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7.4 Further Analysis of the Consequence of Speckle in LDPI 143
of absorption and scattering coefficients was studied by Monte Carlo simulations.
We simulated [19] the situation of a system with a Gaussian beam with 1/e
2
diameter of 1mm. The simulated tissue configuration was a static matrix with thin
dynamic layers at a mutual spacing of 0.25 mm. We recorded both the detection
positions on the tissue surface and the Doppler shift of all detected photons. From
these, the fractions of Doppler-shifted photons f
1
and unshifted photons f
0
, and
the intensity distributions of Doppler-shifted and unshifted light, can be calculated
for use in Eqs. (7.9) and (7.10). While the properties of the dynamic layers were
kept constant, the levels of absorption and scattering were varied. The results are
shown in Figure 7.6a for only the photon statistics and in Figure 7.6b for both
0.016
0.012
0.008
Overall sensitivity
0.004
Absorption coefficient (mm)
−1
0.01 0.02
0.080.04
Absorption coefficient (mm)
−1
0.01 0.02
0.080.04
12
Reduced scattering coefficient (mm)
−1
34
0.000012
0.000016
0.000020
0.000024
Overall sensitivity
12
Reduced scattering coefficient (mm)
−1
34
(a)
(b)
Figure 7.6 Simulated overall sensitivity [20] of scanning
beam LDPI system with 1 mm beam diameter for a range
of absorption and reduced scattering coefficients. (a) Based
on photon statistics only. (b) Speckle effects included.
(Source: Printed with permission of SPIE.)
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144 7 Speckle Effects in Laser Doppler Perfusion Imaging
photon statistics and the speckle effect. Both show a completely different picture.
On the basis of photon statistics only, sensitivity decreases with both absorption
and scattering level of the static medium, since both will reduce the path length
of photons and therefore the probability of Doppler shifting. However, when the
speckle effect is incorporated, the sensitivity increases with the level of scattering
and absorption. Both can be explained, with reference to Figure 7.5, from the fact
that increased absorption and/or scattering leads to a smaller effective detector,
and therefore a larger value of N. These variations completely overrule the trends
that are observed from photon statistics only.
1
0.1
Modulation depth
0.01
m
a
= 0.01 mm
−1
Reduced scattering coefficient (mm
−1
)
1.0 2.0
4.0
3.0
0.0 0.5 1.0
Depth of motion (mm)
1.5 2.0
0.01
Absorption coefficient (mm
−1
)
0.02
0.080.04
0.2
123
Reduced scattering coefficient (mm
−1
)
4
Mean sampling depth (mm)
0.4
0.6
0.8
(a)
(b)
Figure 7.7 Simulated depth sensitivity [20]. (a) Sensitiv-
ity versus motion depth, for a range of reduced scatter-
ing coefficients. All results normalized to unit sensitivity at
zero depth. (b) Mean sampling depth. White symbols: only
photon statistics. Black symbols: speckle effects included.
(Source: Printed with permission from SPIE.)
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References 145
7.4.2
Depth Sensitivity
The same simulated medium as in the previous paragraph was used to analyze
the sensitivity for motion at a certain depth. The result is shown in Figure 7.7a,
which shows the sensitivity versus motion depth for a certain absorption coefficient
and a range of scattering coefficients, and in Figure 7.7b, which shows the mean
sampling depth. Results are based on photon statistics only (White symbols) and
with inclusion of the speckle effect (black symbols). We observe that the effect of
the factor 1/N in Eq. (7.8) is that the sampling depth is largely reduced. This can be
explained by the fact that photons that penetrate deeper in the medium will drift
further away from the point of injection and will therefore occupy a larger part of the
detector. Our results show that the effect is a suppression of sensitivity to motion at
a larger depth. An experimental verification of this behavior is described by in [20].
7.5
Consequences and Concluding Remarks
In this chapter, we surveyed the consequences of the speckled nature of light in
LDPI. The analysis is limited to scanning beam LDPI devices that use a single
collimated laser beam [12, 13]. This speckled character has a significant influence
on the measurement characteristics of these devices. First, the overall sensitivity is
affected, depending on the tissue optical properties. This effect, shown in Figure 7.6,
is additional to the obvious influence of the optical properties on photon path length.
Second, the depth sensitivity is affected. Neglecting the speckle effect when
analyzing the depth sensitivity may lead to an error by a factor of 2, as shown in
Figure 7.6.
Finally, not shown in this chapter but demonstrated in reference [11] the
speckle phenomenon has an influence on the shape of the power spectrum of
photocurrent fluctuations and, therefore, on the estimated average Doppler shift.
The effect is a reduction of the average Doppler shift of 10 –30% compared to the
unknown real value.
Generally, then, speckle leads to an additional cross talk between perfusion
estimations and tissue optical properties. In principle, this cross talk can be
suppressed by increasing the beam diameter. However, this will deteriorate both
the signal level and the spatial resolution, and therefore cannot be considered
as a fundamental solution of the problem. The tissue optical dependence of the
system sensitivity on the tissue optical properties due to the speckle phenomenon
is inevitable in scanning beam laser Doppler perfusion imagers.
References
1. Aizu, Y. and Asakura, T.
(1991) Bio-speckle phenomena
and their application to the
evaluation of blood flow.
Opt. Laser Technol., 23 (4),
205– 219.
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146 7 Speckle Effects in Laser Doppler Perfusion Imaging
2. Asakura, T. and Takai, N. (1981)
Dynamic laser speckles and their
application to velocity measurements
of the diffuse object. Appl. Phys., 25,
179– 194.
3. Fuji, H. et al. (1985) Blood flow
observed by time-varying laser speckle.
Opt. Lett., 10 (3), 104–106.
4. Ruth, B. (1990) Blood flow determina-
tion by the laser speckle method. Int. J.
Microcirc. Clin. Exp., 9, 21–45.
5. Nilsson, G.E., Tenland, T., and Oberg,
P.A. (1980) Evaluation of a laser Doppler
flowmeter for measurement of tissue
blood-flow. IEEE Trans. Biomed. Eng., 27
(10), 597–604.
6. Bonner, R. and Nossal, R. (1981) Model
for laser Doppler measurements of
blood-flow in tissue. Appl. Opt., 20 (12),
2097– 2107.
7. Goodman, J.W. (1976) Some funda-
mental properties of speckle. J. Opt.
Soc. Am., 66 (11), 1145 –1150.
8. Goodman, J.W. (2007) Speckle Phenom-
ena in Optics – Theory and Applications ,
Roberts & Company.
9. Serov, A., Steenbergen, W., and de Mul,
F. (2001) Prediction of the photodetector
signal generated by Doppler-induced
speckle fluctuations: theory and some
validations. J. Opt. Soc. Am. A, 18 (3),
622– 630.
10. Born, M. and Wolf, E. (1985) Principles
in Optics, 6th edn, Cambridge University
Press, Cambridge.
11. Rajan, V. et al. (2006) Speckles in laser
Doppler perfusion imaging. Opt. Lett.,
31 (4), 468– 470.
12. Essex, T.J.H. and Byrne, P.O. (1991)
A laser Doppler scanner for imaging
blood-flow in skin. J. Biomed. Eng., 13
(3), 189–194.
13. Wardell, K., Jakobsson, A., and Nilsson,
G.E. (1993) Laser-Doppler perfusion
imaging by dynamic light-scattering.
IEEE Trans. Biomed. Eng., 40 (4),
309– 319.
14. Draijer, M. et al. (2009) Twente optical
perfusion camera: system overview and
performance for video rate laser Doppler
perfusion imaging. Opt. Express, 17 (5),
3211– 3225.
15. Gu, Q., Hayes-Gill, B.R., and Morgan,
S.P. (2008) Laser Doppler blood flow
complementary metal oxide semicon-
ductor imaging sensor with analog
on-chip processing. Appl. Opt., 47 (12),
2061– 2069.
16. Serov, A. and Lasser, T. (2005)
High-speed laser Doppler perfusion
imaging using an integrating CMOS
image sensor. Opt. Express, 13 (17),
6416– 6428.
17. Serov, A., Steenbergen, W., and de
Mul, F. (2002) Laser Doppler perfusion
imaging with a complimentary metal
oxide semiconductor image sensor. Opt.
Lett., 27 (5), 300 –302.
18. Serov, A., Steinacher, B., and Lasser, T.
(2005) Full-field laser Doppler perfusion
imaging and monitoring with an intel-
ligent CMOS camera. Opt. Express, 13
(10), 3681 –3689.
19. Rajan, V. et al. (2008) Influence of tissue
optical properties on laser Doppler per-
fusion imaging, accounting for photon
penetration depth and the laser speckle
phenomenon. J. Biomed. Opt., 13 (2),
024001.
20. Rajan, V. et al. (2007) Effect of speckles
on the depth sensitivity of laser Doppler
perfusion imaging. Opt. Express, 15 (17),
10911– 10919.
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147
8
Laser Speckle Contrast Analysis (LASCA) for Measuring
Blood Flow
J. David Briers, Paul M. McNamara, Marie Louise O’Connell, and Martin J. Leahy
8.1
Introduction: Fundamentals of Laser Speckle
Laser speckle is a random interference pattern produced when laser light is
scattered from an optically rough surface or a diffuse medium such as biological
tissue [1]. Light from different parts of the surface within a resolution cell (the area
just resolved by the optical system imaging the surface) traverses different optical
path lengths to reach the image plane. The resulting intensity at a given point on the
image is determined by the algebraic addition of all the wave amplitudes arriving
at the point. If the resultant amplitude is zero, a dark speckle is seen at the point;
if all the waves arrive at the point in phase, an intensity maximum is observed.
Laser speckle is a random phenomenon and can only be described statistically.
One important result isan expression for the contrastof a speckle pattern. Assuming
ideal conditions for producing a speckle pattern – highly coherent, single-frequency
laser light and a perfectly diffusing surface with a Gaussian distribution of surface
height fluctuations – it can be shown that the standard deviation of the intensity
variations in the speckle pattern is equal to the mean intensity. In practice, speckle
patterns often have a standard deviation that is less than the mean intensity: this
is observed as a reduction in the contrast of the speckle pattern. It is customary to
define speckle contrast as the ratio of the standard deviation to the mean intensity
Speckle contrast =
σ
I
≤ 1 (8.1)
Although a detailed account of laser speckle statistics [2] is outside the scope of this
chapter, it is worth mentioning at this point that the size of the individual speckles
has, in general, nothing to do with the structure of the surface producing them.
It is entirely determined by the aperture of the optical system used to observe the
speckle pattern. If the speckle pattern is being observed directly by the human eye,
it is the pupil of the eye that determines the speckle size. More importantly, if a
camera is used, it is the setting of the aperture stop that determines the speckle
size. This can have a serious effect if the aperture is used to control the exposure
of the photograph.
Microcirculation Imaging, First Edition. Edited by Martin J. Leahy.
2012 Wiley-VCH Verlag GmbH & Co. KGaA. Published 2012 by Wiley-VCH Verlag GmbH & Co. KGaA.
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148 8 Laser Speckle Contrast Analysis (LASCA) for Measuring Blood Flow
8.2
Time-Varying Speckle
When an object moves, the speckle pattern it produces changes. For small move-
ments of a solid object, the speckles move with the object – that is, they remain
correlated. For larger motions, the speckles ‘‘decorrelate’’ and the speckle pattern
changes completely. Decorrelation also occurs when the light is scattered from
a large number of individual moving scatterers, such as particles in a fluid. An
individual speckle appears to ‘‘twinkle’’ like a star. This phenomenon has come to
be known as time-varying speckle. It is frequently seen when living organisms are
observed under laser light illumination.
It is reasonable to assume that the frequency spectrum of the fluctuations should
be dependent on the velocity of the motion. It should therefore be possible to
obtain information about the motion of the scatterers from a study of the temporal
statistics of the speckle fluctuations. This analysis can be based on the techniques of
either photon correlation spectroscopy or laser Doppler velocimetry. It is not intuitively
obvious that time-varying speckle and Doppler-induced fluctuations are identical.
The theory of time-varying speckle starts with the classical (though random)
interference pattern produced when light beams of the same frequency interfere.
The fluctuations are caused by the changes in optical path lengths of the interfering
beams, which is caused by the movement of the scatterers. Doppler fluctuations,
on the other hand, are explained by the beating effect that occurs when two waves
of slightly different frequency are superimposed, the difference being due to the
frequency shift induced by the Doppler effect when light is scattered by a moving
object. Thus the speckle explanation is based on the superposition of waves of
the same frequency, whereas in the Doppler explanation, the superimposed waves
have different frequencies. Despite these apparent differences in approach, it can be
shown mathematically that the two explanations lead to identical equations linking
the fluctuations to the velocity distribution of the scatterers [3, 4]. Thus, the two
approaches are merely different ways of looking at the same physical phenomenon.
8.3
Full-Field Speckle Methods
One problem with using the temporal statistics of time-varying speckle is that
measurements are made at only one point in the speckle pattern (a single speckle).
If an area is to be analyzed, it is necessary to scan the detector over the field. If a map
of velocity distribution is required, some method of scanning the area of interest
is necessary. Such a map is of particular importance if blood flow is to be used as
a diagnostic tool. Fujii et al. [5] used a scanning technique in their application of
time-differentiated speckle to measure skin capillary blood flow. They used a linear
CCD array to monitor simultaneously a line of speckles and a scanning mirror
to extend this to a two-dimensional area. They later applied the technique to the
mapping of retinal blood flow [6 –9]. They produced a microcirculation map of
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8.4 Single-Exposure Speckle Photography 149
the retina by illuminating the retina with light from a diode laser, scanning and
storing the speckle images, and then calculating the differences between successive
images. The parameter they use is the ratio of the mean intensity to the intensity
difference, a quantity they call ‘‘normalized blur’’ and which they use as a measure
of velocity. Experiments on a rabbit eye showed good correlation with invasive
methods [7].
Scanning has also been applied to the laser Doppler technique [8, 10, 11], and
commercial scanning Doppler systems that can provide full-field monitoring of
capillary blood flow over quite large areas of the body are now on the market.
The problem of either processing or storing vast amounts of data demands a
compromise between cost and performance, and the systems offered suffer from
both long scanning times and a loss of spatial resolution [10, 12]. Nevertheless,
they are useful as a diagnostic tool and offer easily interpreted false-color maps of
blood velocity and/or flow.
The scanning necessary to apply time-varying speckle techniques to map velocity
distributions results in the collection of a vast amount of data and usually takes
an appreciable time (typically 5 min for 256 × 256 pixels). Ideally, a full-field
technique would avoid the need for scanning. One such technique is the ‘‘global
Doppler’’ method [13]. This directly converts velocity to intensity (or false color) by
the ingenious device of measuring how much of the return light is absorbed by
a substance of known absorption/frequency properties. Unfortunately, resolution
problems limit the technique to fairly high velocities, and it is not suitable for
biomedical applications. Further advances in this area are described in Chapter 6.
There is another way to avoid the need to scan – by using the spatial statistics
(specifically the contrast) of time-integrated fluctuating speckle patterns. If the
integration time is comparable with the period of the fluctuations, it is clear that
the effect will be a reduction in the speckle contrast.
8.4
Single-Exposure Speckle Photography
The use of such time-integrated speckle led to a technique for flow visualization in the
early 1980s that simultaneously achieved full-field operation (withoutscanning) and
very simple data gathering and processing. It was originally called ‘‘single-exposure
speckle photography’’ [14], to distinguish it from the then well-known technique
of double-exposure speckle photography for measuring displacement and strain.
Single-exposure speckle photography was developed primarily for the measurement
of retinal blood flow [15, 16]. The basic technique is simply to photograph the retina
under laser illumination using an exposure time that is of the same order as the
decorrelation time of the intensity fluctuations. It is clear that a very short exposure
time will ‘‘freeze’’ the speckle and result in a high-contrast speckle pattern, whereas
a long exposure time will allow the speckles to average out, leading to a low contrast.
In general, the velocity distribution in the field of view is mapped as variations
in speckle contrast. In the original technique, high-pass optical spatial filtering of
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150 8 Laser Speckle Contrast Analysis (LASCA) for Measuring Blood Flow
the resulting photographs converted these contrast variations to more easily seen
intensity variations [17]. Later work introduced digital image processing of the
speckle photographs, including a color coding of the velocities [18].
8.5
Laser Speckle Contrast Analysis (LASCA)
By 1990, digital techniques were sufficiently advanced to allow the photographic
stage to be eliminated. By measuring the speckle contrast directly and converting
the data to a false-color image, a fully digital, real-time technique for mapping skin
capillary blood flow was developed [19]. The technique was called ‘‘laser speckle
contrast analysis’’ or LASCA. Some researchers still use this name [20, 21], but
others have introduced names such as laser speckle contrast imaging (LSCI) [22]
or laser speckle imaging (LSI) [23]. For the sake of simplicity, we shall use the term
LASCA in this chapter, as this was the name used in the original 1996 work [24].
In the past decade, there has been an accelerated adoption of LASCA technology
as the neuroscience community has used it to measure blood flow in the brain.
Over the past decade, there have been more than 100 publications on the topic,
significantly advancing its theory and application. For example, Bandyopadhyay
et al. [25] presented an important correction in the calculation of laser speckle
contrast and its relation to temporal fluctuations. This was an important calibration
procedure for handling static scattering from the skull, which would otherwise
confuse the estimate of blood flow from spatial laser speckle contrast [26], while
Li et al. [27] showed that temporal laser speckle contrast imaging, or TLSCI, is
intrinsically less sensitive to static scattering. More recently, Kirkpatrick et al. [28]
and Duncan et al. [2], presented important theoretical work quantifying the impact
of speckle size and sampling windows on the statistics of laser speckle, while others
have demonstrated more efficient algorithms for calculating speckle contrast to
enable real-time visualization [29, 30]. The technique is also being incorporated into
multimodal imaging schemes to obtain additional information about the tissue
[31–34].
The experimental setup for LASCA is very simple, as shown in Figure 8.1.
Diverging laser light illuminates the object under investigation, which is imaged
by a CCD camera (or equivalent). The image is captured by a frame grabber (or
equivalent), and the data is passed to a personal computer for processing by custom
software. Usually, the operator has several options at his disposal. In the original
LASCA technique [24], this included the exposure time, the number of pixels
over which the local contrast was computed, the scaling of the contrast map, and
the choice of colors for coding the contrast. The choice of the number of pixels
over which to compute the speckle contrast is important: with too few pixels, the
statistics will be compromised, whereas with too many pixels, the spatial resolution
is sacrificed.
A survey of the literature indicates that the community has empirically settled on
a window size of 7 × 7 pixels as a reasonable trade-off between spatial resolution
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8.6 The Question of Speckle Size 151
Frame
grabber
CCD
camera
Laser
light
PC
Target
Figure 8.1 Basic setup for laser speckle contrast analysis (LASCA).
and uncertainty in the estimated speckle contrast, but all this depends on camera
resolution, speckle size, and desired contrast resolution [35]. (A square with sides of
an odd number of pixels was chosen so that thecomputed contrast could be assigned
to the central pixel.) The speckle contrast is quantified by the usual parameter of
the ratio of the standard deviation to the mean (σ /
I
) of the intensities recorded
for each pixel in the square (Eq. (8.1)). The pixel square is then moved along by 1
pixel and the calculation repeated: this overlapping of the pixel squares results in a
much smoother image than would be obtained by using contiguous squares, and at
little cost in terms of additional processing time. It must be remembered, though,
that this overlapping of the squares does not lead to an increase in resolution,
which is determined by the size of square used: there is a trade-off between spatial
resolution and reliable statistics.
If the object under investigation contains moving scatterers, such as blood cells,
each speckle will be fluctuating in intensity. A time-integrated image therefore
shows a reduction in speckle contrast because of the averaging of the intensity of
each speckle over the integration time. In practice, the exposure time can be very
short, typically 0.02 s, and the processing time is less than 1 s for the whole frame
[36], effectively making it a real-time technique. (Recent work has further reduced
the processing time to milliseconds, making real-time video possible.)
8.6
The Question of Speckle Size
A complicating factor for the technique is that of speckle size. As mentioned in
Section 8.1, this is determined entirely by the aperture of the imaging system used
(in this case the camera). The pixels of the detector are effectively sampling the
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152 8 Laser Speckle Contrast Analysis (LASCA) for Measuring Blood Flow
speckle pattern. If the speckles are smaller than the pixels, some averaging will
occur and the technique rapidly becomes less effective. If the speckles are much
larger than the pixels, only a few speckles will be sampled by the square of pixels
used, and the statistics will become unreliable. It was previously thought that the
best compromise was to arrange for the speckle size to be equal to the pixel size.
However, recent studies have shown that an increased contrast is achieved when
the minimum speckle size is twice the camera pixel size [2, 35]. Assuming that the
Nyquist sampling criterion is met, it was determined that the variation of the esti-
mated speckle contrast could be expressed by a dimensionless width parameter.The
formulation reveals that the variation in the speckle contrast dramatically decreases
as the window size is increased to 7 × 7, but then, diminishing returns are obtained
for larger window sizes. Essentially, increasing the speckle size means that a given
window of pixels is estimating the speckle contrast from fewer speckles. Ultimately,
smaller window sizes resulted in an underestimation of the speckle contrast [2].
The speckle size can be easily set by choosing an appropriate setting for the
aperture of the camera lens. However, this removes the possibility of using the
aperture to control the amount of light reaching the detector, the usual function of
a variable lens aperture. Further, the shutter speed cannot be used to do this, as it
is determined by the need to match the velocities being measured, and some other
method of controlling the amount of light reaching the detector must be found.
8.7
Theory
The principle of LASCA is very simple. A time-integrated image of a moving object
exhibits blurring. In the case of a laser speckle pattern, this appears as a reduction in
the speckle contrast, defined (and measured) as the ratio of the standard deviation
of the intensity to the mean intensity. This occurs whatever be the ‘‘movement’’ of
the speckle. For random velocity distributions, each speckle fluctuates in intensity.
For lateral motion of a solid object, on the other hand, the speckles also move
laterally and become ‘‘smeared’’ on the image, but a reduction in speckle contrast
still occurs. For fluid flow, the situation might be a combination of both these types
of ‘‘movement.’’ In each case, the problem for quantitative measurements is the
establishment of a relationship between speckle contrast and velocity (or velocity
distribution).
It is clear that there must be a link between the flow velocity and the amount of
blurring. The higher the velocity, the faster are the fluctuations and the more is the
blurring that occurs in a given integration time. By making certain assumptions, the
following mathematical relationship can be established between speckle contrast
and the temporal statistics of the fluctuating speckle [14]:
σ
2
s
(T) =
1
T
T
0
˜
g
2
(τ )dτ (8.2)
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