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Biomedical Optics Express

Biomedical Optics Express

  • Editor: Joseph A. Izatt
  • Vol. 5, Iss. 4 — Apr. 1, 2014
  • pp: 1275–1289
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Speckle contrast optical tomography: A new method for deep tissue three-dimensional tomography of blood flow

Hari M. Varma, Claudia P. Valdes, Anna K. Kristoffersen, Joseph P. Culver, and Turgut Durduran  »View Author Affiliations


Biomedical Optics Express, Vol. 5, Issue 4, pp. 1275-1289 (2014)
http://dx.doi.org/10.1364/BOE.5.001275


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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

Over the years, noninvasive optical imaging of microvascular blood flow has received much attention in biomedical research [1

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

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]

]. Laser Doppler flowmetry (LDF) [5

5. C. Riva, B. Ross, and G. B. Benedek, “Laser doppler measurements of blood flow in capillary tubes and retinal arteries,” Invest. Ophthalmol. Visual Sci. 11, 936–944 (1972).

7

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]

] and laser speckle flowmetry (LSF) [8

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

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

] are two promising techniques for two-dimensional, relatively superficial imaging of blood flow using dynamic laser speckles. Diffuse correlation spectroscopy (DCS) [3

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

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]

] based on similar physical principles as LSF and LDF but for deep tissues has been extended to three-dimensional tomography (diffuse correlation tomography (DCT) [11

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

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]

]). However DCT instrumentation is limited by fairly low signal-to-noise, low dynamic range in terms of detectable intensity and relatively expensive detectors that have kept the detector channel counts to less than ∼28 [17

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]

]. In this work, we present a new speckle contrast based tomographic approach- speckle contrast optical tomography (SCOT)- that provides efficient three-dimensional imaging of flow in turbid media that can be expanded to significantly large detector channel counts.

In LSF, a camera (CCD or CMOS), records the scattered light emanating from a tissue that is uniformly or broadly illuminated with a laser source. The presence of moving scatterers induces time varying random fluctuations of the scattered intensity resulting in dynamic speckles. The camera integrates the dynamic speckle pattern during the exposure time and gives an integrated spatial intensity distribution. The spatial and temporal blurring of this integrated intensity pattern is studied using a statistical quantity called speckle contrast. Since LSF uses a uniformly illuminated laser source for probing, the penetration depth is limited to superficial layers of the tissue (typically less than 1 mm).

DCS, on the other hand, uses point sources and a photon diffusion model to probe deep tissues. DCT is normally carried out in an analogous fashion to diffuse optical tomography (DOT) [3

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

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

]. It is a model based, computed tomography method where advanced inverse problem methods are utilized. Since, DOT has emerged as a promising technology in a variety of fields ranging from optical mammography to functional neuroimaging where it primarily measures parameters such as blood oxygen saturation and blood volume as well as the distribution of contrast agents, we expect that a practical 3D tomography of blood flow in large tissue volumes would find rapid acceptance in the field.

The main disadvantage of DCS, and hence DCT, is the low signal-to-noise ratio (SNR) and the limited dynamic range which arises due to the fact that the intensity autocorrelation function has to be computed by measuring each speckle independently using single mode or few-mode fibers with small collection areas, i.e. collecting photons of order 10,000 per second. Since SNR increases as the square root of number of speckles, obtaining significant benefits in SNR as well as the dynamic range by doing multi-speckle measurements [17

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]

] by employing a large number of detectors at a given spot on the tissue surface is not feasible for the type of dense sampling [20

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]

] that is desired for practical DCT applications.

An alternative would be to employ large arrays of detectors, for example using a photon-counting CCD, to capture the intensity fluctuations of many speckles simultaneously and then computing the intensity autocorrelation. This has been attempted in DWS studies [21

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

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

] but it is not yet feasible for biomedical applications where the dynamics of the speckles, i.e. the decay of the intensity auto-correlation, is much faster than the frame rates achievable by current camera technologies. We note that the speckle contrast measured using a point source illumination with varying coherence was utilized in [23

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]

] to measure the optical scattering coefficient rather than blood flow.

In this work, we present a new speckle contrast based tomographic system- speckle contrast optical tomography (SCOT)- that allows us to to probe heterogeneities in the dynamics of turbid media, like blood flow in deep tissues. SCOT is similar to DCS in the sense that it works in the multiple scattering, photon diffusion regime. SCOT merges the deep tissue flow measurement capabilities of DCS and the relatively inexpensive detectors with high frame rates as used in LSF. An improved SNR and a broad field-of-view is achieved by using a 2D detector configuration whereas the simultaneous measurement of large number of speckles (of order a million) over many source-detector separations gives the ability for depth resolution. We describe the theoretical background, derive a perturbation equation based on first Born approximation, describe an inversion algorithm and demonstrate the feasibility of the technique in tissue simulating liquid phantoms.

The rest of the paper is organized as follows. The theory and the inversion algorithm of SCOT are explained in Section 2. We describe the speckle flow imaging with correlation diffusion based photon propagation model and derive the sensitivity relation for the SCOT based on Born approximation. The inversion algorithm for SCOT based on the derived sensitivity relation is also presented. The experimental method of SCOT is presented in section 3 where we describe the optical instrumentation needed to acquire the intensity data for computing the speckle contrast. The reconstruction results are presented in Section 4 and the discussion and conclusions drawn from these studies are presented in Section 5.

2. Theory and algorithm

2.1. Correlation diffusion equation and the laser speckle contrast

The propagation of light in multiple scattering media with static scatterers characterized by the optical absorption (μa) and scattering (μs) coefficients obeys the radiative transfer equation (RTE) [3

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]

]. Under a set of well-defined and validated assumptions, one can arrive at the diffusion equation model [3

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]

] for photon propagation which is a a simplified form of RTE. Similarly, in order to study the photon propagation under dynamic scatterers, correlation transport equation (CTE) [24

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

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]

] is adopted, which is analogous to the RTE for static scatterers. From CTE, again using the diffusion approximation, correlation diffusion equation (CDE) [10

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]

, 12

12. D. A. Boas, “Diffuse photon probes of structural and dynamical properties of turbid media: theory and biomedical applications,” Ph.D. thesis, University of Pennsylvania (1996).

, 26

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]

] is derived which describes the propagation of electric field (E) autocorrelation G1(r, τ) = <E*(r, t)E(r, t + τ)> as given in Eq. 1:
D(r)G1(r,τ)+(μa(r)+13μsk02<Δr2(τ)>)G1(r,τ)=S0(rr0),
(1)
where the reduced scattering coefficient μ′s = (1 − g)μs, g is the anisotropy factor of scattering, D=13(μa+μs) is the optical diffusion coefficient, and S0(rr0) is the isotropic point source located at r = r0. k0=2πn0λ is the modulus of propagation vector of light where n0 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.

The dynamics of the scattering medium is modeled by <Δr2(τ)> which is the mean square displacement (MSD) of the scatterers [3

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]

]. When the motion of scatterers is modeled as Brownian motion then <Δr2(τ)>= 6DBτ, where DB is the particle diffusion coefficient. Under a directed capillary flow of the scatterers the MSD is given by <Δr2(τ)>= V2τ2 where V2 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 <Δr2(τ)>= 6DBτ + V2τ2. Other more general formulations are also possible to account for different types of motion and effects [3

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]

, 27

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]

].

In speckle contrast based measurements, instead of g2(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 (σI) of measured intensity to its mean (μI) value in the spatial domain, i.e. [32

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

],
κ(r,T)=σI(r,T)μI(r,T),
(2)
where 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.

By using the Siegert relation the speckle contrast can be expressed in terms of the normalized field autocorrelation g1(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]

],
κ2(r,T)=2βT0T|g1(r,τ)|2(1τT)dτ.
(3)

The typical behavior of 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 μa = 0.03cm−1, μ′s = 6.31cm−1, DB = 10−8cm2/s, β = 0.5 and λ = 7.85 × 10−5cm 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.

Fig. 1 The computed speckle contrast based on CDE model in transmission geometry with DB = 10−8cm2/s: (a) Speckle contrast versus exposure time (b) Speckle contrast versus source detector separation (r).

2.2. Born approximation for speckle contrast optical tomography

In order to be able to carry out tomography, we should first develop the inverse imaging problem where the 3D distribution of MSD, Δr2(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.

We derive the linear Born approximation [19

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

, 30

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

, 34

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]

] to reconstruct Δr2(τ) from κ as explained below. In the future, this could readily be extended to other perturbation methods and non-linear approaches.

For simplicity, we adopt the following notations: C0=13μsk02, Cb=2μsκ02τ and Cv=13μsκ02τ2. We first consider the homogeneous solution G10(r,τ) which solves Eq. 1 with Δr2(τ) = Δr2(τ)0 where Δr2(τ)0=6DB0τ+(V2)0τ2 is the baseline MSD. Due to the presence of perturbation in MSD by an amount Δr2(τ)δ=6DBδτ+(V2)δτ2 the G10(r,τ) will get perturbed by an amount Gsc, resulting in perturbed field correlation G1(r,τ)=G10(r,τ)+Gsc(r,τ). Here G1(r, τ) can be obtained by solving Eq. 1 with MSD Δr2(τ) = Δr2(τ)0 + Δr2(τ)δ.

Substituting the above expression for G1 in Eq. 1, and subtracting Eq. 1 from the resulting equation, an expression for Gsc can be written as,
Gsc(rd,rs,τ)=C0ΩG(r,rd,τ)(G10(r,rs,τ)+Gsc(r,rs,τ))Δr2(τ)δdr.
(4)
Here G(r′, rd, τ) is the Green’s function for the operator given by Eq. 1 with Δr2(τ)δ = 0.

The first Born approximation states that scattered field Gsc is negligibly small compared to the background (homogeneous) field G10, i.e., GscG10, and hence the above nonlinear integral equation reduces to a linear integral equation in Gsc given by
Gsc(rd,rs,τ)=(CbΩG(r,rd,τ)G10(r,rs,τ)DBδdr+CvΩG(r,rd,τ)G10(r,rs,τ)(V2)δdr).
(5)
Since κ depends on the normalized field autocorrelation g1(τ), in order to compute the perturbations in measurement due to flow term Vb, we divide the expression G1(r,τ)=G10(r,τ)+Gsc(r,τ) by G1(r, 0) on both sides resulting in
G1(r,τ)G1(r,0)=G10(r,τ)G1(r,0)+Gsc(r,τ)G1(r,0),
(6)
which gives
G1(r,τ)G1(r,0)=G10(r,τ)G10(r,0)G10(r,0)G1(r,0)+Gsc(r,τ)G1(r,0).
(7)
When τ = 0, Gsc = 0 and hence G10(r,0)=G1(r,0) which implies
g1(r,τ)=g10(r,τ)+Gsc(r,τ)G1(r,0).
(8)
Hence, the measurement κ corresponding to the inhomogeneous normalized field autocorrelation g1 under perturbations due to flow is given by
κ2(r,T)=2βT0T[g10(r,τ)+Gsc(r,τ)G1(r,0)]2(1τT)dτ=2βT0T[(g10(r,τ)2+2g10(r,τ)Gsc(r,τ)G1(r,0)+(Gsc(r,τ)G1(r,0))2](1τT)dτ.
In order to simplify the above expression, the condition for first Born approximation (GscG10) is again invoked resulting in
g10(r,τ)2+(Gsc(r,τ)G1(r,0))2=[G10(r,τ)G10(r,0)]2+[Gsc(r,τ)G1(r,0)]2G10(r,τ)2+Gsc(r,τ)2G10(r,0)2G10(r,τ)2G10(r,0)2=g10(r,τ)2.
(9)
Therefore, the expression for κ reduces to
κ2(r,T)=2βT0Tg10(r,τ)2(1τT)+4βT0T(1τT)g10(r,τ)Gsc(r,τ)G1(r,0).
(10)

By defining the baseline speckle contrast as κ022βT0Tg10(r,τ)2(1τT) and Δκ2κ2κ02, we proceed to write the change in speckle contrast due the perturbation in MSD Δr2(τ)δ as
Δκ2=4βT0T(1τT)g10(r,τ)G1(r,0)[CbΩG(r,rd,τ)G10(r,rs,τ)DBδdr+CvΩG(r,rd,τ)G10(r,rs,τ)(V2)δdr]dτ.
(11)
Here κ0 is the baseline speckle contrast measured without any perturbation in MSD i.e., when Δr2(τ)δ = 0. We consider a special case of the above sensitivity relation with DBδ=0 and (V2)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 V2. We note that the impression of the blood flow in living tissues on the measured quantity is better modelled as Δr2(τ) = DBτ. Here we have adopted a flow imaging experiment which uses Δr2(τ) = V2τ2. The sensitivity relation in Eq. 11 can be employed for living tissue by setting V = 0 and (V2)δ = 0 and considering DB0 as the baseline flow with DBδ as the perturbation in flow from the baseline value. The estimation of errors associated with the Born’s approximation in the context of DOT is addressed in [35

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

] which can be used to estimate the errors for the results presented here, but it is beyond the scope of this paper..

In order to demonstrate that the so-called banana path of photon propagation is still preserved for the derived Jacobian, in the special case that is considered, we compute the right hand side of the sensitivity relation in Eq. 11 in a three dimensional discretized grid (please see Section 2.3 and Fig. 2(b)). Here analytic Green’s function for semi-infinite geometry with homogeneous distribution of tissue properties is used to compute the Jacobian. But the expression for the Jacobian as given in Eq. 11 is derived without any assumption on the form of the solution and hence this formalism permits the use of other forward solvers for CDE in Eq. 1.

Fig. 2 The geometry of the scanning in SCOT along with the plot of Jacobian and location of the tube used to generate the flow.

The plot of logarithm of Jacobian in the YZ plane for X co-ordinate=1.8 cm for a source and the detector positioned at (x,y,z)=(1.8 0 2.0) and (x,y,z)=(1.8 1.5 1.08) respectively is shown in Fig. 2(a).

2.3. Inversion algorithm

The computation of the sensitivity relation (Eq. 11) is done in a discretized slab geometry which corresponds to the size of the object to be imaged in the experiment as well as the scanning pattern of the source relative to detector positions. The computational domain is a rectangular slab of size Nx × Ny × Nz 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 (Y = 0) and the intensity images are collected from the X–Z plane at Y = Ny (plane ABCD) which serves as the detector plane.

The first part of the computational algorithm involves the computation of the speckle contrast from experimentally measured raw intensity images. In order to calculate speckle contrast, X–Y co-ordinates of sources from the intensity images were computed using the centre of mass of images and with the help of a Canny edge detecting algorithm. For each Ns sources we have used Nd detectors in the detector plane ABCD.

We discretize the (X,Y,Z) co-ordinates into vx, vy and vz points respectively, which gives a total of vx × vy × vz voxels for the three dimensional slab geometry. The baseline speckle contrast (κ0) corresponding to the DB0, is computed using the semi-infinite Green’s function solution of CDE in Eq. 1. The term in left hand side of the Eq. 11, which is the perturbation in speckle contrast due to the flow relative to its baseline value (E = Δκ2), 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 NV = vx × vy × vz voxels and for Ns × Nd source-detector pairs which gives the Jacobian matrix (J) of size (Ns × Nd) × NV.

In particular, we discretize the (X,Y,Z) co-ordinates into 14, 8 and 16 points respectively which gives a total of 1792 voxels with a volume of 0.008 cm3 for the three dimensional slab geometry. Finally, the Jacobian is normalized to get =JB where ‘○’ denotes Hadamard product defined as (JB)i,j = (J)i,j (B)i,j (point wise multiplication). Here B is a matrix of size Ns × Nd) × NV whose rows are the vector b such that bi=1ai i =1...Nv and a=(cT+λ2max(cT)) [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]

,36

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]

]. Here indices in suffix position is used to denote the elements of the matrix, the vector c, of size NV × 1, is the sum of rows of the Jacobian and ’T’ notates the transpose of the matrix.

In the discretized domain, Eq. 11 can be re-casted in terms of the normalized Jacobian which gives
(V2)δ=BTJ˜T(J˜TJ˜+λI)1Δκ2
(12)
where, due to the ill-posedness of the system of equation, we have used λ = λ1max(diag(S)). Here S is the diagonal matrix obtained using the singular value decomposition of the matrix . We have found, by trial and error, that λ2 = 10λ1 where we have taken λ1 = 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

The experimental set up is shown in Fig. 3. A temperature controlled continuous laser diode (Thorlabs L785P090, 785 nm, 90 mW) was focused down to a beam of <1 mm diameter to probe the sample. The sample, which consists of a 1% Lipofundin MCT/LCT solution (B.BRAUN, Germany) in water with μa=0.03 cm−1, μ′s =6.31 cm−1 (both at 785nm) was filled in a transparent plastic container of size Nx = 3.8 cm, Ny = 1.5 cm and Nz = 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 × 10−4 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.

Fig. 3 Speckle contrast optical tomography (SCOT): General experimental setup consisting of a point laser source, galvo-controlled scanning, CCD and the data processing unit.

A pair of galvo controlled mirrors were used to scan the laser point source in the bottom X–Z plane of the container. We have used Ns = 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).

For the analysis, for each source, Nd = 75 detectors were defined, located at XZ plane for Y=1.5 cm thus comprising a total of Ns × Nd = 5625 source-detector pairs which serves as the SCOT data. 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

In all experiments, the theoretical speckle contrast as shown in Fig. 1(b) will be different from the speckle contrast computed from experimentally measured intensity images due to noise. One of the detrimental noise sources is the shot noise which obey the Poisson statistics. Hence the speckle contrast due to shot noise can be written as κs=γIγI=1γI, where 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, κs, which is inversely proportional to intensity and hence directly proportional to 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

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

] which is κc=(κ2κs2) where κs=1γI with 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.

Fig. 4 Speckle contrast due to Brownian motion: (a) The speckle contrast computed with (κc) and without (κ) shot noise correction for the Lipofundin phantom, (b) theoretical speckle contrast fitted for DB against the corrected speckle contrast (κc)

4. Results

4.1. Validation of the point source model and data

The speckle contrast due to the Brownian motion represented by κ0 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 (μa = 0.03 cm−1) and scattering coefficient (μ′s = 6.31 cm −1) while the algorithm minimizes for DB. 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 DB = 1.68 × 10−8 cm2/s we obtained by the fitting algorithm is in agreement with the DB = 1.95 × 10−8 cm2/s that we obtained using DCS measurement.

In Fig. 5 we plot the perturbation in the speckle contrast from its background value along the Z-direction as a function of two different flows. Here v1 is three times higher than the other (v2). 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.

Fig. 5 The perturbation in speckle contrast from the background, Δκ2, for two different velocities differing approximately by three folds (v1 = 3v2).

4.2. Experimental demonstration of SCOT

As noted above, we consider only those detectors that lie within 2.3 cm of each source position. As sources are illuminated in a bounded rectangular geometry, the number of detectors per each source within a distance of 2.3 cm will vary according to the source positions. Therefore, the size of the Jacobian with the new set of detectors is 5269 × 1792. The normalized system of equation, as given in Equation 12, is solved for (V2), whose square root gives the flow velocity.

The reconstructed flow profile, for the highest value of flow (3.18 cm/s), as a three dimensional slice plot is shown in Fig. 6(a). Similar plots for the velocities 1.06 cm/s and 0.32 cm/s are shown in Figs. 6(b) and 6(c) respectively. The tube is clearly visible, albeit with a relatively poor resolution as expected from DOT images. Different velocities provide different amounts of contrast. To quantify the observed changes in velocity, we assign a predetermined volume (matching the original position of the tube) which comprises of the rectangular region formed by (X=0.5 to 3.1 cm, Y=0.65 cm to 0.85 cm and Z=1.7 cm to 1.9 cm) with a total volume of 0.1040 cm3. 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]

].

Fig. 6 The three dimensional slice plot of the reconstructed flow velocity for original velocity of 3.18 cm/s, 1.06 cm/s and 0.43 cm/s are shown in (a), (b) and (c) respectively. The volume integral of the reconstructed velocity against the original velocity is shown in (d) where the volume integration is done in a predetermined volume.

5. Discussions and conclusion

We have presented a new tomographic imaging method, speckle contrast optical tomography (SCOT), to measure the blood flow distributed in deeper regions of the tissue. We have proposed and demonstrated that by scanning the point laser source over the sample and acquiring multiple speckle measurements simultaneously using a array of detectors like a CCD or CMOS camera, we are able to model the speckle statistics with a photon diffusion model, fit the model to the data to obtain information about sample dynamics and use in a tomographic inverse problem.

After deriving the physical model and a linearized inversion model, we have carried experiments in a liquid phantom with a cylindrical inclusion with varying flow rates of scatters in the transmission geometry. We were able to obtain three dimensional reconstructions and quantify the flow rate over a large range. For the 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.

In general, the inverse problem associated with the recovery of MSD from the speckle contrast measurement can be re-casted as nonlinear optimization problem. We have adopted the standard procedure to linearize the non-linear inverse problem using the Born approximation. We have derived the sensitivity relation connecting the perturbations in speckle contrast measurements from its baseline value to the changes in flow velocity. In the discretized geometry, the sensitivity relation is called the Jacobian which was plotted for a given source-detector pair to show that it preserves the so-called banana path as observed in DOT. We have further plotted the corrected speckle contrast as a function of spatial co-ordinates versus different values of flow velocity to show the sensitivity of the data for inversion to the perturbations in flow. Having computed the Jacobian and measure the perturbation in speckle contrast from its baseline value, we presented the recovery of the flow velocity using Tikhonov-regularized least square minimization. In a nutshell, SCOT overcomes the limited dynamic range and low SNR of DWS (and hence DCT) and lower penetration depth of LSF and provides a three dimensional distribution of flow in tissue using faster and cheaper instrumentation.

In past, various proposals were made to improve the depth of imaging. One study [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]

] proposed a line beam scanning illumination instead of the uniform illumination. However, while promising, this study did not develop a physical model that allowed a quantitative measurement. In another study [41

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 2, 1553–1563 (2011). [CrossRef] [PubMed]

], the authors have used a transmission geometry through the finger joint, 1.0–1.5 cm, but did not use a proper physical model or achieved any depth resolution. Depth sensitive speckle contrast measurement using a sinusoidally modulated source is used in [42

43. R. Bi, J. Dong, and K. Lee, “Deep tissue flowmetry based on diffuse speckle contrast analysis,” Opt. Lett. 38, 1401–1403 (2013). [CrossRef] [PubMed]

], where a pair of confined flows separated 2 mm apart and at a depth of 2 mm and 4 mm were measured. Although this work presents the relation connecting speckle contrast to the electric field autocorrelation for sinusoidal source excitation, a quantitative recovery of the effective flow velocity from the measured speckle contrast was not presented.

Previously, deep tissue blood flow measurements was made possible by diffuse correlation spectroscopy (DCS) [3

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

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

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]

] which relies on the measurement of the intensity autocorrelation of the random fluctuations of multiply-scattered light intensity. DCS is essentially a unification of the multiple scattering models for LSF and diffuse wave spectroscopy (DWS) in the setting of the tissue optics. A coherent laser point source is used in DCS which enables us to probe several centimetres of tissue with high temporal (≈100ms) but limited spatial(≈0.1 cm) resolution. Traditional DCS measurement employs individual detectors to capture the temporal statistics of intensity from each speckle [11

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]

, 12

12. D. A. Boas, “Diffuse photon probes of structural and dynamical properties of turbid media: theory and biomedical applications,” Ph.D. thesis, University of Pennsylvania (1996).

, 45

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]

].

We acknowledge that the spatial localization of the reconstructed flow velocity values needs further improvement as evident from the Figs. 6(a)–(d). This suggests us to use a denser scanning of the the source in the XZ plane as the slices near to the source positions are having good spatial localization of the flow profile. This also reduces the inherent ill-posedness associated with the inverse problems involving the diffusion equation. This type of denser scanning would have been prohibitively expensive and/or time consuming with the current DCT technologies. However, with SCOT, this can readily be implement.

The limited linear range in measuring the flow velocity as revealed by the plot in Fig. 6(d) suggest to adopt an iterative Born inversion instead of the first Born approximation. As discussed above, the estimation of errors associated with the Born’s approximation was previously carried out in the context of DOT [35

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]

] and was experimentally demonstrated [39

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

]. A more accurate nonlinear-iterative reconstruction algorithm can be adopted to improve the accuracy of the reconstruction as was already presented for DCT and DOT using numerical methods such as FEM [13

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

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

].

Deep tissue perfusion imaging is considered as one of the important imaging problems in the medical imaging research. In this work, we have developed and experimentally demonstrated a new optical method for three dimensional imaging of blood flow in tissues. Speckle contrast optical tomography (SCOT) is based on laser speckle contrast approaches and combines the physical models utilized in diffuse correlation tomography (DCT) with laser speckle flowmetry (LSF). The main potential advantage of SCOT is that it combines the deep perfusion imaging capability of DCS along with the rapid data acquisition possible in LSF even with a very dense sampling of sources and detectors without being prohibitively expensive. This makes SCOT a promising technique for deep perfusion imaging.

Acknowledgments

The project was funded by Fundació Cellex Barcelona, Marie Curie IRG (FP7, RTPAMON), Instituto de Salud Carlos III (DOMMON, FIS), Ministerio de Economía y Competitividad (PHOTOSTROKE), Institució CERCA ( DOCNEURO, PROVAT-002-11), Generalitat de Catalunya, European Regional Development Fund (FEDER/ERDF), LASERLAB Europe III (Bioptichal) and National Institutes of Health (NIH) grant R01-NS078223 (J.P.C). C.P.V. acknowledges Erasmus Mundus Joint Doctorate program Europhotonics grant No. 159224-1-2009-1-FR. The camera was provided courtesy of Hamamatsu Spain.

References and links

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]

2.

M. J. Leahy, J. G. Enfield, N. T. Clancy, J. ODoherty, P. McNamara, and G. E. Nilsson, “Biophotonic methods in microcirculation imaging,” Med. Laser Appl. 22, 105–126 (2007). [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]

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]

5.

C. Riva, B. Ross, and G. B. Benedek, “Laser doppler measurements of blood flow in capillary tubes and retinal arteries,” Invest. Ophthalmol. Visual Sci. 11, 936–944 (1972).

6.

M. Stern, “In vivo evaluation of microcirculation by coherent light scattering.” Nature 254, 56–58 (1975). [CrossRef] [PubMed]

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]

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]

12.

D. A. Boas, “Diffuse photon probes of structural and dynamical properties of turbid media: theory and biomedical applications,” Ph.D. thesis, University of Pennsylvania (1996).

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]

14.

H. M. Varma, B. Banerjee, D. Roy, A. K. Nandakumaran, and R. M. Vasu, “Convergence analysis of the newton algorithm and a pseudo-time marching scheme for diffuse correlation tomography,” J. Opt. Soc. Am. A 27, 259–267 (2010). [CrossRef]

15.

N. Hyvönen, A. K. Nandakumaran, H. M. Varma, and R. M. Vasu, “Generalized eigenvalue decomposition of the field autocorrelation in correlation diffusion of photons in turbid media,” Math. Meth. Appl. Sci. (2012).

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]

18.

T. Binzoni, T. S. Leung, D. Boggett, and D. Delpy, “Non-invasive laser doppler perfusion measurements of large tissue volumes and human skeletal muscle blood rms velocity,” Phys. Med. Biol. 48, 2527 (2003). [CrossRef] [PubMed]

19.

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

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]

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]

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]

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]

28.

R. Bonner and R. Nossal, “Model for laser doppler measurements of blood flow in tissue,” Appl. Opt , 20, 2097–2107 (1981). [CrossRef] [PubMed]

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 369, 4390–4406 (2011). [CrossRef]

30.

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

31.

C. Xu and Q. Zhu, “Light shadowing effect of large breast lesions imaged by optical tomography in reflection geometry,” J. Biomed. Opt. 15, 036003 (2010). [CrossRef] [PubMed]

32.

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

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]

34.

M. v. van Rossum and T. M. Nieuwenhuizen, “Multiple scattering of classical waves: microscopy, mesoscopy, and diffusion,” Rev. Mod. Phys. 71, 313 (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]

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. 28, 2061–2063 (2003). [CrossRef] [PubMed]

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. 37, 3774–3776 (2012). [CrossRef] [PubMed]

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. 43, 21–28 (2011). [CrossRef] [PubMed]

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 2, 1553–1563 (2011). [CrossRef] [PubMed]

43.

R. Bi, J. Dong, and K. Lee, “Deep tissue flowmetry based on diffuse speckle contrast analysis,” Opt. Lett. 38, 1401–1403 (2013). [CrossRef] [PubMed]

44.

R. Bi, J. Dong, and K. Lee, “Multi-channel deep tissue flowmetry based on temporal diffuse speckle contrast analysis,” Opt. Express 21, 22854–22861 (2013). [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]

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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References

  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]
  2. M. J. Leahy, J. G. Enfield, N. T. Clancy, J. ODoherty, P. McNamara, and G. E. Nilsson, “Biophotonic methods in microcirculation imaging,” Med. Laser Appl.22, 105–126 (2007). [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]
  4. T. Durduran and A. G. Yodh, “Diffuse correlation spectroscopy for non-invasive, micro-vascular cerebral blood flow measurement,” NeuroImage85, 51–63 (2014). [CrossRef]
  5. C. Riva, B. Ross, and G. B. Benedek, “Laser doppler measurements of blood flow in capillary tubes and retinal arteries,” Invest. Ophthalmol. Visual Sci.11, 936–944 (1972).
  6. M. Stern, “In vivo evaluation of microcirculation by coherent light scattering.” Nature254, 56–58 (1975). [CrossRef] [PubMed]
  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]
  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. Express14, 1125–1144 (2006). [CrossRef] [PubMed]
  12. D. A. Boas, “Diffuse photon probes of structural and dynamical properties of turbid media: theory and biomedical applications,” Ph.D. thesis, University of Pennsylvania (1996).
  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. A26, 1472–1483 (2009). [CrossRef]
  14. H. M. Varma, B. Banerjee, D. Roy, A. K. Nandakumaran, and R. M. Vasu, “Convergence analysis of the newton algorithm and a pseudo-time marching scheme for diffuse correlation tomography,” J. Opt. Soc. Am. A27, 259–267 (2010). [CrossRef]
  15. N. Hyvönen, A. K. Nandakumaran, H. M. Varma, and R. M. Vasu, “Generalized eigenvalue decomposition of the field autocorrelation in correlation diffusion of photons in turbid media,” Math. Meth. Appl. Sci. (2012).
  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]
  18. T. Binzoni, T. S. Leung, D. Boggett, and D. Delpy, “Non-invasive laser doppler perfusion measurements of large tissue volumes and human skeletal muscle blood rms velocity,” Phys. Med. Biol.48, 2527 (2003). [CrossRef] [PubMed]
  19. S. R. Arridge and J. C. Schotland, “Optical tomography: forward and inverse problems,” Inverse Problems25, 123010 (2009). [CrossRef]
  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]
  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. Transfer52, 713–727 (1994). [CrossRef]
  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. A14, 192–215 (1997). [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. Express2, 2047 (2011). [CrossRef] [PubMed]
  28. R. Bonner and R. Nossal, “Model for laser doppler measurements of blood flow in tissue,” Appl. Opt, 20, 2097–2107 (1981). [CrossRef] [PubMed]
  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., A369, 4390–4406 (2011). [CrossRef]
  30. S. R. Arridge, “Optical tomography in medical imaging,” Inverse Problems15, R41 (1999). [CrossRef]
  31. C. Xu and Q. Zhu, “Light shadowing effect of large breast lesions imaged by optical tomography in reflection geometry,” J. Biomed. Opt.15, 036003 (2010). [CrossRef] [PubMed]
  32. J. D. Briers, “Laser doppler, speckle and related techniques for blood perfusion mapping and imaging,” Physiol. Measurement22, R35 (2001). [CrossRef]
  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]
  34. M. v. van Rossum and T. M. Nieuwenhuizen, “Multiple scattering of classical waves: microscopy, mesoscopy, and diffusion,” Rev. Mod. Phys.71, 313 (1999). [CrossRef]
  35. V. A. Markel and J. C. Schotland, “On the convergence of the born series in optical tomography with diffuse light,” Inverse Problems23, 1445 (2007). [CrossRef]
  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.28, 2061–2063 (2003). [CrossRef] [PubMed]
  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.37, 3774–3776 (2012). [CrossRef] [PubMed]
  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.43, 21–28 (2011). [CrossRef] [PubMed]
  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. Express2, 1553–1563 (2011). [CrossRef] [PubMed]
  43. R. Bi, J. Dong, and K. Lee, “Deep tissue flowmetry based on diffuse speckle contrast analysis,” Opt. Lett.38, 1401–1403 (2013). [CrossRef] [PubMed]
  44. R. Bi, J. Dong, and K. Lee, “Multi-channel deep tissue flowmetry based on temporal diffuse speckle contrast analysis,” Opt. Express21, 22854–22861 (2013). [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. A14, 185–191 (1997). [CrossRef]

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