Year : 2009 Dynamic laser speckle imaging of cerebral blood

Laser speckle imaging (LSI) based on the speckle contrast analysis is a simple and robust technique for imaging of heterogeneous dynamics. LSI finds frequent application for dynamical mapping of cerebral blood flow, as it features high spatial and temporal resolution. However, the quantitative interpretation of the acquired data is not straightforward for the common case of a speckle field formed by both by moving and localized scatterers such as blood cells and bone or tissue. Here we present a novel processing scheme, we call dynamic laser speckle imaging (dLSI), that can be used to correctly extract the temporal correlation parameters from the speckle contrast measured in the presence of a static or slow-evolving background. The static light contribution is derived from the measurements by cross-correlating sequential speckle images. In-vivo speckle imaging experiments performed in the rodent brain demonstrate that dLSI leads to improved results. The cerebral hemodynamic response observed through the thinned and intact skull are more pronounced in the dLSI images as compared to the standard speckle contrast analysis. The proposed method also yields benefits with respect to the quality of the speckle images by suppressing contributions of non-uniformly distributed specular reflections. Dynamic laser speckle imaging of cerebral blood flow P. Zakharov 1,2 , A.C. Völker 1 , M.T. Wyss 3,4 , F. Haiss 4 , N. Calcinaghi 4 , C. Zunzunegui 4 , A. Buck 3 , F. Scheffold 1 and B. Weber 4 * 1 Department of Physics, University of Fribourg, 1700 Fribourg, Switzerland 2 Solianis Monitoring AG, 8050 Zürich, Switzerland 3 Division of Nuclear Medicine, University Hospital Zurich, 8091 Zurich, Switzerland 4 Institute of Pharmacology and Toxicology, University of Zurich, 8091 Zurich, Switzerland * bweber@pharma.uzh.ch Abstract: Laser speckle imaging (LSI) based on the speckle contrast analysis is a simple and robust technique for imaging of heterogeneous dynamics. LSI finds frequent application for dynamical mapping of cerebral blood flow, as it features high spatial and temporal resolution. However, the quantitative interpretation of the acquired data is not straightforward for the common case of a speckle field formed by both by moving and localized scatterers such as blood cells and bone or tissue. Here we present a novel processing scheme, we call dynamic laser speckle imaging (dLSI), that can be used to correctly extract the temporal correlation parameters from the speckle contrast measured in the presence of a static or slow-evolving background. The static light contribution is derived from the measurements by cross-correlating sequential speckle images. In-vivo speckle imaging experiments performed in the rodent brain demonstrate that dLSI leads to improved results. The cerebral hemodynamic response observed through the thinned and intact skull are more pronounced in the dLSI images as compared to the standard speckle contrast analysis. The proposed method also yields benefits with respect to the quality of the speckle images by suppressing contributions of non-uniformly distributed specular reflections. Laser speckle imaging (LSI) based on the speckle contrast analysis is a simple and robust technique for imaging of heterogeneous dynamics. LSI finds frequent application for dynamical mapping of cerebral blood flow, as it features high spatial and temporal resolution. However, the quantitative interpretation of the acquired data is not straightforward for the common case of a speckle field formed by both by moving and localized scatterers such as blood cells and bone or tissue. Here we present a novel processing scheme, we call dynamic laser speckle imaging (dLSI), that can be used to correctly extract the temporal correlation parameters from the speckle contrast measured in the presence of a static or slow-evolving background. The static light contribution is derived from the measurements by cross-correlating sequential speckle images. In-vivo speckle imaging experiments performed in the rodent brain demonstrate that dLSI leads to improved results. The cerebral hemodynamic response observed through the thinned and intact skull are more pronounced in the dLSI images as compared to the standard speckle contrast analysis. The proposed method also yields benefits with respect to the quality of the speckle images by suppressing contributions of non-uniformly distributed specular reflections. ©2009 Optical Society of America OCIS codes: (110.6150) Speckle imaging; (170.7050) Turbid media; (170.3880) Medical and biological imaging; (110.4280) Noise in imaging systems. References and links 1. C. Ayata, H. K. Shin, S. Salomone, Y. Ozdemir-Gursoy, D. A. Boas, A. K. Dunn, and M. A. Moskowitz, “Pronounced hypoperfusion during spreading depression in mouse cortex,” J. Cereb. Blood Flow Metab. 24(10), 1172–1182 (2004). 2. J. D. Briers, “Laser Doppler, speckle and related techniques for blood perfusion mapping and imaging,” Physiol. Meas. 22(4), R35–R66 (2001). 3. 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(3), 195–201 (2001). 4. 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(1), 28–30 (2003). 5. T. Durduran, M. G. Burnett, G. Q. Yu, C. Zhou, D. Furuya, A. G. Yodh, J. A. Detre, and J. H. Greenberg, “Spatiotemporal quantification of cerebral blood flow during functional activation in rat somatosensory cortex using laser-speckle flowmetry,” J. Cereb. Blood Flow Metab. 24(5), 518–525 (2004). 6. J. S. Paul, H. Al Nashash, A. R. Luft, and T. M. Le, “Statistical mapping of speckle autocorrelation for visualization of hyperaemic responses to cortical stimulation,” Ann. Biomed. Eng. 34(7), 1107–1118 (2006). 7. B. Weber, C. Burger, M. T. Wyss, G. K. von Schulthess, F. Scheffold, and A. Buck, “Optical imaging of the spatiotemporal dynamics of cerebral blood flow and oxidative metabolism in the rat barrel cortex,” Eur. J. Neurosci. 20(10), 2664–2670 (2004). 8. A. B. Parthasarathy, W. J. Tom, A. Gopal, X. J. Zhang, and A. K. Dunn, “Robust flow measurement with multiexposure speckle imaging,” Opt. Express 16(3), 1975–1989 (2008). (C) 2009 OSA 3 August 2009 / Vol. 17, No. 16 / OPTICS EXPRESS 13904 #110273 $15.00 USD Received 17 Apr 2009; revised 1 Jul 2009; accepted 22 Jul 2009; published 27 Jul 2009 9. P. Zakharov, A. Völker, A. Buck, B. Weber, and F. Scheffold, “Quantitative modeling of laser speckle imaging,” Opt. Lett. 31(23), 3465–3467 (2006). 10. H. Cheng, Q. Luo, S. Zeng, S. Chen, J. Cen, and H. Gong, “Modified laser speckle imaging method with improved spatial resolution,” J. Biomed. Opt. 8(3), 559–564 (2003). 11. H. Y. Cheng, and T. Q. Duong, “Simplified laser-speckle-imaging analysis method and its application to retinal blood flow imaging,” Opt. Lett. 32(15), 2188–2190 (2007). 12. P. C. Li, S. L. Ni, L. Zhang, S. Q. Zeng, and Q. M. Luo, “Imaging cerebral blood flow through the intact rat skull with temporal laser speckle imaging,” Opt. Lett. 31(12), 1824–1826 (2006). 13. D. D. Duncan, and S. J. Kirkpatrick, “Can laser speckle flowmetry be made a quantitative tool?” J. Opt. Soc. Am. A 25(8), 2088–2094 (2008). 14. T. Smausz, D. Zölei, and B. Hopp, “Real correlation time measurement in laser speckle contrast analysis using wide exposure time range images,” Appl. Opt. 48(8), 1425–1429 (2009). 15. A. C. Völker, P. Zakharov, B. Weber, F. Buck, and F. Scheffold, “Laser speckle imaging with an active noise reduction scheme,” Opt. Express 13(24), 9782–9787 (2005). 16. P. Zakharov, S. Bhat, P. Schurtenberger, and F. Scheffold, “Multiple-scattering suppression in dynamic light scattering based on a digital camera detection scheme,” Appl. Opt. 45(8), 1756–1764 (2006). 17. A. F. Fercher, and J. D. Briers, “Flow Visualization by Means of Single-Exposure Speckle Photography,” Opt. Commun. 37(5), 326–330 (1981). 18. B. J. Berne, and R. Pecora, Dynamic Light Scattering. With Applications to Chemistry, Biology, and Physics. (Dover Publications, New York, 2000). 19. P. Zakharov, A. Volker, A. Buck, B. Weber, and F. Scheffold, “Non-ergodicity correction in laser speckle biomedical imaging,” Proc. SPIE 6631, 66310D (2009). 20. J. D. Briers, G. Richards, and X. W. He, “Capillary blood flow monitoring using laser speckle contrast analysis (LASCA),” J. Biomed. Opt. 4(1), 164–175 (1999). 21. P. Zakharov, and F. Scheffold, Advances in dynamic light scattering techniques in Light Scattering Reviews 4 (Springer, Heidelberg, 2009). 22. D. A. Boas, and A. G. Yodh, “Spatially varying dynamical properties of turbid media probed with diffusing temporal light correlation,” J. Opt. Soc. Am. A 14(1), 192–215 (1997). 23. S. Yuan, A. Devor, D. A. Boas, and A. K. Dunn, “Determination of optimal exposure time for imaging of blood flow changes with laser speckle contrast imaging,” Appl. Opt. 44(10), 1823–1830 (2005). 24. P. Apollo, Y. Wong, and P. Wiltzius, “Dynamic light scattering with a ccd camera,” Rev. Sci. Instrum. 64(9), 2547–2549 (1993). 25. R. Bandyopadhyay, A. S. Gittings, S. S. Suh, P. K. Dixon, and D. J. Durian, “Speckle-visibility spectroscopy: A tool to study time-varying dynamics,” Rev. Sci. Instrum. 76(9), 093110 (2005). 26. V. Tuchin, Tissue Optics: Light Scattering Methods and Instruments for Medical Diagnosis, Second Edition (SPIE Press, Bellingham, 2007). 27. J. Berwick, D. Johnston, M. Jones, J. Martindale, P. Redgrave, N. McLoughlin, I. Schiessl, and J. E. Mayhew, “Neurovascular coupling investigated with two-dimensional optical imaging spectroscopy in rat whisker barrel cortex,” Eur. J. Neurosci. 22(7), 1655–1666 (2005). 28. A. Devor, A. K. Dunn, M. L. Andermann, I. Ulbert, D. A. Boas, and A. M. Dale, “Coupling of total hemoglobin concentration, oxygenation, and neural a