Abstract

Laser Speckle Contrast Imaging (LSCI) is a flexible, easy-to-implement technique for measuring blood flow speeds in-vivo. In order to obtain reliable quantitative data from LSCI the object must remain in the focal plane of the imaging system for the duration of the measurement session. However, since LSCI suffers from inherent frame-to-frame noise, it often requires a moving average filter to produce quantitative results. This frame-to-frame noise also makes the implementation of rapid autofocus system challenging. In this work, we demonstrate an autofocus method and system based on a novel measure of misfocus which serves as an accurate and noise-robust feedback mechanism. This measure of misfocus is shown to enable the localization of best focus with sub-depth-of-field sensitivity, yielding more accurate estimates of blood flow speeds and blood vessel diameters.

© 2014 Optical Society of America

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References

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  1. J. D. Briers and S. Webster, “Laser speckle contrast analysis (lasca): a nonscanning, full-field technique for monitoring capillary blood flow,”; J. Biomed. Opt. 1, 174–179 (1996).
    [Crossref] [PubMed]
  2. A. K. Dunn, H. Bolay, M. A. Moskowitz, and D. A. Boas, “Dynamic imaging of cerebral blood flow using laser speckle,”; J. Cereb. Blood Flow Metab. 21, 195–201 (2001).
    [Crossref] [PubMed]
  3. A. K. Dunn, A. Devor, H. Bolay, M. L. Andermann, M. A. Moskowitz, A. M. Dale, and D. A. Boas, “Simultaneous imaging of total cerebral hemoglobin concentration, oxygenation, and blood flow during functional activation,””, Opt. Lett..  28, 28–30 (2003).
    [Crossref] [PubMed]
  4. D. D. Duncan and S. J. Kirkpatrick, “Can laser speckle flowmetry be made a quantitative tool?” J. Opt. Soc. Am. A 25, 2088–2094 (2008).
    [Crossref]
  5. M. Draijer, E. Hondebrink, T. Leeuwen, and W. Steenbergen, “Review of laser speckle contrast techniques for visualizing tissue perfusion,”; Lasers Med. Sci. 24, 639–651 (2009).
    [Crossref]
  6. D. A. Boas and A. K. Dunn, “Laser speckle contrast imaging in biomedical optics,” J. Biomed. Opt. 15, 011109 (2010).
    [Crossref] [PubMed]
  7. A. K. Dunn, “Laser speckle contrast imaging of cerebral blood flow,”; Ann. Biomed. Eng. 40, 367–377 (2012).
    [Crossref]
  8. L. M. Richards, E. L. Towle, D. J. Fox, and A. K. Dunn, “Intraoperative laser speckle contrast imaging with retrospective motion correction for quantitative assessment of cerebral blood flow,” Neurophotonics 1, 015006 (2014).
    [Crossref]
  9. J. W. Goodman, “Some fundamental properties of speckle,”; J. Opt. Soc. Am. 66, 1145–1150 (1976).
    [Crossref]
  10. S. Klein, M. Staring, K. Murphy, M. Viergever, and J. Pluim, “elastix: A toolbox for intensity-based medical image registration,” IEEE Trans. Med. Imaging 29, 196–205 (2010).
    [Crossref]
  11. R. C. Gonzalez and R. E. Woods, Digital Image Processing3 ed. (Prentice-Hall, Inc., Upper Saddle River, NJ, USA, 2006).
  12. A. Levin, R. Fergus, F. Durand, and W. Freeman, “Image and depth from a conventional camera with a coded aperture,” ACM Trans. Graph. 26, 70 (2007).
    [Crossref]
  13. T. Yeo, S. Ong, Jayasooriah, and R. Sinniah, “Autofocusing for tissue microscopy,” Image and Vision Computing 11, 629–639 (1993).
    [Crossref]
  14. J.-M. Geusebroek, F. Cornelissen, A. W. Smeulders, and H. Geerts, “Robust autofocusing in microscopy,”; Cytometry 39, 1–9 (2000).
    [Crossref] [PubMed]
  15. N. Kehtarnavaz and H.-J. Oh, “Development and real-time implementation of a rule-based auto-focus algorithm,” Real-Time Imaging 9, 197–203 (2003).
    [Crossref]
  16. M. Liebling and M. Unser, “Autofocus for digital fresnel holograms by use of a fresnelet-sparsity criterion,” J. Opt. Soc. Am. A 21, 2424–2430 (2004).
  17. M. A. Bueno-Ibarra, J. Alvarez-Borrego, L. Acho, and M. C. Chavez-Sanchez, “Fast autofocus algorithm for automated microscopes,” Opt. Eng. 44, 063601 (2005).
  18. S. L. Jacques, “Optical properties of biological tissues: a review,” Phys. Med. Biol. 58, R37 (2013).
    [Crossref] [PubMed]
  19. J. F. Kenney and E. S. Keeping, Mathematics of Statistics, Part 1 (Van NostrandPrinceton, NJ, 1962), 3 ed.
  20. Y. Atchia, H. Levy, S. Dufour, and O. Levi, “Rapid multiexposure in vivo brain imaging system using vertical cavity surface emitting lasers as a light source,”; Appl. Opt. 52, C64–C71 (2013).
    [Crossref] [PubMed]
  21. L. Lindvere, R. Janik, A. Dorr, D. Chartash, B. Sahota, J. G. Sled, and B. Stefanovic, “Cerebral microvascular network geometry changes in response to functional stimulation,”; NeuroImage 71, 248–259 (2013).
    [Crossref] [PubMed]

2014 (1)

L. M. Richards, E. L. Towle, D. J. Fox, and A. K. Dunn, “Intraoperative laser speckle contrast imaging with retrospective motion correction for quantitative assessment of cerebral blood flow,” Neurophotonics 1, 015006 (2014).
[Crossref]

2013 (3)

S. L. Jacques, “Optical properties of biological tissues: a review,” Phys. Med. Biol. 58, R37 (2013).
[Crossref] [PubMed]

Y. Atchia, H. Levy, S. Dufour, and O. Levi, “Rapid multiexposure in vivo brain imaging system using vertical cavity surface emitting lasers as a light source,”; Appl. Opt. 52, C64–C71 (2013).
[Crossref] [PubMed]

L. Lindvere, R. Janik, A. Dorr, D. Chartash, B. Sahota, J. G. Sled, and B. Stefanovic, “Cerebral microvascular network geometry changes in response to functional stimulation,”; NeuroImage 71, 248–259 (2013).
[Crossref] [PubMed]

2012 (1)

A. K. Dunn, “Laser speckle contrast imaging of cerebral blood flow,”; Ann. Biomed. Eng. 40, 367–377 (2012).
[Crossref]

2010 (2)

S. Klein, M. Staring, K. Murphy, M. Viergever, and J. Pluim, “elastix: A toolbox for intensity-based medical image registration,” IEEE Trans. Med. Imaging 29, 196–205 (2010).
[Crossref]

D. A. Boas and A. K. Dunn, “Laser speckle contrast imaging in biomedical optics,” J. Biomed. Opt. 15, 011109 (2010).
[Crossref] [PubMed]

2009 (1)

M. Draijer, E. Hondebrink, T. Leeuwen, and W. Steenbergen, “Review of laser speckle contrast techniques for visualizing tissue perfusion,”; Lasers Med. Sci. 24, 639–651 (2009).
[Crossref]

2008 (1)

2007 (1)

A. Levin, R. Fergus, F. Durand, and W. Freeman, “Image and depth from a conventional camera with a coded aperture,” ACM Trans. Graph. 26, 70 (2007).
[Crossref]

2005 (1)

M. A. Bueno-Ibarra, J. Alvarez-Borrego, L. Acho, and M. C. Chavez-Sanchez, “Fast autofocus algorithm for automated microscopes,” Opt. Eng. 44, 063601 (2005).

2004 (1)

2003 (2)

2001 (1)

A. K. Dunn, H. Bolay, M. A. Moskowitz, and D. A. Boas, “Dynamic imaging of cerebral blood flow using laser speckle,”; J. Cereb. Blood Flow Metab. 21, 195–201 (2001).
[Crossref] [PubMed]

2000 (1)

J.-M. Geusebroek, F. Cornelissen, A. W. Smeulders, and H. Geerts, “Robust autofocusing in microscopy,”; Cytometry 39, 1–9 (2000).
[Crossref] [PubMed]

1996 (1)

J. D. Briers and S. Webster, “Laser speckle contrast analysis (lasca): a nonscanning, full-field technique for monitoring capillary blood flow,”; J. Biomed. Opt. 1, 174–179 (1996).
[Crossref] [PubMed]

1993 (1)

T. Yeo, S. Ong, Jayasooriah, and R. Sinniah, “Autofocusing for tissue microscopy,” Image and Vision Computing 11, 629–639 (1993).
[Crossref]

1976 (1)

Acho, L.

M. A. Bueno-Ibarra, J. Alvarez-Borrego, L. Acho, and M. C. Chavez-Sanchez, “Fast autofocus algorithm for automated microscopes,” Opt. Eng. 44, 063601 (2005).

Alvarez-Borrego, J.

M. A. Bueno-Ibarra, J. Alvarez-Borrego, L. Acho, and M. C. Chavez-Sanchez, “Fast autofocus algorithm for automated microscopes,” Opt. Eng. 44, 063601 (2005).

Andermann, M. L.

Atchia, Y.

Boas, D. A.

D. A. Boas and A. K. Dunn, “Laser speckle contrast imaging in biomedical optics,” J. Biomed. Opt. 15, 011109 (2010).
[Crossref] [PubMed]

A. K. Dunn, A. Devor, H. Bolay, M. L. Andermann, M. A. Moskowitz, A. M. Dale, and D. A. Boas, “Simultaneous imaging of total cerebral hemoglobin concentration, oxygenation, and blood flow during functional activation,””, Opt. Lett..  28, 28–30 (2003).
[Crossref] [PubMed]

A. K. Dunn, H. Bolay, M. A. Moskowitz, and D. A. Boas, “Dynamic imaging of cerebral blood flow using laser speckle,”; J. Cereb. Blood Flow Metab. 21, 195–201 (2001).
[Crossref] [PubMed]

Bolay, H.

Briers, J. D.

J. D. Briers and S. Webster, “Laser speckle contrast analysis (lasca): a nonscanning, full-field technique for monitoring capillary blood flow,”; J. Biomed. Opt. 1, 174–179 (1996).
[Crossref] [PubMed]

Bueno-Ibarra, M. A.

M. A. Bueno-Ibarra, J. Alvarez-Borrego, L. Acho, and M. C. Chavez-Sanchez, “Fast autofocus algorithm for automated microscopes,” Opt. Eng. 44, 063601 (2005).

Chartash, D.

L. Lindvere, R. Janik, A. Dorr, D. Chartash, B. Sahota, J. G. Sled, and B. Stefanovic, “Cerebral microvascular network geometry changes in response to functional stimulation,”; NeuroImage 71, 248–259 (2013).
[Crossref] [PubMed]

Chavez-Sanchez, M. C.

M. A. Bueno-Ibarra, J. Alvarez-Borrego, L. Acho, and M. C. Chavez-Sanchez, “Fast autofocus algorithm for automated microscopes,” Opt. Eng. 44, 063601 (2005).

Cornelissen, F.

J.-M. Geusebroek, F. Cornelissen, A. W. Smeulders, and H. Geerts, “Robust autofocusing in microscopy,”; Cytometry 39, 1–9 (2000).
[Crossref] [PubMed]

Dale, A. M.

Devor, A.

Dorr, A.

L. Lindvere, R. Janik, A. Dorr, D. Chartash, B. Sahota, J. G. Sled, and B. Stefanovic, “Cerebral microvascular network geometry changes in response to functional stimulation,”; NeuroImage 71, 248–259 (2013).
[Crossref] [PubMed]

Draijer, M.

M. Draijer, E. Hondebrink, T. Leeuwen, and W. Steenbergen, “Review of laser speckle contrast techniques for visualizing tissue perfusion,”; Lasers Med. Sci. 24, 639–651 (2009).
[Crossref]

Dufour, S.

Duncan, D. D.

Dunn, A. K.

L. M. Richards, E. L. Towle, D. J. Fox, and A. K. Dunn, “Intraoperative laser speckle contrast imaging with retrospective motion correction for quantitative assessment of cerebral blood flow,” Neurophotonics 1, 015006 (2014).
[Crossref]

A. K. Dunn, “Laser speckle contrast imaging of cerebral blood flow,”; Ann. Biomed. Eng. 40, 367–377 (2012).
[Crossref]

D. A. Boas and A. K. Dunn, “Laser speckle contrast imaging in biomedical optics,” J. Biomed. Opt. 15, 011109 (2010).
[Crossref] [PubMed]

A. K. Dunn, A. Devor, H. Bolay, M. L. Andermann, M. A. Moskowitz, A. M. Dale, and D. A. Boas, “Simultaneous imaging of total cerebral hemoglobin concentration, oxygenation, and blood flow during functional activation,””, Opt. Lett..  28, 28–30 (2003).
[Crossref] [PubMed]

A. K. Dunn, H. Bolay, M. A. Moskowitz, and D. A. Boas, “Dynamic imaging of cerebral blood flow using laser speckle,”; J. Cereb. Blood Flow Metab. 21, 195–201 (2001).
[Crossref] [PubMed]

Durand, F.

A. Levin, R. Fergus, F. Durand, and W. Freeman, “Image and depth from a conventional camera with a coded aperture,” ACM Trans. Graph. 26, 70 (2007).
[Crossref]

Fergus, R.

A. Levin, R. Fergus, F. Durand, and W. Freeman, “Image and depth from a conventional camera with a coded aperture,” ACM Trans. Graph. 26, 70 (2007).
[Crossref]

Fox, D. J.

L. M. Richards, E. L. Towle, D. J. Fox, and A. K. Dunn, “Intraoperative laser speckle contrast imaging with retrospective motion correction for quantitative assessment of cerebral blood flow,” Neurophotonics 1, 015006 (2014).
[Crossref]

Freeman, W.

A. Levin, R. Fergus, F. Durand, and W. Freeman, “Image and depth from a conventional camera with a coded aperture,” ACM Trans. Graph. 26, 70 (2007).
[Crossref]

Geerts, H.

J.-M. Geusebroek, F. Cornelissen, A. W. Smeulders, and H. Geerts, “Robust autofocusing in microscopy,”; Cytometry 39, 1–9 (2000).
[Crossref] [PubMed]

Geusebroek, J.-M.

J.-M. Geusebroek, F. Cornelissen, A. W. Smeulders, and H. Geerts, “Robust autofocusing in microscopy,”; Cytometry 39, 1–9 (2000).
[Crossref] [PubMed]

Gonzalez, R. C.

R. C. Gonzalez and R. E. Woods, Digital Image Processing3 ed. (Prentice-Hall, Inc., Upper Saddle River, NJ, USA, 2006).

Goodman, J. W.

Hondebrink, E.

M. Draijer, E. Hondebrink, T. Leeuwen, and W. Steenbergen, “Review of laser speckle contrast techniques for visualizing tissue perfusion,”; Lasers Med. Sci. 24, 639–651 (2009).
[Crossref]

Jacques, S. L.

S. L. Jacques, “Optical properties of biological tissues: a review,” Phys. Med. Biol. 58, R37 (2013).
[Crossref] [PubMed]

Janik, R.

L. Lindvere, R. Janik, A. Dorr, D. Chartash, B. Sahota, J. G. Sled, and B. Stefanovic, “Cerebral microvascular network geometry changes in response to functional stimulation,”; NeuroImage 71, 248–259 (2013).
[Crossref] [PubMed]

Jayasooriah,

T. Yeo, S. Ong, Jayasooriah, and R. Sinniah, “Autofocusing for tissue microscopy,” Image and Vision Computing 11, 629–639 (1993).
[Crossref]

Keeping, E. S.

J. F. Kenney and E. S. Keeping, Mathematics of Statistics, Part 1 (Van NostrandPrinceton, NJ, 1962), 3 ed.

Kehtarnavaz, N.

N. Kehtarnavaz and H.-J. Oh, “Development and real-time implementation of a rule-based auto-focus algorithm,” Real-Time Imaging 9, 197–203 (2003).
[Crossref]

Kenney, J. F.

J. F. Kenney and E. S. Keeping, Mathematics of Statistics, Part 1 (Van NostrandPrinceton, NJ, 1962), 3 ed.

Kirkpatrick, S. J.

Klein, S.

S. Klein, M. Staring, K. Murphy, M. Viergever, and J. Pluim, “elastix: A toolbox for intensity-based medical image registration,” IEEE Trans. Med. Imaging 29, 196–205 (2010).
[Crossref]

Leeuwen, T.

M. Draijer, E. Hondebrink, T. Leeuwen, and W. Steenbergen, “Review of laser speckle contrast techniques for visualizing tissue perfusion,”; Lasers Med. Sci. 24, 639–651 (2009).
[Crossref]

Levi, O.

Levin, A.

A. Levin, R. Fergus, F. Durand, and W. Freeman, “Image and depth from a conventional camera with a coded aperture,” ACM Trans. Graph. 26, 70 (2007).
[Crossref]

Levy, H.

Liebling, M.

Lindvere, L.

L. Lindvere, R. Janik, A. Dorr, D. Chartash, B. Sahota, J. G. Sled, and B. Stefanovic, “Cerebral microvascular network geometry changes in response to functional stimulation,”; NeuroImage 71, 248–259 (2013).
[Crossref] [PubMed]

Moskowitz, M. A.

Murphy, K.

S. Klein, M. Staring, K. Murphy, M. Viergever, and J. Pluim, “elastix: A toolbox for intensity-based medical image registration,” IEEE Trans. Med. Imaging 29, 196–205 (2010).
[Crossref]

Oh, H.-J.

N. Kehtarnavaz and H.-J. Oh, “Development and real-time implementation of a rule-based auto-focus algorithm,” Real-Time Imaging 9, 197–203 (2003).
[Crossref]

Ong, S.

T. Yeo, S. Ong, Jayasooriah, and R. Sinniah, “Autofocusing for tissue microscopy,” Image and Vision Computing 11, 629–639 (1993).
[Crossref]

Pluim, J.

S. Klein, M. Staring, K. Murphy, M. Viergever, and J. Pluim, “elastix: A toolbox for intensity-based medical image registration,” IEEE Trans. Med. Imaging 29, 196–205 (2010).
[Crossref]

Richards, L. M.

L. M. Richards, E. L. Towle, D. J. Fox, and A. K. Dunn, “Intraoperative laser speckle contrast imaging with retrospective motion correction for quantitative assessment of cerebral blood flow,” Neurophotonics 1, 015006 (2014).
[Crossref]

Sahota, B.

L. Lindvere, R. Janik, A. Dorr, D. Chartash, B. Sahota, J. G. Sled, and B. Stefanovic, “Cerebral microvascular network geometry changes in response to functional stimulation,”; NeuroImage 71, 248–259 (2013).
[Crossref] [PubMed]

Sinniah, R.

T. Yeo, S. Ong, Jayasooriah, and R. Sinniah, “Autofocusing for tissue microscopy,” Image and Vision Computing 11, 629–639 (1993).
[Crossref]

Sled, J. G.

L. Lindvere, R. Janik, A. Dorr, D. Chartash, B. Sahota, J. G. Sled, and B. Stefanovic, “Cerebral microvascular network geometry changes in response to functional stimulation,”; NeuroImage 71, 248–259 (2013).
[Crossref] [PubMed]

Smeulders, A. W.

J.-M. Geusebroek, F. Cornelissen, A. W. Smeulders, and H. Geerts, “Robust autofocusing in microscopy,”; Cytometry 39, 1–9 (2000).
[Crossref] [PubMed]

Staring, M.

S. Klein, M. Staring, K. Murphy, M. Viergever, and J. Pluim, “elastix: A toolbox for intensity-based medical image registration,” IEEE Trans. Med. Imaging 29, 196–205 (2010).
[Crossref]

Steenbergen, W.

M. Draijer, E. Hondebrink, T. Leeuwen, and W. Steenbergen, “Review of laser speckle contrast techniques for visualizing tissue perfusion,”; Lasers Med. Sci. 24, 639–651 (2009).
[Crossref]

Stefanovic, B.

L. Lindvere, R. Janik, A. Dorr, D. Chartash, B. Sahota, J. G. Sled, and B. Stefanovic, “Cerebral microvascular network geometry changes in response to functional stimulation,”; NeuroImage 71, 248–259 (2013).
[Crossref] [PubMed]

Towle, E. L.

L. M. Richards, E. L. Towle, D. J. Fox, and A. K. Dunn, “Intraoperative laser speckle contrast imaging with retrospective motion correction for quantitative assessment of cerebral blood flow,” Neurophotonics 1, 015006 (2014).
[Crossref]

Unser, M.

Viergever, M.

S. Klein, M. Staring, K. Murphy, M. Viergever, and J. Pluim, “elastix: A toolbox for intensity-based medical image registration,” IEEE Trans. Med. Imaging 29, 196–205 (2010).
[Crossref]

Webster, S.

J. D. Briers and S. Webster, “Laser speckle contrast analysis (lasca): a nonscanning, full-field technique for monitoring capillary blood flow,”; J. Biomed. Opt. 1, 174–179 (1996).
[Crossref] [PubMed]

Woods, R. E.

R. C. Gonzalez and R. E. Woods, Digital Image Processing3 ed. (Prentice-Hall, Inc., Upper Saddle River, NJ, USA, 2006).

Yeo, T.

T. Yeo, S. Ong, Jayasooriah, and R. Sinniah, “Autofocusing for tissue microscopy,” Image and Vision Computing 11, 629–639 (1993).
[Crossref]

ACM Trans. Graph. (1)

A. Levin, R. Fergus, F. Durand, and W. Freeman, “Image and depth from a conventional camera with a coded aperture,” ACM Trans. Graph. 26, 70 (2007).
[Crossref]

Ann. Biomed. Eng. (1)

A. K. Dunn, “Laser speckle contrast imaging of cerebral blood flow,”; Ann. Biomed. Eng. 40, 367–377 (2012).
[Crossref]

Appl. Opt. (1)

Cytometry (1)

J.-M. Geusebroek, F. Cornelissen, A. W. Smeulders, and H. Geerts, “Robust autofocusing in microscopy,”; Cytometry 39, 1–9 (2000).
[Crossref] [PubMed]

IEEE Trans. Med. Imaging (1)

S. Klein, M. Staring, K. Murphy, M. Viergever, and J. Pluim, “elastix: A toolbox for intensity-based medical image registration,” IEEE Trans. Med. Imaging 29, 196–205 (2010).
[Crossref]

Image and Vision Computing (1)

T. Yeo, S. Ong, Jayasooriah, and R. Sinniah, “Autofocusing for tissue microscopy,” Image and Vision Computing 11, 629–639 (1993).
[Crossref]

J. Biomed. Opt. (2)

J. D. Briers and S. Webster, “Laser speckle contrast analysis (lasca): a nonscanning, full-field technique for monitoring capillary blood flow,”; J. Biomed. Opt. 1, 174–179 (1996).
[Crossref] [PubMed]

D. A. Boas and A. K. Dunn, “Laser speckle contrast imaging in biomedical optics,” J. Biomed. Opt. 15, 011109 (2010).
[Crossref] [PubMed]

J. Cereb. Blood Flow Metab. (1)

A. K. Dunn, H. Bolay, M. A. Moskowitz, and D. A. Boas, “Dynamic imaging of cerebral blood flow using laser speckle,”; J. Cereb. Blood Flow Metab. 21, 195–201 (2001).
[Crossref] [PubMed]

J. Opt. Soc. Am. (1)

J. Opt. Soc. Am. A (2)

Lasers Med. Sci. (1)

M. Draijer, E. Hondebrink, T. Leeuwen, and W. Steenbergen, “Review of laser speckle contrast techniques for visualizing tissue perfusion,”; Lasers Med. Sci. 24, 639–651 (2009).
[Crossref]

NeuroImage (1)

L. Lindvere, R. Janik, A. Dorr, D. Chartash, B. Sahota, J. G. Sled, and B. Stefanovic, “Cerebral microvascular network geometry changes in response to functional stimulation,”; NeuroImage 71, 248–259 (2013).
[Crossref] [PubMed]

Neurophotonics (1)

L. M. Richards, E. L. Towle, D. J. Fox, and A. K. Dunn, “Intraoperative laser speckle contrast imaging with retrospective motion correction for quantitative assessment of cerebral blood flow,” Neurophotonics 1, 015006 (2014).
[Crossref]

Opt. Eng. (1)

M. A. Bueno-Ibarra, J. Alvarez-Borrego, L. Acho, and M. C. Chavez-Sanchez, “Fast autofocus algorithm for automated microscopes,” Opt. Eng. 44, 063601 (2005).

Opt. Lett. (1)

Phys. Med. Biol. (1)

S. L. Jacques, “Optical properties of biological tissues: a review,” Phys. Med. Biol. 58, R37 (2013).
[Crossref] [PubMed]

Real-Time Imaging (1)

N. Kehtarnavaz and H.-J. Oh, “Development and real-time implementation of a rule-based auto-focus algorithm,” Real-Time Imaging 9, 197–203 (2003).
[Crossref]

Other (2)

J. F. Kenney and E. S. Keeping, Mathematics of Statistics, Part 1 (Van NostrandPrinceton, NJ, 1962), 3 ed.

R. C. Gonzalez and R. E. Woods, Digital Image Processing3 ed. (Prentice-Hall, Inc., Upper Saddle River, NJ, USA, 2006).

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Figures (5)

Fig. 1
Fig. 1 (a) Vessel kurtosis as a function of misfocus (vertical position, z, of the objective compared to the plane of best focus) for an arbitrary vessel in a mouse brain. Images were taken using an Olympus microscope (model BX61WI) using a 4× objective, NA = 0.1. A 680 nm vertical-cavity surface-emitting laser was used for illumination, while the camera exposure time was set to 5 ms. (b) (i) - (vii) show the normalized relative flow speed maps of the vessel for the different misfocus values indicated in (a), (x is the transverse direction across the vessel). (c) (i) - (vii) show the normalized relative flow speed profiles obtained by averaging the results in (b) along the columns.
Fig. 2
Fig. 2 Algorithm for computing misfocus measure ζ: initially, a square ROI is chosen for a given vessel in the field of view of the SFI map. Then, for each frame (i) the square ROI is cropped, (ii) the ROI is multiplied by a circular mask to improve the calculation of vessel orientation, (iii) a covariance matrix, Σ, is computed for the circular ROI and then Σ is eigen-decomposed, (iv) the eigenvectors of Σ, which reflect vessel orientation, are used to rotate the image in (ii) via a bilinear interpolation, (v) the cross-sectional flow profile is extracted from averaging along the pixel columns. The profile is cropped and re-centred to normalize-out small in -plane displacements (vi), the kurtosis is computed as per Eq. (2) for the given depth (red circle), (vii) the kurtosis valueis multiplied by the negative of the smaller Σ (i.e., the cross-variance of circular ROI – the “apparent width” of the vessel) to arrive at ζ. The maximum value of ζ corresponds to the location of best focus for the vessel.
Fig. 3
Fig. 3 Experimental setup. An 10× objective lens and a f = 200 mm tube lens create an intermediate image, that is Fourier-transformed and projected onto an SLM. The SLM imposes quadratic phase patterns onto the beam, thus shifting the location of the plane of best focus. The image is then inverse-Fourier-transformed onto a camera. The SLM is designed to work only in one polarization, hence a polarizer is used to reject the polarization that would not be affected by the SLM.
Fig. 4
Fig. 4 Comparison between the tranverse flow profile mean and the flow profile measure ζ as a means by which to estimate best focus location. (a) Field of view showing a SFI map of vessels in a somatosensory cortex in a rat, temporally averaged over 64 frames. The white square shows the ROI selected for analysis. (b) A zoomed-in depiction of the ROI selected from the temporally averaged SFI map in (a). (c) A non-averaged SFI map from a single speckle image in the selected ROI, showing the noise level of the SFI signal. (d) Transverse SFI flow profile vs. axial (z-) position of the objective with respect to the brain tissue. Every vertical slice represents a temporal average of 64 individually recentered SFI flow profiles acquired at a given depth. (e) Transverse flow profile mean (i.e., the average SFI from a recentered flow profile) verses z-position. The green curve shows the mean value of this measure for the 64 frames acquired at each z-step, while the shaded area represents the standard deviation. The vertical lines show the range of ambiguity in finding the best focus. (f) Our measure ζ as a function of misfocus (z-position). The vertical lines show the range of ambiguity in finding the best focus.
Fig. 5
Fig. 5 Closed loop autofocus results using ζ as a feedback mechanism. (a) Full field of view relative flow velocity map of a rat cortex recorded on the camera. The white square shows the ROI selected for autofocus computation. (b) The axial position of the focal plane of the microscope objective with respect to the location of best focus. The shaded regions in (b-f) indicate that an attempt by the autofocus system to detect and correct for an induced misfocus. The unshaded regions indicate no attempt to correct for misfocus (i.e., flat phase mask applied to the SLM). (c) The value of the proposed metric ζ. The horizontal line shows the threshold for refocusing. (d) Speckle flow index inside the vessel. (e) A 15-frame moving average of the speckle flow index. (f) The power of a lens displayed on the SLM surface. (g) Axial motion-induced relative error across the entire field of view for axial objective positions of (from left to right) 0 μm, 70 μm, 0 μm, and −70 μm with respect to the plane of best focus. Every tile is an average of each focal condition for the uncorrected temporal windows. (h) Relative error, as in (g), with correction using the SLM.

Equations (3)

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Kurt ( X ) = E [ ( X μ ) 4 ] ( E [ ( X μ ) 2 ] ) 2
Kurt ( x ) = v ( x i ) v ( x i ) ( x i x ) 4 ( v ( x i ) ( x i x ) 2 ) 2
ζ ( x ) v ( x i ) ( x i x ) 4 v ( x i ) ( x i x ) 2

Metrics