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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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11.3 Microcirculation Parameters Quantified by PAT 265
1.0
SO
2
0.6
PA
50 µm
Figure 11.5 Optical-resolution photoacoustic microscopy of
the hemoglobin oxygen saturation in a living mouse ear.
11.3.3
Blood Flow and Cell Counting
Blood flow is closely associated with neuronal activity and tissue metabolism. More
importantly, combining the measurements of blood flow, [HbT], sO
2
, and vessel
morphology has the potential to quantify tissue oxygen metabolism with high
spatial resolution. Recently, Fang et al. [32] reported the photoacoustic Doppler
effect, where the relative motion between the photoacoustic source and acoustic
detector induces a Doppler shift in the detected photoacoustic signal with respect
to the source modulation frequency. They measured flow in tissue-mimicking
phantoms using photoacoustics based on intensity-modulated continuous-wave
laser excitation [33].
A schematic of the photoacoustic Doppler flowmetry (PADF) system is shown in
Figure 11.6a [33]. Optically absorbing particles with a flow speed of v are excited with
an intensity-modulated laser beam to induce photoacoustic waves. The detected
photoacoustic waves undergo a Doppler shift expressed as [32]
f
PAD
= f
0
v
v
s
cos θ (11.3)
where f
0
is the frequency of the intensity modulation, v
s
is the speed of sound, and
θ is the angle subtended by the propagating direction of the detected photoacoustic
wave and the traveling direction of the particles. As shown in Figure 11.6b, f
PAD
is linearly proportional to v. A flow speed as low as 27 µms
−1
can be measured
through f
PAD
.
Although the PADF described in Figure 11.6 can detect blood flows as slow as the
average capillary flow, it can detect the flow only along the ultrasonic axis. Recently,
Fang and Wang [34] modified OR-PAM for M-mode photoacoustic particle flow
imaging, which is able to detect capillary flow perpendicular to the ultrasonic
axis. M-mode means ‘‘motion mode,’’ where the dual optical and ultrasonic foci
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266 11 Photoacoustic Tomography of Microcirculation
Container
Intensity
modulated
light
Lens
Ultrasonic transducerTo lock-in detector
0.01 0.1 1
0.01
0.1
1
Average flow speed (mm s
−1
)
Doppler shift (Hz)
(a)
(b)
Scattering medium
L
V
θ
Figure 11.6 (a) Schematic of photoacoustic Doppler
flowmetry. (b) Photoacoustic Doppler shift versus particle
flow speed.
Diode laser,
905 nm, 15 ns, 10 kHz
Driver Amplifier Boxcar
Ultrasonic
transducer
Epidermis
Dermis
Blood
vessel
Er
y
throc
y
tes Melanoma cell
Flow
Acoustic
waves
Time, 10 s
400 ns
30 ms
Figure 11.7 Schematic of photoacoustic flow cytometry.
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11.4 Biomedical Applications 267
are kept stationary to record the motion of absorbing particles along the acoustic
axis. Yao et al. further extended this technology to all types of blood vessels with
a concept called photoacoustic Doppler bandwidth broadening and applied it to
measure the microvascular blood flow in small animals in vivo [10, 35].
The M-mode photoacoustic flowmetry that Fang et al. used for transverse flow
measurements can also be applied to monitoring cell trafficking in the micro-
circulation. In photoacoustic flow cytometry (Figure 11.7), a pulsed laser beam
is focused and kept stationary within a targeted blood vessel or lymph vessel,
and individual cells passing through this detection point are excited and detected
with an ultrasonic transducer [36, 37]. On the basis of the spectral features of
endogenous chromophores (such as melanin) or exogenous contrast agents (such
as nanoparticles), single metastatic cells or circulating bacteria can be detected,
identified, and destroyed in vivo in real time [29, 30, 37, 38].
11.4
Biomedical Applications
Since PAT can image and characterize multiple important microcirculation param-
eters, including [HbT], sO
2
, blood flow, and vascular morphology, it holds broad
applications in microcirculation-related physiological, pathophysiological, and clin-
ical studies. A few recent applications are covered here, and more applications can
be found in the literature [11].
11.4.1
Longitudinal Monitoring of Tumorlike Angiogenesis
A major obstacle in tumor or ischemic angiogenesis research is the lack of imaging
tools for studying the temporal features of angiogenesis in intact animals. Ex
vivo histology requires animal termination and thus disables chronic monitoring.
In vivo optical microscopy, such as intravital microscopy, confocal microscopy,
or multiphoton microscopy, allows repetitive imaging; however, the required
angiographic contrast agents may alter the behavior of the intrinsic microcircula-
tion. OR-PAM, enabling in vivo label-free imaging of the intact microvasculature
down to single capillaries, is ideal for longitudinal studies of tumor or ischemic
angiogenesis.
Recently, we applied OR-PAM to study the kinetics of hypoxia-inducible
factor (HIF)-mediated angiogenesis in a transgenic mouse model [39]. Continuous
induction of doxycycline caused the overexpression of HIF-1α, a master regulatory
factor of angiogenesis. Throughout 30 day HIF activation, OR-PAM clearly
observed a progressive and ultimately remarkable increase in microvascular
density and tortuosity in transgenic mice (Figure 11.8). By contrast, control
experiments on a nontransgenic mouse evidenced no noticeable changes in
microvascular morphology (Figure 11.9).
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268 11 Photoacoustic Tomography of Microcirculation
Day 0
Day 1
Day 14
Day 30
500 µm
Day 3
Figure 11.8 Longitudinal monitoring of HIF-mediated
angiogenesis in a transgenic mouse using optical-resolution
photoacoustic microscopy. Optical wavelength: 570 nm.
Day 0
Day 1
Day 3
Day 14
Day 30
500 µm
Figure 11.9 Longitudinal monitoring of a nontransgenic
mouse using optical-resolution photoacoustic microscopy.
Optical wavelength: 570 nm.
11.4.2
Transcranial Imaging of Cortical Microvasculature
Advances in cortical microvascular imaging of small animal models greatly
facilitate the understanding of neurovascular coupling, the relationship between
local neural activity and subsequent changes in cerebral hemodynamics. However,
high-resolution cortical imaging through intact mouse skulls remains challenging
for pure optical modalities because the optical scattering and absorption of the
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11.4 Biomedical Applications 269
skull significantly degrade the spatial resolution and signal-to-noise ratio (SNR).
Moreover, the invasive procedure and the required injection of angiographic agents
discourage longitudinal monitoring of brain plasticity, the changing of brain
function to compensate for injury or disease and to adapt to new environments.
Label-free PAT provides a solution by breaking through the optical diffusion limit.
AR-PAM has successfully demonstrated mouse brain imaging through both intact
scalp and skull [27]. This noninvasive feature is highly desirable for chronic studies
of cortical plasticity. However, its current spatial resolution (70 µm laterally) is
not adequate to resolve capillary networks that are directly coupled with neuronal
metabolism. By moderately sacrificing the penetration depth, OR-PAM has enabled
in vivo capillary imaging through intact mouse skulls [9], which is favorable for
chronic studies.
The label-free OR-PAM imaged oxygen gradients in the mouse cortical micro-
circulation (Figure 11.10). Two optical wavelengths were used for sO
2
mapping.
Figure 11.10a,b shows the maximum amplitude projection (MAP) images of a
2 × 2mm
2
ROI acquired at 570 and 578 nm, respectively. Figure 11.10c shows the
corresponding 3D microvascular morphology, and Figure 11.10d shows the sO
2
mapping. In Figure 11.10a, the microvessels labeled with CL appear to be single
capillaries. In Figure 11.10d, the red vessels (sO
2
values >90%) are believed to be
arteries, whereas the green ones (sO
2
values as low as 60 –70%) are likely to be
veins. However, there is no clear cutoff between arteries and veins in sO
2
values;
instead, the blood oxygenation of microvasculature is closely associated with its
branching order and the local tissue metabolic rate [40, 41]. To demonstrate the
oxygen gradients in the cortical microcirculation, we selectively analyzed a postcap-
illary venular tree (Figure 11.10e) and a precapillary arteriolar tree (Figure 11.10f)
extracted from Figure 11.10d. A negative linear correlation between the branching
order and the mean sO
2
value was observed in both the arteriolar and venular trees
(arteriolar tree: R
2
= 0.98, p = 0.01; venular tree: R
2
= 0.75, p = 0.14), as shown
in Figure 11.10g. The oxygen gradient in the arteriolar tree (∼12% from order
1 to 4) implies that, besides capillaries, precapillary arterioles may also participate
in blood –tissue oxygen exchange, while the oxygen gradient in the venular tree
(∼9% from order 1 to 4) suggests that a diffusional shunt is present between
arterioles and venules.
11.4.3
Label-Free Ophthalmic Microangiography
Visual impairment due to pathological ocular microcirculation is highly preva-
lent worldwide. Fluorescence angiography, the current gold standard for ocular
microvascular imaging, requires the injection of contrast agents that can cause
pain, complications, or even death [42]. Moreover, the angiographic agents may fail
to perfuse if there is conspicuous leakage in the pathological vasculature.
Recently, OR-PAM has demonstrated label-free microvascular imaging in both
the anterior and posterior ocular segments [21, 43]. Figure 11.11a shows a projected
angiogram of the iris microcirculation obtained by label-free OR-PAM at 570 nm
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270 11 Photoacoustic Tomography of Microcirculation
CL
Optical absorption
Max
1.0 Min
0.3
PA
sO
2
200 µm
Venular tree
V4(b)
V2(b)
V4(a)
V3(d)
V2(a)
V3(b)
V3(c)
V1
V3(a)
A3(a)
A4(a)
A3(c)
A4(b)
A3(b)
A2(a)
A3(d)
A
1
(a)
A1(b)
A2(b)
Arteriolar tree
1234
Vascular branching order
60
65
70
75
80
85
90
95
100
Hemoglobin oxygen saturation (%)
Arteriolar tree
Venular tree
(a) (b)
(c)
(d)
(e)
(f)
(g)
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11.4 Biomedical Applications 271
Figure 11.10 Label-free optical-resolution photoacoustic microscopy of oxygen gradients in a
living mouse cortex through the intact skull. Maximum amplitude projection images acquired at
(a) 570 and (b) 578 nm. (c) Volumetric mapping of the total hemoglobin concentration acquired
at 570 nm. (d) Vessel-by-vessel mapping of the hemoglobin oxygen saturation (sO
2
). (e) A venular
tree with branching orders (boxed by blue dashed lines in (d)). (f) An arteriolar tree with branching
orders (boxed by red dashed lines in (d)). (g) The sO
2
value versus the vascular branching order
in the precapillary arteriolar tree and the postcapillary venular tree. The square and round markers
represent the mean sO
2
values in each branching order, and the error bars stand for the standard
deviations of the mean sO
2
values. The calculated sO
2
values are shown in the color bar. PA:
photoacoustic signal amplitude. The scale bar applies to (a), (b), and (d).
←−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−
Optical absorption
Min Max
200 µm
20 µm
LIC
(a)
RBC
0102030
Position (mm)
0
0.2
0.4
0.6
0.8
1
Normalized PA amplitude
RIA
RCB
CP
MIC
sO
2
0.6 1.0
(b)
(c)
(d)
200 µm
Raw data
Gaussian fit
Figure 11.11 Label-free optical-resolution
photoacoustic microscopy of the iris
microvasculature in a living mouse. (a)
Maximum amplitude projection (MAP)
image acquired at 570 nm. (b) Close-up of
the boxed area in (a). The arrows indicate
individual red blood cells (RBCs) in iris cap-
illaries. (c) Photoacoustic amplitude profile
of a RBC crossed by the red dashed line
in (b). (d) MAP image of the same region
in (a) overlaid by the hemoglobin –oxygen
saturation mapping of the boxed region of
interest. CP, ciliary process; LIC, lesser iris
circle; MIC, major iris circle; PA, photoacous-
tic signal; RCB, recurrent choroidal branch;
and RIA, radial iris artery.
[21]. The tight optical focusing and the high detection sensitivity enabled OR-PAM
to visualize individual RBCs traveling along iris capillaries (Figure 11.11b, a
close-up of the boxed region in Figure 11.11a). According to the Gaussian fit of
the cross-sectional photoacoustic signal profile (Figure 11.11c), an imaged RBC
(crossed by the red dashed line in Figure 11.11b) was estimated to be 6 µm
in diameter. Note that the laser exposure applied here was 30 times below the
American National Standards Institute (ANSI) safety limit. The low-level exposure,
in combination with the label-free feature, enables repetitive imaging. As a simple
demonstration, the region in Figure 11.11a was reimaged in Figure 11.11d.
With the focal plane lowered toward the posterior segment, Figure 11.11d better
visualized the peripheral vascular structures such as the ciliary process (CP), the
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272 11 Photoacoustic Tomography of Microcirculation
recurrent choroidal branch (RCB), and the major iris circle (MIC) that bifurcates
into multiple radial iris arteries (RIAs), except that the lesser iris circles (LICs) were
slightly blurred. Besides the out-of-focus issue, Figures 11.11a,d show essentially
no difference in the microvascular morphology, which implies that this laser
exposure level did not cause any visible destruction to the ocular vasculature. In
addition to vascular morphology, we further obtained a vessel-by-vessel sO
2
map
of a smaller ROI with dual wavelengths (570 and 578 nm) (the dashed line box in
Figure 11.11d), where we could clearly identify the RIAs, and we also could observe
the transition in blood oxygenation from arteries to veins.
Laser beam
14th day 21st day 28th day
Tumor
7th day 14th day
Tumor
Tumor
Tumor
Transducer
Laser
beam
1432
Weeks after tumor inoculation
0
4
8
12
16
20
CTC (number min
−1
)
(a)
(b)
(c)
Figure 11.12 Melanoma tumor growth in mouse ear (a)
and (b) abdomen. (c) The time courses of the circulating
rate of melanoma cells in a 150 µm abdomen vessel after
tumor inoculation in the ear (open circle) and abdomen
(open square). Filled circles and filled squares are the cor-
responding mean values.
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11.5 Discussion and Perspectives 273
11.4.4
Real-Time Monitoring and Destruction of Circulating Cancer Cells
The circulation of tumor cells in the blood stream is prognostic of cancer metastasis.
However, due to the limited sensitivity, conventional methods have difficulty in
detecting sparse circulating tumor cells before malignant metastasis. Photoacoustic
flow cytometry introduced in Section 11.3.3 provides a promising solution.
Photoacoustic counting of circulating melanoma cells has been shown in the
mouse ear (Figure 11.12a) and the abdomen (Figure 11.12b) with melanoma
tumor inoculation [30]. A 150 µm diameter vessel that is ∼1.5 mm from the
primary tumor margin in the abdomen was subject to a four-week monitoring
by photoacoustic flow cytometry. One week after the abdomen tumor inoculation,
∼3 melanoma cells were detected in the abdomen vessel per minute, which was
likely due to migration and intravasation. Starting from the second week, more
melanoma cells (
>
7 cells min
−1
) were detected in the abdominal vessel, which
likely indicated the starting of the metastatic process in the vicinity of the primary
tumor. By contrast, there were very few melanoma cells detected in the abdominal
vessel even three weeks after tumor inoculation in the mouse ear, which suggests
that prominent metastasis did not occur until three to four weeks after tumor
inoculation (Figure 11.12c).
Note that with increased laser energy photoacoustic flow cytometry can further
destroy tumor cells. For example, after 1 h exposure to the abdominal skin vessels
with a laser pulse energy of 450 mJ cm
−2
(the photodamage threshold of the
circulating tumor cells), Zharov et al. observed a significant decrease in the tumor
cell detection rate, from 12 cells min
−1
down to 0.5–1 cells min
−1
. However, in the
few days following this procedure, the circulating tumor cell count recovered back
to the initial levels as new tumor cells entered into the microcirculation.
11.5
Discussion and Perspectives
PAT, capable of detecting endogenous hemoglobin contrast with highly scalable
spatial resolution and penetration depth, holds great potential for fundamen-
tal physiological research and clinical care. Its primary advantages include the
following:
1) Relying on the endogenous hemoglobin contrast guarantees that OR-PAM
images only RBC-perfused microvasculature, the functional vascular subset
responsible for tissue oxygen supply.
2) The high nonradiative quantum yield of hemoglobin enables high imaging
sensitivity, down to the single RBC level (contrast-to-noise ratio is ∼20 dB with
570 nm laser excitation).
3) The label-free feature and low laser exposure level enable repetitive imaging
for disease monitoring and treatment evaluation.
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274 11 Photoacoustic Tomography of Microcirculation
AR-PAM OR-PAM
Relative HbT
sO
2
sO
2
Relative HbT
Molecular Human HumanMolecular
Epidermal-
dermal junctionEpidermis
Micro-
vessels
Figure 11.13 Current status of acoustic- and
optical-resolution photoacoustic microscopy for microcircu-
lation imaging.
4) Spectroscopic measurements enable functional imaging of sO
2
on a single
vascular basis.
5) Multiparameter imaging of the microcirculation (including [HbT], sO
2
, blood
flow, and vascular morphology) holds the potential for quantification of the
oxygen metabolism rate within a single modality, guaranteeing the same
sample time and volume.
Currently, the two microscopy implementations of PAT – AR-PAM and
OR-PAM – have achieved in vivo high-resolution functional [8, 9, 16, 22, 44] and
molecular [45, 46] imaging of the microcirculation in small animal models. With a
laser exposure level well within the ANSI safety standards, AR-PAM has further
demonstrated human subcutaneous imaging [16] and OR-PAM also has shown its
potential in human cutaneous imaging (Figure 11.13).
Although promising, PAT still requires further refinements in the following
aspects.
11.5.1
Fluence Compensation for Accurate sO
2
Measurements
As mentioned in Section 11.3.2, the optical fluence is assumed to be wavelength
independent during sO
2
quantification, which is an approximation. However, to
recover more accurate sO
2
values, fluence compensation becomes necessary. The
inaccuracy in sO
2
calculation due to the wavelength-dependent optical scattering
of the sebaceous gland shown in Figure 11.5 is a good example.
Substantial efforts have been undertaken on this topic [31, 47–50]. However,
the existing numerical models require further refinements to take into account
the strong heterogeneity in the structure and composition of biological tissues,
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