Abstract

Multispectral optoacoustic (photoacoustic) tomography (MSOT) is a hybrid modality that can image through several millimeters to centimeters of diffuse tissues, attaining resolutions typical of ultrasound imaging. The method can further identify tissue biomarkers by decomposing the spectral contributions of different photo-absorbing molecules of interest. In this work we investigate the performance of blind source unmixing methods and spectral fitting approaches in decomposing the contributions of fluorescent dyes from the tissue background, based on MSOT measurements in mice. We find blind unmixing as a promising method for accurate MSOT decomposition, suitable also for spectral unmixing in fluorescence imaging. We further demonstrate its capacity with temporal unmixing on real-time MSOT data obtained in-vivo for enhancing the visualization of absorber agent flow in the mouse vascular system.

© 2011 Optical Society of America

Full Article  |  PDF Article

References

  • View by:
  • |
  • |
  • |

  1. H. Zhang, K. Maslov, G. Stoica, and L. Wang, “Functional photoacoustic microscopy for high-resolution and noninvasive in vivo imaging,” Nat. Biotechnol. 24, 848–851 (2006).
    [CrossRef] [PubMed]
  2. M. Xu, and L. Wang, “Photoacoustic imaging in biomedicine,” Rev. Sci. Instrum. 77, 041101 (2006).
    [CrossRef]
  3. V. Ntziachristos, “Going deeper than microscopy: the optical imaging frontier in biology,” Nat. Methods 7, 603–614 (2010).
    [CrossRef] [PubMed]
  4. C. G. A. Hoelen, F. F. M. de Mul, R. Pongers, and A. Dekker, “Three-dimensional photoacoustic imaging of blood vessels in tissue,” Opt. Lett. 23, 648–650 (1998).
    [CrossRef]
  5. H. Fang, K. Maslov, and L. V. Wang, “Photoacoustic doppler effect from flowing small light-absorbing particles,” Phys. Rev. Lett. 99, 184501 (2007).
    [CrossRef] [PubMed]
  6. P.-C. Li, S.-W. Huang, C.-W. Wei, Y.-C. Chiou, C.-D. Chen, and C.-R. C. Wang, “Photoacoustic flow measurements by use of laser-induced shape transitions of gold nanorods,” Opt. Lett. 30, 3341–3343 (2005).
    [CrossRef]
  7. V. Ntziachristos, and D. Razansky, “Molecular imaging by means of multispectral optoacoustic tomography (MSOT),” Chem. Rev. 110, 2783–2794 (2010).
    [CrossRef] [PubMed]
  8. H.-P. Brecht, R. Su, M. Fronheiser, S. A. Ermilov, A. Conjusteau, and A. A. Oraevsky, “Whole-body threedimensional optoacoustic tomography system for small animals,” J. Biomed. Opt. 14, 064007 (2009).
    [CrossRef]
  9. J. Gamelin, A. Maurudis, A. Aguirre, F. Huang, P. Guo, L. V. Wang, and Q. Zhu, “A real-time photoacoustic tomography system for small animals,” Opt. Express 17, 10489–10498 (2009).
    [CrossRef] [PubMed]
  10. A. Buehler, E. Herzog, D. Razansky, and V. Ntziachristos, “Video rate optoacoustic tomography of mouse kidney perfusion,” Opt. Lett. 35, 2475–2477 (2010).
    [CrossRef] [PubMed]
  11. G. Busse, and A. Rosencwaig, “Subsurface imaging with photoacoustics,” Appl. Phys. Lett. 36, 815–816 (1980).
    [CrossRef]
  12. A. Rosencwaig, “Potential clinical applications of photoacoustics,” Clin. Chem. 28, 1878–1881 (1982).
    [PubMed]
  13. X. Wang, X. Xie, G. Ku, L. V. Wang, and G. Stoica, “Noninvasive imaging of hemoglobin concentration and oxygenation in the rat brain using high-resolution photoacoustic tomography,” J. Biomed. Opt. 11, 024015 (2006).
    [CrossRef] [PubMed]
  14. D. Razansky, C. Vinegoni, and V. Ntziachristos, “Multispectral photoacoustic imaging of fluorochromes in small animals,” Opt. Lett. 32, 2891–2893 (2007).
    [CrossRef] [PubMed]
  15. D. Razansky, M. Distel, C. Vinegoni, R. Ma, M. Perrimon, R. W. Koster, and V. Ntziachristos, “Multispectral opto-acoustic tomography of deep-seated fluorescent proteins in vivo,” Nat. Photonics 3, 412–417 (2009).
    [CrossRef]
  16. P.-C. Li, C.-R. C. Wang, D.-B. Shieh, C.-W. Wei, C.-K. Liao, C. Poe, S. Jhan, A.-A. Ding, and Y.-N. Wu, “In vivo photoacoustic molecular imaging withsimultaneous multiple selective targeting using antibody-conjugated gold nanorods,” Opt. Express 16, 18605–18615 (2008).
    [CrossRef]
  17. A. Taruttis, E. Herzog, D. Razansky, and V. Ntziachristos, “Real-time imaging of cardiovascular dynamics and circulating gold nanorods with multispectral optoacoustic tomography,” Opt. Express 18, 19592–19602 (2010).
    [CrossRef] [PubMed]
  18. D. Razansky, J. Baeten, and V. Ntziachristos, “Sensitivity of molecular target detection by multispectral optoacoustic tomography (MSOT),” Med. Phys. 36, 939–945 (2009).
    [CrossRef] [PubMed]
  19. A. Rosenthal, D. Razansky, and V. Ntziachristos, “Quantitative optoacoustic signal extraction using sparse signal representation,” IEEE Trans. Med. Imaging 28, 1997–2006 (2009).
    [CrossRef] [PubMed]
  20. I. T. Jolliffe, Principal Component Analysis, 2nd ed. (Springer, 2002).
  21. A. Cichocki, R. Zdunek, A. H. Phan, and S. I. Amari, Nonnegative Matrix and Tensor Factorizations: Applications to Exploratory Multi-way Data Analysis and Blind Source Separation, 1st ed. (Wiley, 2009).
    [PubMed]
  22. R. Tauler, B. Kowalski, and S. Fleming, “Multivariate curve resolution applied to spectral data from multiple runs of an industrial process,” Anal. Chem. 65, 2040–2047 (1993).
    [CrossRef]
  23. A. Hyvärinen, J. Karhunen, and E. Oja, Independent Component Analysis, Adaptive and Learning Systems for Signal Processing, Communications, and Control, 1st ed. (Wiley InterScience, 2002).
  24. M. Funaro, E. Oja, and H. Valpola, “Independent component analysis for artefact separation in astrophysical images,” Neural Netw. 16, 469–478 (2003).
    [CrossRef] [PubMed]
  25. B. A. Draper, K. Baek, M. S. Bartlett, and J. R. Beveridge, “Recognizing faces with pca and ica,” Comput. Vis. Image Underst. 91, 115–137 (2003).
    [CrossRef]
  26. J. Yang, D. Zhang, A. F. Frangi, and J. Y. Yang, “Two-dimensional pca: a new approach to appearance-based face representation and recognition,” IEEE Trans. Pattern Anal. Mach. Intell. 26, 131–137 (2004).
    [CrossRef] [PubMed]
  27. E. M. C. Hillman, and A. Moore, “All optical anatomical co registration for molecular imaging of small animals using dynamic contrast,” Nat. Photonics 1(9), 526–530 (2007).
    [CrossRef]
  28. H. Xu, and B. W. Rice, “In-vivo fluorescence imaging with a multivariate curve resolution spectral unmixing technique,” J. Biomed. Opt. 14, 064011 (2009).
    [CrossRef]
  29. A.-S. Montcuquet, L. Herv’e, F. Navarro, J.-M. Dinten, and J. I. Mars, “Nonnegative matrix factorization: a blind spectra separation method for in vivo fluorescent optical imaging,” J. Biomed. Opt. 15, 056009 (2010).
    [CrossRef] [PubMed]
  30. S. Clémençon, and S. Slim, “On portfolio selection under extreme risk measure: the heavy-tailed ICA Model,” Int. J. Theor. Appl. Finance 10, 449–474 (2007).
    [CrossRef]
  31. N. Keshava, “A survey of spectral unmixing algorithms,” Lincoln Lab. J. 14, 55–78 (2003).
  32. E. Moore, “On the reciprocal of the general algebraic matrix,” Bull. Am. Math. Soc. 26, 394–395 (1920).
  33. R. Penrose, “A generalized inverse for matrices,” in Proceedings of the Cambridge Philosophical Society (1955) Vol. 51, pp. 406–412.
    [CrossRef]
  34. K. Pearson, “On lines and planes of closest fit to a system of points in space,” London, Edinburgh Dublin Philos, Mag. J. Sci. 6, 559–572 (1901).
  35. S. M. Kay, Fundamentals of Statistical Signal Processing, 1st ed. (Prentice Hall PTR, 1993),Vol. 1.
  36. J. Nash, “The singular-value decomposition and its use to solve least-squares problems,” in Compact Numerical Methods for Computers: Linear Algebra and Function Minimization, 2nd ed. (Inst. of Physics Pub., 1990), pp. 30–48.
  37. L. Le Cam, “The central limit theorem around 1935,” Stat. Sci. 1, 78–91 (1986).
    [CrossRef]
  38. A. Hyvrinen, “Fast and robust fixed-point algorithms for independent component analysis,” IEEE Trans. Neural Netw. 10, 626–634 (1999).
    [CrossRef]
  39. M. Xu, and L. V. Wang, “Universal back-projection algorithm for photoacoustic computed tomography,” Phys. Rev. E Stat. Nonlin. Soft Matter Phys. 71, 016706 (2005).
    [CrossRef]

2010 (5)

V. Ntziachristos, “Going deeper than microscopy: the optical imaging frontier in biology,” Nat. Methods 7, 603–614 (2010).
[CrossRef] [PubMed]

V. Ntziachristos, and D. Razansky, “Molecular imaging by means of multispectral optoacoustic tomography (MSOT),” Chem. Rev. 110, 2783–2794 (2010).
[CrossRef] [PubMed]

A.-S. Montcuquet, L. Herv’e, F. Navarro, J.-M. Dinten, and J. I. Mars, “Nonnegative matrix factorization: a blind spectra separation method for in vivo fluorescent optical imaging,” J. Biomed. Opt. 15, 056009 (2010).
[CrossRef] [PubMed]

A. Buehler, E. Herzog, D. Razansky, and V. Ntziachristos, “Video rate optoacoustic tomography of mouse kidney perfusion,” Opt. Lett. 35, 2475–2477 (2010).
[CrossRef] [PubMed]

A. Taruttis, E. Herzog, D. Razansky, and V. Ntziachristos, “Real-time imaging of cardiovascular dynamics and circulating gold nanorods with multispectral optoacoustic tomography,” Opt. Express 18, 19592–19602 (2010).
[CrossRef] [PubMed]

2009 (6)

J. Gamelin, A. Maurudis, A. Aguirre, F. Huang, P. Guo, L. V. Wang, and Q. Zhu, “A real-time photoacoustic tomography system for small animals,” Opt. Express 17, 10489–10498 (2009).
[CrossRef] [PubMed]

H. Xu, and B. W. Rice, “In-vivo fluorescence imaging with a multivariate curve resolution spectral unmixing technique,” J. Biomed. Opt. 14, 064011 (2009).
[CrossRef]

H.-P. Brecht, R. Su, M. Fronheiser, S. A. Ermilov, A. Conjusteau, and A. A. Oraevsky, “Whole-body threedimensional optoacoustic tomography system for small animals,” J. Biomed. Opt. 14, 064007 (2009).
[CrossRef]

D. Razansky, M. Distel, C. Vinegoni, R. Ma, M. Perrimon, R. W. Koster, and V. Ntziachristos, “Multispectral opto-acoustic tomography of deep-seated fluorescent proteins in vivo,” Nat. Photonics 3, 412–417 (2009).
[CrossRef]

D. Razansky, J. Baeten, and V. Ntziachristos, “Sensitivity of molecular target detection by multispectral optoacoustic tomography (MSOT),” Med. Phys. 36, 939–945 (2009).
[CrossRef] [PubMed]

A. Rosenthal, D. Razansky, and V. Ntziachristos, “Quantitative optoacoustic signal extraction using sparse signal representation,” IEEE Trans. Med. Imaging 28, 1997–2006 (2009).
[CrossRef] [PubMed]

2008 (1)

2007 (4)

D. Razansky, C. Vinegoni, and V. Ntziachristos, “Multispectral photoacoustic imaging of fluorochromes in small animals,” Opt. Lett. 32, 2891–2893 (2007).
[CrossRef] [PubMed]

E. M. C. Hillman, and A. Moore, “All optical anatomical co registration for molecular imaging of small animals using dynamic contrast,” Nat. Photonics 1(9), 526–530 (2007).
[CrossRef]

S. Clémençon, and S. Slim, “On portfolio selection under extreme risk measure: the heavy-tailed ICA Model,” Int. J. Theor. Appl. Finance 10, 449–474 (2007).
[CrossRef]

H. Fang, K. Maslov, and L. V. Wang, “Photoacoustic doppler effect from flowing small light-absorbing particles,” Phys. Rev. Lett. 99, 184501 (2007).
[CrossRef] [PubMed]

2006 (3)

H. Zhang, K. Maslov, G. Stoica, and L. Wang, “Functional photoacoustic microscopy for high-resolution and noninvasive in vivo imaging,” Nat. Biotechnol. 24, 848–851 (2006).
[CrossRef] [PubMed]

M. Xu, and L. Wang, “Photoacoustic imaging in biomedicine,” Rev. Sci. Instrum. 77, 041101 (2006).
[CrossRef]

X. Wang, X. Xie, G. Ku, L. V. Wang, and G. Stoica, “Noninvasive imaging of hemoglobin concentration and oxygenation in the rat brain using high-resolution photoacoustic tomography,” J. Biomed. Opt. 11, 024015 (2006).
[CrossRef] [PubMed]

2005 (2)

P.-C. Li, S.-W. Huang, C.-W. Wei, Y.-C. Chiou, C.-D. Chen, and C.-R. C. Wang, “Photoacoustic flow measurements by use of laser-induced shape transitions of gold nanorods,” Opt. Lett. 30, 3341–3343 (2005).
[CrossRef]

M. Xu, and L. V. Wang, “Universal back-projection algorithm for photoacoustic computed tomography,” Phys. Rev. E Stat. Nonlin. Soft Matter Phys. 71, 016706 (2005).
[CrossRef]

2004 (1)

J. Yang, D. Zhang, A. F. Frangi, and J. Y. Yang, “Two-dimensional pca: a new approach to appearance-based face representation and recognition,” IEEE Trans. Pattern Anal. Mach. Intell. 26, 131–137 (2004).
[CrossRef] [PubMed]

2003 (3)

M. Funaro, E. Oja, and H. Valpola, “Independent component analysis for artefact separation in astrophysical images,” Neural Netw. 16, 469–478 (2003).
[CrossRef] [PubMed]

B. A. Draper, K. Baek, M. S. Bartlett, and J. R. Beveridge, “Recognizing faces with pca and ica,” Comput. Vis. Image Underst. 91, 115–137 (2003).
[CrossRef]

N. Keshava, “A survey of spectral unmixing algorithms,” Lincoln Lab. J. 14, 55–78 (2003).

1999 (1)

A. Hyvrinen, “Fast and robust fixed-point algorithms for independent component analysis,” IEEE Trans. Neural Netw. 10, 626–634 (1999).
[CrossRef]

1998 (1)

1993 (1)

R. Tauler, B. Kowalski, and S. Fleming, “Multivariate curve resolution applied to spectral data from multiple runs of an industrial process,” Anal. Chem. 65, 2040–2047 (1993).
[CrossRef]

1986 (1)

L. Le Cam, “The central limit theorem around 1935,” Stat. Sci. 1, 78–91 (1986).
[CrossRef]

1982 (1)

A. Rosencwaig, “Potential clinical applications of photoacoustics,” Clin. Chem. 28, 1878–1881 (1982).
[PubMed]

1980 (1)

G. Busse, and A. Rosencwaig, “Subsurface imaging with photoacoustics,” Appl. Phys. Lett. 36, 815–816 (1980).
[CrossRef]

1920 (1)

E. Moore, “On the reciprocal of the general algebraic matrix,” Bull. Am. Math. Soc. 26, 394–395 (1920).

1901 (1)

K. Pearson, “On lines and planes of closest fit to a system of points in space,” London, Edinburgh Dublin Philos, Mag. J. Sci. 6, 559–572 (1901).

Aguirre, A.

Baek, K.

B. A. Draper, K. Baek, M. S. Bartlett, and J. R. Beveridge, “Recognizing faces with pca and ica,” Comput. Vis. Image Underst. 91, 115–137 (2003).
[CrossRef]

Baeten, J.

D. Razansky, J. Baeten, and V. Ntziachristos, “Sensitivity of molecular target detection by multispectral optoacoustic tomography (MSOT),” Med. Phys. 36, 939–945 (2009).
[CrossRef] [PubMed]

Bartlett, M. S.

B. A. Draper, K. Baek, M. S. Bartlett, and J. R. Beveridge, “Recognizing faces with pca and ica,” Comput. Vis. Image Underst. 91, 115–137 (2003).
[CrossRef]

Beveridge, J. R.

B. A. Draper, K. Baek, M. S. Bartlett, and J. R. Beveridge, “Recognizing faces with pca and ica,” Comput. Vis. Image Underst. 91, 115–137 (2003).
[CrossRef]

Brecht, H.-P.

H.-P. Brecht, R. Su, M. Fronheiser, S. A. Ermilov, A. Conjusteau, and A. A. Oraevsky, “Whole-body threedimensional optoacoustic tomography system for small animals,” J. Biomed. Opt. 14, 064007 (2009).
[CrossRef]

Buehler, A.

Busse, G.

G. Busse, and A. Rosencwaig, “Subsurface imaging with photoacoustics,” Appl. Phys. Lett. 36, 815–816 (1980).
[CrossRef]

Chen, C.-D.

Chiou, Y.-C.

Clémençon, S.

S. Clémençon, and S. Slim, “On portfolio selection under extreme risk measure: the heavy-tailed ICA Model,” Int. J. Theor. Appl. Finance 10, 449–474 (2007).
[CrossRef]

Conjusteau, A.

H.-P. Brecht, R. Su, M. Fronheiser, S. A. Ermilov, A. Conjusteau, and A. A. Oraevsky, “Whole-body threedimensional optoacoustic tomography system for small animals,” J. Biomed. Opt. 14, 064007 (2009).
[CrossRef]

de Mul, F. F. M.

Dekker, A.

Ding, A.-A.

Dinten, J.-M.

A.-S. Montcuquet, L. Herv’e, F. Navarro, J.-M. Dinten, and J. I. Mars, “Nonnegative matrix factorization: a blind spectra separation method for in vivo fluorescent optical imaging,” J. Biomed. Opt. 15, 056009 (2010).
[CrossRef] [PubMed]

Distel, M.

D. Razansky, M. Distel, C. Vinegoni, R. Ma, M. Perrimon, R. W. Koster, and V. Ntziachristos, “Multispectral opto-acoustic tomography of deep-seated fluorescent proteins in vivo,” Nat. Photonics 3, 412–417 (2009).
[CrossRef]

Draper, B. A.

B. A. Draper, K. Baek, M. S. Bartlett, and J. R. Beveridge, “Recognizing faces with pca and ica,” Comput. Vis. Image Underst. 91, 115–137 (2003).
[CrossRef]

Ermilov, S. A.

H.-P. Brecht, R. Su, M. Fronheiser, S. A. Ermilov, A. Conjusteau, and A. A. Oraevsky, “Whole-body threedimensional optoacoustic tomography system for small animals,” J. Biomed. Opt. 14, 064007 (2009).
[CrossRef]

Fang, H.

H. Fang, K. Maslov, and L. V. Wang, “Photoacoustic doppler effect from flowing small light-absorbing particles,” Phys. Rev. Lett. 99, 184501 (2007).
[CrossRef] [PubMed]

Fleming, S.

R. Tauler, B. Kowalski, and S. Fleming, “Multivariate curve resolution applied to spectral data from multiple runs of an industrial process,” Anal. Chem. 65, 2040–2047 (1993).
[CrossRef]

Frangi, A. F.

J. Yang, D. Zhang, A. F. Frangi, and J. Y. Yang, “Two-dimensional pca: a new approach to appearance-based face representation and recognition,” IEEE Trans. Pattern Anal. Mach. Intell. 26, 131–137 (2004).
[CrossRef] [PubMed]

Fronheiser, M.

H.-P. Brecht, R. Su, M. Fronheiser, S. A. Ermilov, A. Conjusteau, and A. A. Oraevsky, “Whole-body threedimensional optoacoustic tomography system for small animals,” J. Biomed. Opt. 14, 064007 (2009).
[CrossRef]

Funaro, M.

M. Funaro, E. Oja, and H. Valpola, “Independent component analysis for artefact separation in astrophysical images,” Neural Netw. 16, 469–478 (2003).
[CrossRef] [PubMed]

Gamelin, J.

Guo, P.

Herv’e, L.

A.-S. Montcuquet, L. Herv’e, F. Navarro, J.-M. Dinten, and J. I. Mars, “Nonnegative matrix factorization: a blind spectra separation method for in vivo fluorescent optical imaging,” J. Biomed. Opt. 15, 056009 (2010).
[CrossRef] [PubMed]

Herzog, E.

Hillman, E. M. C.

E. M. C. Hillman, and A. Moore, “All optical anatomical co registration for molecular imaging of small animals using dynamic contrast,” Nat. Photonics 1(9), 526–530 (2007).
[CrossRef]

Hoelen, C. G. A.

Huang, F.

Huang, S.-W.

Hyvrinen, A.

A. Hyvrinen, “Fast and robust fixed-point algorithms for independent component analysis,” IEEE Trans. Neural Netw. 10, 626–634 (1999).
[CrossRef]

Jhan, S.

Keshava, N.

N. Keshava, “A survey of spectral unmixing algorithms,” Lincoln Lab. J. 14, 55–78 (2003).

Koster, R. W.

D. Razansky, M. Distel, C. Vinegoni, R. Ma, M. Perrimon, R. W. Koster, and V. Ntziachristos, “Multispectral opto-acoustic tomography of deep-seated fluorescent proteins in vivo,” Nat. Photonics 3, 412–417 (2009).
[CrossRef]

Kowalski, B.

R. Tauler, B. Kowalski, and S. Fleming, “Multivariate curve resolution applied to spectral data from multiple runs of an industrial process,” Anal. Chem. 65, 2040–2047 (1993).
[CrossRef]

Ku, G.

X. Wang, X. Xie, G. Ku, L. V. Wang, and G. Stoica, “Noninvasive imaging of hemoglobin concentration and oxygenation in the rat brain using high-resolution photoacoustic tomography,” J. Biomed. Opt. 11, 024015 (2006).
[CrossRef] [PubMed]

Le Cam, L.

L. Le Cam, “The central limit theorem around 1935,” Stat. Sci. 1, 78–91 (1986).
[CrossRef]

Li, P.-C.

Liao, C.-K.

Ma, R.

D. Razansky, M. Distel, C. Vinegoni, R. Ma, M. Perrimon, R. W. Koster, and V. Ntziachristos, “Multispectral opto-acoustic tomography of deep-seated fluorescent proteins in vivo,” Nat. Photonics 3, 412–417 (2009).
[CrossRef]

Mars, J. I.

A.-S. Montcuquet, L. Herv’e, F. Navarro, J.-M. Dinten, and J. I. Mars, “Nonnegative matrix factorization: a blind spectra separation method for in vivo fluorescent optical imaging,” J. Biomed. Opt. 15, 056009 (2010).
[CrossRef] [PubMed]

Maslov, K.

H. Fang, K. Maslov, and L. V. Wang, “Photoacoustic doppler effect from flowing small light-absorbing particles,” Phys. Rev. Lett. 99, 184501 (2007).
[CrossRef] [PubMed]

H. Zhang, K. Maslov, G. Stoica, and L. Wang, “Functional photoacoustic microscopy for high-resolution and noninvasive in vivo imaging,” Nat. Biotechnol. 24, 848–851 (2006).
[CrossRef] [PubMed]

Maurudis, A.

Montcuquet, A.-S.

A.-S. Montcuquet, L. Herv’e, F. Navarro, J.-M. Dinten, and J. I. Mars, “Nonnegative matrix factorization: a blind spectra separation method for in vivo fluorescent optical imaging,” J. Biomed. Opt. 15, 056009 (2010).
[CrossRef] [PubMed]

Moore, A.

E. M. C. Hillman, and A. Moore, “All optical anatomical co registration for molecular imaging of small animals using dynamic contrast,” Nat. Photonics 1(9), 526–530 (2007).
[CrossRef]

Moore, E.

E. Moore, “On the reciprocal of the general algebraic matrix,” Bull. Am. Math. Soc. 26, 394–395 (1920).

Navarro, F.

A.-S. Montcuquet, L. Herv’e, F. Navarro, J.-M. Dinten, and J. I. Mars, “Nonnegative matrix factorization: a blind spectra separation method for in vivo fluorescent optical imaging,” J. Biomed. Opt. 15, 056009 (2010).
[CrossRef] [PubMed]

Ntziachristos, V.

A. Buehler, E. Herzog, D. Razansky, and V. Ntziachristos, “Video rate optoacoustic tomography of mouse kidney perfusion,” Opt. Lett. 35, 2475–2477 (2010).
[CrossRef] [PubMed]

V. Ntziachristos, and D. Razansky, “Molecular imaging by means of multispectral optoacoustic tomography (MSOT),” Chem. Rev. 110, 2783–2794 (2010).
[CrossRef] [PubMed]

A. Taruttis, E. Herzog, D. Razansky, and V. Ntziachristos, “Real-time imaging of cardiovascular dynamics and circulating gold nanorods with multispectral optoacoustic tomography,” Opt. Express 18, 19592–19602 (2010).
[CrossRef] [PubMed]

V. Ntziachristos, “Going deeper than microscopy: the optical imaging frontier in biology,” Nat. Methods 7, 603–614 (2010).
[CrossRef] [PubMed]

D. Razansky, M. Distel, C. Vinegoni, R. Ma, M. Perrimon, R. W. Koster, and V. Ntziachristos, “Multispectral opto-acoustic tomography of deep-seated fluorescent proteins in vivo,” Nat. Photonics 3, 412–417 (2009).
[CrossRef]

D. Razansky, J. Baeten, and V. Ntziachristos, “Sensitivity of molecular target detection by multispectral optoacoustic tomography (MSOT),” Med. Phys. 36, 939–945 (2009).
[CrossRef] [PubMed]

A. Rosenthal, D. Razansky, and V. Ntziachristos, “Quantitative optoacoustic signal extraction using sparse signal representation,” IEEE Trans. Med. Imaging 28, 1997–2006 (2009).
[CrossRef] [PubMed]

D. Razansky, C. Vinegoni, and V. Ntziachristos, “Multispectral photoacoustic imaging of fluorochromes in small animals,” Opt. Lett. 32, 2891–2893 (2007).
[CrossRef] [PubMed]

Oja, E.

M. Funaro, E. Oja, and H. Valpola, “Independent component analysis for artefact separation in astrophysical images,” Neural Netw. 16, 469–478 (2003).
[CrossRef] [PubMed]

Oraevsky, A. A.

H.-P. Brecht, R. Su, M. Fronheiser, S. A. Ermilov, A. Conjusteau, and A. A. Oraevsky, “Whole-body threedimensional optoacoustic tomography system for small animals,” J. Biomed. Opt. 14, 064007 (2009).
[CrossRef]

Pearson, K.

K. Pearson, “On lines and planes of closest fit to a system of points in space,” London, Edinburgh Dublin Philos, Mag. J. Sci. 6, 559–572 (1901).

Perrimon, M.

D. Razansky, M. Distel, C. Vinegoni, R. Ma, M. Perrimon, R. W. Koster, and V. Ntziachristos, “Multispectral opto-acoustic tomography of deep-seated fluorescent proteins in vivo,” Nat. Photonics 3, 412–417 (2009).
[CrossRef]

Poe, C.

Pongers, R.

Razansky, D.

A. Buehler, E. Herzog, D. Razansky, and V. Ntziachristos, “Video rate optoacoustic tomography of mouse kidney perfusion,” Opt. Lett. 35, 2475–2477 (2010).
[CrossRef] [PubMed]

A. Taruttis, E. Herzog, D. Razansky, and V. Ntziachristos, “Real-time imaging of cardiovascular dynamics and circulating gold nanorods with multispectral optoacoustic tomography,” Opt. Express 18, 19592–19602 (2010).
[CrossRef] [PubMed]

V. Ntziachristos, and D. Razansky, “Molecular imaging by means of multispectral optoacoustic tomography (MSOT),” Chem. Rev. 110, 2783–2794 (2010).
[CrossRef] [PubMed]

D. Razansky, M. Distel, C. Vinegoni, R. Ma, M. Perrimon, R. W. Koster, and V. Ntziachristos, “Multispectral opto-acoustic tomography of deep-seated fluorescent proteins in vivo,” Nat. Photonics 3, 412–417 (2009).
[CrossRef]

A. Rosenthal, D. Razansky, and V. Ntziachristos, “Quantitative optoacoustic signal extraction using sparse signal representation,” IEEE Trans. Med. Imaging 28, 1997–2006 (2009).
[CrossRef] [PubMed]

D. Razansky, J. Baeten, and V. Ntziachristos, “Sensitivity of molecular target detection by multispectral optoacoustic tomography (MSOT),” Med. Phys. 36, 939–945 (2009).
[CrossRef] [PubMed]

D. Razansky, C. Vinegoni, and V. Ntziachristos, “Multispectral photoacoustic imaging of fluorochromes in small animals,” Opt. Lett. 32, 2891–2893 (2007).
[CrossRef] [PubMed]

Rice, B. W.

H. Xu, and B. W. Rice, “In-vivo fluorescence imaging with a multivariate curve resolution spectral unmixing technique,” J. Biomed. Opt. 14, 064011 (2009).
[CrossRef]

Rosencwaig, A.

A. Rosencwaig, “Potential clinical applications of photoacoustics,” Clin. Chem. 28, 1878–1881 (1982).
[PubMed]

G. Busse, and A. Rosencwaig, “Subsurface imaging with photoacoustics,” Appl. Phys. Lett. 36, 815–816 (1980).
[CrossRef]

Rosenthal, A.

A. Rosenthal, D. Razansky, and V. Ntziachristos, “Quantitative optoacoustic signal extraction using sparse signal representation,” IEEE Trans. Med. Imaging 28, 1997–2006 (2009).
[CrossRef] [PubMed]

Shieh, D.-B.

Slim, S.

S. Clémençon, and S. Slim, “On portfolio selection under extreme risk measure: the heavy-tailed ICA Model,” Int. J. Theor. Appl. Finance 10, 449–474 (2007).
[CrossRef]

Stoica, G.

X. Wang, X. Xie, G. Ku, L. V. Wang, and G. Stoica, “Noninvasive imaging of hemoglobin concentration and oxygenation in the rat brain using high-resolution photoacoustic tomography,” J. Biomed. Opt. 11, 024015 (2006).
[CrossRef] [PubMed]

H. Zhang, K. Maslov, G. Stoica, and L. Wang, “Functional photoacoustic microscopy for high-resolution and noninvasive in vivo imaging,” Nat. Biotechnol. 24, 848–851 (2006).
[CrossRef] [PubMed]

Su, R.

H.-P. Brecht, R. Su, M. Fronheiser, S. A. Ermilov, A. Conjusteau, and A. A. Oraevsky, “Whole-body threedimensional optoacoustic tomography system for small animals,” J. Biomed. Opt. 14, 064007 (2009).
[CrossRef]

Taruttis, A.

Tauler, R.

R. Tauler, B. Kowalski, and S. Fleming, “Multivariate curve resolution applied to spectral data from multiple runs of an industrial process,” Anal. Chem. 65, 2040–2047 (1993).
[CrossRef]

Valpola, H.

M. Funaro, E. Oja, and H. Valpola, “Independent component analysis for artefact separation in astrophysical images,” Neural Netw. 16, 469–478 (2003).
[CrossRef] [PubMed]

Vinegoni, C.

D. Razansky, M. Distel, C. Vinegoni, R. Ma, M. Perrimon, R. W. Koster, and V. Ntziachristos, “Multispectral opto-acoustic tomography of deep-seated fluorescent proteins in vivo,” Nat. Photonics 3, 412–417 (2009).
[CrossRef]

D. Razansky, C. Vinegoni, and V. Ntziachristos, “Multispectral photoacoustic imaging of fluorochromes in small animals,” Opt. Lett. 32, 2891–2893 (2007).
[CrossRef] [PubMed]

Wang, C.-R. C.

Wang, L.

M. Xu, and L. Wang, “Photoacoustic imaging in biomedicine,” Rev. Sci. Instrum. 77, 041101 (2006).
[CrossRef]

H. Zhang, K. Maslov, G. Stoica, and L. Wang, “Functional photoacoustic microscopy for high-resolution and noninvasive in vivo imaging,” Nat. Biotechnol. 24, 848–851 (2006).
[CrossRef] [PubMed]

Wang, L. V.

J. Gamelin, A. Maurudis, A. Aguirre, F. Huang, P. Guo, L. V. Wang, and Q. Zhu, “A real-time photoacoustic tomography system for small animals,” Opt. Express 17, 10489–10498 (2009).
[CrossRef] [PubMed]

H. Fang, K. Maslov, and L. V. Wang, “Photoacoustic doppler effect from flowing small light-absorbing particles,” Phys. Rev. Lett. 99, 184501 (2007).
[CrossRef] [PubMed]

X. Wang, X. Xie, G. Ku, L. V. Wang, and G. Stoica, “Noninvasive imaging of hemoglobin concentration and oxygenation in the rat brain using high-resolution photoacoustic tomography,” J. Biomed. Opt. 11, 024015 (2006).
[CrossRef] [PubMed]

M. Xu, and L. V. Wang, “Universal back-projection algorithm for photoacoustic computed tomography,” Phys. Rev. E Stat. Nonlin. Soft Matter Phys. 71, 016706 (2005).
[CrossRef]

Wang, X.

X. Wang, X. Xie, G. Ku, L. V. Wang, and G. Stoica, “Noninvasive imaging of hemoglobin concentration and oxygenation in the rat brain using high-resolution photoacoustic tomography,” J. Biomed. Opt. 11, 024015 (2006).
[CrossRef] [PubMed]

Wei, C.-W.

Wu, Y.-N.

Xie, X.

X. Wang, X. Xie, G. Ku, L. V. Wang, and G. Stoica, “Noninvasive imaging of hemoglobin concentration and oxygenation in the rat brain using high-resolution photoacoustic tomography,” J. Biomed. Opt. 11, 024015 (2006).
[CrossRef] [PubMed]

Xu, H.

H. Xu, and B. W. Rice, “In-vivo fluorescence imaging with a multivariate curve resolution spectral unmixing technique,” J. Biomed. Opt. 14, 064011 (2009).
[CrossRef]

Xu, M.

M. Xu, and L. Wang, “Photoacoustic imaging in biomedicine,” Rev. Sci. Instrum. 77, 041101 (2006).
[CrossRef]

M. Xu, and L. V. Wang, “Universal back-projection algorithm for photoacoustic computed tomography,” Phys. Rev. E Stat. Nonlin. Soft Matter Phys. 71, 016706 (2005).
[CrossRef]

Yang, J.

J. Yang, D. Zhang, A. F. Frangi, and J. Y. Yang, “Two-dimensional pca: a new approach to appearance-based face representation and recognition,” IEEE Trans. Pattern Anal. Mach. Intell. 26, 131–137 (2004).
[CrossRef] [PubMed]

Yang, J. Y.

J. Yang, D. Zhang, A. F. Frangi, and J. Y. Yang, “Two-dimensional pca: a new approach to appearance-based face representation and recognition,” IEEE Trans. Pattern Anal. Mach. Intell. 26, 131–137 (2004).
[CrossRef] [PubMed]

Zhang, D.

J. Yang, D. Zhang, A. F. Frangi, and J. Y. Yang, “Two-dimensional pca: a new approach to appearance-based face representation and recognition,” IEEE Trans. Pattern Anal. Mach. Intell. 26, 131–137 (2004).
[CrossRef] [PubMed]

Zhang, H.

H. Zhang, K. Maslov, G. Stoica, and L. Wang, “Functional photoacoustic microscopy for high-resolution and noninvasive in vivo imaging,” Nat. Biotechnol. 24, 848–851 (2006).
[CrossRef] [PubMed]

Zhu, Q.

Anal. Chem. (1)

R. Tauler, B. Kowalski, and S. Fleming, “Multivariate curve resolution applied to spectral data from multiple runs of an industrial process,” Anal. Chem. 65, 2040–2047 (1993).
[CrossRef]

Appl. Phys. Lett. (1)

G. Busse, and A. Rosencwaig, “Subsurface imaging with photoacoustics,” Appl. Phys. Lett. 36, 815–816 (1980).
[CrossRef]

Bull. Am. Math. Soc. (1)

E. Moore, “On the reciprocal of the general algebraic matrix,” Bull. Am. Math. Soc. 26, 394–395 (1920).

Chem. Rev. (1)

V. Ntziachristos, and D. Razansky, “Molecular imaging by means of multispectral optoacoustic tomography (MSOT),” Chem. Rev. 110, 2783–2794 (2010).
[CrossRef] [PubMed]

Clin. Chem. (1)

A. Rosencwaig, “Potential clinical applications of photoacoustics,” Clin. Chem. 28, 1878–1881 (1982).
[PubMed]

Comput. Vis. Image Underst. (1)

B. A. Draper, K. Baek, M. S. Bartlett, and J. R. Beveridge, “Recognizing faces with pca and ica,” Comput. Vis. Image Underst. 91, 115–137 (2003).
[CrossRef]

IEEE Trans. Med. Imaging (1)

A. Rosenthal, D. Razansky, and V. Ntziachristos, “Quantitative optoacoustic signal extraction using sparse signal representation,” IEEE Trans. Med. Imaging 28, 1997–2006 (2009).
[CrossRef] [PubMed]

IEEE Trans. Neural Netw. (1)

A. Hyvrinen, “Fast and robust fixed-point algorithms for independent component analysis,” IEEE Trans. Neural Netw. 10, 626–634 (1999).
[CrossRef]

IEEE Trans. Pattern Anal. Mach. Intell. (1)

J. Yang, D. Zhang, A. F. Frangi, and J. Y. Yang, “Two-dimensional pca: a new approach to appearance-based face representation and recognition,” IEEE Trans. Pattern Anal. Mach. Intell. 26, 131–137 (2004).
[CrossRef] [PubMed]

Int. J. Theor. Appl. Finance (1)

S. Clémençon, and S. Slim, “On portfolio selection under extreme risk measure: the heavy-tailed ICA Model,” Int. J. Theor. Appl. Finance 10, 449–474 (2007).
[CrossRef]

J. Biomed. Opt. (4)

H. Xu, and B. W. Rice, “In-vivo fluorescence imaging with a multivariate curve resolution spectral unmixing technique,” J. Biomed. Opt. 14, 064011 (2009).
[CrossRef]

A.-S. Montcuquet, L. Herv’e, F. Navarro, J.-M. Dinten, and J. I. Mars, “Nonnegative matrix factorization: a blind spectra separation method for in vivo fluorescent optical imaging,” J. Biomed. Opt. 15, 056009 (2010).
[CrossRef] [PubMed]

X. Wang, X. Xie, G. Ku, L. V. Wang, and G. Stoica, “Noninvasive imaging of hemoglobin concentration and oxygenation in the rat brain using high-resolution photoacoustic tomography,” J. Biomed. Opt. 11, 024015 (2006).
[CrossRef] [PubMed]

H.-P. Brecht, R. Su, M. Fronheiser, S. A. Ermilov, A. Conjusteau, and A. A. Oraevsky, “Whole-body threedimensional optoacoustic tomography system for small animals,” J. Biomed. Opt. 14, 064007 (2009).
[CrossRef]

Lincoln Lab. J. (1)

N. Keshava, “A survey of spectral unmixing algorithms,” Lincoln Lab. J. 14, 55–78 (2003).

London, Edinburgh Dublin Philos, Mag. J. Sci. (1)

K. Pearson, “On lines and planes of closest fit to a system of points in space,” London, Edinburgh Dublin Philos, Mag. J. Sci. 6, 559–572 (1901).

Med. Phys. (1)

D. Razansky, J. Baeten, and V. Ntziachristos, “Sensitivity of molecular target detection by multispectral optoacoustic tomography (MSOT),” Med. Phys. 36, 939–945 (2009).
[CrossRef] [PubMed]

Nat. Biotechnol. (1)

H. Zhang, K. Maslov, G. Stoica, and L. Wang, “Functional photoacoustic microscopy for high-resolution and noninvasive in vivo imaging,” Nat. Biotechnol. 24, 848–851 (2006).
[CrossRef] [PubMed]

Nat. Methods (1)

V. Ntziachristos, “Going deeper than microscopy: the optical imaging frontier in biology,” Nat. Methods 7, 603–614 (2010).
[CrossRef] [PubMed]

Nat. Photonics (2)

D. Razansky, M. Distel, C. Vinegoni, R. Ma, M. Perrimon, R. W. Koster, and V. Ntziachristos, “Multispectral opto-acoustic tomography of deep-seated fluorescent proteins in vivo,” Nat. Photonics 3, 412–417 (2009).
[CrossRef]

E. M. C. Hillman, and A. Moore, “All optical anatomical co registration for molecular imaging of small animals using dynamic contrast,” Nat. Photonics 1(9), 526–530 (2007).
[CrossRef]

Neural Netw. (1)

M. Funaro, E. Oja, and H. Valpola, “Independent component analysis for artefact separation in astrophysical images,” Neural Netw. 16, 469–478 (2003).
[CrossRef] [PubMed]

Opt. Express (3)

Opt. Lett. (4)

Phys. Rev. E Stat. Nonlin. Soft Matter Phys. (1)

M. Xu, and L. V. Wang, “Universal back-projection algorithm for photoacoustic computed tomography,” Phys. Rev. E Stat. Nonlin. Soft Matter Phys. 71, 016706 (2005).
[CrossRef]

Phys. Rev. Lett. (1)

H. Fang, K. Maslov, and L. V. Wang, “Photoacoustic doppler effect from flowing small light-absorbing particles,” Phys. Rev. Lett. 99, 184501 (2007).
[CrossRef] [PubMed]

Rev. Sci. Instrum. (1)

M. Xu, and L. Wang, “Photoacoustic imaging in biomedicine,” Rev. Sci. Instrum. 77, 041101 (2006).
[CrossRef]

Stat. Sci. (1)

L. Le Cam, “The central limit theorem around 1935,” Stat. Sci. 1, 78–91 (1986).
[CrossRef]

Other (6)

A. Hyvärinen, J. Karhunen, and E. Oja, Independent Component Analysis, Adaptive and Learning Systems for Signal Processing, Communications, and Control, 1st ed. (Wiley InterScience, 2002).

I. T. Jolliffe, Principal Component Analysis, 2nd ed. (Springer, 2002).

A. Cichocki, R. Zdunek, A. H. Phan, and S. I. Amari, Nonnegative Matrix and Tensor Factorizations: Applications to Exploratory Multi-way Data Analysis and Blind Source Separation, 1st ed. (Wiley, 2009).
[PubMed]

S. M. Kay, Fundamentals of Statistical Signal Processing, 1st ed. (Prentice Hall PTR, 1993),Vol. 1.

J. Nash, “The singular-value decomposition and its use to solve least-squares problems,” in Compact Numerical Methods for Computers: Linear Algebra and Function Minimization, 2nd ed. (Inst. of Physics Pub., 1990), pp. 30–48.

R. Penrose, “A generalized inverse for matrices,” in Proceedings of the Cambridge Philosophical Society (1955) Vol. 51, pp. 406–412.
[CrossRef]

Cited By

OSA participates in CrossRef's Cited-By Linking service. Citing articles from OSA journals and other participating publishers are listed here.

Alert me when this article is cited.


Figures (5)

Fig. 1
Fig. 1

Optoacoustic reconstruction of an axial slice at the neck area at 4 representative excitation wavelengths. The green and the cyan highlighted areas in the first image indicate the locations of the ICG and Cy7 implantations.

Fig. 2
Fig. 2

Comparison of the performance of the unmixing methods, showing the respective source components calculated for the ICG and Cy7 inclusions and the tissue background with the three unmixing methods.

Fig. 3
Fig. 3

Absorption spectra of ICG and Cy7 (solid lines) and the corresponding spectra estimated by the ICA analysis (lines with markers). Dashed lines are fitting curves for the ICA spectra.

Fig. 4
Fig. 4

Temporal optoacoustic images of the pelvic region (axial slices). a) high contrast (to enhance the anatomical features) b–d) reconstruction in three representative time points. i.: injection vein, m.: monitor vein.

Fig. 5
Fig. 5

Temporal unmixing of tissue components. a–c) the first three principal components of PCA. d) the temporal profiles associated with these components. e–g) the two vein components after the combined PCA-ICA analysis and their overlay onto the anatomical image. h) the temporal profiles of the injection in the monitor vein.

Tables (1)

Tables Icon

Table 1 Signal-to-Background Ratio (SBR) of the Two Inclusion Areas for the Different Unmixing Methods

Equations (5)

Equations on this page are rendered with MathJax. Learn more.

S + = S T ( S S T ) 1 .
R pinv = M S + .
R PCA = U PCA T M .
R I C A = U I C A T M .
R PCA / ICA = U ICA T R PCA = U ICA T U PCA T M .

Metrics