Photoacoustic (PA) Imaging can estimate the spatial distribution of oxygen saturation (sO2) and total hemoglobin concentration (HbT) in blood, and be co-registered with B-Mode ultrasound images of the surrounding anatomy. This study will focus on the development of a PA imaging mode on a commercially available array based micro-ultrasound (μUS) system that is capable of creating such images.

1. Development and Validation of a Combined Photoacoustic Micro-
Ultrasound System for In Vivo Oxygen Saturation Estimation
A. Needles*a, A. Heinmillera, P. Ephrata, D. Batesa,C. Bilan-Traceya, C. Theodoropoulosa, D.
Hirsona, F.S. Fostera,b
a
VisualSonics Inc., 3080 Yonge St., Toronto, ON, Canada;
b
Sunnybrook Health Sciences Centre, 2075 Bayview Ave., Toronto, ON, Canada.
ABSTRACT
Photoacoustic (PA) Imaging can estimate the spatial distribution of oxygen saturation (sO 2) and total hemoglobin
concentration (HbT) in blood, and be co-registered with B-Mode ultrasound images of the surrounding anatomy. This
study will focus on the development of a PA imaging mode on a commercially available array based micro-ultrasound
(μUS) system that is capable of creating such images. The system will then be validated in vivo against a complementary
technique for measuring partial pressure of oxygen in blood (pO 2). The pO2 estimates are converted to sO2 values based
on a standard dissociation curve found in the literature. Finally, the system will be used for assessing oxygenation in a
murine model of ischemia, both during injury and recovery.
Keywords: Photoacoustics; Micro-ultrasound; Small animal imaging; Oxygen saturation; Hemoglobin; Ischemia.
1. INTRODUCTION
Photoacoustic (PA) Imaging is sensitive to differences in optical absorption from biological tissues but detects these
signals with ultrasound. PA Imaging exploits the photoacoustic effect, whereby an acoustic wave is generated from an
object that is illuminated by pulsed electromagnetic radiation. By illuminating tissue, a thermoelastic expansion can
occur, and is dependent on the optical absorption at the excitation wavelength of the light. This expansion creates an
ultrasound wave that can be detected with an ultrasound transducer. The most commonly accepted PA scanners use
either a tomographic (PAT) [1] or a planar geometry with a linear array transducer [2],[3]. The tomographic approach
offers a large effective aperture for data collection, but suffers from a low frame rate, due to the need for hundreds to
thousands of laser pulses per frame. The use of a linear array allows a 2-D frame to be acquired with just a few laser
pulses, providing much higher frame rates.
Because PA Imaging is dependent on optical absorption, it may be used for discriminating blood from tissue signals.
Specifically, the oxygenation of hemoglobin (Hb), the blood protein that carries oxygen to tissues, can be assessed with
PA Imaging. Hemoglobin with bound oxygen (HbO2) has different absorption characteristics than deoxygenated
hemoglobin (Hb) [4],[5], shown in Fig. 1. By imaging with different wavelengths of light, estimates of the percentage
oxygen saturation (sO2) of blood, as well as the total hemoglobin concentration (HbT), can be derived and displayed as a
parametric map. This map can then be superimposed on a B-Mode ultrasound image to register the PA signal to specific
anatomy.
Ischemia - or the lack of blood supply to a tissue - and subsequent reperfusion induces physiological and biochemical
changes in the affected tissue and is an important area of study since the damage that occurs as a result is clinically
important in diabetes, stroke, and heart disease. The ability to image sO2 and HbT in ischemic or reperfused tissue could
prove to be a valuable research tool in the study of ischemia and potential interventions and treatments. Currently,
ischemia is measured using techniques such as laser Doppler [6],[7], which only images tissue blood flow; near infra-red
spectroscopy [8], which is cable of measuring blood oxygenation, but with poor spatial resolution; and magnetic
resonance techniques [9], which are expensive.
*aneedles@visualsonics.com; phone 1 416 484-5000; fax 1 416 484-5001; www.visualsonics.com

2. Linear array based micro-ultrasound imaging systems are capable of imaging small animal models of human disease
[10], as demonstrated by Foster et al. [10]. This system has a 64 channel beamformer, and is capable of operating linear
arrays in the 12-50 MHz range. The first focus of this study will be the development of PA Imaging on this linear array
technology for imaging the microvasculature in vivo, and making estimates sO2 and HbT. The goal will be to enable the
collection of both ultrasound and PA images of the same plane, such that they are co-registered and can be overlaid. The
second focus will then be to validate the PA-derived estimates of sO2 against a complementary technique for estimating
sO2 based on measuring partial pressure of oxygen in blood (pO 2). Finally, the system will be used to image an in vivo
murine model of ischemia induced in the hind limb, with an emphasis placed on generating parametric maps of
physiological significance, namely sO2 and HbT as a function of time.
Figure 1. Extinction coefficient spectra for oxygenated (HbO2) and deoxygenated (Hb) blood [4]. Deoxygenated
blood absorbs light more strongly at 750 nm and oxygenated blood absorbs more strongly at 850 nm
2. METHODS
2.1 Instrumentation
A modified μUS system (Vevo 2100, VisualSonics Inc.) was operated with a linear array transducer (MS250, fc = 21
MHz, 256 elements). The transducer was retrofitted with a housing that held rectangular fiber optic bundles (25.4 x 1.25
mm) to either side, at an angle of 30º relative to the imaging plane. The rectangular bundles were bifurcated ends of a
single bundle that was coupled to a tunable laser (Rainbow NIR Nd:YAG, OPOTEK Inc., Carlsbad CA, 680-970 nm, 20
Hz). The μUS system was synchronized with the laser and PA signals were generated with a fluence < 20 mJ/cm2. The
commercial name of the μUS system combined with the laser is the Vevo LAZR Photoacoustic Imaging System
(Visualsonics Inc.). If required, 3-D data sets were collected with the Vevo LAZR system by linearly translating the
transducer (with integrated optical fibers) with a stepper motor over a region of interest, while collecting each image of
the 3-D stack.
For dual-wavelength imaging, PA images were collected with two different methods. The first method consisted simply
of collecting single wavelength datasets at 750 and 850 nm and processing parametric maps of sO2 and HbT offline with
a two-wavelength approach [11]. The second method was performed on the Vevo LAZR system by alternately
collecting image frames at 750 nm and 850 nm in real-time while processing images of sO2 and HbT at 0.5-1.0 frames
per second (fps). The low frame rate in the second method was due to the tuning time of the laser system between the
two wavelengths. The advantage of the second method was that images of sO2 and HbT could be displayed live on the
system during the course of an experiment, as well as in 3-D if acquired with the stepper motor.
2.2 Image Reconstruction
Parallel acquisition was used for image formation. In this mode, for each laser pulse the PA signals were captured on a
quarter of the MS250 array (64 elements). The signals were baseband quadrature sampled [12], and beamformed in
software using a delay and sum method [13]. Up to 128 elements could be used to form a synthetic aperture (spanning
multiple quarters) to beamform an image. The beamforming was performed with dedicated PC software running on the
μUS system. For a full width image, four laser pulses were required, producing a frame rate of 5 fps (i.e. one quarter of

3. the 20 Hz laser repetition rate). For smaller fields of view, requiring fewer receive elements, the frame rate increased
proportionally, up to a maximum of 20 fps. For single wavelength datasets the magnitude of the resulting complex
beamformed signal was taken, and displayed as in image of relative magnitude. The magnitude values of two single
wavelength datasets served as the input to the dual-wavelength algorithm for estimating sO2 and HbT.
2.3 In vivo Validation of Oxygen Saturation Estimates
All validation experiments we conducted using the first method of acquisition (offline). The jugular vein of adult female
CD1 mice (n = 3) was exposed under anesthesia and a 25 gauge needle was inserted into the vein, pointing caudally. pO 2
and body temperature were measured using the OxyLab pO 2 E Series tissue oxygenation and temperature monitor
(Oxford Optronix, Oxfordshire, UK). The probe was inserted through a 25 gauge needle into the jugular vein.
Positioning of the needle and probe in the vein was verified by a B-Mode scan (not shown). Due to strong reflection
artifacts from the needle in B-Mode and PA-Mode, images and oxygen saturation data were collected from the
contralateral jugular vein. sO2 estimates were obtained with PA imaging while inhaled oxygen was varied by mixing
100% oxygen with 5% oxygen at different concentrations to create hypoxic and hyper-oxygenated states in the animal.
For the sO2 estimates, PA images were collected at 750 and 850 nm once the measured pO 2 stabilized. This was repeated
for three mice and parametric maps of sO2 were processed offline with a two-wavelength approach for all animals. A
region of interest (ROI) was created for the set of sO 2 images for each experiment to include a section of the jugular
vein, and the average sO2 was calculated based on this ROI for each selected pO2 value. These sO2 values from the ROI
were then averaged for all three mice and compared to predicted sO 2 values. Predicted sO2 values were obtained from an
online calculation based on work by Kelman [14]. Finally, linear regression was performed on the relationship between
predicted and estimated sO2. All experiments were conducted under an approved VisualSonics’ animal care protocol,
sanctioned by the Department of Comparative Medicine at the University of Toronto (Toronto, Canada).
2.4 In vivo Imaging of Hind Limb Ischemia
A CD1 mouse was anaesthetized and placed on an apparatus whereby a suture thread (inside a length of polyethylene
tubing) could be drawn tight around the upper part of the right hindlimb. A bout of ischemia was induced by tightening
the loop around the proximal thigh and securing it with a haemostat. 2-D and 3-D PA imaging of the hindlimb was
performed before, during and after ischemia. A region of interest was selected proximal to (non-ischemic portion of the
limb) and distal from (ischemic portion) the tourniquet during ischemia and reperfusion. sO2 and HbT values were
plotted as a function of time from these regions. The bout of ischemia was induced for 3.5mins, before the suture thread
was loosened allowing for reperfusion. All experiments were conducted under an approved VisualSonics’ animal care
protocol, sanctioned by the Department of Comparative Medicine at the University of Toronto (Toronto, Canada).
3. RESULTS
3.1 Photoacoustic Imaging of the Microvasculature
Fig. 2 illustrates an example of a single wavelength (750 nm) PA 3-D dataset of superficial vessels in the mouse
abdomen, displayed in a top-down view. This figure demonstrates the ability of the system to detect PA signal from the
microvasculature as a series of 2-D images rendered in 3-D.

4. Figure 2. Single wavelength Photoacoustic image (750 nm) of superficial microvessels in the mouse abdomen.
Image was obtained with the new combined system by translating the array transducer with light fibers in 3-D,
with a linear stepper motor.
3.2 In vivo Validation of Oxygen Saturation Estimates
Fig. 3 shows PA images of the jugular vein acquired at 750 nm (top left) and 850 nm (top right). These single
wavelength images were used to generate a parametric map of sO 2 (bottom). ROIs were analyzed around the anterior
walls of the jugular, and average sO2 values were generated. The oxyhemoglobin dissociation curve [14], which plots
pO2 on the x-axis against sO2 on the y-axis, is shown in Fig. 4 (magenta markers). The estimated sO 2 in Fig. 4 (blue
markers) shows the average sO2 values estimated with PA (n = 3) taken at the corresponding pO 2 values. The correlation
between estimated and expected sO2 is shown in Fig. 5. Performing linear regression resulted in a correlation coefficient
of 0.82 and a P-value < 0.01.
Figure 3. Photoacoustic images of the mouse jugular vein acquired at 750 nm (left) and 850 nm (right) were used
to generate a parametric map of sO2 (bottom). ROIs were analyzed around the anterior walls of the jugular vein,
and average sO2 values were generated.

5. Expected and Estimated Oxyhemoglobin Dissociation Curve
1
0.9
0.8
0.7
0.6
sO2
0.5
0.4
0.3
Expected (from Kelman, 1966)
0.2
PA Estimate
0.1
0
0 10 20 30 40 50 60 70 80 90 100
pO2 [mmHg]
Figure 4. The oxyhemoglobin dissociation curve which plots pO2 on the x-axis against sO2 on the y-axis. The
magenta curve shows expected sO2 values derived from the literature based on measured pO2 values. The blue
curve shows the average sO2 values estimated with PA, taken at the corresponding pO2 values.
Correlation Between Estimated and Expected sO2
1.2
1
0.8
Expected sO2
0.6
y = 1.2522x - 0.053
R2 = 0.8246
0.4
0.2
0
0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1
Estimated sO2
Figure 5. Correlation between PA derived sO2 estimates and expected sO2 values derived from Kelman, 1966 [14].
3.3 In vivo Imaging of Hind Limb Ischemia
The effects of ischemia induced by the suture thread in the hind limb are shown in Fig. 6. In Fig. 6 a parametric map of
sO2 (displayed as 0% oxygen in blue to 100% oxygen in red) is overlaid on top of the B-Mode ultrasound image of the
hindlimb. Key landmarks such as the suture thread, the skin line and the femur are noted on the image. In the left panel
of Fig. 6, the hind limb is seen with the suture thread in place but the blood in both the proximal (left of the suture
thread) and distal (right of the suture thread) regions under non-ischemic conditions (time point = 0 s ). After tightening
the suture thread it can be observed qualitatively in the right panel of Fig. 6 that the sO2 within the distal region has
decreased significantly (time point = 218 s). The results were quantified by drawing regions of interest, at equivalent
depths, within the proximal and distal regions, and are summarized in Fig. 7. The average sO2 signal in the distal
(ischemic) portion of the hindlimb (see Fig. 7 light blue curve) decreased immediately and steadily to a minimum of
approximately 25% after approximately 3.5 minutes, while the proximal (non-ischemic) portion of the limb (see Fig. 7
red curve) showed relatively constant sO2 during the bout of ischemia. Following reperfusion, the sO2 in the distal
portion rose steadily to near the pre-ischemic level. In addition to sO2, HbT was also assessed in both regions. The Hbt

6. remained relatively constant in the proximal region of the hindlimb (Fig. 7 burgundy curve) while the distal region
showed an increase upon reperfusion (Fig. 7 dark blue curve). It should also be noted that in the 3-D renderings of the
hindlimb before and during ischemia (not shown), it was evident that the ischemic limb showed low sO2 values
(indicated by a darker blue colour) whereas the reperfused limb showed a much higher sO2 signal. These results were
visible throughout the entirety of the limb.
Figure 6. PA images of sO2 in the hind limb overlaid on a B-Mode ultrasound image. sO2 is shown under non-
ischemic (left) and ischemic (right) conditions. The sO2 overlay is displayed from 0% (blue) to 100% (red).
Figure 7. Average estimated sO2 and Hbt as measured by selecting ROIs that encompass the proximal or distal
regions of the hindlimb. Ischemia was induced at approximately 5 seconds and alleviated at approximately 218
seconds.
4. DISCUSSION AND CONCLUSIONS
The new Vevo LAZR system, a combined PA/µUS system with linear array based technology, has been demonstrated
with single wavelength 2-D imaging up to 20 fps, over a wide range of wavelengths. Acquisition was extended to 3-D by
linearly translating the imaging plane with a linear stepper motor. The capability of the system to generate parametric

7. images of HbT and sO2 was also validated in vivo and subsequently demonstrated in a murine model of induced
ischemia.
Changes in pO2 values measured with the OxyLab probe and sO2 values estimated with PA imaging were compared as a
method of validating the system. PA estimates of sO 2, correlated well with alterations in inhaled O2 concentration, down
to sO2 values of 30% (Fig. 4 and 5). Below 30% the estimates became unreliable and did not correlate well with the
expected results based on the dissociation curve. Dynamic energy monitoring was not applied in this study, so
fluctuations in laser energy from pulse-to-pulse were not accounted for. This may introduce error into the measurements
and should be investigated further. Potentially, active energy monitoring could be implemented in the future. Ideally, the
system would be able to record the actual fluence generated by the laser source and compensate for this prior to
performing the estimate of sO2. Other sources of error may include differences between the actual and assumed optical
properties of the tissue used in the calculations, as well the fact that the dissociation curve used in this study was for
human blood. It is possible that there are small physiological differences with mouse blood that were not accounted for.
Finally, the fact that the pO2 measurements and PA estimates were acquired in the contralateral jugular veins of the same
animal, may also have been a source of error. Moving to larger animals, such as the rat, could allow the pO 2
measurement and PA estimate to be acquired from the same blood vessel, without corrupting the PA signal with image
artifact from the catheter tip. Alternatively, other methods for measuring oxygenation such as blood-gas analysis or pulse
oximetry could be used for validating the PA estimates. More work is required to understand why the error in the
estimates is observed at lower concentrations of oxygen in the blood. Further development of the algorithms used to
calculate sO2, determination of more appropriate ROIs for analysis, and improvement in beamforming and image quality
may also contribute to the improvement of sO 2 estimation with PA.
In terms of relative quantification, however, (i.e. not measuring against an absolute quantity such as pO2) the system
shows great promise for monitoring in vivo changes in sO2 and HbT. These changes may be induced through forced
changes in physiological state, as was the case in this study, or through a response to therapy. The latter case was the
motivation for examining the ability of the system to monitor sO 2 and HbT in the tourniquet-based mouse hindlimb
model of acute ischemia/reperfusion. The system was able to perform real-time measurement of estimated sO2 and Hbt
in the affected hindlimb. The system also allowed for the comparison of non-ischemic and ischemic regions of the
hindlimb in the same plane, over the same time course and with high anatomical and temporal resolution. This result
demonstrates the great potential of the system to investigate the physiology underlying ischemia with in vivo animal
models. Future experiments could look at even more sophisticated disease models where ischemia is induced through
other biological processes.
PA imaging for preclinical research is anticipated to be a powerful imaging modality for the future. The developments
presented here allow for the integration of PA imaging fused with B-Mode ultrasound. These images depict regions of
blood flow and provide functional information of blood oxygen saturation and relative hemoglobin levels. Gathering this
type of functional information is not possible with µUS alone. PA imaging utilizes the optical absorption properties of
blood, and retains the image resolution and imaging depths of µUS, something not possible with purely optical based
imaging techniques. The addition of PA imaging to a previously developed µUS system offers unique potential to add
functional information to pre-clinical studies not currently available on other imaging platforms.
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