JBO-130724R 6..6 ++ - JBO_19_1

Retrospective respiration-gated whole-

body photoacoustic computed

tomography of mice

Jun Xia

Wanyi Chen

Konstantin Maslov

Mark A. Anastasio

Lihong V. Wang

Downloaded From: http://biomedicaloptics.spiedigitallibrary.org/ on 06/20/2016 Terms of Use: http://spiedigitallibrary.org/ss/TermsOfUse.aspx

Retrospective respiration-gated whole-body

photoacoustic computed tomography of mice

Jun Xia, Wanyi Chen, Konstantin Maslov, Mark A. Anastasio, and Lihong V. Wang

Washington University in St. Louis, Optical Imaging Lab, Department of Biomedical Engineering, One Brookings Drive, Saint Louis, Missouri 63130

Abstract.

Photoacoustic tomography (PAT) is an emerging technique that has a great potential for preclinical

whole-body imaging. To date, most whole-body PAT systems require multiple laser shots to generate one cross-

sectional image, yielding a frame rate of <

1

Hz. Because a mouse breathes at up to 3 Hz, without proper gating

mechanisms, acquired images are susceptible to motion artifacts. Here, we introduce, for the first time to our

knowledge, retrospective respiratory gating for whole-body photoacoustic computed tomography. This new

method involves simultaneous capturing of the animal

’

s respiratory waveform during photoacoustic data acquis-

ition. The recorded photoacoustic signals are sorted and clustered according to the respiratory phase, and an

image of the animal at each respiratory phase is reconstructed subsequently from the corresponding cluster. The

new method was tested in a ring-shaped confocal photoacoustic computed tomography system with a hardware-

limited frame rate of 0.625 Hz. After respiratory gating, we observed sharper vascular and anatomical images at

different positions of the animal body. The entire breathing cycle can also be visualized at

20

frames

∕

cycle.

2014

Society of Photo-Optical Instrumentation Engineers (SPIE)

[DOI:

10.1117/1.JBO.19.1.016003

]

Keywords: photoacoustic computed tomography; small-animal whole-body imaging; respiratory motion; retrospective motion gating.

Paper 130724R received Oct. 6, 2013; revised manuscript received Nov. 20, 2013; accepted for publication Nov. 27, 2013; published

online Jan. 6, 2014.

Photoacoustic tomography (PAT) is an emerging small-animal

whole-body imaging technique that has drawn great interest in

recent years.

–

PAT is based on the photoacoustic effect, which

converts absorbed optical energy into pressure via thermoelas-

tic expansion. The generated pressure waves are detected by

ultrasonic transducers placed in multiple positions, and the

complete dataset is then computed to reconstruct an image

of the absorbed optical energy density in the tissue. The con-

version to acoustic waves enables PAT to generate high-reso-

lution images in the optically diffusive regime. In order to

capture photoacoustic waves traveling along different direc-

tions, small-animal whole-body PAT systems typically have

a large receiving aperture, achieved either by mechanically

scanning a transducer array

or by using an array with a

large number of elements.

However, even in the latter

case, due to the limited number of data acquisition channels,

it is challenging to capture signals from all elements with a

single-laser shot. Therefore, the majority of whole-body

PAT systems have a frame rate of <

1Hz

. Due to acoustic dis-

tortion from the lungs, whole-body PAT systems mainly focus

on regions below the chest, where respiration is the main cause

of motion. Because a mouse breathes at up to 3 Hz, images

acquired with a frame rate of <

1Hz

are susceptible to respi-

ratory motion artifacts. Respiratory gating is also essential in

many imaging applications, where accurate localization of

organs is required. For instance, in high-intensity focused

ultrasound (HIFU) tumor treatment, the respiration-induced

organ displacement can be larger than the focus of the treat-

ment beam.

To reduce respiratory motion, whole-body PAT imaging has

employed different animal mounting schemes. For instance,

Brecht et al.

used pretensioned fiberglass rods to minimize

animal movement. Lam et al.

laid the animal on its back to

image the kidney region. However, all these approaches can

only partially resolve the problem, and they are only applicable

to the specific system. In this report, we propose a retrospective

respiratory gating method that is widely applicable to different

whole-body PAT systems. In our approach, the respiratory

waveform is recorded during photoacoustic data acquisition,

where photoacoustic signals from different views are captured

at a constant speed as usual. After the experiment, the entire

dataset is sorted and clustered according to respiratory phases.

We then reconstruct the photoacoustic image using only data

acquired from the same respiratory phase, greatly diminishing

respiratory motion artifacts.

A key requirement of this approach is the accurate monitor-

ing of the animal

’

s respiratory waveform. Because photoacous-

tic experiments require water coupling, the animal is fully or

partially immersed in water. Therefore, conventional electrical-

based respiratory monitoring approaches, such as impedance

pneumography,

cannot be employed. In addition, to ensure

transmission of both light and sound, we cannot mount any

opaque devices, such as a pressure sensor or a strain gage,

on the animal body. Alternatively, one can use intubation and

ventilation to precisely control the breathing cycle.

However,

that procedure requires special training, and repeated intubation

for longitudinal monitoring may damage the animal

’

s trachea or

vocal cords.

Here, we devise an approach that takes advantage

of the water coupling. In this approach, a pressure sensor is used

to continuously monitor fluctuations in the water level, which

directly correlates to changes in the animal

’

s corporeal volume.

This method demands minimal hardware modification and is

applicable to any whole-body PAT system.

Address all correspondence to: Lihong V. Wang, E-mail:

lhwang@wustl.edu

Journal of Biomedical Optics

016003-1

January 2014

•

Vol. 19(1)

Journal of Biomedical Optics 19(1), 016003 (January 2014)

Downloaded From: http://biomedicaloptics.spiedigitallibrary.org/ on 06/20/2016 Terms of Use: http://spiedigitallibrary.org/ss/TermsOfUse.aspx

Figure

1(a)

is a schematic diagram of the ring-shaped con-

focal photoacoustic computed tomography (RC-PACT) system

with respiratory motion detection. A 10-Hz pulse-repetition rate

Ti:sapphire laser (LS-2137, Symphotic TII) is used as the exci-

tation source. The laser beam is first converted into a ring-

shaped beam by a conical lens and then redirected to the animal

body by an optical condenser. The photoacoustic signals are

detected by a 512-element full-ring transducer array with 5-

MHz central frequency and

>

80%

bandwidth.

The array

data is acquired by a 64-channel data acquisition system

(DAQ) with 40-MHz sampling rate. Due to the limited data

transfer speed, the DAQ system can capture data from only

every other laser shot, yielding a full-ring acquisition time of

1.6 s. A detailed description of the imaging system and animal

mounting schemes can be found in Refs.

, and

. To capture

the respiratory waveform, we used a pressure sensor (PXCPC,

OMEGA Engineering Inc., Stamford, Connecticut). As shown

in Fig.

1(a)

, the input of the pressure sensor is connected to a

plastic tube. The other end of the tube is immersed in the cou-

pling water, which compresses the air in the tube. Therefore,

when the water level varies, the air pressure in the tube also

changes. The output of the pressure sensor is amplified and

then digitized by a data acquisition system (PCI-6220,

National Instruments, Austin, Texas) at a 100-Hz sampling

rate. Figure

1(b)

shows a section of the pressure signal acquired

over 25 s. Pressure peaks due to periodic breathing can be

clearly seen for every 3 s.

The data processing steps are illustrated in Fig.

. In each

experiment, we continuously acquire 180 image frames over

a span of 4.8 min and then sort the data according to the res-

piratory waveform. For signals acquired at each laser shot,

we first identify the temporal position in the respiratory wave-

form and then assign it a phase value, ranging from 0% to 100%,

determined by its relative position between two adjacent respi-

ratory peaks. The data are then sorted according to the respira-

tory phase and evenly clustered into 20 sets. The number of

clusters is chosen to ensure that each cluster contains data from

all 512 transducer elements, i.e., a full

π

imaging aperture is

covered. Due to

∶

multiplexing, the full-ring array is divided

into eight segments, and each laser pulse generates data from

one segment. Within a cluster, different array segments may

have produced data with different numbers of copies. Therefore,

we first average the data from each segment according to its

number of copies and then combine all of them to form a single

full-ring dataset, which is used to reconstruct a photoacoustic

image for the given cluster. Merging images from all clusters

produces a continuous video of the entire respiratory cycle.

Because photoacoustic image reconstruction is also a data aver-

aging process, the effect of the different number of data copies at

each array segment is mitigated after the reconstruction. As each

cluster has approximately the same total number of data copies

(

180

×

∕

), the final reconstructed image at each frame has a

similar signal-to-noise ratio. To mitigate image artifacts induced

by acoustic reflectors, such as the spine and GI tract, we used the

half-time image reconstruction principle.

Because the main

purpose of this study was to compensate for respiratory motion,

rather than performing quantitative analysis, a noniterative half-

time reconstruction algorithm was employed that is operated by

directly back-projecting the first half of the raw data.

Figure

shows

in vivo

cross-sectional images acquired from

the liver region of a 2-month-old nude mouse. The mouse was

anesthetized with isoflurane, which slowed its respiratory rate to

1.25 s

∕

breath

. Without motion compensation, each image frame

is thus acquired over a period of 1.28 (i.e.,

1.6

∕

1.25

) breathing

cycles with eight laser pulses. As expected, the ungated image

[Fig.

3(a)

] is appreciably more blurry than the gated image

[Fig.

3(b)

], especially for the hepatic vasculature. The skin

boundary and cross sections of main blood vessels, such as

vena cava, are also less clear in the ungated image due to the

respiratory motion.

To better illustrate the respiratory effect, we plot the temporal

changes of photoacoustic amplitude from a small region marked

with red circles in Figs.

3(a)

and

3(b)

. Both Figs.

3(c)

and

3(d)

contain data from 90 frames of images of the red circled region.

It can be seen that respiratory gating not only allows us to visu-

alize the breathing cycle coherently but also improves the tem-

poral resolution. In Fig.

3(d)

, we can see the amplitude drop with

body expansion, which moves the skin vessel out of the red

circled region. In contrast, Fig.

3(c)

shows only randomized

amplitude fluctuation. Video

shows the ungated images

acquired at

1.6 s

∕

frame

, and Video

shows the gated images

resampled to

0.065 s

∕

frame

, corresponding to 20 frames/

Fig. 1

(a) Schematic diagram of the ring-shaped confocal photo-

acoustic computed tomography system with respiratory motion gat-

ing. (b) A section of the respiratory waveform as monitored by the

pressure sensor.

Image

acquisition

over 4.8

minutes

Phase

assignment

for data

acquired

each

laser

shot

Data sorting

and clustering

Signal

averaging

within each

cluster

Image

reconstruction

Fig. 2

Flow chart illustrating the data processing steps.

Journal of Biomedical Optics

016003-2

January 2014

•

Vol. 19(1)

Xia et al.: Retrospective respiration-gated whole-body photoacoustic computed tomography of mice

Downloaded From: http://biomedicaloptics.spiedigitallibrary.org/ on 06/20/2016 Terms of Use: http://spiedigitallibrary.org/ss/TermsOfUse.aspx

respiratory cycle. Compared to the random body movements

seen in Video

, we clearly see the rhythmic respiratory expan-

sion and contraction of the animal body. It should be noted that

the resampled 15.4-Hz (i.e.,

∕

0.065 s

) frame rate is faster than

the 10-Hz laser pulse repetition rate. This improvement is due to

both the noninteger ratio of the laser

’

s pulse repetition rate

(10 Hz) to the respiratory rate (

∼

0.8 Hz

) and the fluctuation

of the respiratory cycle (

over the imaging period).

It should be emphasized that the role of respiratory gating is

twofold. First, it improves the image sharpness because image

reconstruction is performed using only data acquired at the same

respiratory phase. Second, it improves the signal-to-noise ratio,

as data acquired at the same respiratory phase can be construc-

tively averaged. To better illustrate the two effects, we attached

Video

, which was generated using only a single copy of data at

each respiratory phase (without constructive averaging). It can

be seen that while the vessel sharpness here is comparable to that

in Video

, the ring-shaped electronic noise is more noticeable.

We also imaged the kidney region, where more organs can be

visualized. Although the kidneys are farther away from the

lungs than the liver, the effect of respiratory motion is still evi-

dent. The kidneys, spleen, spine, and vascular network in the

uncorrected Fig.

4(a)

are more blurred than the counterparts

in the motion-compensated Fig.

4(b)

. The skin and abdominal

vessels are also difficult to identify in Fig.

4(a)

. We also plot in

Figs.

4(c)

and

4(d)

the temporal photoacoustic signal changes

within a red circle placed in between the skin and spleen. In

Fig.

4(d)

, we can clearly see the signal increase due to body

expansion, which moves the spleen to the red circled region.

The ungated and gated imaging frames are also compiled as

Videos

and

, respectively. The rhythmic respiratory motion

of the animal body, as well as the movements of its organs, can

be clearly observed in Video

The rate of animal respiration can be slowed by increasing

the isoflurane concentration in the inhalation gas. In another

study, we imaged a mouse with a respiratory rate of

∼

0.31 Hz

which is slower than our imaging frame rate (0.625 Hz).

Figures

5(a)

and

5(b)

compare the ungated and gated images.

Although the blurs caused by the respiratory motion are not

as obvious as in the previous cases, the hepatic vascular

structures are still clearer in the motion-compensated image

[Fig.

5(b)

]. In addition, the signal-to-noise ratio is also

improved, as can be seen from the disappearance of the system

’

electronic-noise artifact in Fig.

5(b)

. Because each gated image

is an average of approximately nine (

180

∕

) projections, the

signal

–

to-noise ratio is improved by three times. Therefore,

even when the respiratory rate is slower than the imaging

frame rate, respiratory gating is still beneficial. Figures

5(c)

(a)

(b)

Spine

Vena cava

Portalvein

Hepatic

artery

Skin

vessels

Hepatic

vessels

5mm

0123456

0.010

0.015

0.020

0.025

0.030

Time (s)

0 20406080100120140

0.005

0.010

0.015

0.020

0.025

PA amplitude (a.u.)

Time (s)

(c)

(d)

Fig. 3

In vivo

mouse cross-sectional photoacoustic images acquired around the liver region. (a) Image

reconstructed without respiratory motion gating (Video

, MPEG, 2.47 MB) [URL:

http://dx.doi.org/10

.1117/1.JBO.19.1.016003.1

]. (b) Image reconstructed with respiratory motion gating (Video

, MPEG,

10.6 MB) [URL:

http://dx.doi.org/10.1117/1.JBO.19.1.016003.2

]. The hepatic vessels in the red box

are enlarged to show the effect of respiratory motion correction. (c) and (d) show temporal changes

in photoacoustic amplitude within the red circled regions in (a) and (b), respectively. Video

, MPEG,

10.9 MB) [URL:

http://dx.doi.org/10.1117/1.JBO.19.1.016003.3

] was generated using a single copy of

data at each respiratory phase. The motion-gated image and videos were reconstructed from data

acquired over 4.8 min.

Journal of Biomedical Optics

016003-3

January 2014

•

Vol. 19(1)

Xia et al.: Retrospective respiration-gated whole-body photoacoustic computed tomography of mice

Downloaded From: http://biomedicaloptics.spiedigitallibrary.org/ on 06/20/2016 Terms of Use: http://spiedigitallibrary.org/ss/TermsOfUse.aspx

Spleen

Kidney

Spine

GI tracts

Abdominal

vessel

amplitude

(a.u.)

Skin

vessels

(a)

(b)

5 mm

0 20406080100120140

-0.001

0.000

0.001

0.002

PA amplitude (a.u.)

Time (s)

(c)

(d)

012345

-0.02

-0.01

0.00

0.01

0.02

0.03

0.04

Time (s)

Fig. 4

In vivo

small-animal cross-sectional photoacoustic images acquired around the kidney region.

(a) Image reconstructed without respiratory motion gating (Video

, MPEG, 2.75 MB) [URL:

http://dx

.doi.org/10.1117/1.JBO.19.1.016003.4

]. (b) Image reconstructed with respiratory motion gating

(Video

, MPEG, 11.8 MB) [URL:

http://dx.doi.org/10.1117/1.JBO.19.1.016003.5

]. The abdominal ves-

sels are enlarged to show the effect of respiratory motion correction. (c) and (d) show temporal changes

in photoacoustic amplitude within the red circled regions in (a) and (b), respectively. The motion-gated

image and video were reconstructed from data acquired over 4.8 min.

0 20406080100120140

0.004

0.006

0.008

0.010

0.012

PA amplitude (a.u.)

Time (s)

Spine

Vena cave

PA amplitude (a.u.)

Electronic noise

Portal

vein

Hepatic

artery

(a)

(b)

(c)

(d)

0246810121416

0.007

0.008

0.009

0.010

Time (s)

5mm

Skin

vessels

Fig. 5

In vivo

small-animal cross-sectional photoacoustic images acquired around the liver region.

(a) Image reconstructed without respiratory motion gating (Video

, MPEG, 2.16 MB) [URL:

http://dx

.doi.org/10.1117/1.JBO.19.1.016003.6

]. (b) Image reconstructed with respiratory motion gating. The

hepatic vessels are enlarged to show the effect of respiratory motion (Video

, MPEG, 9.22 MB)

[URL:

http://dx.doi.org/10.1117/1.JBO.19.1.016003.7

]. (c) and (d) show temporal changes in photo-

acoustic signal within the red circled regions in (a) and (b), respectively. The motion-gated image

and video were reconstructed from data acquired over 4.8 min.

Journal of Biomedical Optics

016003-4

January 2014

•

Vol. 19(1)

Xia et al.: Retrospective respiration-gated whole-body photoacoustic computed tomography of mice

Downloaded From: http://biomedicaloptics.spiedigitallibrary.org/ on 06/20/2016 Terms of Use: http://spiedigitallibrary.org/ss/TermsOfUse.aspx

and

5(d)

show changes in photoacoustic amplitude within the

red circles in Figs.

5(a)

and

5(b)

, respectively. As expected,

Fig.

5(d)

shows periodic drop in photoacoustic amplitude due

to body expansion, which moves the skin vessel out of the

red circled region. Compared to Fig.

4(d)

, Fig.

5(d)

has a longer

resting period between breaths. This phenomenon is commonly

observed in respiratory depression caused by high isoflurane

concentrations.

The same effect can also be seen in Video

In summary, we implemented, for the first time, respiratory

gating in mouse whole-body PAT. Taking advantage of the water

coupling, we used a pressure sensor to monitor the water level

fluctuation induced by animal respiration. This noninvasive

procedure lends itself to other whole-body PAT systems. We

demonstrated the improvement in imaging quality under differ-

ent respiratory rates and at multiple anatomical locations.

Respiratory gating also allows us to sort and resample the

data to a much higher frame rate, allowing visualization of

the entire breathing cycle. In the respiration-gated videos (vid-

eos

, and

), we can clearly see the rhythmic movement of

the liver, spleen, and kidneys. Because the respiratory waveform

can be accessed in real time, after a control experiment is per-

formed, our gating method can also potentially permit accurate

tumor targeting during HIFU (Ref.

) and radiation

therapies.

In the current study, because our excitation laser could not be

triggered at a rate other than 10 Hz, we opted for retrospective

respiratory gating. Using laser systems with flexible triggering

options, we can also employ prospective respiratory gating,

which can be used for high-speed imaging of a chosen respira-

tory phase. The same data processing principle can be used for

cardiac gating. Compared to image-based gating approaches,

monitoring of the respiratory or cardiac waveform is immune to

image noises and the data processing is computationally less

intensive. Therefore, we expect that our proposed method

will be widely used to improve the image quality and broaden

the applications of small-animal whole-body PAT.

Acknowledgments

The authors appreciate Prof. James Ballard

’

s close reading of

the manuscript. This work was sponsored in part by National

Institutes of Health grants DP1 EB016986 (NIH Director

’

Pioneer Award), R01 EB016963, R01 CA134539, R01

EB010049, and R01 CA159959. L.W. has a financial interest

in Microphotoacoustics, Inc. and Endra, Inc., which, however,

did not support this work. K.M. has a financial interest in

Microphotoacoustics, Inc., which, however, did not support

this work.

References

1. J. Xia et al.,

“

Whole-body ring-shaped confocal photoacoustic com-

puted tomography of small animals in vivo,

”

J. Biomed. Opt.

(5),

050506 (2012).

2. M. R. Chatni et al.,

“

Tumor glucose metabolism imaged in vivo in

small animals with whole-body photoacoustic computed tomography,

”

J. Biomed. Opt.

(7), 076012 (2012).

3. G. S. Filonov et al.,

“

Deep-tissue photoacoustic tomography of a genet-

ically encoded near-infrared fluorescent probe,

”

Angewandte Chemie

Int. Edition

(6), 1448

–

1451 (2012).

4. H.-P. Brecht et al.,

“

Whole-body three-dimensional optoacoustic

tomography system for small animals,

”

J. Biomed. Opt.

(6), 064007

(2009).

5. L. V. Wang and S. Hu,

“

Photoacoustic tomography: in vivo imaging

from organelles to organs,

”

Science

335

(6075), 1458

–

1462 (2012).

6. J. Xia and L. Wang,

“

Small-animal whole-body photoacoustic tomog-

raphy: a review,

”

IEEE Trans. Biomed. Eng.

(99), 1

–

10 (2013).

7. L. Xiang et al.,

“

4-D photoacoustic tomography,

”

Sci. Rep.

(1113), 1

–

(2013).

8. M. Pernot, M. Tanter, and M. Fink,

“

3-D real-time motion correction

in high-intensity focused ultrasound therapy,

”

Ultrasound Med. Biol.

(9), 1239

–

1249 (2004).

9. R. B. Lam et al.,

“

Dynamic optical angiography of mouse anatomy

using radial projections,

”

Proc. SPIE

7564

, 756405 (2010).

10. V. P. Seppa, J. Viik, and J. Hyttinen,

“

Assessment of pulmonary flow

using impedance pneumography,

”

IEEE Trans. Biomed. Eng.

(9),

2277

–

2285 (2010).

11. D. Cavanaugh et al.,

“

In vivo respiratory-gated micro-CT imaging in

small-animal oncology models,

”

Mol. Imaging

(1), 55

–

62 (2004).

12. A. Oshodi et al.,

“

Airway injury resulting from repeated endotracheal

intubation: possible prevention strategies,

”

Pediatr. Crit. Care Med.

(1), E34

–

E39 (2011).

13. J. Gamelin et al.,

“

A real-time photoacoustic tomography system for

small animals,

”

Opt. Express

(13), 10489

–

10498 (2009).

14. J. Xia et al.,

“

Three-dimensional photoacoustic tomography based on

the focal-line concept,

”

J. Biomed. Opt.

(9), 090505 (2011).

15. M. A. Anastasio et al.,

“

Half-time image reconstruction in thermoacous-

tic tomography,

”

IEEE Trans. Med. Imaging

(2), 199

–

210 (2005).

16. D. R. Cleary et al.,

“

A novel, non-invasive method of respiratory

monitoring for use with stereotactic procedures,

”

J. Neurosci.

Methods

209

(2), 337

–

343 (2012).

17. M. H. Phillips et al.,

“

Effects of respiratory motion on dose uniformity

with a charged particle scanning method,

”

Phys. Med. Biol.

(1), 223

–

234 (1992).

18. A. Taruttis et al.,

“

Motion clustering for deblurring multispectral opto-

acoustic tomography images of the mouse heart,

”

J. Biomed. Opt.

(1),

016009 (2012).

Jun Xia

earned his PhD degree at the University of Toronto and is

currently a postdoctoral fellow at Washington University in St.

Louis, under the mentorship of Dr. Lihong V. Wang. His research

interests are the development of novel biomedical imaging techniques

including photoacoustic, photothermal and ultrasonic imaging. He has

published more than 20 peer-reviewed journal articles in photoacous-

tic and photothermal research.

Wanyi Chen

is currently an undergraduate student at Washington

University in St. Louis and a research assistant in Dr. Lihong V.

Wang

’

s lab. Her research interests are in photoacoustic and ultra-

sonic imaging.

Konstantin Maslov

graduated from Moscow Institute of Physics and

Technology, Moscow, Russia and received a PhD in physical acous-

tics from Moscow State University, Russia, in 1993. Currently,

he is a research associate professor in the Biomedical Engineer-

ing department at Washington University in St Louis, Missouri.

His research interests include ultrasonics, optical, photoacoustic

and photothermal imaging.

Mark A. Anastasio

earned his PhD degree at the University of

Chicago and is currently a professor of biomedical engineering at

Washington University in St. Louis. His research interests include

tomographic image reconstruction, imaging physics, and the develop-

ment of novel computed biomedical imaging systems. He has con-

ducted extensive research in the fields of diffraction tomography,

x-ray phase-contrast x-ray imaging, and photoacoustic tomography.

Lihong V. Wang

earned his PhD degree at Rice University, Houston,

Texas. He currently holds the Gene K. Beare distinguished professor-

ship of Biomedical Engineering at Washington University in St. Louis.

He has published 342 peer-reviewed journal articles and delivered

370 keynote, plenary, or invited talks. His Google Scholar h-index

and citations have reached 81 and over 26,000, respectively.

Journal of Biomedical Optics

016003-5

January 2014

•

Vol. 19(1)

Xia et al.: Retrospective respiration-gated whole-body photoacoustic computed tomography of mice

Downloaded From: http://biomedicaloptics.spiedigitallibrary.org/ on 06/20/2016 Terms of Use: http://spiedigitallibrary.org/ss/TermsOfUse.aspx