Review of laser speckle contrast techniques for visualizing tissue perfusion
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- Draijer, M., Hondebrink, E., van Leeuwen, T. et al. Lasers Med Sci (2009) 24: 639. doi:10.1007/s10103-008-0626-3
When a diffuse object is illuminated with coherent laser light, the backscattered light will form an interference pattern on the detector. This pattern of bright and dark areas is called a speckle pattern. When there is movement in the object, the speckle pattern will change over time. Laser speckle contrast techniques use this change in speckle pattern to visualize tissue perfusion. We present and review the contribution of laser speckle contrast techniques to the field of perfusion visualization and discuss the development of the techniques.
Imaging blood flow in the tissue is of major importance in the clinical environment [1–10]. Over recent decades, several techniques have been developed for imaging tissue perfusion. Most of these techniques [10–13] exploit the interference pattern generated from diffusely backscattered light from the skin . Currently, laser speckle contrast techniques are gaining interest [15, 16]. Laser speckle contrast techniques are based on the spatial and temporal statistics of the speckle pattern. The motion of particles in the illuminated medium causes fluctuations in the speckle pattern on the detector. These intensity fluctuations blur the image and reduce the contrast to an extent that is related to the speed of the illuminated objects, such as moving red blood cells. In this paper, we will present the principles and various implementations of the speckle contrast method, review the contribution of laser speckle contrast techniques to the field of perfusion imaging, and describe their technical development.
What are speckles?
What is speckle contrast?
Theories relating speckle contrast to particle speed
Recently, Duncan et al.  stated that the Lorentzian velocity distribution model is only applicable for Brownian motion whereas an inhomogeneous (Gaussian) distribution is valid for ordered motion. They claimed that the proper model for the combined effect (i.e., Brownian motion and ordered motion) is a Voigt velocity distribution, which is the result of a convolution of a Lorentzian and Gaussian velocity distribution.
Speckle contrast flow measurement techniques
Methods of measuring tissue blood flow with laser speckle contrast techniques
Contrast is determined in 1 image over 5 x 5 or 7 x 7 pixels.
Laser speckle imaging 
Contrast is determined in 1 pixel over 25 or 49 images.
A sequence of two rapid speckle recordings is taken in 1 image. The resulting fringes contain information about the movement.
Forerunner of LASCA, based on the same principle.
Laser speckle temporal contrast analysis 
Contrast is determined in one pixel over a sequence of images.
Spatial & temporal
Combination of LASCA and LSI.
Laser speckle flowgraphy 
Spatial & temporal
The contrast is determined based on an area of 3 x 3 pixels, in 3 speckle images.
Spatial derived contrast with averaging 
Contrast is determined based on averaging a sequence of LASCA-images.
Temporal laser speckle contrast analysis 
Spatial & temporal
Contrast is determined based on averaging a sequence of LSI-images.
Spatial laser speckle contrast analysis 
Contrast is determined based on averaging a sequence of LASCA-images.
Multi-exposure speckle imaging 
Contrast is determined in 1 image over 7 x 7 pixels. Exposure time is kept constant and T is controlled by laser pulse duration.
Double- and single-exposure speckle photography
Archbold and Ennos  invented double-exposure speckle photography [28, 29], a technique which was the forerunner of LASCA (see Section Laser speckle contrast analysis (LASCA)). Strictly speaking, double-speckle photography is not a speckle contrast technique since each of the two exposures is a snapshot rather than a blurred image. Double-exposure speckle photography is based on the principle that a photographic record of two identical and mutually displaced speckle structures gives rise to parallel straight fringes in the Fourier plane. The spacing and orientation of these fringes is related to the displacement and direction between both photographs. This makes the technique only applicable for solid bodies or fluids with a stationary flow pattern . Iwai and Shigeta  developed a digital version of double-exposure speckle photography. To obtain a velocity map, the whole image should be divided into small regions for analysis over which the velocity can be assumed to be spatially constant. The analysis of these fringe-patterns is complicated compared to analysis performed in speckle contrast techniques, which is a disadvantage.
The first real speckle contrast technique, using a long exposure time, was single-speckle photography . Single-exposure speckle photography  was a laborious process (i.e., making and developing a photograph and analysis of the negative film).
Laser speckle contrast analysis (LASCA)
LASCA is fast and inexpensive, but there are technical details which should be taken into account for proper measurements. To adjust the “sensitivity” of the LASCA setup to a certain velocity, the integration time can be adjusted. As the integration time changes, the noise in the measurement also changes. Yuan et al.  identified a relation linking sensitivity, noise, and camera exposure time. They found that with an increasing exposure time up to 2 ms, the sensitivity to relative speckle changes increased. However, the noise in the speckle contrast also increases with increasing exposure time. The optimal contrast-to-noise ratio was found to be at 5 ms, so Yuan et al. suggested that ∼5 ms is an optimal exposure time for LASCA measurements in the brains of rodents.
With “classical” LASCA, all depth information about perfusion is lost, so Zimnyakov and Misnin  modified the setup by making use of a localized moving light source in combination with spatial filtering to reveal depth-resolved information about the micro circulation. When a dynamic layer below a static layer is imaged, the resulting speckle pattern will be composed of a stationary speckle pattern in the inner zone of the CCD camera and a dynamic speckle pattern in the outer zone. So by placing filtering diaphragms on the sample, depth information can be obtained. As a consequence of the stationary speckle pattern, the contrast will not drop to 0 for long integration times. To quantify that Zimnyakov and Misnin introduced the term residual contrast.
Laser speckle imaging (LSI)
Nothdurft and Yao  showed that by adjusting the capture parameters (e.g., exposure time, incident power, and time interval between subsequent capture), LSI is able to reveal structures that are hidden under the surface. Surface and subsurface inhomogeneities depend differently on these capture parameters, so by tuning the capture parameters, the image contrast values of the surface and subsurface targets can be changed. When the contrast of the surface inhomogeneity is within the noise level of the background image, the surface effect is essentially removed from the image. They did not test LSI on tissue perfusion; that was done by Li et al.  who named the technique differently, Laser Speckle Temporal Contrast Analysis (LSTCA), but it is based on the same principle as LSI. They presented images of the cerebral blood flow of a rat through the intact skull by making use of temporal averaging of the speckle pattern. They used an exposure time of 5 ms, which is of the same order as that suggested by Yuan et al.  and an interval time of 25 ms, resulting in a real-time video frame rate of 33 Hz. They furthermore showed that LSTCA significantly improves the visualization of the blood vessels with respect to LASCA due to the fact that the speckle pattern on the detector is built up of a stationary and a dynamic part. They stated that the stationary part produced by the skull is mainly dependent on local properties of the skull and is therefore temporally homogeneous. So the contrast value in the LSTCA process is not influenced by the stationary part, whereas in the LASCA process, the stationary part will influence the contrast value and lower the SNR.
Völker et al.  modified LSI by positioning a rotating diffuser, which can be controlled by a motor, to illuminate the sample with a random speckle pattern. In this way, they could suppress the noise in LSI. If the diffuser rotates slowly (e.g., one rotation per hour), temporal fluctuations will occur at time scale τ0. However, if the exposure time T of the camera is chosen to be smaller than τ0, subsequent speckle images will be statistically independent and analyzing a large number of images results in the perfect averaging of the contrast without loss of spatial resolution. They showed that the noise level scales with N − 0.5, with N being the number of independent speckle images.
Bandyopadhyay et al.  and Zakharov et al.  pointed out recently that the commonly used LSI equation (i.e., Eq. 3) involves an approximation (i.e., τc ≪ T for Lorentzian velocity distribution) that could result in incorrect data analysis. Cheng and Duong  investigated the contribution of such approximation and its impact on LSI data analysis. They showed that the approximation is valid for calculating blood flow changes rather than absolute values for τc ≪ T.
Furthermore, they introduced a time-efficient LSI analysis method by making use of the asymptotic approximation of the commonly used LSI equation (i.e., Eq. 3) instead of using the Newtonian iterative method to solve that equation. Based on these findings, Parthasarathy et al.  presented a new multi-exposure speckle imaging (MESI) instrument based on their robust speckle model that has potential to obtain quantitative baseline flow measures and overcomes their criticism of LASCA and LSI (e.g., lack of quantitative accuracy and the inability to predict flows in the presence of static scatterers such as an intact or thinned skull). To keep the noise contribution of the camera (e.g., readout noise, thermal noise) constant while changing the integration time, they used a fixed exposure time for the camera and gated a laser diode during each exposure to effectively vary the speckle exposure duration T.
LASCA has the disadvantage of a lack of spatial resolution whereas LSI has the disadvantage of a lack of temporal resolution. Therefore, several researchers [52–56] have developed techniques which are combinations of LASCA and LSI. Forrester et al. [52, 53] developed Laser Speckle Perfusion Imaging (LSPI), Tan et al.  developed LASCA using Spatially Derived Contrast with Averaging (SDCav), Konishi et al.  developed Laser Speckle Flowgraphy (LSFG) and Le et al.  introduced tLASCA and sLASCA as temporal and spatial equivalents of LASCA.
Tan et al.  modified the “classical” LASCA to SDCav by introducing averaging over multiple contrast maps, resulting in a decrease in root mean square (RMS) of the value of 1/τc with an increasing number of averages. A few years later, this technique of averaging over multiple contrast maps was presented by Le et al.  under the name sLASCA. They also introduced tLASCA, a technique in which averaging in the spatial domain is performed on contrast maps obtained using LSI. They showed that tLASCA give better results and is faster than sLASCA and LSI.
All techniques discussed here have advantages and disadvantages. Imaging blood flow using LASCA gives a higher temporal resolution compared to LSI and LSFG, so for fast-changing perfusion levels it is the best candidate. LSI on the other hand provides the best spatial resolution, which makes it suitable for producing detailed perfusion images. LSFG is a combination between these two techniques, which makes it ideal for situations where a trade-off between temporal and spatial resolution is needed.
Usually, a speckle pattern is built up from a dynamic and a static part. As is shown by Yuan et al. , the static part does not influence the contrast in LSI, which results in a higher SNR for LSI compared to LASCA and LSFG.
The laser speckle contrast techniques discussed above can be used in a wide variety of biomedical applications, and several researchers have presented in-vivo results. DaCosta  used it to monitor the heartbeat of a human volunteer in a non-invasive way. Sadhwani et al.  showed that the thickness of a Teflon layer could be determined by using laser speckle contrast techniques, so both, they and Zimnyakov and Misnin , suggested that laser speckle contrast techniques could be used for burn depth diagnosis.
Richards and Briers  showed that contrast images obtained using LASCA give a good picture of the movement of red blood cells in the hand of a volunteer. Cheng and Duong  and Konishi et al.  even used LSI and LSFG, respectively, to map the ocular blood flow in the retina.
Ramirez-San-Juan et al.  used chicken chorioallantoic membrane (CAM) to prove that the use of the Gaussian-based approach reveals more details such as small vessels than the Lorentzian-based approach.
Several researchers reported contrast images of perfusion in rodents [5, 41, 42, 45, 55, 56, 59–64]. Yuan et al.  used changes in contrast images of the rat brain after electrical stimulation to obtain the optimal exposure time. Kubota  used LSFG to investigate the effects of diode laser therapy on blood flow in skin flaps in the rat model. To assess changes in blood flow during photo dynamic therapy (PDT), Kruijt et al.  used LSPI to monitor the vasculature response in arteries, veins and tumor microvasculature in a rat skin-fold observation chamber. Smith et al.  used contrast images to image the microvascular blood flow using an in vivo rodent dorsal skin-fold model during PDT, pulsed dye laser (PDL) irradiation and a combination of both on port wine stains. Dunn et al.  used LASCA to map the cerebral flow of a rat and simultaneously measure the perfusion using a laser Doppler probe. They showed that there is a good agreement between the flow in-vivo measured with both techniques. Several researchers like Cheng et al. , Tan et al. , Li et al. , Murari et al.  and Le et al. , did similar work to image the cerebral flow but used temporal averaging. Zhu et al.  monitored thermal-induced changes in tumor blood flow and microvessels in mice by using LASCA, and showed that deformation of vessel is a main factor for changing the blood perfusion of a microvessel.
Besides visualizing blood flow, LASCA can also be used to characterize the composition of atherosclerotic plaques, as achieved by Nadkarni et al. [66, 67], who measured the speckle decorrelation time τc, which provides an index of plaque viscoelasticity and helps characterize the composition of the plaque, which can be used to identify high-risk lesions. They showed that LASCA is highly sensitive to changes in the plaque composition so it can be used to identify thin-cap fibroatheromas.
Comparison with laser Doppler perfusion imaging
Currently, there are two major techniques that are used to image tissue perfusion. Besides laser speckle contrast techniques, laser Doppler perfusion imaging (LDPI) is used to image the perfusion. In LDPI, optical Doppler shifts are analyzed from the temporal intensity fluctuations that are caused by the dynamic speckle pattern. A number of locally measured power spectra of these intensity fluctuations is converted into a perfusion image.
Until recently, LASCA had the advantage over LDPI of being a full-field technique, whereas LDPI was a scanning technique. This scanning mode resulted in long measurement times, which made LDPI less favorable for the clinical environment. This advantage decreased when LDPI became a full-field technique by introducing a high-speed CMOS camera for the detection of the Doppler-shifted light [68–71]. From that moment on, both techniques had a measurement time in the millisecond range.
The introduction of the high-speed CMOS cameras in LDPI directly reveals another advantage of LASCA over LDPI. To perform LASCA measurements, an inexpensive camera that can achieve a frame-rate of 200 Hz (i.e., an integration time of 5 ms) is sufficient, whereas for LDPI, a state-of-the-art high-speed camera that can achieve a frame-rate of about 25 kHz is needed.
On the other hand, the physics behind LDPI is well known and it is shown that, for low blood concentrations, the concentration of red blood cells and their average velocity are both linearly represented by the perfusion estimation given in LDPI. Bonner and Nossal  published a widely accepted theoretical model of laser Doppler measurements to determine these parameters of blood flow in tissue.
For LASCA and related speckle contrast techniques, a model linking the measurement outcome to the perfusion, is not available. The reading of LASCA is based on blurring of the speckles on the detector. To link this blurring with the average velocity of red blood cells, assumptions should be made about an appropriate velocity distribution (e.g., Lorentzian, Gaussian, Voigt) the fraction of moving red blood cells and other parameters (e.g., particle size). With the wide variety of biological applications, this is a major challenge. So yet there is no proper model linking the speckle contrast to the perfusion. To our knowledge, determination of the concentration of red blood cells with LASCA has not been shown to be possible.
Forrester et al.  compared LDPI with some of the laser speckle contrast techniques discussed in this paper. They imaged digits of a human hand and the joint capsule and muscle in a rabbit knee, and suggested that laser speckle contrast techniques are a good and fast alternative for LDPI and therefore should be further developed. Furthermore, the higher temporal resolution of LASCA made it more sensitive to the hyperaemic response after an occlusion. Besides Forrester et al., several other researchers have performed a comparison between both techniques [51, 71–73]. Briers  compared both techniques from a more theoretical point of view and postulated the essential equivalence of both techniques. He therefore encouraged some cross fertilization of ideas between both techniques. Serov and Lasser  compared LASCA and LDI in their hybrid imaging system. They not only compared imaging quality and speed but also sensitivity for flow parameters such as speed and concentration. In their measurements, LASCA turned out be faster (i.e., ten frames per second) but had a poorer spatial resolution. Thompson and Andrews  postulated a method to gain the quantitative advantages of LDPI while keeping the speed of LASCA. They claim that by making use of a temporal autocorrelation function of the LASCA measurement, a perfusion index comparable to the index of LDPI can be obtained.
Speckle contrast techniques are gaining interest in the field of tissue perfusion imaging. In this paper, we have presented the principles and various implementations of the speckle contrast methods, reviewed the contribution of these techniques to the field of perfusion imaging and described their technical development.
Speckle contrast techniques have advantages over their main counterpart, laser Doppler perfusion imaging (LDPI). Speckle contrast techniques need only one or a few frames to determine the tissue perfusion, which makes it fast. They also need a low-frame-rate camera only, which makes them inexpensive techniques. However, they also have one major disadvantage with respect to LDPI; the readings in LDPI can be related to the Doppler effect, which is described by a theory that is widely accepted and understood. For LASCA this is not the case, since, for example, it is still unknown which velocity distribution (e.g., Voigt, Lorentzian, or Gaussian) should be used. The need to assume a specific velocity distribution to relate the speckle contrast to the tissue perfusion makes the technique less generally applicable.
Once there is a consensus about a theoretical model for LASCA that connects the contrast unambiguously to the perfusion level, it can become one of the leading techniques for measuring tissue-perfusion maps.
We thank Jurjen Couperus and Koen Thuijs for their involvement in setting up the LASCA-instrument. This work has been funded by the Technology Foundation STW (Grant No 06443) and Perimed AB, Sweden.
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