Amilkumar K. Reddy et al 2007 Pulsed Doppler signal processing for use in mice Aplications

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IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING, VOL. 52, NO. 10, OCTOBER 2005
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Pulsed Doppler Signal Processing for Use in Mice:
Applications
Anilkumar K. Reddy*, Member, IEEE, George E. Taffet, Yi-Heng Li, Sang-Wook Lim, Thuy T. Pham,
Jennifer S. Pocius, Mark L. Entman, Lloyd H. Michael, and Craig J. Hartley, Senior Member, IEEE
Abstract—We have developed a high-frequency, high-resolution Doppler spectrum analyzer (DSPW) and compared its
performance against an adapted clinical Medasonics spectrum
analyzer (MSA) and a zero-crossing interval histogram (ZCIH)
used previously by us to evaluate cardiovascular physiology in
mice. The aortic velocity (means
SE: 92.7
2.5 versus 82.2
1.8 cm/s) and aortic acceleration (8194
319 versus 5178
191 cm/s2 ) determined by the DSPW were significantly higher
compared to those by the MSA. Aortic ejection time was shorter
1.8 ms) and the isovolumic relaxation
(48.3 0.9 versus 64.6
was longer (17.6 0.6 versus 13.5 0.6 ms) when determined by
the DSPW because it generates shorter temporal widths in the
velocity spectra when compared to the MSA. These data indicate
that the performance of the DSPW in evaluating cardiovascular
physiology was better than that of the MSA. There were no significant differences between the aortic pulse wave velocity determined
by using the ZCIH (391 16 cm/s) and the DSPW (394 20 cm/s).
Besides monitoring cardiac function, we have used the DSPW for
studying peripheral vascular physiology in normal, transgenic,
and surgical models of mice. Several applications such as the
detection of high stenotic jet velocities ( 4 m/s), vortex shedding
frequencies (250 Hz), and subtle changes in wave shapes in peripheral vessels which could not obtained with clinical Doppler
systems are now made possible with the DSPW.
Index Terms—High-frequency pulsed Doppler ultrasound,
mouse cardiac function, mouse cardiovascular physiology,
pulse-wave velocity, stenotic jet velocities, vortex shedding
frequencies.
I. INTRODUCTION
T
RANSGENIC and gene-targeted (knockout) models of
mice are increasingly used to study cardiovascular disease
processes and to some extent have replaced the traditional
large animal models [1], [2]. Although it is easy to breed,
house, and maintain them, the small size of mice makes the
measurement of hemodynamic parameters difficult. Despite
Manuscript received August 20, 2004; revised March 26, 2005. This work was
supported in part by National Institutes of Health under Grant HL-52364, Grant
HL-22512, and Grant AG-13251, and in part by the Texas Advanced Technology
Program. The work of Y.-H. Li was supported in part by a grant from National
Science Council, Taipei, Taiwan, and the College of Medicine, National Cheng
Kung University, Tainan, Taiwan. Dr. Lim was supported by Pochon CHA University, South Korea. Asterisk indicates corresponding author.
*A. K. Reddy is with the Baylor College of Medicine, Houston, TX, 77030
USA (e-mail: [email protected]).
Y.-H. Li was with the Baylor College of Medicine and is currently with National Cheng Kung University, Tainan, Taiwan.
S-W. Lim was with the Baylor College of Medicine and is currently with
Pochon CHA University, Bundang-gu, South Korea.
G. E. Taffet, T. T. Pham, J. S. Pocius, M. L. Entman, L. H. Michael and
C. J. Hartley are with Baylor College of Medicine and DeBakey Heart Center,
Houston, TX, 77030 USA.
Digital Object Identifier 10.1109/TBME.2005.855709
the fact that mice have high heart rates ( 600 beats/min) many
researchers continue to measure blood velocities in mice using
clinical systems that have poor spatial resolution because they
operate at low frequencies (2–5 MHz), have Doppler probes
with large footprints that cannot be oriented properly, and use
spectrum analyzers that have poor temporal resolution [3]–[5].
Previously we have used custom made 10- and 20-MHz pulsed
Doppler probes and a clinical Medasonics (Vasculab SP25A)
spectrum analyzer (MSA) to measure blood velocities from
various vessels in mice [6]. Cardiac (aortic and mitral) blood
velocity signals of mice were measured using a 10-MHz pulsed
Doppler probe and parameters such as the peak and average
velocities, and timing of various events in the cardiac cycle
were calculated from the screen-captured images of real-time
display of velocity spectrograms obtained using the MSA
[6]–[8]. The 10- and 20-MHz Doppler probes provided high
spatial resolutions and small foot prints. However, the temporal
resolution was limited to about 8 ms because of the fast Fourier
transform (FFT) window length and the slow sweep speed [6],
[7]. This time resolution is not adequate to measure cardiac
timing in mice where the total cardiac period is on the order
of 100 ms. The maximum bandwidth (frequency range) of the
MSA is 24 kHz which is barely adequate to measure velocities
in most cases. However, in mouse models of aortic stenosis
the peak blood flow velocities just past the site at the arch can
exceed 400 cm/s producing Doppler shifts greater than 100 kHz
[9].
Blood flow velocity signals from the aortic arch and abdominal aorta were measured using a 20-MHz pulsed Doppler probe,
to calculate pulse wave velocity (PWV). An indicator of arterial
compliance, PWV was determined by measuring the difference
in time of onset of the blood velocity signals at two sites along a
vessel separated by a known distance. Given the fast rise times
of the blood flow velocity signal in mice, a zero-crossing interval histogram (ZCIH) [6] was used to maximize the temporal
resolution of the Doppler signals, and the PWVs obtained by
this method in mice were reported [10]. The time resolution of
the ZCIH improves with the increase in Doppler frequency, but
the frequency resolution is nonquantitative even with noise free
Doppler signals, allowing only for estimates of spectral information [6].
Thus the need arises for a Doppler data acquisition and analysis system which can sample the data at high rates, have a high
bandwidth, provide high temporal resolution, and be capable of
acquiring and analyzing multiple cardiovascular signals from a
mouse. In the preceding article [11] we described such a system.
Here, we discuss the performance and applications of the above
system in evaluating mouse cardiovascular physiology.
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II. METHODS
A. Instrumentation
1) Doppler Signal/Spectrum Analyzers: The Doppler signal
processing workstation (DSPW), a real-time signal acquisition
and spectrum analyzer system (Indus Instruments, Houston,
Texas) was described in the previous paper (Design and Evaluation) [11]. The MSA is a real-time spectrum analyzer and has
a frequency response from 500 Hz to 24 kHz, with a temporal
resolution of 8 ms, a highest sweep speed of 1-s full scale, and
eight sampling rates extending from 3.2 kHz to 38.4 kHz. The
sampling rates depend upon the displayed frequency ranges
with a sampling rate of 3.2 kHz in the range 563 to 2038 Hz
and up to a sampling rate of 38.4 kHz in the range 6.75 to
24.45 kHz. Also, the MSA has a 280 Hz high-pass filter to
eliminate low-frequency noise, and two anti-aliasing filters at
8 kHz for ranges up to 8 kHz, and at 24 kHz for ranges 12 kHz
and higher. The spectrogram is displayed on a Sanyo RGB
color monitor (Model DMC 8500). Since the ECG signal could
not be displayed along with the Doppler spectrogram, a 1-ms
R-peak trigger pulse generated by the ECG module was added
to one of the quadrature audio signals. This R-trigger pulse
generated a vertical line in the spectrogram after processing by
the FFT spectrum analyzer. This vertical line served as an ECG
timing marker. The screen-captured image was printed using
a video printer (model UP-870MD, Sony) to obtain hard-copy
output which was later scanned to save the image on a computer. The zero crossing interval histogram (ZCIH) analyzer
is a time-domain Doppler signal analyzer, which continuously
displays the frequency of consecutive zero crossings of the
Doppler signal. The output of the ZCIH was displayed on an
oscilloscope and simultaneously acquired by a data acquisition
system at a sampling rate of 37 kHz.
2) Analog Doppler Signal Processors: The high-frequency pulsed Doppler analog mainframe (Baylor College
of Medicine, Houston, Texas) can generate pulse repetition
frequencies (PRF) of 31.25, 62.5, or 125 kHz and can operate
at ultrasonic frequencies of 5, 10, or 20 MHz. For velocity
measurements in mice 10 or 20 MHz are typically used. The
mainframe accommodates several modules which include 10and 20-MHz pulsed Doppler modules, blood pressure module,
ECG module, and temperature control module.
The Doppler modules generate 10- or 20-MHz signals pulsed
at either 62.5 kHz or 125 kHz allowing for the measurement of
blood velocity signals with Doppler shifts of up to 31.25 kHz
or 62.5 kHz respectively, to be resolved without aliasing [12].
measured in frequency (kHz) were conThe Doppler shifts
verted to velocities (cm/s) using the Doppler equation
, where is the speed of sound (1540 m/s)
is the ultrasonic frequency (10 or 20 MHz). A 10-MHz
and
pulsed Doppler probe is used to obtain cardiac (aortic outflow
and mitral inflow) blood velocity signals from mice while a
20 MHz probe is used to obtain signals from peripheral vessels
that are within 2–4 mm of the skin surface in mice.
We used custom built 10- and 20-MHz Doppler probes with a
1.0 mm (20 MHz) or a 1.5 mm (10 MHz) square of piezoelectric
material (PZT-5A) mounted at the end of a 2.25-mm-diameter
10-cm-long stainless-steel tubing. An epoxy lens is molded to
the front face of the crystal to focus the sound beam at 4 mm
(20 MHz) or 6 mm (10 MHz). The sample volume is about
0.3–0.5 mm in length and 0.3–0.5 mm in diameter at the focal
point of the lens giving an estimated volume of 0.02–0.08 l.
The sample volume dimensions are on the order of or slightly
smaller than the diameter of the mouse aorta [13].
3) Pressure Amplifier Module: The blood pressure module
consists of a custom built bridge amplifier which has a frequency
response of 0–2 kHz with balance and gain adjustments and
the option to obtain mean pressure. Pressure sensors used in
mice include the RADI Pressure Wire (RADI Medical Systems) for aortic [14] and ventricular pressure measurements,
Millar Mikro-tip catheter transducer (SPR-671, Millar Instruments, Houston, Texas) for tonometric pressure measurements,
and an external pressure transducer (Meritrans #K09-03 243P,
Merit Medical Systems, Inc.) connected to a fluid filled catheter
for the measurement of carotid [15] and iliac artery pressures.
The RADI catheter was designed as an angioplasty guide wire
and has a 3-cm-long floppy wire tip distal to the pressure sensor.
The distal segment was removed and epoxy was used to smooth
the tip before the catheter was used in the mouse. A custom
signal conditioner was built to interface the RADI catheter to
our pressure amplifier module [14]. The catheters and the pressure amplifier were calibrated for each experiment from 0 to
250 mmHg using a mercury manometer.
4) ECG Amplifier Module: The ECG module consisting of
a custom built differential amplifier module with frequency response extending from 0.1 to 2 kHz was used to record ECG’s
(Lead I, II, III, AVR, AVF, AVL) from limb electrodes. A mouse
ECG board (Baylor College of Medicine, Houston, Texas) was
designed and built for use in mouse studies. The ECG board has
a large ground plane and contains four electrodes, a DC powered
heating pad consisting of an array of surface mount resistors,
and a temperature sensor. A custom-built temperature control
module was used to regulate the temperature of the heating pad
using the mouse body temperature or the board temperature as
feedback [9].
B. Animal Protocols
All studies (invasive and noninvasive) were performed on
anesthetized mice. The mice were anesthetized with either gas
anesthesia or two different intraperitoneally (IP) administered
anesthetics depending upon the type of study. The gas anesthesia was administered at a continuous flow rate (20 ml/min)
of a mixture containing 1% isoflurane combined with 99%
oxygen. One of the IP administered anesthetics used was a
rodent cocktail (ketamine 42.8 mg/ml, xylazine 8.6 mg/ml, and
acepromazine 1.4 mg/ml) given at 0.5 l/g of body weight.
The other IP administered anesthetic was pentobarbital cocktail
(1.6 ml of 50 mg/ml pentobarbital sodium, 4.0 ml of 200 proof
ethyl alcohol, and 16.0 ml of 0.9% saline) given at 10 l/g
of body weight. The mouse was anesthetized and placed on
the ECG/heater board with the board temperature adjusted
to maintain mouse body temperature at 37 1 C. The limbs
of the mouse were taped to the four electrodes as shown in
Fig. 1, the quality of ECG assessed, and the electrode contact
optimized if needed. All animal protocols were approved by
the Institutional Animal Care and Use Committee of Baylor
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Fig. 1. Experimental setup for the measurement of Doppler flow velocity signals along with ECG and blood pressure signals in mice. Several possible
configurations are shown in this setup. For example, three Doppler probes are shown but only one Doppler probe can be used at any time to measure blood
velocity signals.
Fig. 2. (a). Aortic and mitral flow velocity signals from an anesthetized mouse. Four cardiac cycles of aortic velocity signal were simultaneously processed by
the DSPW and the MSA (above), and displayed at same sweep speed of 1-s full scale. The vertical dashed lines before each aortic wave processed by the MSA is
the ECG marker which overlaps the mitral valve click which can be clearly seen in the signal processed by the DSPW. The mitral signals were similarly processed
and displayed (below). (b). Flow velocity signals measured at the aortic arch and at the abdominal aorta in an anesthetized mouse. Four cardiac cycles of each
velocity signal were simultaneously processed by the DSPW and the ZCIH, and displayed at the same sweep speed of 1-s full scale. A vertical dashed line is drawn
to indicate the alignment of the ECG R-peak which was used as a reference to measure the pulse transit time.
College of Medicine in accordance with the National Institutes
of Health Guide for the Care and Use of Laboratory Animals
(DHHS Publication no. 85–23, Revised 1985, Office of Science
and Health Reports, Bethesda, MD).
C. Noninvasive Measurements
Cardiac Doppler signals were obtained from the aortic root
and the mitral inflow track using a 10-MHz probe (Fig. 1).
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Fig. 3. A cardiac cycle showing both aortic and mitral flow velocity spectrograms from an anesthetized mouse. The segments were captured offline when upper
and lower panels were displayed at a sweep speed of 500-ms full scale. The aortic and mitral signals were obtained separately using a 10-MHz probe. The probe
was oriented to place sample volume in the aortic outlet region and then reoriented to place sample volume in the mitral inlet region. The Doppler signals were
sampled at 125 kilo samples/s. A 512-sample cFFT window was used to generate the velocity spectrogram. Several cardiac parameters are identified and defined.
Doppler signals from each mouse were acquired and processed by both the DSPW and MSA (at about the same time
if not simultaneously). Cardiac parameters were extracted
during analysis offline from data obtained by both systems
and compared. Doppler signals from many peripheral vessels
(carotid, femoral, tail, renal, aortic arch, abdominal aorta) were
obtained using a 20-MHz probe, the most common being the
measurements from two aortic sites to determine aortic PWV as
described elsewhere [10]. Doppler signals from the aortic arch
and abdominal aorta were acquired and processed by both the
DSPW and the ZCIH systems. Aortic PWV determined from
signals obtained by both systems was compared.
The noninvasive measurement of systolic and diastolic blood
pressure in mice using a tail-cuff and a 20-MHz Doppler probe is
important. The procedure to measure blood pressure by tail-cuff
has been described in detail elsewhere [15]. Briefly, a tail-cuff
(10 mm long and 7 mm diameter) was placed close to the base of
the tail of an anesthetized mouse. A 3–4 mm epoxy cuff with a
20-MHz piezo-electric crystal (1 mm ) embedded at an angle of
45 was placed distally near the tail-cuff and adjusted to obtain
the best velocity signal. The tail-cuff pressure was increased
until the velocity signal sensed by the Doppler probe in the tail
disappeared (above systolic). Then the pressure in the cuff was
decreased slowly ( 3–5 mmHg/s). The pressure at which the
signal first appeared was defined as systolic pressure and the
pressure at which the signal became continuous was defined as
diastolic pressure. The tail blood flow velocity signal sensed
by the Doppler probe and the tail-cuff pressure signal sensed
by the Meritrans pressure sensor were processed and stored for
analysis offline.
D. Invasive Measurements
The most common invasive measurement made in mice is
blood pressure. Blunt dissection techniques were used on an
anesthetized mouse to expose and isolate the carotid artery
which was then cannulated with either a fluid-filled catheter
connected to a Meritrans pressure sensor to measure pressure
in the carotid artery or with RADI pressure catheter to measure
pressures in the ascending aorta and left ventricle. Similar surgical procedure was performed in the lower abdominal section
to access the iliac artery for cannulation with a fluid-filled
catheter. For the measurement of tonometric pressure using a
Millar catheter the artery of interest was exposed but unopened.
The pressure sensor was placed under the exposed artery such
that the arterial wall was positioned flat on the sensor.
E. Data Analysis
All data are presented in the form of mean SE. Comparisons
of the cardiac parameters analyzed by the DSPW and the MSA,
and PWV determined from peripheral velocity signals measured
by the DSPW and the ZCIH were made with paired Student’s
t-test using a significance level of 0.05.
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TABLE I
CARDIAC PARAMETERS EXTRACTED FROM AORTIC AND MITRAL FLOW VELOCITY SIGNALS PROCESSED USING THE MSA AND THE DSPW SYSTEMS
III. RESULTS
Typical mouse cardiac and peripheral Doppler signals are
shown in Fig. 2. The cardiac signals (aortic and mitral) were
obtained from the same mouse, with the same cardiac cycles
processed by the DSPW and the MSA, and displayed at the
same sweep speed of 1-s full scale [Fig. 2(a)]. Similarly, each of
the peripheral signals (aortic arch and abdominal aorta) was obtained from the same mouse and the data from the same cardiac
cycles were processed by the DSPW and the ZCIH [Fig. 2(b)].
Shown in Fig. 3 is an expanded cycle of composite cardiac
Doppler signal (both aortic and mitral components) of a mouse.
The Doppler audio signals were sampled at 125 ksps, the spectrogram was calculated using a 256-point complex FFT (cFFT)
window, and displayed at a sweep speed of 200 ms full scale.
Several parameters that can be measured from both aortic and
mitral signals are also shown. Normally the aortic and mitral
signals are measured separately [as shown in Fig. 2(a)] to maximize their peak velocities.
Some of the important cardiac parameters obtained from
normal mice using the DSPW and the MSA are summarized
in Table I. Also shown in the table are % changes in parameter
values when measured by DSPW from the values measured
by MSA. The “p-value” is the statistical probability value
obtained from the paired Student t-test comparisons. The peak
E-velocity and peak aortic velocity were significantly higher
when measured by the DSPW. Although not significant, peak
A-velocity was also higher when measured by the DSPW (The
large variability in peak A-velocity resulted in the lack of statistical significance). On the other hand, timing parameters such
as time to aortic peak and ejection time (ET) were significantly
greater when measured by the MSA.
A similar trend, albeit not significant, was observed in the
measurement of time to E-peak, E-deceleration time, and pre-
Fig. 4. A cardiac cycle showing both aortic arch and abdominal aorta
flow velocity waves from an anesthetized mouse The Doppler signals
corresponding to these waves were obtained using a 20-MHz probe and
processed simultaneously by the DSPW (left) and the ZCIH (right).
ejection time when measured by the MSA. Isovolumic relaxation time (IVRT) was significantly higher when measured by
the DSPW. We were unable to measure isovolumic contraction time (IVCT) with the MSA but could consistently measure it with the DSPW which allowed us to calculate the TEI
index. A reliable robust measure of cardiac function, the TEI
index is calculated as (IVRT IVCT)/ET [16]. Mean velocity and
mean acceleration of aortic flow were significantly higher when
measured by the MSA. Fig. 4 shows the calculation of PWV
using a single cycle of aortic arch and abdominal velocity signals processed by the DSPW and the ZCIH to illustrate the calculation of PWV. No significant differences were observed in
the aortic PWV determined in 24 mice using the two systems
(ZCIH: 391 16, DSPW: 394 20).
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Fig. 5. Cardiac and peripheral blood flow velocity signals along with ECG signal in a mouse. The cardiac (aortic and mitral) signals were obtained using a 10-MHz
probe and the peripheral signals were obtained using a 20-MHz probe. All the Doppler signals were sampled at 125 ksps and processed using a 512-sample cFFT
to generate the velocity spectrogram. Part of the figure was adapted from Hartley et al., [9] and reprinted with permission from ILAR (Institute for Laboratory
Animal Research) journal.
IV. DISCUSSION
A high-speed, high-bandwidth, real-time Doppler signal processing workstation (DSPW) which provides high temporal
resolution and is capable of acquiring and analyzing multiple cardiovascular signals from a mouse was developed and
evaluated. In the initial part of this section we discuss the
performance of the DSPW versus the MSA in the measurement
of cardiac signals, and versus the ZCIH in the measurement
of pulse wave velocity. In the later part we discuss several
applications of the DSPW that are used for cardiovascular
phenotyping in the mouse.
A. Performance of the DSPW Versus the MSA and the ZCIH
The velocity and timing parameters shown in Table I are the
important ones among the parameters listed in Fig. 3 and are
routinely used to evaluate cardiac function in a mouse. The velocity parameters extracted from cardiac (aortic and mitral) flow
velocity signals are comparable to those measured in humans
[17], rats [18], and dogs [19]. Also, the aortic PWV in mice is
similar to that of humans [20] and other animals [21], [22].
1) Measurement of Peak Velocities: We found that higher
peak velocities were obtained when the Doppler signals were
processed by the DSPW versus those processed by the MSA.
While the DSPW uses up to 95% energy level to detect the peak
velocities the level used by the MSA could be lower than 95%.
Typically 75–95% is energy level range used for detecting peak
velocity [23]. Another possibility is that in the MSA the baseline
noise could have been subtracted before displaying the spectrogram thereby reducing the maximum frequencies. In addition,
the fastest sweep speed is limited to 1-s full scale in the MSA
thus limiting pixel resolution at the spectrogram peaks.
2) Measurement of Timing of Cardiac Events: The timing
of cardiac events determined from the spectrograms obtained
by both the DSPW and the MSA were longer than the actual
time. This is because the measurement of time events from the
spectrogram is influenced by the cFFT-window sample size in
addition to the sampling rate and sweep speed used to calculate the spectrogram. While the cFFT window of the MSA is
limited to 8-ms [6], [7], the cFFT window of the DSPW can be
made as small as 0.5 ms at a sampling rate of 125 kHz. Therefore, with a 256-sample cFFT the timing events measured by the
MSA are much longer when compared to those measured by the
DSPW with the exception of isovolumic relaxation time. Since
each spectral window in the MSA is longer, the trailing edge
of the velocity spectrum becomes much wider than that of the
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Fig. 6. Spectral envelope signals from the aortic arch of a wild-type mouse and
an apolipoprotein E knockout (ApoE-/-) mouse showing systemic differences
in acceleration of the velocity waveforms. Acceleration can be divided into
two phases, A1 and A2, with A2 being smaller than A1 in wild-type mice and
larger than A1 in ApoE-/- mice. We hypothesized that the differences were due
to altered peripheral wave reflections. Reproduced from Hartley et al., Am. J.
Physiol. Heart Circ. Physiol., 279:H2326–H2334, 2000, with permission from
American Physiological Society.
DSPW thus making the period of isovolumic relaxation time to
appear shorter when determined using the MSA. We could not
determine isovolumic contraction time using the MSA because
the exact locations of the trailing edge of the inflow and leading
edge of the outflow velocities were difficult to discern (see Fig. 3
for the definitions of the above parameters).
3) Measurement of Pulse Wave Velocity: Aortic PWV has
been used in humans and large animals to evaluate stiffness of
the vessel wall segment between two measurement sites [10],
[19]. We determined PWV using the time intervals from R-peak
to the onset of aortic arch and abdominal velocity signals (Fig. 4)
and the separation distance between these sites. No significant
differences were observed in the PWV determined by both systems. The values of PWV are similar to those previously reported in mice [10], [24]. Although the time resolution of the
ZCIH system is good [8], it only provides an average of center
frequency [21] and, therefore, cannot be used to measure peak
velocities.
B. Applications of DSPW
Cardiac inflow and outflow velocity signals obtained with a
10-MHz pulsed Doppler probe and various peripheral arterial
velocity signals obtained with a 20-MHz pulsed Doppler probe
in mice and processed using the DSPW are shown in Fig. 5. The
velocity signals from various locations were obtained based on
the knowledge of the anatomy of the mouse and the estimated
angles of the Doppler beam with the axial direction of flow. The
origination of most of these signals has been previously confirmed with invasive procedures. These signals obtained using
the DSPW are of exceptional quality and detail and could not
be obtained using the MSA or the ZCIH.
1) Peripheral Vascular Signals: Blood velocity signals
from peripheral arterial sites can be analyzed by the DSPW
for alterations in peak and mean velocities, and specifically
for changes in the shapes of the waveform caused by specific
phenotypes which would be not possible with the MSA. For
Fig. 7. (a). Analysis of a mouse peripheral velocity signal. The above signal
was obtained from a mouse carotid artery using a 20-MHz pulsed Doppler
probe, sampled at 125 ksps, and the velocity spectrogram was calculated using
a 256-sample cFFT window. Also shown in the figure are the calculations
for pulsatility index (PI) and resistance index (RI) that characterize the local
compliance and downstream resistance of the artery. (b). Doppler velocity
signals from the right and left carotid arteries before aortic banding and from
the same locations after banding. While the pulsatility index of the left and
right carotid velocity was similar before aortic banding, it increased 5-fold
after banding. (b) reproduced from Li et al., J. Gerontol., 58A:895–899,
2003, Copyright © The Gerontological Society of America. Reproduced by
permission of the publisher.
example, we could not have detected the biphasic upstroke of
the aortic arch velocity signal in apolipoprotein-E knockout
mice using the MSA or other clinical Doppler systems because
their best temporal resolution is longer than the duration of
the initial phase of the velocity upstroke (see Fig. 6) [24]. We
hypothesized that this change in the wave shape could have
been caused by stronger wave reflections from the stenosed
carotid bifurcation in ApoE-KO mice [9].
Peripheral vascular signals are analyzed to evaluate the function of the proximal and distal arterial systems which are responsible for maintaining adequate peripheral perfusion. Typically
peripheral vessel function is characterized by pulsatility index
and resistance index which are used to estimate local compliance and distal arterial resistance. Shown in Fig. 7(a) is the
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Fig. 8. Doppler velocity signals across aortic band and pressure tracings
from both carotid arteries (left panel). Peak value (V) of jet velocity and
instantaneous carotid pressure gradient ( P) were measured. Pressure gradient
estimated by simplified Bernoulli equation ( P 4V ) was plotted against the
carotid catheter pressure gradient (right lower panel). The right upper panel
shows the position of the probe and sample volume (SV) for measuring the
jet velocity across aortic band. LCPr, left carotid pressure; RCPr, right carotid
pressure. The right lower panel shows correlation between the pressure gradient
estimated by simplified Bernoulli equation from Doppler studies (4V ) and
measured from carotid catheterization studies ( P). The five different symbols
represent data from five different mice. Reproduced from Li et al., Ultrasound
Med. Biol., 29(9):128l–1289, 2003, Elsevier.
1
1=
1
analysis process of a peripheral (carotid artery in this case)
arterial velocity signal. Using such analysis we have shown that
16 month old mice take longer time to establish adequate and
equal mean flow velocity in the carotid arteries in response to
transverse aortic stenosis [Fig. 7(b)] when compared to mice
aged 4 months [25].
2) Validation of the Simplified Bernoulli Equation in
Mice: One of the important applications of the DSPW was the
measurement of stenotic jet velocity in mice with transverse
aortic banding to estimate the pressure gradient across the aortic
stenosis using the simplified Bernoulli equation ( P 4V ).
Previously, we were unable to measure high-velocity signals
across the stenotic jet using the MSA because its frequency
range was limited to 24 kHz (92.4 cm/s with 20-MHz pulsed
Doppler with speed of sound in soft tissue/blood 1540 cm/s).
Using the DSPW we recorded stenotic jet velocities which
were higher than 110 kHz ( 400 cm/s with 20-MHz pulsed
Doppler) [9], [11]. In contrast, Fard et al. measured stenotic
jet velocities of about 3.1 m/s in mice using a 12-MHz clinical Doppler system (Hewlett-Packard 5500, Andover, MA)
[26]. Another group reported stenotic jet velocities of about
4.5 m/s in mice (with angle correction) using a 10-MHz clinical
Doppler system (HDI3000/5000, ATL, Inc., Bothell, WA) [27].
However, they acknowledged that higher correction angles may
result in the overestimation of velocities. While a 10 change
in angle within 0–20 range results in maximum error of 4.5%,
the same in 60–75 range results in a maximum error of 16.4%.
Clinical Doppler systems typically use angle corrections in the
60–70 range making velocity measurement less precise. Continuous wave Doppler has been used in some cases to measure
blood velocities in mice [28] to overcome the frequency range
limitation of some clinical pulsed Doppler systems.
The accuracy of the simplified Bernoulli equation has been
demonstrated in the estimation of the pressure gradient across
stenotic valves in patients [29]–[31]. However, some of the un-
Fig. 9. The detection of vortex shedding frequencies in the peak aortic velocity
distal to the location of an aortic stenosis caused by banding. The signal was
obtained using a 20-MHz pulsed Doppler probe with the crystal mounted to
the side of a 22-gauge needle and passed down the esophagus. The transient
oscillations in peak velocity occur at a frequency of about 250 Hz.
derlying assumptions used in deriving P 4V may not hold
in mice due to higher wall shear stress resulting in a short inlet
length and ultimately a nearly parabolic velocity profile in the
aortic arch. Despite these questions our data (Fig. 8) [13] indicate that the accuracy of the simplified Bernoulli equation in
estimating the pressure drop in banded mice is similar to that of
clinical systems used in patients.
3) Detection of Vortex Shedding Frequencies: In the mouse
model of aortic banding we were able to detect high-frequency
velocity fluctuations in peak aortic velocity distal to a stenosis
(Fig. 9). We used a 20-MHz esophageal pulse Doppler probe to
measure this signal. Given that the aortic diameter in a mouse is
less than 1 mm, the vortices have to be smaller than 1 mm. The
ability to detect these 250 Hz fluctuations in velocity demonstrates that the sample volume of the 20-MHz Doppler probe
is quite small and that the DSPW can detect these vortex shedding frequencies. Gosling and King [32] briefly discussed the
changes in peak blood velocity due to vortices in flow distal
to stenosis. However, the signals were of poor resolution and
the vortex shedding frequencies were not discernible. To our
knowledge no other group has reported such findings in mice
thus reflecting the lack of studies on such significant hemodynamic perturbations in this mouse model [9].
4) Arterial Pulse Wave Velocity: One of the important
and commonly used applications of peripheral blood velocity
measurements is the determination of arterial PWV as an index
of arterial stiffness [10]. Pressure and velocity waves travel
faster in stiffer vessels than in compliant vessels. Determination
of PWV (Fig. 4) requires the measurement of velocity signals
from two arterial sites separated by a known distance (SD). The
transit time (PTT) of the velocity pulse from the first site to
the second site is measured and PWV is calculated as SD/PTT.
Previously we reported elevated PWV in Apolipoprotein E
knockout mice compared to wild-type mice [24] but such
differences were not apparent at rest in other models such as
the -smooth muscle actin knockout mouse [33]. However
the differences can be brought out with pharmacological interventions (Fig. 10). Other mouse models such as Matrix GLA
protein KO mice have greatly elevated PWV at rest.
REDDY et al.: PULSED DOPPLER SIGNAL PROCESSING FOR USE IN MICE: APPLICATIONS
Fig. 10. Pulse wave velocity in normal and transgenic mice (-SMA-/-, Matrix
GLA-/-). Reprinted with permission from ILAR journal, 43(3):147–158, 2002,
Institute for Laboratory Animal Research, National Academies, 500 Fifth Street
NW, Washington DC 20001 (www.national-academies.org/ilar).
Fig. 11. Peak aortic flow velocity followed for 5–6 months in mice subjected
, 2-hour occlusion followed by reperfusion
, and
to sham operation
permanent occlusion
. Data are % of preoperative values and are expressed
as means SE. Preoperative values: sham (n 15), 104 20 cm/s; permanent
occlusion (n
24), 111 17 cm/s; reperfusion (n
13), 102 10 cm/s.
-p<0.05, permanent occlusion versus sham; #-p<0.05, reperfusion versus
sham. Reproduced from Michael et al., Am. J. Physiol. 277:H660–H668, 1999,
with permission from American Physiological Society.
6
z
=
()
(1)
6
=
()
6
=
6
5) Myocardial Infarction and Remodeling in Mice: In the
mouse studies of myocardial infarction and ventricular remodeling following reperfusion we quantified myocardial tissue
repair through the measurement of peak aortic flow velocity
(systolic function) and peak early filling velocity (diastolic function) in mice subjected to sham operation, permanent coronary
occlusion, and 2-hour occlusion followed by reperfusion. While
the mice with permanent occlusion showed significant and sustained reduction in peak aortic flow velocity, the reperfusion
group recovered to preocclusion levels of peak velocity in 2
weeks (Fig. 11). Also, peak early velocity was significantly
reduced in mice with permanent occlusion whereas the reperfused animals recovered to about 90% of preocclusion levels
in 2 weeks. These data demonstrate that mice with permanent
coronary occlusion are incapable of compensating unrelieved
ischemia [34].
6) Aortic Input Impedance in Mice: Aortic input impedance
is defined as pressure/flow, but it is often calculated using velocity instead of flow because velocity is a measured parameter
1779
and conversion to flow is prone to errors [35]. Normally the luthe value of which depends on
minal average flow velocity,
the velocity profile in lumen of the vessel is used in calculation.
(spatial peak velocity)
For a blunt velocity profile
and for a parabolic velocity profile
[23]. Shown
in Fig. 12 is the procedure to calculate input impedance using
pressure and
. Using aortic input impedance we characterized the effects of aging on aortic stiffness in mice. We found
that the impedance curves resemble those from humans; and, as
in man, the peripheral resistance (modulus of impedance at zero
frequency), characteristic impedance (average of impedance
modulus from second to tenth harmonics) and location of first
minimum, all increase with age [36].
7) Noninvasive Blood Pressure Measurement in Mice: We
recently developed, validated, and reported a noninvasive
method to obtain systolic and diastolic pressures in mice using
a Doppler flow sensor and a tail-cuff. The principle of operation
is described briefly earlier in Methods and in detail elsewhere
[15]. Data were obtained from normal mice (n 17) at baseline
and after methoxamine administration. Regression analysis of
the data showed high correlation between tail-cuff and catheter
pressures (R
0.91 systolic, R
0.89 diastolic). We used
the method to differentiate phenotypes of seven unmarked
mice. Based on pressures obtained at baseline and in response
to changes in blood volume and methoxamine administration,
three mice were correctly identified as -smooth muscle actin
knockouts and four as controls (Fig. 13). The genotypes were
confirmed with DNA tests. Thus, this method can reliably
measure systolic and diastolic pressures in mice to differentiate
phenotypes.
8) Flow Velocity Measurements in Prenatal, Neonatal, and
Juvenile Mice: The usefulness of the DSPW in quantifying
the velocities and time events in the cardiac cycle of mice to
study the developmental changes in left and right ventricular
diastolic function from prenatal to juvenile mice has been
demonstrated by Zhou et al. [37]. Flow velocity signals from
mitral and tricuspid orifices were measured in embryonic,
neonatal, preweaning, and postweaning stages (Fig. 14) using
an image-guided 20-MHz Doppler (ultrasound biomicroscope
VS40, VisualSonics, Toronto, Canada) and the signals were
acquired and analyzed by the DSPW. Parameters such as peak
velocities of E- and A-waves, peak E/A ratio, peak E/E-A area,
and isovolumic relaxation time were extracted from the mitral
and tricuspid inflow signals. The progression of these parameters
showed that age-related changes were mainly associated with
increases in peak-E velocity up to 3 weeks postnatal at which
time maturation of diastolic function was observed [37].
C. Limitations and Recommendations
The limitation of our system is that we measure velocity
signals by placing the sample volume in the blood stream at
a desired location without image guidance. We overcome this
limitation with knowledge of mouse anatomy and the shape
and timing of the signals with respect to ECG signal. However,
in-utero measurements (in fetal mice) such as those described
by Zhou et al. [37] cannot be made with our system without the
use of image guidance.
1780
IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING, VOL. 52, NO. 10, OCTOBER 2005
Fig. 12. Determination of input impedance. Discrete Fourier transform (DFT) was performed separately on the digitized pressure and average velocity waveforms
followed by the calculation of pressure and velocity moduli. Impedance modulus was computed by dividing the pressure modulus by velocity modulus at each
harmonic. The magnitudes of P(f) and V(f) at higher harmonics (although distinct) are not quite discernible in the figure due the large amplitudes at the first and
second harmonics. Reproduced from Reddy et al., Proceedings of the 25th IEEE/EMBS Conference, 276–278, 2003, with permission from IEEE.
j j
j j
6
Fig. 13. Mean ( SE) systolic and diastolic tail-cuff pressures from -smooth
muscle actin knockout (n = 3) and littermate control (n = 4) mice at
chronological events; baseline (Bsl), 600 l blood withdrawal (W6), 600 l
blood re-infusion (16), stabilized (Stb), 3 min post-methoxamine (Mx3),
6 min post-methoxamine (Mx6), and final withdrawal of 200 l of blood
(W2).
V. CONCLUSION
We have developed a high-frequency high-speed Doppler
spectrum analyzer specifically targeted for use in mice. This
system provides a significant improvement over the previously
used adapted clinical spectrum analyzer and zero crossing
interval histogram with respect to temporal resolution and
frequency range. The DSPW system has a time resolution of
0.1 ms and 100 ms sweep speed, both about 10 times better
than the best clinical Doppler systems used in mice. The small
foot prints of the 10- and 20-MHz Doppler probes allow us to
position the ultrasound beam almost parallel to the flow. This
kind of positioning in not possible with clinical probes and
small errors in angle corrections (in the range of 30 –90 ) may
cause significant errors in the estimations of peak velocities.
In disturbed flows it is important to have the ultrasound beam
point to the direction of main flow in order to measure the
Fig. 14. Developmental changes in the flow velocity signals at the mitral and
tricuspid orifices starting from embryonic (14.5 day of gestation) to adult (12
weeks). The waveform in each cardiac cycle has two peaks. The first peak
is the early (E) flow and the second peak is the atrial (A) flow. Except for
the embryonic stage the direction of flow was toward the tip of the probe.
The velocity signals were obtained using a 20-MHz pulsed Doppler probe
and processed by DSPW. Reproduced from Zhou et al., Am. J. Physiol.
285:H1563–H1575, 2003, with permission from American Physiological
Society.
maximum velocities. With our 20-MHz probe and the DSPW
system (0–125 kHz frequency response), we were able to obtain
high-resolution stenotic jet velocity signals with peaks greater
than 4 m/s in mice. In clinical systems where the frequency
range of pulsed Doppler is limited continuous wave Doppler
is used to measure high-velocity signals. However, we can
use pulsed Doppler at very high frequencies in mice which
cannot be done even in humans with clinical Doppler systems.
Detection of vortex shedding frequencies in the flow distal to
the aortic stenosis in mice by our 20-MHz esophageal probe
and the ability of the DSPW to process and display such signals
demonstrate that the performance of our system is much better
than that of the clinical systems used in mice.
REDDY et al.: PULSED DOPPLER SIGNAL PROCESSING FOR USE IN MICE: APPLICATIONS
ACKNOWLEDGMENT
The authors wish to acknowledge the assistance of L. N.
Ochao, R. Kwun, M. Theresa, R. Hartley, A. Tumang on this
project. They wish to thank Dr. Y.-Q. Zhou and Dr. L. Adamson
of the University of Toronto, Toronto, ON, Canada, for providing them figures from their work. Finally, they would like to
thank J. Brooks for editorial review.
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Anilkumar K. Reddy (S’93–M’97) was born in
Kalwakole, Andhra Pradesh, India, in 1963. He
received the B.E. degree in electronics and communication engineering from Osmania University
(Chaitanya Bharathi Institute of Technology), Hyderabad, India, in 1985, the M.S. degree in biomedical
engineering from The University of Akron, Akron,
OH, in 1991, and the Ph.D. degree in bioengineering
from Texas A&M University, College Station, in
1996.
From 1997 to 2001, he was a Postdoctoral Fellow
in the Section of Cardiovascular Sciences in the Department of Medicine, Baylor
College of Medicine, Houston, TX, as a Postdoctoral Associate, and worked on
the development of ultrasound instrumentation for small animal research. Since
2001, has been a member of the faculty and is currently Assistant Professor of
Medicine. His past experience includes working as a Teaching and a Research
Assistant, an Instructor, and a Research/Design Engineer. His interests include
development of instrumentation for use in mice and the study the cardiovascular system of normal, disease, and transgenic models of mice. His past work
involved analysis of physiological signals.
Dr. Reddy is a member of American Physiological Society, Houston Society for Engineering in Medicine and Biology, and since 2002 has served on a
National Institutes of Health (NIH) study section that reviews Small Business
Grants. He is an ad hoc reviewer for IEEE TBME, ABME, and AJP journals.
He was an invited speaker at the 3rd International Congress on Cardiovascular
Disease in Taiwan in 2004. He is a recipient of a Research Career Development
Award (2005–10) from NIH.
Yi-Heng Li was born in Taipei, Taiwan, in 1963.
He received the M.D. degree from the Department
of Medicine, Kaohsiung Medical University, Kaohsiung, Taiwan, in 1988, and the Ph.D. degree from
the Institute of Basic Medical Science, National
Cheng Kung University (NCKU), Tainan, Taiwan,
in 2000. He is currently an Associate Professor in
the Cardiology Division, Department of Internal
Medicine, College of Medicine, National Cheng
Kung University, Tainan, Taiwan. His past work
included Military Service (Taiwan), Resident in
Internal Medicine, Fellow and Chief Resident, Attending Physician, Lecturer,
and Visiting Postdoctoral Fellow. His research interests are atherosclerosis and
related vascular diseases.
Dr. Li is a member of Formosan Medical Association and Societies of Internal
Medicine, Cardiology, Ultrasound in Medicine, and Critical Care & Emergency
Medicine in Taiwan. He has received Young Investigator Award (Taiwan Soc. of
Cardiology), Best Paper of the Year Award (NCKU), First Prize of the Special
Grant Excellent Research (The 64th Annual Scientific Meeting of the Japanese
Circ. Soc.), and Medical Research Award (Taiwan Soc. of Vascular Disease &
Atherosclerosis).
Sang-Wook Lim was born in Kwangju Ci, Jeon
Nam Do, South Korea, in 1963. He received the
M. D. degree from the College of Medicine, Yonsei
University, Seoul, South Korea, in 1988 and the
Ph.D. degree from the department of Pharmacology,
the College of Medicine, Korea University, Seoul,
in 1997.
He is currently an Associate Professor in the
Department of Cardiology, College of Medicine,
Pochon CHA University, Bundang-Gu, South Korea,
where he is in charge of patient care and cardiac
catheterization laboratory. His research interests include therapeutic and diagnostic strategies for advanced heart failure. He authored and co-authored many
articles regarding ischemic heart disease and heart failure that were published
in Korean journals.
Dr. Lim is a member of Korean Circulation Society and a member of Korea
Society of Internal Medicine.
George E. Taffet received the B.A. degree in mathematics and M.D. degrees from Brown University,
Providence, RI, in 1979 and 1982, respectively.
From 1982–86 he did his Internship and Residency
in Internal Medicine at Baylor College of Medicine,
Houston, TX. He was a Clinical Fellow in Geriatric
Medicine and then a Research Fellow in Cardiovascular Sciences. Since 1988, he has been a faculty and
is currently Associate Professor of Medicine in the
Sections of Cardiovascular Sciences and Geriatrics
in the Department of Medicine at Baylor College of
Medicine. He is an Attending Physician at Houston VA Medical Center. He is
Chief of Geriatrics at Baylor College of Medicine and a faculty associate of the
Huffington Center on Aging. He is principle investigator on several research
grants focused on aging and the cardiovascular system.
Dr. Taffet is a member of American Geriatric Society, International Society
of Heart Research; Gerontological Society of America; Texas Geriatrics Society
(Board Member); American Academy of the Advancement of Science, American Heart Association—Basic Science Council, and Association of Directors
of Geriatric Academic Programs. He has been an Expert Panel Member of Heart
Failure with Diastolic Dysfunction and Aging in Disparate Species at National
Institute on Aging and of Adaptation to Space/Physiological Factors Associated with Normal Aging at NASA. He is a member of the National Institutes
of Health (NIH) GRM study section, Ad Hoc Reviewer for National Institute
for Nursing Research, USDA, VA Research Service, and AHPCR Guidelines
Cardiac Rehabilitation, and Geriatric and Cardiology Spokesman for John A.
Hartford Foundation.
Thuy T. Pham had received the B.S. degree in biology from the University of Houston, Houston, TX,
in 1990.
She has been with Baylor College of Medicine,
Houston, since 1990 and is currently a Research
Technician in the Section of Cardiovascular Sciences in the Department of Medicine. Her expertise
includes Murine Ultrasound Sonography and data
analysis.
Jennifer Pocius received the B.S. degree in Biology from University of
Houston, Houston, TX, in 1974.
From 1974 to 1975, she worked as a Laboratory Technician at University of
Texas and has been at Baylor College of Medicine, Houston, since 1975. and
is currently Research Associate in the Section of Cardiovascular Sciences in
the Department of Medicine. Her expertise is in mouse surgery and routinely
performs coronary LAD occlusion surgeries in mice. In addition, she is also in
charge of cardiovascular murine laboratory.
Ms. Pocius received an Achievement Award from VA Medical Center in 1994.
REDDY et al.: PULSED DOPPLER SIGNAL PROCESSING FOR USE IN MICE: APPLICATIONS
Mark L. Entman pursued his Pre-med education at
University of Florida, Gainesville, and received the
M.D. degree in Medicine from Duke University Medical School, Durham, NC, in 1963.
He was an Intern at Johns Hopkins University
(Osler Service), Baltimore, MD, from 1963–1964.
From 1964–1968, he was a Postdoctoral Fellow and
Resident in Cardiology and Internal Medicine. He
has been with Baylor College of Medicine, Houston,
TX, since 1970 and is currently Professor and Head,
Section of Cardiovascular Science, Department of
Medicine and Scientific Director of DeBakey Heart Center and the William
J. Osher Professor of Cardiovascular Research in 2003. His research interests
include role of inflammation in cardiac injury and repair and identification of
potential therapeutic targets. The work is done primarily in altered mice.
Dr. Entman is a fellow of American College of Cardiology, and a member
of American Society of Clinical Investigation and Association of American
Physicians. He is a past recipient of American College of Cardiology Young Investigator Award, the Award for Outstanding Research-International Society of
Heart Research, The Roussell Award for Cardiology Research, and an NHLBI
research MERIT award. He is also a past recipient of the Duke University Medical Center Distinguished Alumnus award.
Lloyd H. Michael received the A.A. degree from
Keystone College, La Plume, PA, in 1962, the B.S.
degree in biology from Moravian College, Bethlehem, PA, in 1964, the M.S. degree in physiology
from Kent State University, Kent, OH, in 1966, and
the Ph.D. degree in physiology from the University
of Ottawa School of Medicine, Ottawa, ON, Canada,
in 1973.
He completed Postdoctoral Fellowships in myocardial biology at Baylor College of Medicine,
Houston, TX, and in clinical immunology at Columbia College of Physicians and Surgeons, New York, NY. He has been with
Baylor College of Medicine since 1976 and is Professor of Medicine in the
Department of Medicine, Section of Cardiovascular Sciences, Director of the
DeBakey Heart Center Core Animal laboratories and Senior Associate Dean
of Medical College Admissions. His current research areas include myocardial
ischemia and animal models to investigate the role of inflammation, leukocytes,
and complement in myocardial damage; the mechanics of cardiac muscle
contraction. He is an editorial board member for the American Journal of
Physiology, a council member for AAALAC Int’l, and a National Institutes of
Health (NIH) study section peer reviewer.
1783
Craig J. Hartley (S’64–M’66–SM’88) received the
B.S.E.E. and Ph.D. degrees from the University of
Washington, Seattle, in 1966 and 1970, respectively.
From 1970 to 1973, he was a Postdoctoral Fellow
in Bioengineering at Rice University, Houston,
TX. Since 1973, he has been with Baylor College
of Medicine, Houston, where he is currently a
Professor of Medicine in the section of Cardiovascular Sciences. He is also an adjunct Professor of
Bioengineering at Rice University and Professor of
Electrical Engineering at the University of Houston.
Since 1968, he has been active in the development of ultrasonic methods to
measure blood flow and cardiovascular function in man and in animal models
of human diseases. He is principle investigator on several research grants and
has received a Research Career Development Award and a MERIT award from
the National Institutes of Health (NIH).
Dr. Hartley is a member of the American Institute of Ultrasound in Medicine,
the American Physiological Society, the Cardiovascular Systems Dynamic Society, and since 1993 has served on an NIH study section that reviews Small
Business Grants. In 1993, he received the Laufman Prize for career achievement from the Association for the Advancement of Medical Instrumentation,
and in 1998 became an AIMBE fellow. He is the regional representative to the
EMBS administrative committee, was treasurer of the EMBS-BMES 2002 Joint
Conference in Houston, and is currently chair of the EMBS Awards Committee.
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