaussian Markov Random Fields: Theory and Applications, Vol. 104 of Monographs on Statistics and Applied Probability (Chapman & Hall, 2005).
  31. C. Lieberman, K. Willcox, and O. Ghattas, “Parameter and state model reduction for large-scale statistical inverse problems,” SIAM J. Sci. Comput. 32, 2523–2542 (2010). [CrossRef]
  32. M. Schweiger, S. R. Arridge, and I. Nissilä, “Gauss-Newton method for image reconstruction in diffuse optical tomography,” Phys. Med. Biol. 50, 2365–2386 (2005). [CrossRef]

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