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Optics Express

Optics Express

  • Editor: Andrew M. Weiner
  • Vol. 21, Iss. 19 — Sep. 23, 2013
  • pp: 22854–22861
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Multi-channel deep tissue flowmetry based on temporal diffuse speckle contrast analysis

Renzhe Bi, Jing Dong, and Kijoon Lee  »View Author Affiliations


Optics Express, Vol. 21, Issue 19, pp. 22854-22861 (2013)
http://dx.doi.org/10.1364/OE.21.022854


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Abstract

Recently, diffuse speckle contrast analysis (DSCA) was introduced as a competent modality for deep tissue blood flow measurement, where the speckle contrast is calculated over spatial domain on the CCD image of diffuse reflectance. In this paper, we introduce time-domain DSCA where temporal statistics are used for speckle contrast calculation and results in the same deep tissue flow measurement. This new modality is especially suitable for multi-channel real-time flowmetry, and we demonstrate its performance on human arm during cuff occlusion test. Independent component analysis (ICA) study on multi-channel data shows promising results about underlying physiology.

© 2013 OSA

1. Introduction

Blood perfusion, or microcirculation, plays a critical role in human health, because it is responsible for transport of oxygen and nutrients to tissue, as well as blood pressure regulation. Compromised blood perfusion can lead to various diseases, such as stroke and cerebral infarction. Existing methods for blood perfusion measurement being used in clinical environment include Laser Doppler Flowmetry (LDF), contrast-enhanced computational tomography (CT) and MRI based Arterial Spin Labeling (ASL), etc. However, each of them has its own limitation: LDF is mostly applied on superficial layer, CT and MRI suffer from lack of flexibility and high expense.

Although DSCA has many advantages over existing modalities, it is not without disadvantages. For blood perfusion measurement, the CCD camera can’t be easily mounted on the subject’s skin. Furthermore, any movement of the subject can cause defocus which affects the result very much. Thus in real practice, the fiber based probe that can be fixed on the skin surface is preferred. One way to achieve this in DSCA is to incorporate temporal laser speckle contrast analysis (TLSCA) [18

18. D. A. Boas and A. K. Dunn, “Laser speckle contrast imaging in biomedical optics,” J. Biomed. Opt. 15(1), 011109 (2010). [CrossRef] [PubMed]

, 24

24. P. C. Li, S. L. Ni, L. Zhang, S. Q. Zeng, and Q. M. Luo, “Imaging cerebral blood flow through the intact rat skull with temporal laser speckle imaging,” Opt. Lett. 31(12), 1824–1826 (2006). [CrossRef] [PubMed]

] to make a multi-channel device out of one CCD camera. Here temporal laser speckle contrast Kt is applied for deep tissue blood flow measurement rather than spatial laser speckle contrast Ks.

In this paper, a cost effective multi-channel deep tissue flowmetry based on the temporal domain diffuse speckle contrast analysis (tDSCA) method is introduced. Compared with spatial domain DSCA (sDSCA), tDSCA is more practical to serve as a bedside blood flow monitor, and multi-channel friendly. Because one CCD camera is inherently a multi-channel detector with millions of pixels, the tDSCA device can be expanded into multiple channels without loss of temporal resolution or additional cost. Both flow-controlled phantom experimental result and in-vivo result are demonstrated and discussed with respect to simulation results.

2. Theory

In LSCA, Ks is defined as the ratio of the spatial standard deviation of the speckle intensity to the mean intensity [25

25. R. Bandyopadhyay, A. S. Gittings, S. S. Suh, P. K. Dixon, and D. J. Durian, “Speckle-visibility spectroscopy: A tool to study time-varying dynamics,” Rev. Sci. Instrum. 76(9), 093110–093121 (2005). [CrossRef]

27

27. A. F. Fercher and J. D. Briers, “Flow visualization by means of single-exposure speckle photography,” Opt. Commun. 37(5), 326–330 (1981). [CrossRef]

],
Ks=σsI
(1)
where the subscript s means spatial operation. Usually 7 × 7 adjacent pixels from one single image are used to calculate Ks [18

18. D. A. Boas and A. K. Dunn, “Laser speckle contrast imaging in biomedical optics,” J. Biomed. Opt. 15(1), 011109 (2010). [CrossRef] [PubMed]

]. However in TLSCA, temporal standard deviation of the speckle intensity σt is applied instead of σs, and Kt is computed otherwise in the same way as Ks. Thus TLSCA requires multiple images of the same area, out of which Kt is calculated for each pixel [24

24. P. C. Li, S. L. Ni, L. Zhang, S. Q. Zeng, and Q. M. Luo, “Imaging cerebral blood flow through the intact rat skull with temporal laser speckle imaging,” Opt. Lett. 31(12), 1824–1826 (2006). [CrossRef] [PubMed]

]. There exists trade-off between spatial and temporal resolutions, where the spatial analysis offers better temporal resolution while the temporal analysis offers better spatial resolution.

It has been proven that given certain exposure time, the speckle contrast K is determined by the normalized electric field autocorrelationg1(τ). The relationship is given by [25

25. R. Bandyopadhyay, A. S. Gittings, S. S. Suh, P. K. Dixon, and D. J. Durian, “Speckle-visibility spectroscopy: A tool to study time-varying dynamics,” Rev. Sci. Instrum. 76(9), 093110–093121 (2005). [CrossRef]

]
K2(T)=V(T)=2βT0T(1τ/T)[g1(τ)]2dτ
(2)
where T is the exposure time, V is the normalized variance, τ is delay time, β is a constant that depends on the ratio of detector size to the speckle size and polarization. If the detector size is comparable to the speckle size, β is known to be 0.5 for polarized source and unpolarized detector. Although g1(τ) is mostly assumed as simple exponential form in LSCA where mostly singly scattered photons are detected, Eq. (2) holds for general cases, including semi-infinite reflection geometry of diffuse medium, where light diffusely propagates into deep tissue where scatterers move (microcirculation) and back out to the surface before being detected.

In DCS, under the assumption of homogeneous semi-infinite boundary condition, BFI can be derived from unnormalized electric field autocorrelation function G1(τ) which has the following expression [4

4. T. Durduran, R. Choe, W. B. Baker, and A. G. Yodh, “Diffuse optics for tissue monitoring and tomography,” Rep. Prog. Phys. 73(7), 076701 (2010). [CrossRef]

, 6

6. J. Dong, R. Bi, J. H. Ho, P. S. P. Thong, K.-C. Soo, and K. Lee, “Diffuse correlation spectroscopy with a fast Fourier transform-based software autocorrelator,” J. Biomed. Opt. 17(9), 097004 (2012). [CrossRef] [PubMed]

],
G1(r,τ)=3μs'4π[exp(kD(τ)r1)r1exp(kD(τ)r2)r2].
(3)
HerekD(τ)=3μs'μa+αμs'2k02r2(τ), μs' is reduced scattering coefficient, μa is absorption coefficient, α is the fraction of dynamic photon scattering events in medium, r1=r2+z02,r2=r2+(z0+2zb)2, and r is source-detector separation, z0=1/μs', zb=2(1Reff)/3μs'(1+Reff), where Reff represents effective reflection coefficient. r2(τ)represents the mean square displacement of the moving scatterers after a delay time τ. After repeated observation that the real experimental data fitted better with Brownian motion model than with random flow model, r2(τ)=6Dbτ became widely accepted, where Db is the effective diffuse coefficient [4

4. T. Durduran, R. Choe, W. B. Baker, and A. G. Yodh, “Diffuse optics for tissue monitoring and tomography,” Rep. Prog. Phys. 73(7), 076701 (2010). [CrossRef]

, 6

6. J. Dong, R. Bi, J. H. Ho, P. S. P. Thong, K.-C. Soo, and K. Lee, “Diffuse correlation spectroscopy with a fast Fourier transform-based software autocorrelator,” J. Biomed. Opt. 17(9), 097004 (2012). [CrossRef] [PubMed]

]. Furthermore, several groups have shown that the fitted αDb correlates well with other blood flow measurement modalities, such as ASL-MRI and transcranial Doppler ultrasound [4

4. T. Durduran, R. Choe, W. B. Baker, and A. G. Yodh, “Diffuse optics for tissue monitoring and tomography,” Rep. Prog. Phys. 73(7), 076701 (2010). [CrossRef]

, 28

28. S. A. Carp, G. P. Dai, D. A. Boas, M. A. Franceschini, and Y. R. Kim, “Validation of diffuse correlation spectroscopy measurements of rodent cerebral blood flow with simultaneous arterial spin labeling MRI; towards MRI-optical continuous cerebral metabolic monitoring,” Biomed. Opt. Express 1(2), 553–565 (2010). [CrossRef] [PubMed]

30

30. E. M. Buckley, D. Hance, T. Pawlowski, J. Lynch, F. B. Wilson, R. C. Mesquita, T. Durduran, L. K. Diaz, M. E. Putt, D. J. Licht, M. A. Fogel, and A. G. Yodh, “Validation of diffuse correlation spectroscopic measurement of cerebral blood flow using phase-encoded velocity mapping magnetic resonance imaging,” J. Biomed. Opt. 17(3), 037007 (2012). [CrossRef] [PubMed]

]. Thus αDb is accepted as BFI in DCS practice, despite the fact that its dimension is neither of flow rate nor of speed. In this paper we also follow this convention, using αDb as BFI.

We used the similar optical properties as human tissue, where µs = 8 cm−1 and µa = 0.03 cm−1. From the simulation result, 1/K2 is shown to be an increasing function of αDb, with a gradually decreasing slope. However, in the lower range of αDb, the relation is close to linearity. Fortuitously, the reported physiologically relevant maximum values of in vivo αDb are located in the approximate linear range (~1 × 10−6 cm2/s) [4

4. T. Durduran, R. Choe, W. B. Baker, and A. G. Yodh, “Diffuse optics for tissue monitoring and tomography,” Rep. Prog. Phys. 73(7), 076701 (2010). [CrossRef]

, 7

7. L. Gagnon, M. Desjardins, J. Jehanne-Lacasse, L. Bherer, and F. Lesage, “Investigation of diffuse correlation spectroscopy in multi-layered media including the human head,” Opt. Express 16(20), 15514–15530 (2008). [CrossRef] [PubMed]

, 8

8. C. Zhou, G. Q. Yu, D. Furuya, J. H. Greenberg, A. G. Yodh, and T. Durduran, “Diffuse optical correlation tomography of cerebral blood flow during cortical spreading depression in rat brain,” Opt. Express 14(3), 1125–1144 (2006). [CrossRef] [PubMed]

, 10

10. E. M. Buckley, N. M. Cook, T. Durduran, M. N. Kim, C. Zhou, R. Choe, G. Yu, S. Schultz, C. M. Sehgal, D. J. Licht, P. H. Arger, M. E. Putt, H. H. Hurt, and A. G. Yodh, “Cerebral hemodynamics in preterm infants during positional intervention measured with diffuse correlation spectroscopy and transcranial Doppler ultrasound,” Opt. Express 17(15), 12571–12581 (2009). [CrossRef] [PubMed]

, 29

29. G. Q. Yu, T. F. Floyd, T. Durduran, C. Zhou, J. J. Wang, J. A. Detre, and A. G. Yodh, “Validation of diffuse correlation spectroscopy for muscle blood flow with concurrent arterial spin labeled perfusion MRI,” Opt. Express 15(3), 1064–1075 (2007). [CrossRef] [PubMed]

]. This is the theoretical basis for DSCA that 1/K2 can be used as a substitute for the conventional BFI in DCS.

3. Instrument and method

In our 3-channel tDSCA setup, the light source is a continuous-wave laser with a long coherence length (>10 meter) operating at 785 nm (DL785-100-S, ~100mW, CrystaLaser, Reno, Nevada, USA). A high quality VGA camera (Stingray F033, Allied Vision Technology) is used as detector. As shown in Fig. 2(a)
Fig. 2 Schematic of (a) tDSCA setup and (b) the phantom experiment. S1, S2 and S3 are 50:50 fiber splitters, PD is photon detector for power monitoring. Small glass beads are filled inside the hollow tube which is embedded in the solid phantom to provide randomized interstitial space for the flow.
, the laser source is divided equally into four multi-mode fibers with three cascading 50:50 fiber splitters, where three of them are used as source fibers in the probes, and the other one is used for source power monitoring. Three single mode fibers are used as detector fibers in the probes, with the other ends touching onto the CCD chip directly. The CCD camera is controlled by a laptop, with LabVIEW program (National Instrument).

We set the frame rate at 60 fps, and calculate 1/Kt2 every 1 second with 60 data points. Thus the update rate for tDSCA system is 1 Hz. The moving average of 1/Kt2 with a window size of 4 is applied for further data smoothing.

Before the tDSCA test, we performed DCS measurement on the upper surface of the phantom by our software correlator-based DCS system [6

6. J. Dong, R. Bi, J. H. Ho, P. S. P. Thong, K.-C. Soo, and K. Lee, “Diffuse correlation spectroscopy with a fast Fourier transform-based software autocorrelator,” J. Biomed. Opt. 17(9), 097004 (2012). [CrossRef] [PubMed]

], with a source-detector separation of 3 cm. The flow rate was controlled from 0 to 0.35 ml/s, in steps of 0.05 ml/s.

4. Result

After the validation study using flow phantom, we performed in vivo experiment on human forearm and palm using cuff occlusion protocol, with exposure time of 0.2 ms. Two probes were placed on the surface of the inside of forearm, and one more probe was placed on the surface of the hypothenar area, with the same source-detector separation of 1.5 cm. During the experiment, we measured the baseline for 100 s. Then a cuff occlusion was applied on upper arm with a pressure of 200 mmHg for about 75 s, which would lead to a dramatic decrease of blood flow in the forearm and palm. Finally the pressure was released, and the recovery period was also recorded.

The responses from three channels are plotted in Fig. 4
Fig. 4 Results from 3-channel tDSCA system during in vivo experiment with arm-cuff protocol. The positions of the three probes are indicated by corresponding colors on the arm.
. The two channels on forearm show very similar response, while the other channel on palm shows much higher blood flow reading during baseline. The palm channel also shows quite different reactive hyperemia pattern than the other two after the release of the cuff. However, the results from all channels are very similar in amplitude during cuff occlusion period, during which the microcirculation becomes minimal.

The higher baseline BFI in palm can be explained by the presence of many well-perfused small muscles in palm, which makes the palm look red. During arterial cuff occlusion, however, the blood flow underneath the skin is very low in the whole upper limb equally, because the blood supply is blocked. Thus there is little difference of BFI among all channels during cuff occlusion.

5. Discussion

In DSCA, we can take reference from LSCI on the optimal exposure time issue. It is suggested that the exposure time T should be around τc where g1(τ)=1/e, to obtain the best sensitivity to particle dynamics [31

31. S. Yuan, A. Devor, D. A. Boas, and A. K. Dunn, “Determination of optimal exposure time for imaging of blood flow changes with laser speckle contrast imaging,” Appl. Opt. 44(10), 1823–1830 (2005). [CrossRef] [PubMed]

]. Thus T should be about 0.1~1 ms in typical DSCA measurement. We used exposure time of 0.2 ms in our tDSCA system, and it worked properly.

From our simulation result in Fig. 1, there is one-to-one correspondence between αDb and 1/K2, when optical properties, β and exposure time are given. Thus the simulation result can serve as the calibration curve from 1/K2 to the BFI in DCS. Because of the approximate linear relationship in physiologically relevant range of αDb, it is straight forward to use 1/K2 to indicate rBF as a function of time for specific site.

The noise-like fluctuation in the baseline of our in vivo result is due to the low frequency oscillation (LFO) in normal blood flow regulation, not the instrument noise. The main frequency components (0.06 Hz in palm and 0.07 Hz in arm) correspond well with in vivo LFO frequency band [32

32. R. Cheng, Y. Shang, D. Hayes Jr, S. P. Saha, and G. Q. Yu, “Noninvasive optical evaluation of spontaneous low frequency oscillations in cerebral hemodynamics,” Neuroimage 62(3), 1445–1454 (2012). [CrossRef] [PubMed]

, 33

33. P. Kvandal, S. A. Landsverk, A. Bernjak, A. Stefanovska, H. D. Kvernmo, and K. A. Kirkebøen, “Low-frequency oscillations of the laser Doppler perfusion signal in human skin,” Microvasc. Res. 72(3), 120–127 (2006). [CrossRef] [PubMed]

]. Moreover, during cuff occlusion, the readings are much smoother without obvious fluctuation. This confirms the stability of the tDSCA device and certifies that the fluctuation in the baseline origins from blood flow regulation.

We further attempted to separate out the LFO signal from the fluctuating baseline signal using ICA, and the result is promising as shown in Fig. 5. Although one of the positions (palm) is quite far from the other two (arm 1 and arm 2), we were able to see common behaviors among different channels as same brachial artery is responsible for perfusing whole lower arm. We anticipate that a systematic study with more number of channels that cover broader area on human body will result in interesting physiological assessment which is impossible using a single channel perfusion monitoring.

6. Conclusion

In conclusion, tDSCA is reported for the first time for deep tissue blood flow measurement, and a cost effective multi-channel tDSCA flowmetry device based on single CCD camera is demonstrated. tDSCA is clearly a more practical choice for bedside blood flow monitoring on multiple sites than sDSCA is. Taking the advantages of simple instrumentation, easy analysis and low cost into account, tDSCA can be regarded as a good alternative for DCS.

In the future, high sensitivity and high frame rate CCD can greatly increase the time resolution of tDSCA, which will enable observation of fast responses on multiple positions at the same time.

Acknowledgment

This study was partly funded by the Ministry of Education in Singapore, Academic Research Fund Tier 1 grant (RG37/07). The authors wish to thank Dr. Hojin for the inspirational discussion we had.

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2.

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T. Durduran, R. Choe, W. B. Baker, and A. G. Yodh, “Diffuse optics for tissue monitoring and tomography,” Rep. Prog. Phys. 73(7), 076701 (2010). [CrossRef]

5.

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 A Math Phys Eng Sci 369(1955), 4390–4406 (2011). [CrossRef] [PubMed]

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J. Dong, R. Bi, J. H. Ho, P. S. P. Thong, K.-C. Soo, and K. Lee, “Diffuse correlation spectroscopy with a fast Fourier transform-based software autocorrelator,” J. Biomed. Opt. 17(9), 097004 (2012). [CrossRef] [PubMed]

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L. Gagnon, M. Desjardins, J. Jehanne-Lacasse, L. Bherer, and F. Lesage, “Investigation of diffuse correlation spectroscopy in multi-layered media including the human head,” Opt. Express 16(20), 15514–15530 (2008). [CrossRef] [PubMed]

8.

C. Zhou, G. Q. Yu, D. Furuya, J. H. Greenberg, A. G. Yodh, and T. Durduran, “Diffuse optical correlation tomography of cerebral blood flow during cortical spreading depression in rat brain,” Opt. Express 14(3), 1125–1144 (2006). [CrossRef] [PubMed]

9.

N. Roche-Labarbe, A. Fenoglio, H. Radhakrishnan, M. Kocienski-Filip, S. A. Carp, J. Dubb, D. A. Boas, P. E. Grant, and M. A. Franceschini, “Somatosensory evoked changes in cerebral oxygen consumption measured non-invasively in premature neonates ,” Neuroimage , (2013) Epub 28 Jan 2013 ahead of print.

10.

E. M. Buckley, N. M. Cook, T. Durduran, M. N. Kim, C. Zhou, R. Choe, G. Yu, S. Schultz, C. M. Sehgal, D. J. Licht, P. H. Arger, M. E. Putt, H. H. Hurt, and A. G. Yodh, “Cerebral hemodynamics in preterm infants during positional intervention measured with diffuse correlation spectroscopy and transcranial Doppler ultrasound,” Opt. Express 17(15), 12571–12581 (2009). [CrossRef] [PubMed]

11.

T. Durduran, C. Zhou, B. L. Edlow, G. Yu, R. Choe, M. N. Kim, B. L. Cucchiara, M. E. Putt, Q. Shah, S. E. Kasner, J. H. Greenberg, A. G. Yodh, and J. A. Detre, “Transcranial optical monitoring of cerebrovascular hemodynamics in acute stroke patients,” Opt. Express 17(5), 3884–3902 (2009). [CrossRef] [PubMed]

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24.

P. C. Li, S. L. Ni, L. Zhang, S. Q. Zeng, and Q. M. Luo, “Imaging cerebral blood flow through the intact rat skull with temporal laser speckle imaging,” Opt. Lett. 31(12), 1824–1826 (2006). [CrossRef] [PubMed]

25.

R. Bandyopadhyay, A. S. Gittings, S. S. Suh, P. K. Dixon, and D. J. Durian, “Speckle-visibility spectroscopy: A tool to study time-varying dynamics,” Rev. Sci. Instrum. 76(9), 093110–093121 (2005). [CrossRef]

26.

J. D. Briers and S. Webster, “Laser speckle contrast analysis (LASCA): a nonscanning, full-field technique for monitoring capillary blood flow,” J. Biomed. Opt. 1(2), 174–179 (1996). [CrossRef] [PubMed]

27.

A. F. Fercher and J. D. Briers, “Flow visualization by means of single-exposure speckle photography,” Opt. Commun. 37(5), 326–330 (1981). [CrossRef]

28.

S. A. Carp, G. P. Dai, D. A. Boas, M. A. Franceschini, and Y. R. Kim, “Validation of diffuse correlation spectroscopy measurements of rodent cerebral blood flow with simultaneous arterial spin labeling MRI; towards MRI-optical continuous cerebral metabolic monitoring,” Biomed. Opt. Express 1(2), 553–565 (2010). [CrossRef] [PubMed]

29.

G. Q. Yu, T. F. Floyd, T. Durduran, C. Zhou, J. J. Wang, J. A. Detre, and A. G. Yodh, “Validation of diffuse correlation spectroscopy for muscle blood flow with concurrent arterial spin labeled perfusion MRI,” Opt. Express 15(3), 1064–1075 (2007). [CrossRef] [PubMed]

30.

E. M. Buckley, D. Hance, T. Pawlowski, J. Lynch, F. B. Wilson, R. C. Mesquita, T. Durduran, L. K. Diaz, M. E. Putt, D. J. Licht, M. A. Fogel, and A. G. Yodh, “Validation of diffuse correlation spectroscopic measurement of cerebral blood flow using phase-encoded velocity mapping magnetic resonance imaging,” J. Biomed. Opt. 17(3), 037007 (2012). [CrossRef] [PubMed]

31.

S. Yuan, A. Devor, D. A. Boas, and A. K. Dunn, “Determination of optimal exposure time for imaging of blood flow changes with laser speckle contrast imaging,” Appl. Opt. 44(10), 1823–1830 (2005). [CrossRef] [PubMed]

32.

R. Cheng, Y. Shang, D. Hayes Jr, S. P. Saha, and G. Q. Yu, “Noninvasive optical evaluation of spontaneous low frequency oscillations in cerebral hemodynamics,” Neuroimage 62(3), 1445–1454 (2012). [CrossRef] [PubMed]

33.

P. Kvandal, S. A. Landsverk, A. Bernjak, A. Stefanovska, H. D. Kvernmo, and K. A. Kirkebøen, “Low-frequency oscillations of the laser Doppler perfusion signal in human skin,” Microvasc. Res. 72(3), 120–127 (2006). [CrossRef] [PubMed]

OCIS Codes
(030.6140) Coherence and statistical optics : Speckle
(120.3890) Instrumentation, measurement, and metrology : Medical optics instrumentation
(290.1990) Scattering : Diffusion

ToC Category:
Medical Optics and Biotechnology

History
Original Manuscript: June 12, 2013
Revised Manuscript: August 10, 2013
Manuscript Accepted: September 6, 2013
Published: September 20, 2013

Virtual Issues
Vol. 8, Iss. 10 Virtual Journal for Biomedical Optics

Citation
Renzhe Bi, Jing Dong, and Kijoon Lee, "Multi-channel deep tissue flowmetry based on temporal diffuse speckle contrast analysis," Opt. Express 21, 22854-22861 (2013)
http://www.opticsinfobase.org/oe/abstract.cfm?URI=oe-21-19-22854


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References

  1. R. Bi, J. Dong, and K. Lee, “Deep tissue flowmetry based on diffuse speckle contrast analysis,” Opt. Lett.38(9), 1401–1403 (2013). [CrossRef] [PubMed]
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