Basic Doppler ultrasound for clinicians
|Christian Andreas Doppler
(1803 - 1853)
|The Doppler effect
||My cat Doppler
The Doppler effect was discovered by Christian Andreas
Doppler (1803 - 1853), and shows how the frequency of an
emitted wave changes with the velocity of the
emitter or observer. The theory was presented in the royal
Bohemian society of Science in 25th of May1842 (5
listeners at the occasion!), and published in 1843 (119). The premises
for his theoretical work was faulty, as he built his
theory on the work of James Bradley who erroneously
attributed the apparent motion of stars against the
background (parallax effect) to the velocity of the earth
in its orbit (instead of the effect of Earth's position in
orbit on the angle of observation). Further, Doppler
attributed the differences in colour of different stars to
be due to the Doppler effect, assuming all stars to be
white. Finally, he theoretised over the effect of
the motion of double stars that rotate around each
other, assuming a Doppler effect from the
motion. The changes in wavelength from the Doppler
effect, however, is too small to be observed.
However, Doppler did a theoretical derivation of the effect of the motion of the source or observer on the perceived wavelength from the premises of a constant propagation velocity of the waves in the medium, and this is entirely correct, valid both for sound waves and electromagnetic radiation of all kinds. The basis for the Doppler effect is that the propagation velocity of the waves in a medium is constant, so the waves propagates with the same velocity in all directions, and thus there is no addition of the velocity of the waves and the velocity of the source. Thus, as the source moves in the direction of the propagation of the waves, this does not increase the propagation velocity of the waves, but instead increases the frequency.The original derivation of the Doppler principle as well as the extension to reflected waves is explained in more detail here. As a work of theoretical physics, it is thus extremely important. In addition, it has become of practical importance, as the basis for the astronomical measurement of the velocity of galaxies by the red shift of the spectral lines, in Doppler radar, Doppler laser and Doppler ultrasound.
The theory was experimentally validated by the Dutchman
Christoph Hendrik Diderik Buys Ballot (120), with the
Doppler effect on sound waves, who placed musicians along
a railway line and on a flatbed truck, all blowing the
same note, and observed by subjects with absolute pitch,
who observed the tones being a half note higher when the
train was approaching as compared to the stationary
musicians, and a half note lower as the train receded.
(This can be observed in everyday phenomena such as the sound of f.i. an ambulance siren, the pitch (frequency) is higher when the ambulance is coming towards the observer, hanging as it passes, and lower as it goes away.
This is illustrated below:
Doppler effect. As the velocity of sound in air (or
any other medium ) is constant, the sound wave will
propagate outwards in all directions with the same
velocity, with the center at the point where it was
emitted. As the engine moves, the next sound wave is
emitted from a point further forward, i.e. with the
center a little further forward. Thus the distance
between the wave crests is decreased in the direction
of the motion, and increased in the opposite
direction. As the distance between the wave crests is
equal to the wavelength, wavelength decreases (i.e.
sound frequency increases) in front of the engine, and
increases (sound frequency decreases) behind it. This
effect can be heard, as the pitch of the train
whistle is higher coming towards a listener than
moving away, changing as it passes. The effect on the
pitch of the train whistle was published directly, but
later than Doppler and Buys Ballot.
The Doppler effect for a stationary wave source and a moving observer. For the time the wave has moved the distance , the observer has moved the distance closer to the source (determined by the velocity v of the observer), corresponding tho the difference between the real wavelength and the apparent wavelength . This results in an apparent shortening of wavelength (increase in frequency) as shown by Doppler (119) and reproduced below this image. .
The Doppler effect for a moving source and a stationary observer. In the time between waves (which is 1/f0), the source has moved the distance closer to the source, (determined by the velocity v of the source), corresponding tho the difference between the real wavelength and the apparent wavelength . This results in an apparent shortening of wavelength (increase in frequency) as shown by Doppler (119) and reproduced below this image.
Thanks to Hon Chen Eng of University of Toledo who pointed out an inconsistency in the original illustration and showed a better way of illustrating the Doppler effect in this image.
(blue) moving towards a stationary wavesource
with the velocity:
will meet the wave as the wave have moved a distance , which is the perceived wavelength. The observer has moved the distance:
The motion of the wave and the motion of the observer happen during the same time interval:
The motion of the observer thus shortens the original wavelength by , i.e:
The change in
frequency, the Doppler shift is:
|If the source moves toward a
stationary observer with the
In the time the wave moves one wavelength, the source moves the distance:
The motion of the wave and the motion of the source happen during the same time interval:
The distance from the next wave emitted from the new position of the source (small dotted red circle)
to the observer (blue) is shortened by in the direction of the motion, so the new wavelength representing the distance between the first and second waves is
The change in frequency, the Doppler shift is:
If v << c, then:
The Nykvist phenomenon (121) is an effect of
the relation between the sampling frequency and the
observed velocity. If you sample at a certain frequency,
the direction of the motion becomes ambiguous, more
frequent sampling will give the correct direction, less
frequent sampling results in an apparent motion in
the opposite direction. This can be observed with a
stroboscopic light, for instance illuminating the flow of
water, or with old fashioned wagon wheels in old moves
which often seem to revolve slowly backwards when the
wagon moves forwards.
This is illustrated below.
Constant rotation velocity, decreasing sampling frequency:
The easiest is to show how reducing the sampling frequency affects the apparent motion. All circles rotate with the same rotation velocity clockwise. The sampling frequency is reduced from left to right. It can be seen that the red dots is at the same positions when they are seen to move.
8 samples per rotation, the red point is seen in eight positions during the rotation.
4 samples per rotation, the red point is seen to rotate just as fast, but is only seen in four positions
2 samples per rotation, i.e. the sampling frequency is exactly half the rotation frequency. Here, the red dot is only seen in two positions, (but it is evident that it is in the same positions at the same time as in a and b). However, it is impossible to decide which way it is rotating. This is the Nykvist limit; sampling rate = 1/2 rotation rate.
1.5 samples per rotation,or one sample per three quarter rotation, making it seem that the red dot is rotating counter clockwise. Again, the dot is in the same position at the same time as in a and b.
One rotation per 8 samples. The sampling catches the red dot in 8 positions during one rotation.
Rotation velocity twice that i a; one rotation per four samples, the sampling catches the red dot only in four positions during one rotation.
Rotation velocity four times a; one rotation per two samples, this catches the red dot in only two positions, giving directional ambiguity as above.
Rotation velocity six times a; one rotation per 1,5 samples, or 3/4 rotation per sample, giving an apparent counter clockwise rotation.
Sampling from increasing depth
will increase the time for the pulse returning, thus
increasing the sampling interval and decrease the
sampling frequency. The Nykvist limit thus
decreases with depth. This means that pulsed Doppler has
depth resolution, but this leads to a limit to the
velocities that can be measured.
The Nyquist limit is dependent on the depth (D) of the sampling volume. The larger distance from the probe, the longer time for the pulse to pass to the desired depth and back, and the lower the Nyquist limit. The time for the return of the pulse is:
T = 2D/c
and the maximum PRF as PRF = c/2D
is no problem in pulsed tissue
Doppler, as tissue velocities are far below the Nykvist limit. However, in
colour tissue Doppler, harmonic imaging is halving the
effective frequency, and leads to aliasing as shown below.
on the other hand, will measure all
velocities along the ultrasound beam: The beam is
transmitted continuously, and the received echoes are
sampled continuously with no range gating. Thus, there is
no information about the time interval from the signal to
the reflection, and, hence, no information about the depth
of the received signal; the signal may come from any
depth. The continuous Doppler has no Nykvist limit, and
can measure maximal velocities. It is used for measuring
This means that both methods has
limitations: pulsed Doppler has velocity ambiguity at high
velocities, and continuous wave Doppler has depth or range
ambiguity. Thus, for continuous Doppler the pulse length
can be long, as there is no depth resolution, while in
pulsed Doppler it has to be shorter in order to achieve a
sufficient depth resolution.
continuous wave versus pulsed wave, and the Nykvist
effect. Left: Aortic insufficiency shown by cw
Doppler. It van be seen that there are a fair
distribution of velocities in the whole spectrum.
However, There are far more velocities blow 2 m/s. In
this case, the low pass filter is only set to suppress
tissue velocities. If the point is to get a clear
visualisation of the maximal velocities in the jet, at 4
- 6 m/s, the filter should be set higher. Left, the same
patient by pulsed Doppler of the LVOT. The outflow can
be seen as a narrow band, within the velocity range,
while the regurgitant jet has velocities outside the
Nykvist range, and there is total velocity ambiguity.
|The principle of HPRF. Pulses are transmitted with three times the frequency that is necessary to allow the echo from the furthest depth to return. Thus, the echo of pulse 1 will return from level 3 at the same time as the echo of pulse 2 from level 2 and and of pulse 3 from level 1, and there is no way to determine whether a signal is from level 1, 2 or 3.||HPRF pulsed Doppler
recording (right). with one sample volume in mid
ventricle and one in the mitral ostium. The
recording shows a systolic dynamic gradient (due
to inotropic stimulation with dobutamine), as
well as an ordinary mitral inflow curve.
There is no way in the pulsed recording to
determine which velocities that originate from
which sample volume (except from á priori
knowledge, of course, a dynamic gradient like
this is usually mid ventricular, and the mitral
inflow in the annulus is easily
|Two pulses sent toward a scatterer with a time delay t2 - t1 = 1/PRF. Given that the scatterer has a velocity, it will have moved a distance, d, that is a function of the velocity and the time (d = v x t). Thus, pulse 2 travels a longer (or shorter) distance equal to d with the speed of sound, c, before it is reflected.||During the time pulse2 has travelled the distance d to the new position of the scatterer and back to the point of the reflection of pulse 1, i.e. a distance 2d, pulse 1 has travelled the same distance away from the reflection point. (The scatterer will have travelled further, but this is not relevant). Thus the displacement of the waveform of pulse 2 relative to pulse 1, is 2d. This corresponds to a phase shift from pulse 1 to pulse 2 of , and = 4 f0 / v PRF (see below).||By sampling the two pulses simultaneously at two timepoints, as shown in the previous illustration, the phase of each pulse can be determined as shown below. The phase analysis of the relative positions of all four points is done by autocorrelation, a quick (and dirty?) method that allows online computation.|
|Phase analysis. If the waveform is treated as a sine curve, every point on the curve corresponds to an angle, and the phase of the point in the curve can be described by this angle; the phase angle . From the diagram, it's also evident that a full wavelength, , is equivalent to 2, and for every point the corresponding fraction of a wavelength is equivalent to an angle which is the fraction of 2.||However, from the diagram at the top, it is evident that by sampling the waveform only once, the phase is ambiguous, it is not possible to separate the phase of point a from point b. The two points are separated by a quarter of a wavelength, or 90° (). In order to determine the phase of the points unambiguously, the pulse has to be sampled at to points separated by less than a quarter wavelength. Then it can be seen that point a is in increasing phase from a1 to a2, corresponding to a phase angle of 0 - /2 while b is in a decreasing phase corresponding to an angle of /2 - .|
As descibed above, a pulse has a certein bandwidth, describing the frequency content of the pulse. In spectral analysis, this will give a spectrum of a certain width, corresponding to the velocity distribution of flow velocities. In phase analysis, this will correspont to a certain distribution of phase angles as illustrated. Autocorrelation, however, will only result in the average phase angle.
|In the case of
stationary noise (clutter) as f.i.
reverberations, the autocorrelation will
result in an average phase angle that is in
between the signal and the noise. The
clutter noise will have to be removed by a
low velocity filter in order to avoid severe
underestimation of flow velocities.
package acquisition. Time depth diagram of the
position of a moving scatterer. Each dot
represent one pulse. Packages of two pulses
are sent to the scatterer with intervals. The
time between the pulses in one package is
given by 1/PRF, and decides the Nykvist limit.
The time between packages is the time it takes
to build a full sector of lines in colour flow
mode (CFM) and is given by the frame rate
(FR), the time interval being 1/FR. This
decides the temporal resolution of the CFM.
||CFM sector superposed
on a B-mode sector. By reducing sector size,
line density and sampling frequency, the CFM
image can achieve an acceptable frame
rate. This is feasible because the region
of interest for the flow is usually only a part
of the ROI for The B-mode, ,flow being
intracavitary as shown below.
|Power Doppler image
of the renal circulation. The amplitude is a
function of the number of scatterers, i.e. the
number of blood cells with a Doppler shift.
This is shown as the brightness (hue) of the
signal. In addition, direction
of flow can be imaged by different colours
(red - positive flow - towards probe, blue -
negative colours - away from probe), and still
the brightness may show the amplitude.
||Colour flow showing a large mitral regurgitation. Velocities away from the probe is shown in blue (converting to red where there is aliasing), towards the probe is red. In this image, the green colour is used to show the spread (variance) of velocities. This will also reflect areas of high velocities (high variance due to aliasing). The sector with colour flow is seen to be far smaller than the B-mode sector. The image displays the direction, extent and timing of the jet.|
|Recording from a patient with apical hypertrophic cardiomyopathy. Ejection can be seen in blue, and there is a delayed, separate ejection from the apex due to delayed relaxation. There is an ordinary mitral inflow (red), but no filling of the apex in the early phase (E-wave), while the late phase (A-wave) can be seen to fill the apex. Left, a combined image in HPRF and colour M-mode. The PRF is adjusted to place two samples at thr mitral annulus and in the mid ventricle just at the outlet of the apex. The mitral filling is shown by the green arrows, and the late filling of the apex is marked by the blue arrow. In addition, theere is a dynamic mid ventricular gradient shown by the red arrow, with aliasing in the ejection signal in colur Doppler. The delayed ejection from the apex is marked by the yellow arrow (the case is described in (87). The utility of the different methods is evident: HPRF (or cw Doppler) for timing and velocity measurement, but with depth ambiguity, colour M-mode for timing and location of the different jets, direction being displayed by the colour.|
analysis is often done by the process known as autocorrelation. This will
result in a values that does not reflect the spectrum, but
only mean values in the spectrum. But if there is clutter
in the region (stationary echoes), this will be
incorporated in the mean, resulting ion lower values. In
Doppler flow, this can be filered by the high pass filter,
and thus will represent a small problem. In tissue Doppler, this may be a
more significant problem, as the velocities are only about
1/10 of the flow values, and thus clutter may be more
difficult to separate from true velocities. Thus, a
substantial amunt of clutter may reduce autocorrelation
values for tissue Doppler more than pulsed Doppler as
discussed below. In addition,
it is customary to analyse the tissue Doppler values in
native, rather than harmonic imaging, due to the Nykvist limitation. Thus, there
is a greater amount of clutter than if harmonic imaging
had been used, as
in B-mode images.
colour flow, it is important to realise that there may, in
some scanners, be an inverse relation between the gain of
colour Doppler and B-mode. (In some scanners it is
possible to adjust the priority, or to adjust the gain
settings separately). This, however, is an acquisition
finction, and not image adjustment, and thus cannot be
compensated afterwards. This is illustrated below:
Effect on B-mode gain on colour Doppler imaging. Left pulmonary venous flow by pwDoppler, showing a systolic flow component, although low velocities. Middle, colour M-mode of the same patient. Only the diastolic flow component can be seen. Right, reducing B-mode gain increases the gain of colour flow, and the systolic pulmonary venous flow can be seen.
53). It is simply a
question of different filtering of the Doppler signals. The
main principle is that blood has high velocity (Typically
above 50 cm/s, although also all velocities down to zero),
but low density, resulting in low intensity (amplitude)
reflected signals. Tissue has high density, resulting in
high intensity signals, but low velocity (typically below 20
cm/s). The difference in the applications used for the two
sets of signals is mainly differences in filtering, applying
a high pass filter in Doppler flow, and low pass filter in
tissue Doppler (Although the latter is not absolutely
|The diagram to the left shows the
placement of flow and tissue signals on this
intensity (amplitude) / velocity diagram. Velocity
given as the height ogf the bars, intensdity showb
both by the placement on the x axis, as well as
the darkness of the bars, black being the highest
intensity. The flow signals are low intensity but
mostly high velocity, while the tissue is
exclusively low velocity, high intensity. The
heart valves, however, are solid structures which
moves with the velocity of the passing blood,
resulting in high intensity signals giving a
saturation of the Doppler spectrum. A typical flow
curve from the right ventricular outflow tract is
shown to the left, with the valve click.
|Application of a high pass filter (low
velocity reject) shown schematically to the left
and in practice applied to a mitral flow curve to
the right. Velocities lower than the limits of the
green bar (showing the range of the filter) are
removed seen in the dark zone in the middle of the
spectrum. The setting rejects velocities at blood
intensities below 15 - 20 cm/s, which is too high
for normal flow velocities as in this instance,
although may often be useful in continuous
wave Doppler recordings of high velocities in
|The filter is adjustable and is here
reduced to 10 cm/s
|Further reduction in the filter below
10 cm/s results in high intensity signals becoming
visible, especially in early diastole. This is
tissue signals from the mitral ring.
|Fully removing the filter results in a
dense band of high intensity tissue signals around
the baseline. The signal is difficult to analyse,
as it has so high amplitude that the display is
|Decreasing the scale and gain (shown as
all signals being illustrated in lighter colour,
but with the same relative placement on the x
axis), and placing the sample volume near the
mitral ring, discloses the tissue velocity curve
of the ring, still taken with an ordinary Doppler.
The flow signal, having a much lower amplitude, is
removed simply by reducing the gain.
|All modern ultrasound machines today
has separate applications for tissue Doppler which
optimises the signal for this purpose, among other
things by applying a low pass filter that removes
most of the flow velocities. This results in a
|Velocity and strain rate imaging of the same (normal) left ventricle. The colour sector can bee seen to be equal to the B-mode sector.Velocity is red in systole when all parts of the heart muscle moves toward the probe (apex) and blue in diastole. The changes are too quick to observe entirely, to make full use of the information the image has to be stopped and scrolled.||Curved anatomical M-mode (CAMM). A line is drawn from apex to base, and velocity data over time are sampled along the line and displayed in colour along a straight line. The numbers on the curve and the M-mode are included for reference and corresponds to the numbers on the B-mode image. This example shows the septum from the apex to base along one axis, and one heart cycle along the other, in a two - dimensional space - time plot. S: systole, E: early relaxation, A: atrail contraction.|
The information coded in the colour images, is
fundamentally numerical for all varieties of colour Doppler. Thus, the
velocity time traces can be extracted fom any point in the
image as shown below.
Extracted velocity curves from three points in the septum. As in colour flow, the M-mode gives the depth - time - direction information, while the curves give the quantitative information.Thus: 2D images show the whole sector image at one point in time, velocity or strain (rate) traces shows the whole time sequence (f.i. a heart cycle) at one point in space, while CAMM shows the time sequence as well as the length of the line, but only semi quantitative motion / deformation information.
|As the apex is stationary, while the base moves toward the apex in systole, away from the apex in diastole, the ventricle has to show differential motion, between zero at the apex and maximum at the base.||As motion decreases from apex to base, velocities has to as well. This is seen very well in this plot of pwTissue Doppler recordings showing decreasing velocities toward apex. Thus, there is a velocity gradient from apex to base|
|Longitudinal velocity gradient, where v1 and v2 are two different velocities measured at points 1 and 2, and L the length of the segment between those points.||Spatial distribution of systolic velocities as extracted by autocorrelation. This kind of plot is caled a V-plot (247). It shows velocities as near straight lines, and thus, a constant velocity gradient, which is the slope of the curve from base to apex. .|
|The strain rate can be
described by the instantaneous velocity
gradient, in this case between two material
points, but divided by the instantaneous
distance between them. In this description, it
is the relation to the instantaneous length,
that is the clue to the Eulerian reference.
||train rate is calculated as
the velocity gradient between two spatial
points. As there is deformation, new material
points will move into the two spatial points at
each point in time. Thus, the strain that
results from integrating the velocity gradient,
is the Eulerian strain. In
this view, the relation to the spatial, rather
than material reference is very evident.
|Recordings from basal
septal mitral ring in a subject without
substantial clutter. Spectral Doppler shows the
dispersion of velocities, although this is
probably an effect of bandwidth.
The colour Doppler recording is superposed and
aligned with both vertical and horizontal scale.
In this instance can be seen to give values
close to the middle of the spectrum (modal
|Image from another subject in the study shown above (266). In this subjech there is some clutter from reverberations, as seen by the band in systole close to the zero line. In this case the peak velocity by autocorrelation is lower than the modal velocity of the main spectral band, which still was the one closest to the RF M-mode reference. (Figure courtesy of Svein Arne Aase, modified from (266))||Clutter filtering
may reduce the problem, as seen here. There
is aa band of clutter close to zero
velocities, but as seen here, the spectral
modality makes it very easy to separate the
true and clutter velocities. However, the
clutter affects the autocorrelation velocity
(red line), giving lower velocities, but
with clutter filter this effect is removed
(red line) , and the peak value is
substantially higher. Image modified from (268).
|Shadowy reverberations covering the anterior wall in this 2-chamber image. It is differentiated from drop out, as we can se a "fog" of structures covering the anterior wall. The structures are stationary. On the other hand, this is not distinct reverberations shadows, but incoherent clutter.||Recordings from the basal anterior ring in a subject with substantial clutter. The tyrue signal is clearly visible as a normal curve, and can be seen separately from the clutter band, which is the horizontal spectral band along the baseline. The colour Doppler recording is superposed and aligned with both vertical and horizontal scale. The colour Doppler, using the autocorrelation algorithm, results in mean velocities that incorporate both signal and clutter, giving a severe underestimation of velocities.|
|Basal spectral tissue
Doppler curve in the anterior wall. Peak
systolic velocity ca 8.5 cm/s.
||Midwall spectral tissue
Doppler curve in the anterior wall. Peak
systolic velocity ca 6.5 cm/s.
||Apcal spectral tissue
Doppler curve in the anterior wall. Peak
systolic velocity ca 5 cm/s.
|IColour M-mode from the
image shown above. The curved M-mode shows a
fairly homogenous and normal signal in the
inferior wall (top), but more or less random
noise in the anterior wall (bottom), where
the noise is seen as vertical stripes of
||Velocity curves from
the anterior wall, showing noise, and not
much more, but at low level (within ± 0.3
|The principle of the effect of clutter. V-plot with clutter showing how the mean velocities are reduced, compared to the mormal expected values (red line). But in additions the variation of the velocity estimates from pixel to pixel is much higher, resulting in increased noise, but with reduced mean values.||Combined pulsed Doppler
(yellow bands) and colour Doppler green
Aligned horizontally and vertically. The
noise level can be seen to be b´very low,
compared to the peak velocities shown in the
pulsed Doppler recording. The clutter is the
horizontal band around the baseline, and the
width of the spectrum in this case is the
the septum of a normal ventricle. The colour
bands are stationary
||In pulsed wave
tissue Doppler, the clutter will show up as
a high amplitude band of zero velocities,
but the true velocity curves can be seen as
entirely separate from the clutter line, and
thus peak velocities can still be measured.
Ultra high frame rate tissue Doppler is done by combining more principles:
By this method, using two broad, unfocussed (planar) beams,
each covering one wall, as well as 16 MLA and sparse
interleaved B-mode imaging, it has proved possible to
increase the TDI frame rate substatially (172, 268). it has been
possible to increase frame rate to 1200 FPS in 2D
Few beams give high frame rate. Image courtesy of Svein Arne Aase, modified from (172).
Tissue Doppler is still limited to one velocity
direction only. This means that the term
"3-dimensional" refers to a three dimensional distribution
of tissue velocities only, not velocity vectors
in a three dimensional coordinate system. However,
data from the whole ventricle can be put together on a
surface model of the left ventricle.
|3D tissue Doppler is
basically a grid of numerical values on
a ventrricular surface.
Doppler, showing three standard planes, with
the assumption that the angle between them
is 60°, the rest of the data between the
planes are than interpolated. This gives a
circumferential resolution of 60°.
As tissue data as about acquiring (and displaying,
f.i. by colour) numerical data, the method do not have
the same limitation as 3D B-mode. One method is to
combine information from three standard planes, and
then interpolating the data between the planes by for
instance spline. The method has been explained elsewhere.
This has been done both by combining sequentially
acquired standard planes. It could also be done as a
simultaneous triplane acquisition, but at the cost of
a substantially reduced frame rate. Thus, freehand
scanning has been preferred.
This version of three dimensional tissue Doppler may
be used for display, but also for area measurement, as
the data are distributed over a representation of the
(approximate) real ventricular area.
With the Ultra
high frame rate tissue Doppler method, it is
also possible to acquire three dimensional tissue
Doppler in real time.
Using a 3D matrix probe, sending an array of 3x3
broad unfocussed or planar beams, and using a 4x4
matrix of receive beams for each transmit beam, giving
a 16 (or 4x4) MLA, we have been able to achieve a
volume rate of about 500 VPS (280),
i.e. Ultra high frame rate 3D tissue Doppler.
|Principle of beam
formation, showing a matrix of 3x3
wide transmit beams (brown circles) and
for each beam an array of 4x4 receive beams,
i.e. a 16 MLA. (After 280).
||Distribution of the
transmit beams in relation to a cross
section of the ventricle, endocardial
and epicardial surfaces marked with black
lines and arrows. the energu distribution of
the beams is shown by the colour hue. The
transverse plane shown to the right is
marked by the thick line. (After 280).
||Distribution of the transmit beams in an apical plane, the level of the cross section to the left is marked by the thick line. As evident from the illustration, the transmit beams do not cover the whole sector, but will cover most of the walls. (After 280).|
By this method, t is possible to achieve a high
circumferential resolution through the MLA technique
at the same time as a high temporal resolution. The
result can be displaued as a 3D figure, as with
reconstructed 3D, and both curved M-modes and
tivelocity curves can be extracted from this matrix:
|3D surface with tissue velocity display. The ring represents a line for extraction of the curved M-mode shown top, left. (After 280).||Data display from the
3D velocity figure to the right. Top: curved
M-mode, showing the time variation of
apically directed velocities in a ring
around the mid
velocity curves from the basolateral part,
red from UFR 3D TVI, extracted from the 3D
data to the left, blue velocity from the
same point in the same subject, but acquired
bt conventional colur TDI (i.e. a different
(CAMM) of tissue velocities in fundamental
(above) and harmonic (below) imaging. Slight
aliasing can be seen in native
imaging in the e' wave at the base. In harmonic imaging, there is aliasing both in the S' wave, and the e' wave (double).
Doppler curved M-mode in harmonic imaging,
velocity plot (above), strain rate (below).
As can be seen there is heavy aliasing in
velocity plot, but no aliasing in strain rate imaging.
Editor: Asbjorn Støylen, Contact address: firstname.lastname@example.org