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Файл:Ординатура / Хирургия / Библиотека им академика М.И. Перельмана / Книга_3921_Библиотеки_им_академика_М_И_Перельмана
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6.4 Overview of ffLDPI Systems and Obtained Results 123
0 1 2 3 4 5 6 7 8 9 1
0
30
40
50
60
70
80
90
Time
(
s
)
M
1
(a.u.)
Figure 6.4 Perfusion estimated with the time domain
(dashed line) and FFT algorithm (solid line), measured on
the wrist during reperfusion after an upper arm occlusion.
the two algorithms give perfusion estimations with a systematic difference within
15%. In practice, the clinical decisions taken on the basis of images obtained with
both algorithms will be the same. As for the calculation time, when implemented
in LabView (version 7.1), the TD algorithm is twice as fast as the FFT algorithm.
An advantage of the TD algorithm is that it gives the option of a moving average
estimation of the perfusion: in this manner, the estimation of perfusionat time t can
be updated on the basis of two new raw frames, taken at t + t and t + 2t.This
will greatly reduce the amount of data to be stored and transported. Furthermore,
slightly more sophisticated versions of the TD algorithm might allow for high-pass
filtering, leading to a reduction of motion artifacts.
6.4
Overview of ffLDPI Systems and Obtained Results
This section deals with the CMOS-camera-based ffLDPI systems that have been
described in the literature and gives an impression of the current performance. It
must be emphasized that since then, probably progress has been made in terms of
image noise and the potential of the method to provide real-time perfusion images.
In this chapter we focus on the systems developed by the Ecole Polytechnique
F
´
ed
´
erale de Lausanne (EPFL) [12, 13] and the University of Twente (UT) [10]. A
concise overview of these systems is given in Table 6.1. A common feature in
these systems is the wavelength of 671 nm, produced by a DPSS laser. This is
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124 6 Fast Full-Field Laser Doppler Perfusion Imaging
Table 6.1 Overview of specifications of published ffLDPI systems.
EPFL [13] EPFL [12] UTwente [10]
Wavelength (nm) 671 671 671
Laser power (mW) 50 250 400
Imaged tissue area
demonstrated
Ø40 mm 5.5 × 5.5 cm
2
7 × 7cm
2
High-speed camera iMVS 155, Fastcom
Ttechnologies
Home-built Fastcam 1024PCI,
Photron
CMOS array Fuga1000, FillFactory
(logarithmic sensor)
VCA1281, Symagery
(integrating sensor)
Not specified
Raw frame rate in
ROI
16 800 fps in 64 × 8
pixels
12 000 fps in
unknown
subwindow inside
256 × 256 pixels
window
27 000 fps in 128 ×
128 pixels
Perfusion frame rate,
or refresh time
90 s, for 256 × 256
pixels
3.5– 10 s, for 128 and
512 raw frames,
respectively, of 256 ×
256 pixels
5 s, for 1024 raw
frames of 128 × 128
pixels
Remark Real-time monitoring
at single pixel
possible
– Video rate perfusion
movies (26 fps)
possible with offline
processing
currently the only laser that is able to produce visible light of a suitable wavelength
at a sufficient power and coherence length. A potential disadvantage of 671 nm
is the sensitivity of optical absorption at this wavelength to the oxygenation of
hemoglobin in blood. This might make the response of the system dependent
on the oxygenation level. For instance, the path length of the photons through
the tissue and the penetration depth of the light into the blood vessels might be
reduced with decreasing oxygenation. However, at 671 nm, the absorption level
at any physiological oxygenation level is sufficiently low to consider this effect
negligible. In general, because of eye safety for ffLDPI, the use of visible light is
preferred over the potentially better wavelength of 795 nm, at which light absorption
is independent of oxygenation (a so-called isosbestic wavelength). If 795 nm is used,
a visible guiding beam should be used to delineate the location of the invisible
near-infrared laser beam.
6.4.1
Systems Developed by EPFL Lausanne
EPFL Lausanne developed twodifferent ffLDPI systems [12, 13]based on high-speed
CMOS camera. In both systems, a CMOS camera that can only achieve a reasonable
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6.4 Overview of ffLDPI Systems and Obtained Results 125
frame rate in terms of resolvable Doppler frequencies, in a small region of in-
terest, such as 64 × 8 pixels, is used. Hence, a total perfusion image of, for
instance, 256 × 256 pixels is built up by merging images obtained in smaller
subwindows. Their first system [13] was able to perform a scan of 256 × 256
pixels and produce a perfusion image in 90 s. A tissue domain with a diame-
ter of ∼40 mm could be imaged. Furthermore, real-time perfusion monitoring
was possible by reading out a single pixel. The performance of the system
was limited by the 8-bit resolution of the Analog-to-Digital converter (ADC)
and the logarithmic response of the camera. It is not clear how the noise
level is established and how the noise-associated perfusion offset is accounted
for.
Their second system [12] showed an increased performance both in speed
and imaged tissue area; see Table 6.1. The time to perform and present a com-
plete perfusion scan in 256 × 256 pixels was decreased to 3.5 –10 s, depending
on the number of raw images on which a single perfusion frame was based.
A fundamental difference between the two systems is that for the second sys-
tem an integrating sensor is used, which accumulates charges depending on
the number of photons hitting each pixel during a certain integration time.
This will lead to a higher SNR: for an integration time increasing from 40 to
100 µs, the SNR increased by a factor of 3. Apart from this aspect, integrating
CMOS sensors have better characteristics in terms of thermal noise and read-
out noise and the bandwidth is independent of the signal level. Furthermore,
integration provides a means of antialiasing filtering, which nonintegrating sen-
sors lack. The system was applied [14] for intraoperative function brain imaging
on a human patient. The perfusion in an area of 3.5 × 4cm
2
was imaged in
140 × 120 pixels at 1.5 fps. A diode laser at 808 nm was used, which is the isos-
bestic wavelength. Perfusion images were taken while the patient performed a
finger-tapping provocation. Significant perfusion variations of 10 –20% relative to
the baseline value were observed, also correlating with preoperational functional
MRI.
One of the Lausanne systems has been commercialized under the name Flux-
EXPLORER. Schlosser et al. [15] have used this device to study the feasibility of
planning flap surgery and monitoring its result. Their finding was that the method
is unsuitable for identifying the main blood vessel supplying a tissue flap; however,
tissue ischemia could be detected. The FluxEXPLORER systems feature raw frame
acquisition rates that are on the low side compared to the usual bandwidths in
laser Doppler systems. This in particular is true for the integrating sensor [12]
with a frame rate of 12 000 fps, leading to a Doppler bandwidth of 6 kHz only.
Usually, scanning beam laser Doppler perfusion imagers and fiber-optic laser
Doppler perfusion monitors have bandwidths of at least 12.5 kHz for red laser
sources.
More recently, a fast LDPI system based on the technology developed by EPFL
Lausanne has been commercialized by a new spin-off company called A¨ımago.A
frame rate of 16 fps is specified, and it images a region of 7 × 7cm.
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126 6 Fast Full-Field Laser Doppler Perfusion Imaging
6.4.2
Systems Developed by the University of Twente
The first ffLDPI system was realized by Serov et al. [16]. It was based on a Fuga15d
camera, sampling a region of interest of 64 × 64 pixels with a frame acquisition
rate of 9000 fps. Actually, this system rendered images of the zero-order moment
of the power spectrum only, providing information on the concentration of moving
red blood cells. An image was acquired and processed in 7 s. The capabilities of the
method were shown both in vitro, using Intralipid inside a cavity made in a static
Delrin environment, and in vivo, on an occluded and reperfused finger.
Draijer et al. realized the Twente Optical Perfusion Camera (TOPCam) [10]. The
main features of this system are given in Table 6.1.The TOPCam is based on the
Photron 1024 Fastcam high-speed camera. This camera is able to acquire images
at a frame rate of 27 000 fps in a region of interest of 128 × 128 pixels. This gives a
detection bandwidth of 13.5kHz, which is considered sufficient for recording even
the highest Doppler shifts produced by microcirculatory blood flow. For creating
a desired illumination pattern, the so-called engineered diffusers are used. These
are compact beam-shaping elements made of polymer, which diverge a narrow
collimated beam to realize an illumination pattern by the diffractive action of
the micropattern on the element’s surface. In the TOPCam, square beams are
produced with linear sizes up to 15 cm. Such beam patterns can be obtained using
commercially available diffractive optical elements. The intensity distribution is
uniform within ±10%. Another option for creating a homogeneous beam is to
couple the light into a multimode fiber to produce a magnified image of the fiber
core on the tissue. This method was applied by Serov and Lasser [12] and may
produce a better homogeneity than diffractive optics. However, using a circular
illumination pattern is less efficient in combination with the usually rectangular
regions of interest on the CMOS arrays.
The TOPCam can produce a perfusion frame in 5 s. This time is dominated by
the transfer speed between the camera and the computer, since data acquisition by
the camera and processing by the computer only take 380 ms and 1 s, respectively.
However, since the entire region of 128 × 128 pixels can be read out at once, video
rate perfusion images at a frame speed of 26 fps can be produced by acquiring data
until the camera memory is full. For perfusion images of 128 × 128 pixels, with a
maximum internal camera memory of 24 Gb, a perfusion movie of 45 s duration
can be recorded.
The ability of the TOPCam system to image skin perfusion has been demon-
strated in various ways. An example of perfusion data obtained at video rate was
already shown in Figure 6.4. The slow increase of skin perfusion in response to
topical application of a capsicum cream was measured in a region of 5 × 5cm on
the back of the hand of a healthy volunteer [10]. The perfusion image after 14 min
29 s is shown in Figure 6.5. This figure visualizes the first-order moment for
each pixel normalized with the DC value of the photocurrent. DC normalization
(rather than DC
2
normalization) has become common practice in commercial
devices for LDPI. While DC
2
normalization is suitable for correction variations
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6.4 Overview of ffLDPI Systems and Obtained Results 127
Figure 6.5 Skin perfusion image
(M
1
/DC) of the back of the hand in
response to topically applied capsicum
cream. The image is recorded 14 min
after cream application.‘‘UT’’ denotes
‘‘University of Twente.’’
0−50 50 100 150 200
(
a
)(
b
)
Figure 6.6 Perfusion of a second-degree burn at two days
postburn [17]. (a) Image of DC values, and indication of
the 128 × 128 pixel region of interest (ROI) for perfusion
imaging. (b) Perfusion within ROI, expressed by M
1
/DC.
(Reprinted with permission of Elsevier.)
in laser power, normalization by DC may enable a better correction for inho-
mogeneities in the tissue surface condition. Such inhomogeneities may become
dominant if no cross-polarizers are used to suppress the reflection from the tissue
surface.
The suitability of the TOPCam to image burn perfusion has been tested in a
pilot study [17]. The result of imaging a second-degree burn of the hand is shown
in Figure 6.6, where panel (a) shows the raw image taken with the entire CMOS
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128 6 Fast Full-Field Laser Doppler Perfusion Imaging
array and panel (b), the perfusion as measured within the indicated region of
interest. These images show both an advantage and a disadvantage of using CMOS
imaging arrays for perfusion imaging. An advantageous feature is that the wound
topography and the perfusion information can be obtained with the same sensor,
which facilitates fusing both types of information. This is important, since burn
diagnosis will be based on both the wound appearance and the perfusion image.
A disadvantage is the absence of color and the moderate quality of the photograph
taken with the CMOS image. However, taking high-quality color images with a
monochrome imaging array is feasible and is a matter of further sophistication
of the entire system. The general finding in the burn center setting was that the
imaging speed of the system is a big advantage, which balances against the slightly
lower imaging quality in terms of SNR. In general, the sensitivity and resolution
of the method are sufficient. Furthermore, practical issues, such as the presence of
crusts, creams and blisters, and tissue curvature, have a similar impact as identified
by Droog et al. [18] for scanning beam systems.
6.5
Comparative Behavior of CMOS-Based Systems: How Good Are They to Measure
Perfusion?
6.5.1
General Considerations
The generic methodology of LDF is able to quantitatively measure microcirculatory
perfusion in a relative manner, provided that the fraction of Doppler-shifted
photons involved in signal generation is much smaller than one. This is known
from the early work on LDF, both from the theory by Bonner and Nossal [1] and the
experiments by Nilsson et al. [19, 20]. Usually, this condition is met in moderately
perfused tissues, using fiber-optic probes with a small distance between the fibers
for illumination and detection. For scanning beamlaser Doppler perfusion imagers,
this aspect has not been studied in great detail.
The question may arise to what extent ffLDPI based on CMOS arrays is able to
measure perfusion, and how it compares against other methods providing similar
information. Binzoni and Van de Ville [21] used Monte Carlo simulations to study
the performance of ffLDPI systems. They concluded that the first-order moment M
1
could properly describe perfusion, whereas the zero-order moment M
0
was hardly
sensitive to blood volume when the probability of interaction of light with moving
red blood cells exceeded a value of 0.05. They attribute this to the strong forward
scattering of light in tissues. This is only one of the various conditions that leads
to an excessive fraction of Doppler-shifted light, along with too large a distance
between the points of injection and detection of photons. Unlike fiber-optic probes,
in ffLDPI systems, the use of a broad laser beam may lead to relatively large photon
trajectories, and hence to a relatively large probability of interaction of photons
with moving red blood cells.
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6.5 Comparative Behavior of CMOS-Based Systems: How Good Are They to Measure Perfusion? 129
6.5.2
Comparison with Scanning Beam Systems
While most experience in the use of LDPI is built on the use of scanning beam
systems, it is to be expected that eventually, these systems will be discarded in favor
of either CMOS-based ffLDPI devices or laser speckle contrast perfusion imaging
(referred to as LCSPI). In this and the next subsection, we briefly compare ffLDPI
with scanning beam LDPI and LSCPI methods, respectively.
In a few yet unpublished studies performed in our group, when comparing
ffLDPI and scanning beam LDPI images on similar objects, the following features
immediately attracted attention:
•
Perfusion images formed by ffLDPI suffer from structured noise (so-called fixed
pattern noise) and a higher random noise than scanning beam LDPI images. The
fixed pattern noise is a well-known feature of CMOS arrays, which can be partly
suppressed by calibration. The higher random noise is the consequence of the
much smaller detector size in ffLDPI systems. We can regard an ffLDPI device as
having p × q individual detection channels, with p and q denoting the perfusion
image size, compared with a single detection channel in scanning beam systems.
•
Boundaries between dynamic media (e.g., Intralipid) and static media (e.g.,
Delrin) are rendered at a higher resolution with scanning beam systems than
with ffLDPI systems. In tissues, this difference is less pronounced in the absence
of sharp transitions. While for many diagnostic decisions the potentially worse
resolution of ffLDPI systems is not important, it is an interesting feature that
will be elaborated below.
0 200 400 600 800 1000 1200 1400 18001600
PIM3
p
erfusion
(
a.u.
)
0.000
0.005
0.010
0.015
0.020
TOPCam perfusion (a.u.)
Figure 6.7 Pixel-by-pixel comparison of perfusion values
obtained with PIM3 (Perimed AB) versus’ TOPCam, imaging
skin tissue with topically applied capsicum cream.
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130 6 Fast Full-Field Laser Doppler Perfusion Imaging
•
Scanning beam systems appear to be more sensitive to variations in tissue optical
properties than ffLDPI systems. An example is shown in Figure 6.7, which reveals
that the skin perfusion increase generated by capsicum cream as recorded with
scanning beam device PIM3 (Perimed AB) is larger than that recorded with the
TOPCam.
It is tempting to state that scanning beam devices should have a better resolution
than ffLDPI devices, because the latter uses a much broader beam. However, a
closer look reveals that the difference in beam sizes as such cannot lead to a
different imaging resolution. This is illustrated in Figure 6.8. In a scanning beam
device, light is injected into a point and follows shallow or deep trajectories back
to the surface at positions remote from or nearby the injection point. Part of this
light is imaged on a large photodetector. In a full-field device, light is injected in a
broad beam, which we can regard as a large number of collimated beams. A certain
pixel, imaged on the tissue surface, is illuminated by light that followed shallow or
deep trajectories, coming from remote or nearby collimated beams. Hence, from
the point of view of photon migration, there is no fundamental difference between
the two modalities. In other words, the better resolution of scanning beam devices
is not obvious from the fact that they use a narrower beam.
Collimated beam
of scanning LDPI
Image of photodetector
Broad beam of ffLDPI
Image of camera
p
ixel on tissue surface
(a)
(
b
)
On tissue surface
Figure 6.8 Comparison of beam configu-
rations and light trajectories. (a) Scanning
beam LDPI with a collimated beam and light
trajectories ending on a large photodetec-
tor projected on the tissue; (b) ffLDPI with
a broad beam and light trajectories ending
on one camera pixel. The relative trajectory
thicknesses indicate the decay of intensity
with penetration depth.
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6.5 Comparative Behavior of CMOS-Based Systems: How Good Are They to Measure Perfusion? 131
A more probable explanation can be found in the physics behind Eqs. (6.1) and
(6.3) and, in particular, in the role of the number of speckles N on the photodetector.
This is defined as N = A
det
/A
sp
,withA
sp
being the average speckle area and A
det
the effective detection area. According to Eq. (6.3), the speckle area A
sp
is governed
by the solid angle of the light hitting the detector and therefore only dependent
on the detection geometry. However, while the effective detection area A
det
is equal
to the pixel size A
p
in ffLDPI (see Eq. (6.1)), in scanning beam systems, A
det
will
be related to the size of the light-emitting spot on the tissue surface. This size
will depend on the tissue optical properties. This gives scanning beam LDPI an
independent source of cross talk between perfusion readings and optical properties.
This issue is treated in much more detail in Chapter 7 and in its references.
To study the relationship between perfusion readings with ffLDPI and scanning
beam LDPI, perfusion measurements have been performed in Intralipid samples
at various levels of the reduced scattering coefficient. The results are shown in
Figure 6.9. Panel (a) shows a nonlinear relationship between the perfusion values
of the PIM3 and the TOPCam, in a manner that has some qualitative similarity
to the in vivo result in Figure 6.7. Since these samples, unlike perfused tissue, are
0 1000 2000 3000 4000
0.0
0.5
1.0
1.5
2.0
2.5
Perfusion TOPCam
Perfusion PIM3
0
500
1000
1500
2000
2500
3000
3500
4000
Perfusion PIM3
Monte Carlo perfusion (a.u.)
abs = 0.01
0.02
0.04
scatt = 0.5
2
4
0.0
0.5
1.0
1.5
2.0
2.5
Perfusion TOPCam
Monte Carlo perfusion (a.u.)
(a) (b)
(c)
0123456
0123456
Figure 6.9 Comparison of perfusion val-
ues measured with the PIM3 (Perimed AB)
and the TOPCam in Intralipid samples with
various scattering and absorption levels.
(a) TOPCam versus PIM3. (b) PIM3 versus
Monte Carlo simulation results. (c) TOPCam
versus Monte Carlo. Values in the legend are
the absorption and reduced scattering coeffi-
cients in mm
−1
.
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132 6 Fast Full-Field Laser Doppler Perfusion Imaging
completely dynamic, it is difficult to define their ‘‘perfusion level.’’ However, it is
possible to predict the ideal laser Doppler perfusion reading in terms of the first
moment of the power spectrum by performing Monte Carlo simulations. We can
regard the Monte Carlo predictions as a reference method, in that they show how
the Doppler statistics of the detected photons are related to and affected by the
tissue optical properties. The result is shown for the PIM3 and the TOPCam in
panels (b,c), respectively. These figures reveal that the PIM3 perfusion readings
are highly nonlinear with the Monte Carlo values. For varying scattering, the
TOPCam-measured perfusion value hardly changes, while the scanning beam
device shows an extreme sensitivity to varying scattering levels. This opposite
response is related to the effect that the optical properties have on A
det
in the
case of the scanning beam device. The TOPCam perfusion readings, however, are
quite proportional to the predicted values. We emphasize that these ‘‘perfusion
readings’’ in these entirely liquid samples do not estimate the flux of particles,
but the Doppler statistics of the detected light. However, we must conclude that
scanning beam devices are less able than ffLDPI devices to give a one-to-one
relation with the Doppler statistics of the detected light. The difference between
these two modalities must be attributed to the speckle phenomenon that affects
ffLDPI and scanning beam LDPI measurements in a fundamentally different
way.
This difference also forms the explanation for the better spatial resolution of
scanning beam devices. As described in Chapter 7 (see, in particular, Figure 7.6),
the speckle effect reduces the effective probed depth by ∼50% with, as a side effect,
an improved spatial resolution.
6.6
Concluding Remarks
The early results of ffLDPI devices illustrate their potential and their relative
advantages over scanning beam devices. In particular, in view of their improved
imaging speed, it seems inevitable that in the long term, the scanning beam
approach will be replaced by the full-field approach. In addition to a faster speed, it
was also illustrated in this chapter that ffLDPI systems seem to have a better ability
to render the Doppler information contained by the detected light, which may be
regarded as an advantage.
The future of ffLDPI cannot be viewed independently from that of laser speckle
contrast perfusion imaging (LSCPI) [2], which must be regarded as its main
competitor. LSCPI has the advantage of being relatively simple and cheap, since
it does not need a high-speed imaging array. On the other hand, LSCPI is less
quantitative in that the relation between speckle contrast and perfusion is not
described by a physical model, unlike laser Doppler methods, which are supported
by the model of Bonner and Nossal [1].
A theory by Draijer et al. [22] suggests that LSCPI is more sensitive to concentra-
tion variations of moving red blood cells than to variations of red blood cell velocity.
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