Impact of velocity distribution assumption on simplified laser speckle imaging equation

Ramírez-San-Juan, J. C.; Ramos-Garcı́a, R.; Guízar-Iturbide, Ileana; Martínez-Niconoff, G.; Choi, Bernard

doi:10.1364/oe.16.003197

Cited by 124 publications

(129 citation statements)

References 23 publications

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“…Speckle patterns have been used extensively for a variety of metrology applications including surface roughness [2], strain measurement [3] and fluid flow [4][5][6][7][8]. The second-order statistics of a speckle pattern provide a description of the spatial structure of the pattern, with the minimum size of a speckle, or the minimum correlation length of the pattern, being an important parameter in the characterization of this structure [9].…”

Section: J Of Biomedical Photonics and Eng 3(3)mentioning

confidence: 99%

Spatial Poincaré Plots as Descriptors of Speckle Pattern Second-Order Statistics

Majumdar

Kirkpatrick

2017

JBPE

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Abstract. It is demonstrated herein that the use of spatial Poincaré plots provides an efficient means to describe short and long-range correlations in the spatial structure of the measured intensity distribution of scattered coherent fields. The intensity distribution over a row of pixels in single frames of speckle fields with varying speckle sizes was considered. Statistical descriptors from the spatial Poincaré plots for these intensity data with variable lags were used to estimate the short and long-term variations in the measured intensities, and from these descriptors, the minimum speckle size in the speckle patterns was estimated. This approach yielded similar results for speckle size estimates as the more standard method of calculating the power spectral density of the intensity pattern and simultaneously provided information on the relative contributions of short-term and long-term variations in the measured intensity to the spatial structure of the scattered fields.

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Section: J Of Biomedical Photonics and Eng 3(3)mentioning

confidence: 99%

Spatial Poincaré Plots as Descriptors of Speckle Pattern Second-Order Statistics

Majumdar

Kirkpatrick

2017

JBPE

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“…Furthermore in each pixel, from the power spectrum of the time trace the contrast was predicted for different values of T using Eq. (8).…”

Section: T T T−t F (τ)Dτ With T the Integration Time F(ω) And U(ω) Amentioning

confidence: 99%

“…As pointed out by Boas and Dunn in their recent review [6], as yet speckle contrast flowmetry modeling has focused on retrieving velocity information of scattering particles rather than their flux which includes concentration [7][8][9]. The presence of a static speckle component is usually not taken into account, with two recent exceptions [9,10].…”

Section: Introductionmentioning

confidence: 99%

“…Furthermore, although less problematic as long as only relative velocities are measured, models for speckle contrast flowmetry are inspired by Dynamic Light Scattering theories, implying assumptions regarding the dynamics of the particles and the associated optical intensity correlations, which is known to lead to model-dependent velocity estimations [8,12]. Finally they often assume single scattering by moving particles.…”

Section: Introductionmentioning

confidence: 99%

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Relation between the contrast in time integrated dynamic speckle patterns an the power spectral density of their temporal intensity fluctuations

et al. 2010

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Relation between the contrast in time integrated dynamic speckle patterns an the power spectral density of their temporal intensity fluctuations Draijer, M.J.; Hondebrink, E.; Larsson, M.; van Leeuwen, T.G.; van Steenbergen, W. General rightsIt is not permitted to download or to forward/distribute the text or part of it without the consent of the author(s) and/or copyright holder(s), other than for strictly personal, individual use, unless the work is under an open content license (like Creative Commons). Disclaimer/Complaints regulationsIf you believe that digital publication of certain material infringes any of your rights or (privacy) interests, please let the Library know, stating your reasons. In case of a legitimate complaint, the Library will make the material inaccessible and/or remove it from the website. Please Ask the Library: http://uba.uva.nl/en/contact, or a letter to: Library of the University of Amsterdam, Secretariat, Singel 425, 1012 WP Amsterdam, The Netherlands. You will be contacted as soon as possible. Abstract:Scattering fluid flux can be quantified with coherent light, either from the contrast of speckle patterns, or from the moments of the power spectrum of intensity fluctuations. We present a theory connecting these approaches for the general case of mixed static-dynamic patterns of boiling speckles without prior assumptions regarding the particle dynamics.An expression is derived and tested relating the speckle contrast to the intensity power spectrum. Our theory demonstrates that in speckle contrast the concentration of moving particles dominates over the contribution of speed to the particle flux. Our theory provides a basis for comparison of both approaches when used for studying tissue perfusion.

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“…Various optical and image processing methods have been proposed for enhancing the contrast of LSCI in subcutaneous blood flow measurements [1,[8][9][10][11][12][13]. However, LSCI still suffers from low image contrast, although the limitation of laser penetration depth in tissue due to the light scattering property has been partially addressed.…”

Section: Introductionmentioning

confidence: 99%

Contrast Enhancement of Laser Speckle Contrast Image in Deep Vasculature by Reduction of Tissue Scattering

Son¹,

Lee²,

Jung³

2013

Journal of the Optical Society of Korea

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Various methods have been proposed for enhancing the contrast of laser speckle contrast image (LSCI) in subcutaneous blood flow measurements. However, the LSCI still suffers from low image contrast due to tissue turbidity. Herein, a physicochemical tissue optical clearing (PCTOC) method was employed to enhance the contrast of LSCI. Ex vivo and in vivo experiments were performed with porcine skin samples and male ICR mice, respectively. The ex vivo LSCIs were obtained before and 90 min after the application of the PCTOC and in vivo LSCIs were obtained for 60 min after the application of the PCTOC. In order to obtain the skin recovery images, saline was applied for 30 min after the application of the PCTOC was completed. The visible appearance of the tubing under ex vivo samples and the in vivo vasculature gradually enhanced over time. The LSCI increased as a function of time after the application of the PCTOC in both ex vivo and in vivo experiments, and properly recovered to initial conditions after the application of saline in the in vivo experiment. The LSCI combined with the PCTOC was greatly enhanced even in deep vasculature. It is expected that similar results will be obtained in in vivo human studies.

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Impact of velocity distribution assumption on simplified laser speckle imaging equation

Cited by 124 publications

References 23 publications

Spatial Poincaré Plots as Descriptors of Speckle Pattern Second-Order Statistics

Spatial Poincaré Plots as Descriptors of Speckle Pattern Second-Order Statistics

Relation between the contrast in time integrated dynamic speckle patterns an the power spectral density of their temporal intensity fluctuations

Contrast Enhancement of Laser Speckle Contrast Image in Deep Vasculature by Reduction of Tissue Scattering

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