Signal Processing - May 2017 - 97
for reducing the influence of the axial
oscillation on the lateral oscillation. A
fourth-order estimator has been derived
to separately estimate the axial and lateral velocity components as
vx =
mx
2r2T prf
J 1 {R 1 (1)} 0 {R 2 (1)} N
K
O
K +1 {R 2 (1)} 0 {R 1 (1)} O
# arctan K
,
0 {R 1 (1)} 0 {R 2 (1)} O
KK
OO
L -1 {R 1 (1)} 1 {R 2 (1)} P
(10)
and similarly for v z, where R 1 (1) is the
complex lag-one autocorrelation value for
r1 (i) and R 2 (1) is the complex lag-one
autocorrelation value for r2 (i) . 1 denotes
the imaginary part and 0 denotes the
real part of the argument.
This approach has been implemented
on scanners from BK Ultrasound (Herlev,
Denmark), and an example from the aorta
(main artery from the heart) in a short
axis view is shown in Figure 2. During
the contraction of the heart, a rotational
motion of the blood is often found in the
aorta in addition to the main flow direction downward in the body. This rotation
is shown in the figure, where the arrows
indicate direction and magnitude. The
color coding also indicates direction, and
a full 360c rotation is found at the late systolic phase in the cardiac cycle. This demonstrates the spatial variation in angle
throughout the image, precluding the use
of a single angle correction factor.
measurement makes the velocity determination completely independent of the
angle between the ultrasound beam and
the flow direction. Quantification of peak
velocities, volume flow, and other parameters potentially becomes more reliable
and the system also becomes operator
independent.
The 3-MHz probe employed has 32 ×
32 = 1,024 elements each generating
around 40 MB of data/s for a combined
data flow of 40 GB/s, which must be processed in real time. Connection to the
probe through 1,024 coaxial cables is
also a challenge, as such a cable has a
diameter of 3 cm, which makes working
with it cumbersome for clinicians.
Advanced imaging and
continuous data
What currently limits the accuracy and
frame rate of velocity imaging is the
sequential data acquisition. The pulses
are emitted in one direction at a time,
and eight to 16 emissions are needed for
a sufficient velocity accuracy. Imaging
down to 15 cm takes 200 ns /pulse, and
making flow estimation in 32 directions
with eight emissions, combined with an
anatomic image with 128 lines results
in a frame rate of 13 Hz, barely accept-
able for cardiac imaging. There is, thus,
a link between imaging depth, estimation accuracy, and frame rate, which is
currently a major obstacle for fast and
quantitative flow imaging.
This complex problem can be solved
by employing fast imaging schemes
using spherical [8] or plane wave emissions [9], [10]. The full imaging region
is insonified using a spherical or plane
wave, which covers the full imaging
region as shown in Figure 3. The scattered signal is then received by all
the elements and dynamically focused
during receive processing. Such imaging has no transmit focusing, but the
receive focusing can be synthesized by
sending out spherical waves from other
parts of the probe and combine all the
measurements. This is called synthetic
transmit aperture (STA) imaging or
ultrafast imaging for plane waves [11],
[12]. The emitted energy for STA is low,
but this can be compensated for by combining a number of elements and using
long chirp excitation. The scheme gives
dynamic focusing throughout the image
with a frame rate of thousands of frames
per second, if emission sequences
with few emissions are used. Continuous data everywhere in the image are,
0.0 cm
The predominate direction of the flow
in Figure 2 however, is, out of the imaging plane, and a full visualization of the
flow in the human circulation necessitates a complete estimation of the 3-D
velocity vector. The TO approach has
been extended to full 3-D imaging by
employing a 2-D matrix probe with N × N
elements. The ultrasound beam can then
be controlled in both directions. A broad
beam is again emitted, and two apodization patterns are applied to generate four
beams in parallel with two apodization
peaks in both the lateral and orthogonal elevation direction. From these four
beams and eight samples per emission
the axial, lateral, and elevation velocity components can be determined. This
Axial Velocity (cm/s)
-61 0
61
Vector flow challenges
-61 0
61
Transverse
Velocity (cm/s)
(a)
(b)
figure 2. (a) The color wheel used for indicating direction and magnitude. (b) The vector flow image
displays the velocities estimated at the late systolic phase in the ascending aorta in a short axis view.
IEEE Signal Processing Magazine
|
May 2017
|
97
Table of Contents for the Digital Edition of Signal Processing - May 2017
Signal Processing - May 2017 - Cover1
Signal Processing - May 2017 - Cover2
Signal Processing - May 2017 - 1
Signal Processing - May 2017 - 2
Signal Processing - May 2017 - 3
Signal Processing - May 2017 - 4
Signal Processing - May 2017 - 5
Signal Processing - May 2017 - 6
Signal Processing - May 2017 - 7
Signal Processing - May 2017 - 8
Signal Processing - May 2017 - 9
Signal Processing - May 2017 - 10
Signal Processing - May 2017 - 11
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Signal Processing - May 2017 - 14
Signal Processing - May 2017 - 15
Signal Processing - May 2017 - 16
Signal Processing - May 2017 - 17
Signal Processing - May 2017 - 18
Signal Processing - May 2017 - 19
Signal Processing - May 2017 - 20
Signal Processing - May 2017 - 21
Signal Processing - May 2017 - 22
Signal Processing - May 2017 - 23
Signal Processing - May 2017 - 24
Signal Processing - May 2017 - 25
Signal Processing - May 2017 - 26
Signal Processing - May 2017 - 27
Signal Processing - May 2017 - 28
Signal Processing - May 2017 - 29
Signal Processing - May 2017 - 30
Signal Processing - May 2017 - 31
Signal Processing - May 2017 - 32
Signal Processing - May 2017 - 33
Signal Processing - May 2017 - 34
Signal Processing - May 2017 - 35
Signal Processing - May 2017 - 36
Signal Processing - May 2017 - 37
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Signal Processing - May 2017 - 41
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Signal Processing - May 2017 - 43
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Signal Processing - May 2017 - 67
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Signal Processing - May 2017 - 69
Signal Processing - May 2017 - 70
Signal Processing - May 2017 - 71
Signal Processing - May 2017 - 72
Signal Processing - May 2017 - 73
Signal Processing - May 2017 - 74
Signal Processing - May 2017 - 75
Signal Processing - May 2017 - 76
Signal Processing - May 2017 - 77
Signal Processing - May 2017 - 78
Signal Processing - May 2017 - 79
Signal Processing - May 2017 - 80
Signal Processing - May 2017 - 81
Signal Processing - May 2017 - 82
Signal Processing - May 2017 - 83
Signal Processing - May 2017 - 84
Signal Processing - May 2017 - 85
Signal Processing - May 2017 - 86
Signal Processing - May 2017 - 87
Signal Processing - May 2017 - 88
Signal Processing - May 2017 - 89
Signal Processing - May 2017 - 90
Signal Processing - May 2017 - 91
Signal Processing - May 2017 - 92
Signal Processing - May 2017 - 93
Signal Processing - May 2017 - 94
Signal Processing - May 2017 - 95
Signal Processing - May 2017 - 96
Signal Processing - May 2017 - 97
Signal Processing - May 2017 - 98
Signal Processing - May 2017 - 99
Signal Processing - May 2017 - 100
Signal Processing - May 2017 - 101
Signal Processing - May 2017 - 102
Signal Processing - May 2017 - 103
Signal Processing - May 2017 - 104
Signal Processing - May 2017 - 105
Signal Processing - May 2017 - 106
Signal Processing - May 2017 - 107
Signal Processing - May 2017 - 108
Signal Processing - May 2017 - 109
Signal Processing - May 2017 - 110
Signal Processing - May 2017 - 111
Signal Processing - May 2017 - 112
Signal Processing - May 2017 - Cover3
Signal Processing - May 2017 - Cover4
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