Real-time aortic pulse wave velocity measurement during exercise stress testing
Roberts et al. Journal of Cardiovascular Magnetic Resonance
Real-time aortic pulse wave velocity measurement during exercise stress testing
Paul A. Roberts 2
Brett R. Cowan 0
Yingmin Liu 0
Aaron C. W. Lin 1
Poul M. F. Nielsen 2 3
Andrew J. Taberner 2 3
Ralph A. H. Stewart 1
Hoi Ieng Lam 0
Alistair A. Young 0
0 Department of Anatomy with Radiology, Faculty of Medical and Health Sciences, University of Auckland , 85 Park Road, Auckland 1142 , New Zealand
1 Greenlane Cardiovascular Unit, Auckland City Hospital , Auckland , New Zealand
2 Auckland Bioengineering Institute, University of Auckland , Auckland , New Zealand
3 Department of Engineering Science, University of Auckland , Auckland , New Zealand
Background: Pulse wave velocity (PWV), a measure of arterial stiffness, has been demonstrated to be an independent predictor of adverse cardiovascular outcomes. This can be derived non-invasively using cardiovascular magnetic resonance (CMR). Changes in PWV during exercise may reveal further information on vascular pathology. However, most known CMR methods for quantifying PWV are currently unsuitable for exercise stress testing. Methods: A velocity-sensitive real-time acquisition and evaluation (RACE) pulse sequence was adapted to provide interleaved acquisition of two locations in the descending aorta (at the level of the pulmonary artery bifurcation and above the renal arteries) at 7.8 ms temporal resolution. An automated method was used to calculate the foot-to-foot transit time of the velocity pulse wave. The RACE method was validated against a standard gated phase contrast (STD) method in flexible tube phantoms using a pulsatile flow pump. The method was applied in 50 healthy volunteers (28 males) aged 22-75 years using a MR-compatible cycle ergometer to achieve moderate work rate (38 ± 22 W, with a 31 ± 12 bpm increase in heart rate) in the supine position. Central pulse pressures were estimated using a MR-compatible brachial device. Scan-rescan reproducibility was evaluated in nine volunteers. Results: Phantom PWV was 22 m/s (STD) vs. 26 ± 5 m/s (RACE) for a butyl rubber tube, and 5.5 vs. 6.1 ± 0.3 m/s for a latex rubber tube. In healthy volunteers PWV increased with age at both rest (R2 = 0.31 p < 0.001) and exercise (R2 = 0.40, p < 0.001). PWV was significantly increased at exercise relative to rest (0.71 ± 2.2 m/s, p = 0.04). Scan-rescan reproducibility at rest was −0.21 ± 0.68 m/s (n = 9). Conclusions: This study demonstrates the validity of CMR in the evaluation of PWV during exercise in healthy subjects. The results support the feasibility of using this method in evaluating of patients with systemic aortic disease.
Aortic stiffness; Pulse wave velocity; Exercise stress test; Real time imaging
Stiffness of the aorta is an important determinant of
cardiovascular function [1, 2] due to its direct effect on
systolic and diastolic blood pressure. Aortic pulse wave
velocity (PWV) is a surrogate measure of aortic
stiffness which has been shown to independently predict
adverse cardiovascular events and mortality [3–5].
Estimation of PWV by cardiovascular magnetic resonance
(CMR) has been demonstrated to give good
reproducibility in scan-rescan evaluations, with measurements
superior to echocardiography . Variations in PWV
with age and vascular pathologies have also been
reported [2, 7, 8]. However, little is known on how aortic
PWV changes with exercise.
Although cardiac function at rest provides important
information, the power to detect dynamic cardiovascular
dysfunction is greatly enhanced by stress testing [9, 10].
Pharmacological stress tests with agents such as
adenosine and dobutamine have commonly been employed in
conjunction with CMR examinations . However,
exercise provides a physiological challenge to the entire
cardiovascular system , and is generally regarded as
superior to pharmacological stress testing. This is
particularly important when evaluating arterial response to
states of increased cardiac output. Since the aortic wall
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exhibits nonlinear mechanical properties , aortic
stiffness during exercise is expected to change due to pressure
loading [14, 15]. PWV measurement during exercise stress
testing may, therefore, provide additional useful
information on disease status beyond that seen in the resting state.
The aim of this study was to examine and validate a
method of quantifying aortic PWV during exercise using
CMR. The method utilized a real-time velocity sensitive
acquisition, automated quantification of PWV, and
validation in phantoms. Quantification of scan-rescan
reproducibility was also assessed.
A custom-built CMR-compatible cycle ergometer 
was adapted for this study (Fig. 1). The ergometer
comprised a set of pedals mounted on 60 mm radius cranks
coupled to a hub driving an aluminium flywheel with 6:1
ratio using toothed sprockets and belts. Force
transducers (LRF350, FUTEK Advanced Sensor Technology, Inc.,
Irvine, CA, USA) measured the load applied to each pedal
and an optical encoder (HEDS-5540, Avago Technologies,
San José, CA, USA) measured flywheel rotational speed.
Sensor signals were transmitted through short shielded
cables to a battery-powered conditioning and USB digitization
device (USB-6210, National Instruments, Austin, TX, USA)
mounted on the ergometer, and then transmitted via a USB
fibre-optic link (USB Rover 200, Icron Technologies
Corporation, BC, Canada) through a wave guide to a
computer in the MRI control room. Custom software
(LabVIEW 2009, National Instruments, Austin, TX, USA)
was written to calculate and display work rate, and
enable ergometer resistance adjustment, as follows.
A pneumatic actuator (CJPB10-15H6, SMC Corporation,
Tokyo, Japan) pressed a 21 mm diameter braking felt pad
to the outer rim of the flywheel. The actuator’s air supply
pressure determined the force on the pad and hence
resistance. Compressed air from a 2.7 L dive cylinder located in
the control room was regulated to 1000 kPa by a first stage
dive regulator (R1, Atlantis Dive, Auckland, NZ). This fed
a 0–500 kPa electronic pressure regulator (ITV0031-2CL,
SMC Corporation, Tokyo, Japan) adjusted under software
control using the regulator’s 0–5 VDC analogue input via a
USB interface (USB-6008, National Instruments, Austin,
TX, USA). Polyurethane tubing connected the air supply
to the scan room through a wave-guide thereby delivering
the regulated air to the actuator.
The study was approved by the Multi-Region Ethics
Committee of the New Zealand Health and Disability Ethics
Committees, and written informed consent was obtained
from all participants. Fifty healthy volunteers (28 male,
aged 22–75 years) completed rest and exercise protocols.
Strenuous exercise and caffeine were avoided for 24 h,
and food and alcohol were avoided for a minimum of 3 h
prior to CMR examination. Target work rate was set to
raise heart rate by approximately 30 bpm above resting
baseline. Exclusion criteria included pregnancy,
contraindication to CMR, abnormal ECG or atrial fibrillation,
physical limitations preventing cycle exercise, known
cardiovascular disease, current smoker or ceased
smoking within 6 months, and treated or untreated
hypertension >140/90 mmHg (>18.7/12.0 kPa) .
All studies were performed using a 1.5 T MRI scanner
(Avanto, Siemens AG Healthcare Sector, Erlangen,
Germany). The ergometer was fixed to the scanner table
with participants in a supine position (Fig. 1b), and
adjusted to position the heart to within 100 mm of
isocentre while avoiding knee contact with the scanner while
The imaging protocol included standard scouts to
locate the axes of the heart, cine ventricular function,
aortic PWV, and blood pressure with pulse wave analysis at
rest and immediately after exercise. For exercise data
acquisitions, participants cycled at the target work rate until
their heart rate stabilized, and image acquisition was
performed immediately after cessation of cycling with a short
breath-hold at expiration (Fig. 2). After each acquisition,
Fig. 1 Custom-built CMR compatible ergometer. a Schematic showing adjustable position to accommodate different body sizes. b Volunteer cycling
in the scan position
Fig. 2 Work rate (red) and heart rate (green) during the rest and exercise phases of the protocol. Grey bands indicate breath-hold acquisitions
when cycling is ceased
participants were instructed to resume cycling at the
target work rate.
Six short axis slices, equally spaced from base to apex,
and two long axis slices (four chamber and two chamber
views) were acquired using a 3× accelerated balanced
steady state free precession sequence . Typical
parameters were: Base resolution 128 pixels, field of view
(FOV) 300 mm, 82 % rectangular FOV, phase resolution
100 %, slice thickness 6 mm, 6–15 views per segment,
TR 2.6 ms, TE 1.1 ms, and flip angle 80°. Temporal
resolution was 16–40 ms, dependent on heart rate, enabling
acquisition of two slices per breath-hold of 8–10 s
duration at rest, and 5–7 s duration after exercise.
CMR aortic PWV measurement was performed using
a real-time acquisition and evaluation (RACE) sequence,
originally described by Bock et al. . The RACE PWV
method enabled flow sensitive 1D projection imaging of
two independent slices at 7.8 ms temporal resolution per
slice with a 4 s breath-hold. Briefly, a gradient-echo
sequence was modified to make the signal phase dependent
on velocity using a through-slice velocity-encoding
gradient. A 1D projection of the 2D slice was produced
perpendicular to the readout direction by omitting the
phase-encoding gradient. By orientating the slice
perpendicular to the vessel of interest and ensuring no other
vessels were in the same projected voxel, a velocity signal
could be obtained as a combination of stationary and
moving spins. Unlike standard phase-contrast flow sequences,
there was no acquisition of a flow-compensated signal,
which further contributed to improving the temporal
The sequence parameters were velocity encoding (VENC)
250 cm/s, TR 3.9 ms, TE 1.9 ms, flip angle 15 °, image
matrix 512 (pixels) × 512 (time points), bandwidth 673 Hz/
pixel, slice thickness 5 mm, and FOV 300 mm. Signals were
acquired at two axial slices located at the level of the
pulmonary bifurcation and above the renal and superior
mesenteric arteries in alternating TRs, achieving 7.8 ms
temporal resolution for each slice. Data were acquired
for 512 lines for each slice, giving a total acquisition
time of 4 s. The phase-encode direction was set to be
right-left for the superior slice, and anterior-posterior
for the inferior slice (Fig. 3), in order to avoid overlap
of other major vessels in the projection direction. Two
acquisitions were performed during separate
breathholds and the results concatenated.
Blood pressures were acquired at rest and immediately
after exercise using an automated sphygmomanometer
(CardioScope I, Pulsecor Ltd, Auckland, NZ), which also
estimated the central aortic pressure from analysis of
low-frequency suprasystolic waveforms at the occluded
brachial artery. This device has previously been validated
against invasive pressure recordings .
All images were randomized so that analysts were blinded
to participants, rest and exercise. Left ventricular mass and
volume were determined using guide-point modeling .
Briefly, a spatio-temporal finite element model was
interactively customized to all short and long axis slices and
frames simultaneously. Breath-hold mis-registration was
corrected manually with in-plane translations. Mass was
calculated by numerical integration of the model and
averaged over all frames. The time-varying position and
angulation of the mitral valve plane provided the basal cut-off
for the calculation of mass and volume. Previous studies
have shown that this method gives accurate estimation of
mass and volume with a reduced number of
noncontiguous short axis slices in humans  and mice .
Figure 3 shows the PWV analysis method. The vessel
region of interest was selected to encompass the pulsatile
flow signal in the RACE phase image (Fig. 3a) matching
the location of the cross-section of the descending aorta
in an anatomical scout image acquired with the same
Fig. 3 MRI PWV analysis. a Region of interest selection guided by anatomical images (left) and corresponding RACE phase image (right). b Correction
for stationary tissue signal in the complex domain, with proximal (superior) slice signal in the vessel region shown in blue and distal (inferior) signal in
green. b-i Raw data plotted in the complex plane, showing centroids marking slow flow signal. b-ii Complex signal relative to the reference static tissue
point. b-iii Phase waveforms relative to the reference point. b-iv Estimation of foot transit time
imaging parameters (FOV and slice position) as the RACE
acquisition. The complex velocity-sensitive signal was
averaged across the region of interest for all time points.
The stationary tissue signal component was identified and
subtracted using a two-step procedure. Firstly, the data
were plotted for all time points on the complex plane, and
the centroid of the signal data during low or zero flow was
calculated by successively eliminating 66 % of the points
furthest from the centroid. Secondly, a complex vector
representing static tissue was subtracted from each data
point. The position of this reference signal was designed
to provide a robust measure of upstroke during the
pulsatile flow, approximately in the center of the signal arc of
flowing spins which have a flow-dependent magnitude
. This point was defined to have the same phase as the
centroid but was offset from the centroid towards the
origin by a distance of half the magnitude of the peak
pulsatile flow signal relative to the centroid (Fig. 3b). The phase
of the complex difference was then plotted against time
and the PWV determined automatically by the early
systolic fit method [24, 25] (Fig. 3b). Following [24, 25], the
upstroke between 20 and 40 % of the peak value was fitted
by a least squares line and intersected with the baseline,
which was defined to be the minimum of the 10 points
prior to the upstroke, to find the foot of each heart beat.
The foot-to-foot time difference (Δt) between proximal
and distal slices was collected for all heart beats and the
median time difference was used in the following equation
to calculate PWV:
Pulsatile flow phantom
PWV from the CMR RACE sequence was compared to
PWV estimates obtained using a standard 2D gated
phase contrast (PC) flow sequence (Siemens AG
Healthcare Sector, Erlangen, Germany) in a pulsatile flow
phantom. Two phantoms of different compliance were tested.
A 19 mm diameter latex rubber tube and a stiffer 21 mm
butyl rubber tube were mounted in a tank within a volume
of water. A linear motor (STA2510, Copley Motion
Systems LLC, Essex, UK) under software control drove a
piston to push water through a circuit of pipes and one-way
valves, generating repetitive forward moving pulsatile flow
through the compliant phantom section which returned
through a separate hose (Fig. 4). Axial slices were acquired
at ±150 mm from the iso-centre, during a 60 cycles per
minute simulation with 30 and 50 mL stroke volumes for
the latex and rubber phantoms, respectively. RACE
imaging parameters were the same as described above. The
standard 2D gated PC flow acquisition parameters were
VENC 1.5 m/s, TR 9.6 ms, TE 2.0 ms, flip angle 30 °,
bandwidth 554 Hz/pixel, slice thickness 5.5 mm, FOV
320 mm, and 69 % rectangular FOV with a 55 s
acquisition time. Flow encoding was through-plane at the same
Fig. 4 Flow circuit for phantom experiments
RACE PWV repeatability was assessed in nine
asymptomatic volunteers who underwent two evaluations at rest.
These were performed consecutively to minimize diurnal
variations and separated by removing volunteers from the
scanner. Each evaluation consisted of localizers, and two
sequential breath-hold PWV acquisitions. The median
pulse transit times from the sequential data sets were used
to estimate aortic PWV scan-rescan variability.
Rest and exercise data were tested with two tailed paired
t-tests, and differences due to gender with independent
t-tests. Significance level of 0.05 was assumed. The
relationship between age and aortic PWV was tested using
linear correlation and the interaction with exercise was
tested using ANCOVA.
PWV were 22 m/s (standard PC) vs. 26 ± 5 m/s (RACE)
for the stiffer butyl rubber phantom, and 5.5 m/s
(standard PC) vs. 6.1 ± 0.3 m/s (RACE) for the compliant latex
rubber phantom. Over all phantom experiments, the
standard deviation in transit times averaged 2 ms,
approximately half the TR of the RACE sequence.
Additional experiments over five different slice spacings
ranging from 100 to 300 mm in the more compliant
phantom resulted in approximately constant PWV
averaging 5.2 ± 0.3 m/s. Scan-rescan reproducibility from 9
volunteers at rest was −0.21 ± 0.68 m/s (p = NS) (Fig. 5).
Central mean arterial pressures were slightly lower in
the second evaluation (−4 ± 3 mmHg, p < 0.01). Heart rate
was similar in both evaluations (−3 ± 6 bpm, p = NS).
Demographics for the 50 asymptomatic volunteers are
shown in Table 1. Changes in haemodynamic parameters
from rest to exercise are presented in Table 2. Heart rate
increase ranged from 12 to 75 bpm (average 31 ± 12 bpm)
and work rate ranged from 13 to 109 W (average 38 ±
22 W). Changes in left ventricular functional parameters
were all statistically significant (p < 0.01). Approximately
80 % of the cardiac output increase was due to heart rate
increase, while the remainder was due to increased stroke
volume. Mean arterial pressure increased by 5 ± 5 mmHg
between rest and exercise (p < 0.001). Mean arterial
pressure at rest was positively correlated with age at rest
(R2 = 0.17, p = 0.003) and exercise (R2 = 0.11, p = 0.02);
ANCOVA showed no significant interaction between
exercise and age.
PWV could not be determined at either rest or exercise
in 6 participants, due to noisy waveforms and inability of
Table 1 Participant demographics (n = 50, 28 male)
the algorithm to automatically calculate transit time. The
average distance between the proximal and distal slices
was 156 ± 17 mm. PWV was positively correlated with
age, at both rest and during exercise (Fig. 6, R2 = 0.31 for
rest and 0.40 for exercise, p < 0.001). At rest, PWV was
4.2 ± 1.0 m/s for participants aged 20–30 year (n = 6),
increasing to 7.0 ± 2.0 m/s for participants aged 70–80
year (n = 4). PWV was significantly increased at exercise
relative to rest (p = 0.04). ANCOVA, with age as a
covariate, found no significant interaction between
exercise and age.
To evaluate PWV in conditions of elevated heart rate, a
fast acquisition with high temporal resolution and short
Table 2 Changes from rest to moderate exercise
−5 ± 6**
−15 ± 19**
breath-hold duration is essential. Several methods have
been proposed for CMR PWV measurement, including
4D flow , two slice cine flow , and flow-area in a
single slice . However, these methods have limited
application in exercise testing due to long breath-hold
duration. Patients typically cannot hold their breath for
more than 5 s under exercise conditions. Several fast
acquisition methods have been proposed previously, including
cylindrical excitation 1D flow , 1D flow spectroscopy
, cylindrical excitation tagging , multislice comb RF
excitation and 1D readout , complex difference of
velocity encoded projections , and flow-sensitive RACE
with stationary tissue suppression . The RACE method
was chosen for the current study because it offered high
temporal resolution without the need for a velocity
compensated acquisition or complex excitation procedure, both
of which reduce the effective temporal resolution.
Langham et al.  used a similar 1D projection method
in a single slice at the level of the pulmonary bifurcation,
and calculated PWV between ascending and descending
portions of the aorta. A flow-compensated acquisition was
subtracted from a flow-sensitive acquisition in each TR,
giving 10 ms temporal resolution. Gaddum et al.  used
a similar method, with two velocity-sensitive acquisitions
to assess the beat-to-beat variation of PWV and variation
during breath-hold maneuvers. Sliding window
subtraction of acquisitions from two different gradient waveforms
was used to derive a velocity sensitive signal.
PWV transit time can be estimated using a variety of
methods, including time-to-foot, flow-area and
crosscorrelation techniques. In studies comparing different
methods, transit time using the intersection of the baseline
with the upstroke was most reproducible [24, 27] and this
was used in this study. However, errors in the
determination of transit time non-linearly affect errors in PWV. To
illustrate this concept, Fig. 7 shows the estimated error in
the PWV measurement, given a 2 ms error in pulse wave
transit time (the average variation in transit time in our
phantom experiments) and a 150 mm distance between
slices. It can be seen that the error increases nonlinearly
with decreasing transit times, resulting in wide variation in
PWV above 10 m/s. This may explain why the difference
between PWV in the phantom experiments was larger for
the stiffer butyl rubber phantom than for the latex
phantom, and why the scatter increases with PWV in Fig. 6.
Similar arguments can be made for the effect of slice
spacing at a given PWV, since transit time is linear with slice
CMR with supine exercise on an ergometer is an
alternative method to standard treadmill tests using the Bruce
protocol . Supine exercise has previously been used to
evaluate strain and transvalvular blood flow , aortic
compliance , diastolic function in type I diabetes ,
and caval flow in Fontan repair . An advantage of
Cardiac output (L/min1)
RPP (×103 bpm·mmHg)
Cardiac output power (W)
End diastolic volume (mL)
End systolic volume (mL)
Augmentation index (%)
Fig. 6 Aortic PWV versus age, at rest (open circles) and exercise (solid circles)
treadmill tests is that a higher level of exercise is
achievable, however one limitation is that a significant time must
elapse between exercise and image acquisition, during
which the heart rate can decline rapidly (Fig. 2). Our
protocol enables a small number of short duration
breathholds, obtained immediately after cessation of exercise, as
well as repeated exercise periods with interspersed
The PWV results at rest in asymptomatic volunteers are
similar to those obtained in previous studies. Nethononda
et al.  recently reported a large cohort study using the
time-to-foot method, with similar age variation as the
Our study found increased PWV during exercise which
concurred with previous studies of exercise and
pharmacological stress test in asymptomatic volunteers. In a
catheterization study in 13 asymptomatic males, aortic
PWV increased during supine cycle exercise, along with
increased central pressure, and reduced peripheral
resistance . PWV changes during dobutamine stress test
were studied by Puntmann et al. , who found that
PWV increased in men but not women. Steeden et al. 
also found reduced vascular compliance at exercise using
pressure-flow relationships in 20 asymptomatic volunteers.
The limited number of short axis slices used in this study
may lead to small regional abnormalities in ventricular
wall motion being overlooked. Also, the spatial resolution
of the cine images (approximately 2.3 mm) used for
ventricular function was reduced to compensate for the need
for short breath-holds and high temporal resolution. The
methods employed in this study estimate average PWV
between two slices, rather than local PWV at a point, and
therefore are more suited to examination of systemic
rather than local aortic disease.
Fig. 7 Effect of transit time error on PWV estimation, for an error in transit time of 2 ms and a slice spacing of 150 mm. Points show relative errors at
10 and 4 m/s PWV
To our knowledge, this is the first study to demonstrate
a valid method of determining PWV during exercise using
CMR. Comparable PWV measurements were obtained
from the standard 2D gated phase contrast velocity
encoding method in phantoms. Normal volunteers showed an
increase in PWV with age, and an increase in PWV with
exercise. The results suggest this method would be
feasible for evaluation of patients with systemic aortic
CMR: Cardiovascular magnetic resonance; PWV: Pulse wave velocity;
RACE: Real-time acquisition and evaluation; STD: Standard gated phase
contrast; VENC: Velocity encoding value.
PAR participated in the study design, development of pulsatile pump and
ergometer systems, participant recruitment, data acquisition, data analysis,
developed pulsatile pump system, data interpretation and manuscript
editing. BRC participated in the study design, development of pulsatile
pump and ergometer systems, data analysis and interpretation and
manuscript editing. YL participated in software development, data
acquisition and analysis. AL participated in the study design and manuscript
editing. PN and AT participated in the design and development of the
pulsatile pump and ergometer systems, and manuscript editing. RS
participated in the study design, data interpretation and manuscript editing.
HIL implemented the automatic analysis methods. AY participated in the
study design, data analysis, data interpretation and manuscript editing. All
authors read and approved the final manuscript.
This study was funded by the Health Research Council of New Zealand (09/173).
The authors would like to thank the staff at the Centre for Advanced MRI
(CAMRI) where all MRI evaluations were performed. Auckland MRI Research
Group staff John Beck, Agustin Okamura and Ben Wen performed all MRI image
analyses. Research nurses from Auckland District Health Board, Lay Cunningham
and Sue Anderson, and University of Auckland Cardiovascular Research Group,
Melinda Copley, assisted with the recruitment of subjects. Dr Andrew Lowe,
Auckland University of Technology, provided the Pulsecor device.
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