## Speckle contrast optical tomography: A new method for deep tissue three-dimensional tomography of blood flow |

Biomedical Optics Express, Vol. 5, Issue 4, pp. 1275-1289 (2014)

http://dx.doi.org/10.1364/BOE.5.001275

Acrobat PDF (2189 KB)

### Abstract

A novel tomographic method based on the laser speckle contrast, speckle contrast optical tomography (SCOT) is introduced that allows us to reconstruct three dimensional distribution of blood flow in deep tissues. This method is analogous to the diffuse optical tomography (DOT) but for deep tissue blood flow. We develop a reconstruction algorithm based on first Born approximation to generate three dimensional distribution of flow using the experimental data obtained from tissue simulating phantoms.

© 2014 Optical Society of America

## 1. Introduction

1. A. Devor, S. Sakadžić, V. Srinivasan, M. Yaseen, K. Nizar, P. Saisan, P. Tian, A. Dale, S. Vinogradov, M. Franceschini, and D. A. Boas, “Frontiers in optical imaging of cerebral blood flow and metabolism,” J. Cereb. Blood Flow Metab. **32**, 1259–1276 (2012). [CrossRef] [PubMed]

4. T. Durduran and A. G. Yodh, “Diffuse correlation spectroscopy for non-invasive, micro-vascular cerebral blood flow measurement,” NeuroImage **85**, 51–63 (2014). [CrossRef]

7. V. Rajan, B. Varghese, T. G. van Leeuwen, and W. Steenbergen, “Review of methodological developments in laser doppler flowmetry,” Lasers Med. Sci. **24**, 269–283 (2009). [CrossRef]

8. A. F. Fercher and J. D. Briers, “Flow visualization by means of single-exposure speckle photography,” Opt. Commun. **37**, 326–330 (1981). [CrossRef]

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

3. T. Durduran, R. Choe, W. B. Baker, and A. G. Yodh, “Diffuse optics for tissue monitoring and tomography,” Rep. Prog. Phys. **73**, 076701 (2010). [CrossRef]

10. D. A. Boas, L. E. Campbell, and A. G. Yodh, “Scattering and imaging with diffusing temporal field correlations,” Phys. Rev. Lett. **75**, 1855–1858 (1995). [CrossRef] [PubMed]

11. C. Zhou, G. Yu, D. Furuya, J. Greenberg, A. J. Yodh, and T. Durduran, “Diffuse optical correlation tomography of cerebral blood flow during cortical spreading depression in rat brain,” Opt. Express **14**, 1125–1144 (2006). [CrossRef] [PubMed]

16. J. P. Culver, T. Durduran, D. Furuya, C. Cheung, J. H. Greenberg, and A. G. Yodh, “Diffuse optical tomography of cerebral blood flow, oxygenation, and metabolism in rat during focal ischemia,” J. Cereb. Blood Flow Metab. **23**, 911–924 (2003). [CrossRef] [PubMed]

17. G. Dietsche, M. Ninck, C. Ortolf, J. Li, F. Jaillon, and T. Gisler, “Fiber-based multispeckle detection for time-resolved diffusing-wave spectroscopy: characterization and application to blood flow detection in deep tissue,” Appl. Opt. **46**, 8506–8514 (2007). [CrossRef] [PubMed]

3. T. Durduran, R. Choe, W. B. Baker, and A. G. Yodh, “Diffuse optics for tissue monitoring and tomography,” Rep. Prog. Phys. **73**, 076701 (2010). [CrossRef]

19. S. R. Arridge and J. C. Schotland, “Optical tomography: forward and inverse problems,” Inverse Problems **25**, 123010 (2009). [CrossRef]

17. G. Dietsche, M. Ninck, C. Ortolf, J. Li, F. Jaillon, and T. Gisler, “Fiber-based multispeckle detection for time-resolved diffusing-wave spectroscopy: characterization and application to blood flow detection in deep tissue,” Appl. Opt. **46**, 8506–8514 (2007). [CrossRef] [PubMed]

20. Y. Zhan, A. T. Eggebrecht, J. P. Culver, and H. Dehghani, “Image quality analysis of high-density diffuse optical tomography incorporating a subject-specific head model,” Front. Neuroenerg. **4**, 103389 (2012). [CrossRef]

21. V. Viasnoff, F. Lequeux, and D. J. Pine, “Multispeckle diffusing-wave spectroscopy: a tool to study slow relaxation and time-dependent dynamics,” Rev. Sci. Instrum. **73**, 2336–2344 (2002). [CrossRef]

22. A. P. Y. Wong and P. Wiltzius, “Dynamic light scattering with a ccd camera,” Rev. Sci. Instrum. **64**, 2547–2549 (1993). [CrossRef]

23. J. D. McKinney, M. A. Webster, K. J. Webb, and A. M. Weiner, “Characterization and imaging in optically scattering media by use of laser speckle and a variable-coherence source,” Opt. Lett. **25**, 4–6 (2000). [CrossRef]

## 2. Theory and algorithm

### 2.1. Correlation diffusion equation and the laser speckle contrast

*μ*) and scattering (

_{a}*μ*) coefficients obeys the radiative transfer equation (RTE) [3

_{s}3. T. Durduran, R. Choe, W. B. Baker, and A. G. Yodh, “Diffuse optics for tissue monitoring and tomography,” Rep. Prog. Phys. **73**, 076701 (2010). [CrossRef]

**73**, 076701 (2010). [CrossRef]

24. B. J. Ackerson, R. L. Dougherty, N. M. Reguigui, and U. Nobbmann, “Correlation transfer- application of radiative transfer solution methods to photon correlation problems,” J. Thermophys. Heat Transf. **6**, 577–588 (1992). [CrossRef]

25. R. L. Dougherty, B. J. Ackerson, N. M. Reguigui, F. Dorri-Nowkoorani, and U. Nobbmann, “Correlation transfer: development and application,” J. Quant. Spectrosc. Radiat. Transfer **52**, 713–727 (1994). [CrossRef]

10. D. A. Boas, L. E. Campbell, and A. G. Yodh, “Scattering and imaging with diffusing temporal field correlations,” Phys. Rev. Lett. **75**, 1855–1858 (1995). [CrossRef] [PubMed]

26. 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**, 192–215 (1997). [CrossRef]

*E*) autocorrelation

*G*

_{1}(

**r**,

*τ*) = <

*E*

^{*}(

**r**,

*t*)

*E*(

**r**,

*t*+

*τ*)> as given in Eq. 1: where the reduced scattering coefficient

*μ′*= (1 −

_{s}*g*)

*μ*,

_{s}*g*is the anisotropy factor of scattering,

*S*

_{0}(

**r**−

**r**

_{0}) is the isotropic point source located at

**r**=

**r**

_{0}.

*n*

_{0}is the refractive index of free medium and

*λ*is the wavelength of light. Here

**r**= (

*x*,

*y*,

*z*) is the spatial co-ordinate in three dimensional space and

*τ*is the correlation time.

*r*

^{2}(

*τ*)> which is the mean square displacement (MSD) of the scatterers [3

**73**, 076701 (2010). [CrossRef]

*r*

^{2}(

*τ*)>= 6

*D*, where

_{B}τ*D*is the particle diffusion coefficient. Under a directed capillary flow of the scatterers the MSD is given by <Δ

_{B}*r*

^{2}(

*τ*)>=

*V*

^{2}

*τ*

^{2}where

*V*

^{2}is the square of the effective (average) velocity of the scattering particles. We consider here both the Brownian motion as well as the directed flow and hence MSD is given by <Δ

*r*

^{2}(

*τ*)>= 6

*D*+

_{B}τ*V*

^{2}

*τ*

^{2}. Other more general formulations are also possible to account for different types of motion and effects [3

**73**, 076701 (2010). [CrossRef]

27. S. A. Carp, N. Roche-Labarbe, M.-A. Franceschini, V. J. Srinivasan, S. Sakadžić, and D. A. Boas, “Due to intravascular multiple sequential scattering, diffuse correlation spectroscopy of tissue primarily measures relative red blood cell motion within vessels,” Biomed. Opt. Express **2**, 2047 (2011). [CrossRef] [PubMed]

*g*

_{2}(

*τ*), another statistical quantity called the speckle contrast,

*κ*, is used for flow measurement. The speckle contrast is defined as the ratio of the standard deviation (

*σ*) of measured intensity to its mean (

_{I}*μ*) value in the spatial domain, i.e. [32

_{I}32. J. D. Briers, “Laser doppler, speckle and related techniques for blood perfusion mapping and imaging,” Physiol. Measurement **22**, R35 (2001). [CrossRef]

*T*is the exposure time of the detection system.

*κ*varies between zero and one and higher values indicate slower fluctuations of the scatterers. In LSF, single scattering approximations and uniform illumination of the sample of interest is utilized relating

*κ*to blood flow at superficial layers.

*g*

_{1}(

**r**,

*τ*) as [33

33. 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**, 093110 (2005). [CrossRef]

*κ*and

*g*

_{1}(

**r**,

*τ*) as given in Eq. 3 along with the CDE in Eq. 1 can be made if we consider point sources and utilize solutions of Eq. 1 to obtain

*g*

_{1}. We now take this approach which generalize the previous works and can be used to generate quantitative measurements. We use the Green’s function solution of CDE in Eq. 1 for a semi-infinite geometry [16

16. J. P. Culver, T. Durduran, D. Furuya, C. Cheung, J. H. Greenberg, and A. G. Yodh, “Diffuse optical tomography of cerebral blood flow, oxygenation, and metabolism in rat during focal ischemia,” J. Cereb. Blood Flow Metab. **23**, 911–924 (2003). [CrossRef] [PubMed]

13. H. M. Varma, A. K. Nandakumaran, and R. M. Vasu, “Study of turbid media with light: Recovery of mechanical and optical properties from boundary measurement of intensity autocorrelation of light,” J. Opt. Soc. Am. A **26**, 1472–1483 (2009). [CrossRef]

30. S. R. Arridge, “Optical tomography in medical imaging,” Inverse Problems **15**, R41 (1999). [CrossRef]

*κ*with respect to exposure time and the source-detector separation,

**r**, are shown in Figs. 1(a) and (b) respectively. Here we have used

*μ*= 0.03

_{a}*cm*

^{−1},

*μ′*= 6.31

_{s}*cm*

^{−1},

*D*= 10

_{B}^{−8}

*cm*

^{2}/

*s*,

*β*= 0.5 and

*λ*= 7.85 × 10

^{−5}

*cm*for the computation of Green’s function. As expected, the speckle contrast decreases as exposure time is increased for a given source-detector separation and also decreases with increasing source-detector separation. It is this dependence on these parameters that we will utilize in SCOT to obtain three-dimensional (3D) images of the distribution of the dynamics of the probed tissue volume.

### 2.2. Born approximation for speckle contrast optical tomography

*r*

^{2}(

*τ*), inside the volume of interest is recovered from the measurement of the two dimensional distribution of intensity speckle contrast,

*κ*, at the surface using the CDE in Eq. 1.

19. S. R. Arridge and J. C. Schotland, “Optical tomography: forward and inverse problems,” Inverse Problems **25**, 123010 (2009). [CrossRef]

30. S. R. Arridge, “Optical tomography in medical imaging,” Inverse Problems **15**, R41 (1999). [CrossRef]

35. V. A. Markel and J. C. Schotland, “On the convergence of the born series in optical tomography with diffuse light,” Inverse Problems **23**, 1445 (2007). [CrossRef]

*r*

^{2}(

*τ*) from

*κ*as explained below. In the future, this could readily be extended to other perturbation methods and non-linear approaches.

*r*

^{2}(

*τ*) = Δ

*r*

^{2}(

*τ*)

^{0}where

*G*, resulting in perturbed field correlation

_{sc}*G*

_{1}(

**r**,

*τ*) can be obtained by solving Eq. 1 with MSD Δ

*r*

^{2}(

*τ*) = Δ

*r*

^{2}(

*τ*)

^{0}+ Δ

*r*

^{2}(

*τ*)

*.*

^{δ}*G*

_{1}in Eq. 1, and subtracting Eq. 1 from the resulting equation, an expression for

*G*can be written as, Here

_{sc}*G*(

**r′**,

**r**

*,*

_{d}*τ*) is the Green’s function for the operator given by Eq. 1 with Δ

*r*

^{2}(

*τ*)

*= 0.*

^{δ}*G*is negligibly small compared to the background (homogeneous) field

_{sc}*G*given by Since

_{sc}*κ*depends on the normalized field autocorrelation

*g*

_{1}(

*τ*), in order to compute the perturbations in measurement due to flow term

*V*, we divide the expression

_{b}*G*

_{1}(

**r**, 0) on both sides resulting in which gives When

*τ*= 0,

*G*= 0 and hence

_{sc}*κ*corresponding to the inhomogeneous normalized field autocorrelation

*g*

_{1}under perturbations due to flow is given by

*κ*reduces to

*r*

^{2}(

*τ*)

*as*

^{δ}*κ*

_{0}is the baseline speckle contrast measured without any perturbation in MSD i.e., when Δ

*r*

^{2}(

*τ*)

*= 0. We consider a special case of the above sensitivity relation with*

^{δ}*V*

^{2})

^{0}= 0 i.e., there is no perturbation in Brownian motion and the perturbation to the system is introduced as a deterministic flow represented by

*V*

^{2}. We note that the impression of the blood flow in living tissues on the measured quantity is better modelled as Δ

*r*

^{2}(

*τ*) =

*D*. Here we have adopted a flow imaging experiment which uses Δ

_{B}τ*r*

^{2}(

*τ*) =

*V*

^{2}

*τ*

^{2}. The sensitivity relation in Eq. 11 can be employed for living tissue by setting

*V*= 0 and (

*V*

^{2})

*= 0 and considering*

^{δ}### 2.3. Inversion algorithm

*N*×

_{x}*N*×

_{y}*N*as shown in Fig. 2(b). The distribution of the flow inside the tube is depicted in the three dimensional slice plot in Fig. 2(c) where the geometry shown is used for the simulations. As shown in Fig. 2(d), the sources are scanned along the XZ plane (

_{z}*Y*= 0) and the intensity images are collected from the X–Z plane at

*Y*=

*N*(plane ABCD) which serves as the detector plane.

_{y}*N*sources we have used

_{s}*N*detectors in the detector plane ABCD.

_{d}*v*,

_{x}*v*and

_{y}*v*points respectively, which gives a total of

_{z}*v*×

_{x}*v*×

_{y}*v*voxels for the three dimensional slab geometry. The baseline speckle contrast (

_{z}*κ*

_{0}) corresponding to the

*E*= Δ

*κ*

^{2}), is computed by subtracting the baseline speckle contrast from the speckle contrast computed from intensity images measured in the presence of flow. The right hand side term in Eq. 11 is computed using the rectangular geometry with

*N*=

_{V}*v*×

_{x}*v*×

_{y}*v*voxels and for

_{z}*N*×

_{s}*N*source-detector pairs which gives the Jacobian matrix (

_{d}*J*) of size (

*N*×

_{s}*N*) ×

_{d}*N*.

_{V}*cm*

^{3}for the three dimensional slab geometry. Finally, the Jacobian is normalized to get

*J̃*=

*J*○

*B*where ‘○’ denotes Hadamard product defined as (

*J*○

*B*)

*= (*

_{i,j}*J*)

*(*

_{i,j}*B*)

*(point wise multiplication). Here*

_{i,j}*B*is a matrix of size

*N*×

_{s}*N*) ×

_{d}*N*whose rows are the vector

_{V}*b*such that

*i*=1...

*N*and

_{v}16. J. P. Culver, T. Durduran, D. Furuya, C. Cheung, J. H. Greenberg, and A. G. Yodh, “Diffuse optical tomography of cerebral blood flow, oxygenation, and metabolism in rat during focal ischemia,” J. Cereb. Blood Flow Metab. **23**, 911–924 (2003). [CrossRef] [PubMed]

37. J. P. Culver, A. M. Siegel, J. J. Stott, and D. A. Boas, “Volumetric diffuse optical tomography of brain activity,” Opt. Lett. **28**, 2061–2063 (2003). [CrossRef] [PubMed]

*c*, of size

*N*× 1, is the sum of rows of the Jacobian and ’

_{V}*T*’ notates the transpose of the matrix.

*J̃*which gives where, due to the ill-posedness of the system of equation, we have used

*λ*=

*λ*

_{1}

*max*(

*diag*(

*S*)). Here

*S*is the diagonal matrix obtained using the singular value decomposition of the matrix

*J̃*. We have found, by trial and error, that

*λ*

_{2}= 10

*λ*

_{1}where we have taken

*λ*

_{1}= 0.1 gives optimal results. The flow velocity is computed by solving the linear system of regularized discrete sensitivity equations given in Eq. 12.

## 3. Experimental method

*μ*=0.03 cm

_{a}^{−1},

*μ′*=6.31 cm

_{s}^{−1}(both at 785nm) was filled in a transparent plastic container of size

*N*= 3.8 cm,

_{x}*N*= 1.5 cm and

_{y}*N*= 5 cm as shown in Fig. 2(b). The light source was focused on the bottom of the sample and the produced speckle patterns were imaged from the top with a a monochrome scientific complementary metal-oxide-semiconductor camera (sCMOS; Orca flash4.0, Hamamatsu, Japan) at the top surface of the sample. The horizontal field of view was ≈ 4 cm, resulting in a pixel diameter of 3 × 10

_{z}^{−4}cm. A f/# of 16 was chosen to roughly match the speckle size to pixel size. The exposure time of the camera was set to 1 ms. Each pixel over the image corresponds to a specific distance from the source, and hence, the use of a camera provides dense spatial sampling with a large field-of-view.

*N*= 75 different source positions arranged in an array of 3 rows with each having 25 sources. This array is homogeneously distributed in the field of view of the camera (3.9 cm × 3.7 cm in XZ plane) and each source corresponds to one position of the focused laser beam. The laser was set in every position during 0.5 seconds to acquire 35 intensity images per source, with a 1 ms exposure time. The scanning positions of the source and the detectors are depicted in Fig. 2(d).

_{s}*N*= 75 detectors were defined, located at XZ plane for Y=1.5 cm thus comprising a total of

_{d}*N*×

_{s}*N*= 5625 source-detector pairs which serves as the SCOT data.

_{d}*κ*was calculated for each detector position using a 5×5 pixel window. These values are averaged over 35 images corresponding to each source and using Eq. 2 the speckle contrast for each detector is computed.

### 3.1. Shot noise correction in speckle contrast

*γ*is the ratio of full well capacity of CCD/sCMOS camera to its analog-to-digital conversion bits. The presence of shot noise will result in the speckle contrast computed from experimentally measured intensity images to increase with respect to spatial variable

**r**, in contrary to theoretical behavior as shown in Fig. 1(b). This is evident from the expression for speckle contrast due to shot noise,

*κ*, which is inversely proportional to intensity and hence directly proportional to

_{s}**r**for a point source illumination. In order to reduce out the effect of the shot noise in the speckle contrast we define a corrected speckle contrast [38] which is

*γ*= 0.4578 (defined for our specific camera model. The corrected speckle contrast behaves more closer to the speckle contrast derived from theoretical model and hence we use the corrected speckle contrast for SCOT (Please see Fig. 4(a) and 4(b) in section 4). The dynamic range could be further extended by improving the detected SNR and taking into account other significant sources of noise that introduce systematic errors.

## 4. Results

### 4.1. Validation of the point source model and data

*κ*

_{0}has to be determined

*apriori*for the SCOT inversion procedure. Hence the speckle contrast measurement computed with transmitted intensity images from the Lipofundin phantom illuminated by a point source is fitted against the numerically computed speckle contrast using CDE. In the fitting algorithm, based on nonlinear least square minimization, we have used the experimentally measured values of optical absorption (

*μ*= 0.03

_{a}*cm*

^{−1}) and scattering coefficient (

*μ′*= 6.31

_{s}*cm*

^{−1}) while the algorithm minimizes for

*D*. Figure 4(a) shows the speckle contrast, computed from experimental data, with and with out shot noise correction. Figure 4(b) shows the computed speckle contrast (from Equation 3) fitted against the measured speckle contrast (shot noise corrected) as a function of source distance separation in centimeters. We would like to mention that the speckle contrast up to a source-distance separation of 2.3 cm is only used for fitting since the systematic deviations form the theory due to uncorrected noise factors increases considerably after this separation. The

_{B}*D*= 1.68 × 10

_{B}^{−8}

*cm*

^{2}/

*s*we obtained by the fitting algorithm is in agreement with the

*D*= 1.95 × 10

_{B}^{−8}

*cm*

^{2}/

*s*that we obtained using DCS measurement.

*v*

_{1}is three times higher than the other (

*v*

_{2}). We clearly see the velocity dependent change in the speckle contrast due to the flow in the tube. This is essentially a demonstration that the left hand side of Eq. 11 is sensitive to the flow velocity in the tube.

### 4.2. Experimental demonstration of SCOT

*V*

^{2}), whose square root gives the flow velocity.

*cm*

^{3}. A plot of the integrated value of the reconstructed and original velocities in this predetermined volume is shown in Fig. 6(d). The normalization is done by dividing the original and reconstructed flow corresponding to the flow value of 0.85 cm/s. A linear fit, using the data from original velocities ranging from 0.11 cm/s to 1.06 cm/s of the reconstructed flow gives a slope of 0.97. This slope is quite encouraging for this limited range. The underestimation for the larger perturbations, i.e. for larger velocities, is presumably due to the failure of the linearized Born approximation [39

40. H. He, Y. Tang, F. Zhou, J. Wang, Q. Luo, and P. Li, “Lateral laser speckle contrast analysis combined with line beam scanning illumination to improve the sampling depth of blood flow imaging,” Opt. Lett. **37**, 3774–3776 (2012). [CrossRef] [PubMed]

## 5. Discussions and conclusion

*in vivo*applications, this technique would allow us to image local or temporal variations in blood flow for example due to neuronal stimuli, pharmacological or physiological changes in time or heterogeneities due to local ischemia or a tumor.

**73**, 076701 (2010). [CrossRef]

10. D. A. Boas, L. E. Campbell, and A. G. Yodh, “Scattering and imaging with diffusing temporal field correlations,” Phys. Rev. Lett. **75**, 1855–1858 (1995). [CrossRef] [PubMed]

26. 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**, 192–215 (1997). [CrossRef]

11. C. Zhou, G. Yu, D. Furuya, J. Greenberg, A. J. Yodh, and T. Durduran, “Diffuse optical correlation tomography of cerebral blood flow during cortical spreading depression in rat brain,” Opt. Express **14**, 1125–1144 (2006). [CrossRef] [PubMed]

45. M. Heckmeier, S. E. Skipetrov, G. Maret, and R. Maynard, “Imaging of dynamic heterogeneities in multiple-scattering media,” J. Opt. Soc. Am. A **14**, 185–191 (1997). [CrossRef]

35. V. A. Markel and J. C. Schotland, “On the convergence of the born series in optical tomography with diffuse light,” Inverse Problems **23**, 1445 (2007). [CrossRef]

13. H. M. Varma, A. K. Nandakumaran, and R. M. Vasu, “Study of turbid media with light: Recovery of mechanical and optical properties from boundary measurement of intensity autocorrelation of light,” J. Opt. Soc. Am. A **26**, 1472–1483 (2009). [CrossRef]

30. S. R. Arridge, “Optical tomography in medical imaging,” Inverse Problems **15**, R41 (1999). [CrossRef]

*in vivo*.

## Acknowledgments

## References and links

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17. | G. Dietsche, M. Ninck, C. Ortolf, J. Li, F. Jaillon, and T. Gisler, “Fiber-based multispeckle detection for time-resolved diffusing-wave spectroscopy: characterization and application to blood flow detection in deep tissue,” Appl. Opt. |

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20. | Y. Zhan, A. T. Eggebrecht, J. P. Culver, and H. Dehghani, “Image quality analysis of high-density diffuse optical tomography incorporating a subject-specific head model,” Front. Neuroenerg. |

21. | V. Viasnoff, F. Lequeux, and D. J. Pine, “Multispeckle diffusing-wave spectroscopy: a tool to study slow relaxation and time-dependent dynamics,” Rev. Sci. Instrum. |

22. | A. P. Y. Wong and P. Wiltzius, “Dynamic light scattering with a ccd camera,” Rev. Sci. Instrum. |

23. | J. D. McKinney, M. A. Webster, K. J. Webb, and A. M. Weiner, “Characterization and imaging in optically scattering media by use of laser speckle and a variable-coherence source,” Opt. Lett. |

24. | B. J. Ackerson, R. L. Dougherty, N. M. Reguigui, and U. Nobbmann, “Correlation transfer- application of radiative transfer solution methods to photon correlation problems,” J. Thermophys. Heat Transf. |

25. | R. L. Dougherty, B. J. Ackerson, N. M. Reguigui, F. Dorri-Nowkoorani, and U. Nobbmann, “Correlation transfer: development and application,” J. Quant. Spectrosc. Radiat. Transfer |

26. | 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 |

27. | S. A. Carp, N. Roche-Labarbe, M.-A. Franceschini, V. J. Srinivasan, S. Sakadžić, and D. A. Boas, “Due to intravascular multiple sequential scattering, diffuse correlation spectroscopy of tissue primarily measures relative red blood cell motion within vessels,” Biomed. Opt. Express |

28. | R. Bonner and R. Nossal, “Model for laser doppler measurements of blood flow in tissue,” Appl. Opt , |

29. | R. C. Mesquita, T. Durduran, G. Yu, E. M. Buckley, M. N. Kim, C. Zhou, R. Choe, U. Sunar, and A. G. Yodh, “Direct measurement of tissue blood flow and metabolism with diffuse optics,” Philos. Trans. R. Soc., A |

30. | S. R. Arridge, “Optical tomography in medical imaging,” Inverse Problems |

31. | C. Xu and Q. Zhu, “Light shadowing effect of large breast lesions imaged by optical tomography in reflection geometry,” J. Biomed. Opt. |

32. | J. D. Briers, “Laser doppler, speckle and related techniques for blood perfusion mapping and imaging,” Physiol. Measurement |

33. | 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. |

34. | M. v. van Rossum and T. M. Nieuwenhuizen, “Multiple scattering of classical waves: microscopy, mesoscopy, and diffusion,” Rev. Mod. Phys. |

35. | V. A. Markel and J. C. Schotland, “On the convergence of the born series in optical tomography with diffuse light,” Inverse Problems |

36. | B. C. White, “Developing high-density diffuse optical tomography for neuroimaging,” Ph.D. thesis, Washington University in St. Louis (2012). |

37. | J. P. Culver, A. M. Siegel, J. J. Stott, and D. A. Boas, “Volumetric diffuse optical tomography of brain activity,” Opt. Lett. |

38. | S. Yuan, “Sensitivity, noise and quantitative model of laser speckle contrast imaging,” Ph.D. thesis, Tufts University (2008). |

39. | M. A. O’Leary, “Imaging with diffuse photon density waves,” Ph.D. thesis, University of Pennsylvania (1996). |

40. | H. He, Y. Tang, F. Zhou, J. Wang, Q. Luo, and P. Li, “Lateral laser speckle contrast analysis combined with line beam scanning illumination to improve the sampling depth of blood flow imaging,” Opt. Lett. |

41. | J. F. Dunn, K. R. Forrester, L. Martin, J. Tulip, and R. C. Bray, “A transmissive laser speckle imaging technique for measuring deep tissue blood flow: an example application in finger joints,” Lasers Surg. Med. |

42. | A. Mazhar, D. J. Cuccia, T. B. Rice, S. A. Carp, A. J. Durkin, D. A. Boas, and B. J. T. B. Choi, “Laser speckle imaging in the spatial frequency domain,” Biomed. Opt. Express |

43. | R. Bi, J. Dong, and K. Lee, “Deep tissue flowmetry based on diffuse speckle contrast analysis,” Opt. Lett. |

44. | R. Bi, J. Dong, and K. Lee, “Multi-channel deep tissue flowmetry based on temporal diffuse speckle contrast analysis,” Opt. Express |

45. | M. Heckmeier, S. E. Skipetrov, G. Maret, and R. Maynard, “Imaging of dynamic heterogeneities in multiple-scattering media,” J. Opt. Soc. Am. A |

**OCIS Codes**

(110.6150) Imaging systems : Speckle imaging

(170.3880) Medical optics and biotechnology : Medical and biological imaging

(110.6955) Imaging systems : Tomographic imaging

(110.3010) Imaging systems : Image reconstruction techniques

**ToC Category:**

Speckle Imaging and Diagnostics

**History**

Original Manuscript: January 31, 2014

Revised Manuscript: March 10, 2014

Manuscript Accepted: March 11, 2014

Published: March 28, 2014

**Citation**

Hari M. Varma, Claudia P. Valdes, Anna K. Kristoffersen, Joseph P. Culver, and Turgut Durduran, "Speckle contrast optical tomography: A new method for deep tissue three-dimensional tomography of blood flow," Biomed. Opt. Express **5**, 1275-1289 (2014)

http://www.opticsinfobase.org/boe/abstract.cfm?URI=boe-5-4-1275

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