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Reduction of Poisson noise in measured time-resolved data for time-domain diffuse optical tomography

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Abstract

A method to reduce noise for time-domain diffuse optical tomography (DOT) is proposed. Poisson noise which contaminates time-resolved photon counting data is reduced by use of maximum a posteriori estimation. The noise-free data are modeled as a Markov random process, and the measured time-resolved data are assumed as Poisson distributed random variables. The posterior probability of the occurrence of the noise-free data is formulated. By maximizing the probability, the noise-free data are estimated, and the Poisson noise is reduced as a result. The performances of the Poisson noise reduction are demonstrated in some experiments of the image reconstruction of time-domain DOT. In simulations, the proposed method reduces the relative error between the noise-free and noisy data to about one thirtieth, and the reconstructed DOT image was smoothed by the proposed noise reduction. The variance of the reconstructed absorption coefficients decreased by 22% in a phantom experiment. The quality of DOT, which can be applied to breast cancer screening etc., is improved by the proposed noise reduction.

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References

  1. Arridge SR (1999) Optical tomography in medical imaging. Inverse Prob 15:R41–R93

    Article  Google Scholar 

  2. Boas DA, Brooks DH, Miller EL, DiMarzio CA, Kilmer M, Gaudette RJ, Zhang Q (2001) Imaging the body with diffuse optical tomography. IEEE Signal Process Mag 18(6):57–75

    Article  Google Scholar 

  3. Boverman G, Miller EL, Li A, Zhang Q, Chaves T, Brooks DH, Boas DA (2005) Quantitative spectroscopic optical tomography of the breast guided by imperfect a priori structural information. Phys Med Biol 50:3941–3956

    Article  PubMed  Google Scholar 

  4. Bowsher JE, Johnson VE, Turkington TG, Jaszczak RJ, Floyd CE, Coleman RE (1996) Bayesian reconstruction and use of anatomical a priori information for emission tomography. IEEE Trans Med Imaging 15(5):673–686

    Article  PubMed  CAS  Google Scholar 

  5. Eda H, Oda I, Ito Y, Wada Y, Oikawa Y, Tsunazawa Y, Tuchiya Y, Yamashita Y, Oda M, Sassaroli A, Yamada Y, Tamura M (1999) Multichannel time-resolved optical tomographic imaging system. Rev Sci Instrum 70(9):3595–3602

    Article  CAS  Google Scholar 

  6. Gibson AP, Hebden JC, Arridge SR (2005) Recent advances in diffuse optical imaging. Phys Med Biol 50:R1–R43

    Article  PubMed  CAS  Google Scholar 

  7. Gibson AP, Austin T, Everdell NL, Schweiger M, Arridge SR, Meek JH, Wyatt JS, Delpy DT, Hebden JC (2006) Three-dimensional whole-head optical tomography of passive motor evoked responses in the neonate. NueroImage 30:521–528

    Article  CAS  Google Scholar 

  8. Green PJ (1990) Bayesian reconstruction from emission tomography data using modified EM algorithm. IEEE Trans Med Imaging 9(1):84–93

    Article  PubMed  CAS  Google Scholar 

  9. Grosenick D, Wabnitz H, Rinneberg HH, Moesta T, Schlag PM (1999) Development of a time-domain optical mammography and first in vivo applications. Appl Opt 38(13):2927–2943

    Article  PubMed  CAS  Google Scholar 

  10. Grosenick D, Moesta KT, Möller M, Mucke J, Wabnitz H, Gebauer B, Stroszczynski C, Wassermann B, Schlag PM, Rinneberg H (2005) Time-domain scanning optical mammography: I Recording and assessment of mammograms of 154 patients. Phys Med Biol 154(50):2429–2449

    Article  Google Scholar 

  11. Grosenick D, Wabnitz H, Moesta KT, Mucke J, Schlag PM, Rinneberg H (2005) Time-domain scanning optical mammography: II Optical properties and tissue parameters of carcinomas. Phys Med Biol 87(50):2451–2468

    Article  Google Scholar 

  12. Hawrysz DJ, Sevick-Muraca EM (2000) Developments toward diagnostic breast cancer imaging using near-infrared optical measurements and fluorescent contrast agents. Neoplasia 2(5):388–417

    Article  PubMed  CAS  Google Scholar 

  13. Hebden JC, Veenstra H, Dehghani H, Hillman EMC, Schweiger M, Arridge SR, Delpy DT (2001) Three-dimensional time-resolved optical tomography of a conical breast phantom. Appl Opt 40(19):3278–32887

    Article  PubMed  CAS  Google Scholar 

  14. Hebden JC, Gibson A, Yusof RM, Everdell N, Hillman EMC, Delpy DT, Arridge SR, Austin T, Meek JH, Wyatt JS (2002) Three-dimensional optical tomography of the premature infant brain. Phys Med Biol 47:4155–4166

    Article  PubMed  Google Scholar 

  15. Kalman RE (1960) A new approach to linear filtering and prediction problems. Trans ASME D 82:35–45

    Article  Google Scholar 

  16. Logothetis A, Krishnamurthy V (1999) Expectation maximization algorithm for MAP estimation of jump Markov linear systems. IEEE Trans Signal Process 47(8):2139–2156

    Article  Google Scholar 

  17. Marjono A, Okawa S, Gao F, Yamada Y (2007) Light propagation for time-domain fluorescence diffuse optical tomography by convolution using lifetime function. Opt Rev 14(3):131–138

    Article  Google Scholar 

  18. O’Connor DV, Phillips D (1985) Time-correlated single-photon counting. Academic Press, London

    Google Scholar 

  19. Okawa S, Honda S (2005) Reduction of noise from magnetoencephalography data. Med Biol Eng Comput 43:630–637

    Article  PubMed  CAS  Google Scholar 

  20. Qi J (2004) Analysis of lesion detectability in Bayesian emission reconstruction with nonstationary object variability. IEEE Trans Med Imaging 23(3):321–329

    Article  PubMed  Google Scholar 

  21. Schweiger M, Arridge SR, Delpy DT (1993) Application of the finite-element method for the forward and inverse model in optical tomography. J Math Imaging Vis 3:263–283

    Article  Google Scholar 

  22. Vogel CR (2002) Computational method for inverse problems. SIAM, Philadelphia

    Book  Google Scholar 

  23. Yates T, Hebdan C, Gibson A, Everdell N, Arridge SR, Douek M (2005) Optical tomography of the breast using a multi-channel time-resolved imager. Phys Med Biol 50:2503–2517

    Article  PubMed  Google Scholar 

  24. Ye JC, Bouman CA, Webb KJ, Millane RP (2001) Nonlinear multigrid algorithms for Bayesian optical diffusion tomography. IEEE Trans Image Process 10(5):909–922

    Google Scholar 

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Authors and Affiliations

  1. Department of Mechanical Engineering and Intelligent Systems, The University of Electro-Communications, 1-5-1 Chofugaoka, Chofu, Tokyo, 182-8585, Japan

    S. Okawa, Y. Endo & Y. Yamada

  2. Tokyo Institute of Psychiatry, 2-1-8 Kamikitazawa, Setagaya-ku, Tokyo, 156-8585, Japan

    Y. Hoshi

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  1. S. Okawa
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  2. Y. Endo
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  3. Y. Hoshi
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Correspondence to S. Okawa.

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Okawa, S., Endo, Y., Hoshi, Y. et al. Reduction of Poisson noise in measured time-resolved data for time-domain diffuse optical tomography. Med Biol Eng Comput 50, 69–78 (2012). https://doi.org/10.1007/s11517-011-0774-7

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  • DOI: https://doi.org/10.1007/s11517-011-0774-7

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