||Velocity ( m/s)
|Average soft tissue
|Metal||3000 - 6000|
|Basically, a reflected
ultrasound pulse is a waveform. However, storing the
full waveform, called RF data, is demanding in terms of
storage, as each point on the curve would have to be
represented in some way or other. However, if the full
RF data are stored, the amplitude and frequency data
could both be calculated in post processing.
||The pulse has a certain
amplitude. Just storing the amplitude is much les
demanding (corresponding more or less to one number per
pulse). This is the only data that are used in grey
scale imaging, where the amplitude is displayed as
brightness of the point correspåonding to the scatterer
as in B-mode and M-mode.
||However, the reflected
ultrasound pulse has a frequency (or a spectrum of
frequencies), and this can be represented as a numerical
value per image pixel as well, as described in Doppler imaging. Still, the
amount of data is far less than the RF data.
|Fig. 3a. The ultrasound image is built up as a line of echoes based on the time lag and amplitude of the reflected signals.||
3b. The reflected signals can be displayed in three different modes. A-mode (Amplitude) shows the depth and the reflected energy from each scatterer. B-mode (Brightness) shows the energy or signal amplitude as the brightness (in this case the higher energy is shown darker, against a light background) of the point. The bottom scatterer is moving. If the depth is shown in a time plot, the motion is seen as a curve, (and horizontal lines for the non moving scatterers) in a M-mode plot (Motion).
|Effect of size and direction
of the reflecting surface. The two images on the
left shows a perfect reflecting surface. Most of the
energy (but not all, as the wavefront is not flat), will
reflect back to the transducer resulting in a high
amplitude echo, when the surface is perpendicular to the
ultrasound beam. On the other hand, if this surface is
tilted 45º, almost all energy will be reflected away
from the surface, resulting in a very low
amplitude return echo to the probe. The next two
images shows a scatterer with a more curved surface,
resulting in more energy being spread out in different
directions, this will give a lower amplitude signal back
to the probe, but may reflect more energy back
towards the probe if it is tilted, as for instance when
the heart contracts, walls changing direction. Finally,
to the left, a totally irregylar surface will reflect
the sound in all directioons, butt very little net
reflectionstoward the probe.
|The effect of the direction of the reflecting surface in a long axix image of the left ventricle. The echo resulting from the septum-blood interface (arrows) is far stronger in the regions where the surfaces are perpendicular to the ultrasound beamns (blue arrows), compared to the region between where the surface is slanted compared to the ultrasound beams.||Cyclic variations in the amplitude in reflected ultrasound (integrated backscatter) with heart cycle. This reflect the variations in reflexivity, but not myocardial density, as the myocardium is incompressible. Thus, most of the amplitude variations must be due to changes in fibre directions.|
|Attenuation. Imaging of a
homogeneous tissue, f.i. liver will change the apparent
density behind structures with different attenuation.
Behind a structure with high reflexivity (e.g. a calcification), there will be high attenuation, (white; left). Hence, the sector behind receives less energy, and appears less dense (darker), the area behind may even be a full shadow.
Behind a strcture with low reflexivity (e.g. a fluid) there is little attenuation (black; right), the tissue receives more energy and appears denser (brighter - "colouring") than the surrounding tissue.
|Liver with a gallbladder in
front, containing gallstones. The gallstones are dense,
with a shadow behind. The rest of the gallbladdeer is
fluid filled, thus the sector behind the fluid appears
denser than the neighbouring tissue due to "colouring".
|Uncompensated image, showing
decreasing signal intensity (and, hence, visibility)
with depth, due to attenuation.
||Increasing over all gain, will
increase the amplitude of the
signal, and the structures at the
bottom of the sector becomes more visible. But the gain
in the top of the sector are also increased, including
the cavity noise, thus decreasing contast in this part
of the image.
Basically, each slider controls gain selectively at a
||In older models, the
TGC should be set manually to achieve a balanced image:
|Present models, however, have automatic TGC. Thus the default control setting should be neutral to achieve a balanced picture:||Using manual
setting by old habit will result in a double
compensation, with too much gain in the bottom, too
little in the top:
|Image with default gain, reject and compress settings||Principle of gain, reject and compress. All curves display brightness of the display in relation to the amplitude of the rejected signal. An ordinary gain curve is shown in black, using a linear brightness scale, displays the full range of amplitudes. Increasing gain (red curve), will increase all signals, including the weakest, as in the noise. The disadvantage, in addition to increasing noise, is that the strongest signals will be saturated, so details may disappear. Compress is shown as the blue curve. This results in a steeper brightness curve, resulting in less brightness of the weakest echoes, and more brightness of the strongest. Thus, weak echoes may disappear together with background noise, while strong echoes will be saturated, resulting in loss of detail. Finally reject is shown by the light grey zone, siply displaying all signals below a certain amplitude as black. (The black brightnes curve drops abruptly to zero at the reject limit (dark grey line). A combination of high gain and reject will give an effect fairly similar to the compress function.|
|Same image with high gain
(top) showing increased density of the endocardium, but
loss of detail due to brightness saturation and a
corresponding increase in cavity noise and low gain
(bottom), showing reduction in cavity noise, but loss of
detail (see endocardium in lateral wall).
||Same image with increased
reject (top) showing reduction in cavity noise, but also
with slight loss of detail (endocardium in lateral wall)
and compress function (bottom) with less detail in the
myocardium due to increased brightness.
|Second harmonic (1.7/3.5 MHz)
left and fundmental (3.5 MHz) right
images of LV septum, showing how the echo from the
blood/septum interface (arrows) is thicker in harmonic
imaging, due to the reduction in frequency. Observe,
however, how cavity noise is much reduced in harmonic
imaging, resulting in a far more favorable
||The thickness of the surtface
echoes is dependent n the pulse length, and thus also on
the frequency. This picture of the septum
illustrates how the leading-to-leading ASE convention
shown in red, eliminates the pulse length in measurement
(as the echo blooms in both directions), while the Penn
convention will result in increasing overestimation of
the thickness by increasing pulse length as it
incorporates the interface on both sides.
|A pulse is sent out, ultrasound is reflected, and the B-mode line is built up from the reflected signals.||Linear array.
|By making the ultrasound beam
sweep over a sector, the image can be made to build up an
image, consisting of multiple B-mode lines.
||c. In principle, the image is built up line by line, by emitting the pulse, waiting for the reflected echoes before tilting the beam and emitting the next pulse. Resulting in an image being built up with a whole frame taking the time for emitting the total number of pulses corresponding to the total number of lines in the image.|
|Fig. 7A. Mechanical
transducer. The sector is formed by rotating a single
transducer or array of transducers mechanically, firing
one pulse in each direction and then waiting for the
return pulse before rotating the transducer one step. In
this beam there is electronic focusing as well, by an
transducer in a phased array. By stimulating the
transducers in a rapid sequence , the ultrasound will be
sent out in an interference pattern. According to Huygens
principle, the wavefront will behave as a single beam,
thus the beam is formed by all transducers in the array,
and the direction is determined by the time sequence of
the pulses sent to the array. Thus, the beam can be
electronically steeredand will then sweep stepwise over
the sector in the same way as the mechanical transducer in
A, sending a beam in one direction at a time.
|Dynamic focusing. The
same principle of phase steering can be applied to make a
concave wavefront, resulting in focusing of the beam
with its narrowest part a distance from the probe.
Combining the steering in B and C will result in a
focussed beam that sweeps across the sector, as in the
moving image above.
||Resulting Ultrasound beam as shown by a computer simulation, focusing due to the concave wavefront created by the dynamic focusing. The wavelength is exaggerated for illustration purposes. Image Courtesy of Hans Torp.|
Annular focusing in all directions both in plane and transverse to the plane.
Linear focusing in the imaging plane only.
|The lateral resolution
of a beam is dependent on the focal depth, the wavelength
and probe diameter (aperture) of the ultrasound
(Reproduced from Hans Torp by permission)
|Two points in a sector that is to be scanned.||The ultrasound scan will smear the points out according to the lateral resolution in each beam.|
|Thus a small scatterer will
appear to be "smeared out", and the apparent size in the
image is determined by the beam width and pulse length. As the pulse length is less
than the beam width, the object will
be "smeared out" most in the lateral direction.
|Two scatterers at the same
depth, separated laterally by less than the beam width,
will appear as one.
||Two scatterers at different depths will appear
separate if separated by more than the pulse length.
||But, if separated both
laterally and in depth, they will appear as being in the
same line, if lateral separation is within the beam.
|Two different lateral resolutions, the speckles can
be seen to be "smeared". In this case the loss of
resolution in the right image is due to lower line density
. By rights the image should appear as split in different
lines as indicated in the middle, as each beam is
separated, line density being less than optimal relative
to the beam width. Instead the image is interpolated
beween lines. This reduction in line density is done to
achieve a higher frame rate, as illustrated below.
Beam width. Speckles (true
speckles: black) are smeared out across the whole beam
width ( Apparent speckles dark grey, top). This means that
with this beam width the speckles from to different layers
cannot be differentiated, and layer specific motion cannot
Line density. Only the lines
in the ultrasound beams (black) are detected, and can be
tracked, beams between lines are not detected or tracked.
The spaces between lines cannot be seen in the final image
due to image lateral smoothing.
of lines in the depth due to the sector image will both
increase beam width and decrease line density in the far
field. this may result in the line density and width being
adequate (in this example for two layer tracking) in the
near field, but inadequate in the far field, situation
there being analoguous to A.
Focussing. The beams being focussed
at a certain depth mau mean that line density may be
inadequate at the focus depth. Thus speckles in some
layers may be missed. IN general, the default setting will
usually give the best line density at the focus depth, so
unless frame rate is increased, this problem may be minor.
Howewever, line density will decrease ifalso if sector
width is increased, there is a given number of lines for a
given frame rate and depth. In any case, in the far field,
the beams will be broader, and the beam width will be more
like A and C.
Focussing may even result in beams overlapping int the far
field. A speckle in the overlap zone may be smeared out
across two beams.
|As the depth of
the sector determines the time before next pulse can be
sent out, higher depth results in longer time for building
each line, and thus longer time for building the sector
from a given number of lines, i.e. lower frame rate.
||Thus reducing the
desired depth of the sector results in shorter time
between pulses, and thus shorter time for building each
line, shorter time for building the same number of lines,
i.e. higher frame rate. In this case, the depth has been
halved, and the time for building a line is also halved.
case, in the image to the left, the depth has been halved,
reducing the time for building each line to half, thus
also halving the time for building the full sector,
doubling the frame rate.
sector width, but maintaining the line
density, gives unchanged lateral
resolution but higher frame rate, at the
cost of field of view.
||c. Reducing the line density instead and maintaining sector width, results in lower number of lines, i.e. lateral resolution, and gives the same increase in frame rate.|
3D ultrasound increases complexity a lot, resulting in a new set
of additional challenges.
The number of crystals need to be increased, typically from
between 64 and 128 to between 2000 and 3000. However, the probe
footprint still needs to be no bigger than being able to fit
between the ribs. And the aperture size
must still be adequate for image resolution.
The number of data channels increases also by the square, from 64
to 642 = 4096. This means that the transmission
capacity of the probe connector needs to be substatially
increased, and some processing has to take place in the probe
itself to reduce number of transmission channels. .
The number of lines also increase by the square of the number for
2D, given the same line density, meaning that each plane
shall have the same number of lines, and a full volume then shall
be n=built by the same number of planes. This means that given 64
lines per plane, the number of planes should be 64, which means a
total of 64 x 64 = 4096 lines. This means that the frame rate
(usually termed the "volume rate" in 3D imaging), will be 0.19 ms
x 4096 = 778 ms, or about 0.8 secs. Meaning about 1 volume per
heartbeat for a heart rate of 75. This is illustrated below.
|Building a 2D sector with lines.
(Even though each line (and the sector) has a
definite thickness, this is usually not considered in 2D
imaging, except in beamforming for image quality.
||Building a 3D volume. Each plane
has the same number of lines as in the 2D sector to the
left, and takes as long to build. The number of planes
equals the number of lines in each plane. Here is shown
only the building of the first plane (compare with
left), but the time spent on each of the following
planes are in proportoion. The time for a full volume is
then equal to the square of the number of lines in each
|Surface rendering of a 3D volume.
The image shows a cut through the LV between base and
apex, looking down toward the base, the papillary
muscles and mitral valve can be seen. The
illustration also shows that the temporal resolution is
to low to actually show the opening of the mitral valve
during trial systole, only a slight flicker can be seen
at end diastole.
||The same volume, now displayed as
a series of short axis slices from apex (top
left) to base (bottom right). A
slight stiching artefact (spatial discontinuity) can be
seen in the anterior wall (top of each slice).
|3D acquisition of a ventricle with
inferior infarct. The display is shown as the apical
planes to the left, and nine cross sectional planes to
the right, going from the apex (top left) to the base
(bottom right - reading order). The infarct can be seen
as inferoseptal a - to dyskinesia in the basal sections.
The image also illustrates that the software can be
enabled to track the planes, thus eliminating out of
plane artefacts when evaluating wall motion. Note
that there is drop outs that cannot be eliminated by
moving the imaging plane, in the anterior wall. Image
courtesy of Dr. A. Thorstensen .
||Styitching artefacts. In this
volume, reconstructed from four heartbeats, i.e. four
sub volumes, there are stitching artefacts between each
of the sub volumes. This is due to motion of
either the heart (f.i.) because of respiration, or of
the probe. In the inferior wall (bottom of each slice),
the spatial discontinuity is very evident, less
so at the other stiches,, but in the anterior wall there
is a discontinuity that illudes a dyssynergy.
|Illustration of effects of
shadows on an ultrasound beam. Left: no shadow. Middle, a
shadow distant from the beam (e.g. a calcification or the
lung seen at a distance), resulting in a shadow with no
image below it. Left a shadow close to the transducer
surface (e.g. lung edge or rib) will result in a narrow
beam (reduced apparent aperture) which will not be seen as
a shadow in the picture, but rather a reduced lateral
resolution. (Original simulation image to the left
courtesy of Hans Torp, modifications by me.) The effect of
the depth of the origin of the shadows in the images is
shown below, indicated by the green arrows.
|Left, shadow originating at a depth of ca 3 cm, as can seen by the visible structures of the chest wall closer to the probe. The shadow is probably due to the edge of the lung. Right; a small repositioning of the probe solves the problem.||Left shadow originating close to the chest wall (< 1 cm), probably the edge of a costa. It can be seen as a shadow, but the main effect is loss of lateral resolution in the shadow, and again a small repositioning of the probe solves the problem as seen to the right.|
|more pronounced drop out of the anterior wall in this 2-chamber view due to a lung shadow distant from the probe. However, the lateral resolution may be seen to be reduced at the basal part of the border between the picture and the shadow.||Reduced lateral resolution due to costal shadow. The effects of both costae and shadows will vary, according to the distance from the probe. In this case the patient was extremely thin, thus there was virtually no distance between the probe and the costa. In this case, no localised shadow can be seen, the costa was to the left in the image, where resolution is poorest.|
|If the near shadow is in the centre of the probe, the result may be that the beam is split in two, resulting in two apparent apertures. The effect on the image is shown below.||Split image due to two virtual appertures, caused by a near shadow in the middle of the probe footprint.|
|By first glance, this image seems to have OK image quality. The endocardium seems well defined around most of the wall. However, the lateral wall shows good definition mostly in the latter half of the cycle. And shadowy reverberations can be seen in both base and midwall.|
|Simulated beam with focusing,
showing interference pattern dispersing some of the beam
to the sides. (image courtesy of Hans Torp).
||Side lobes from a single focussed ultrasound beam. These side lobes will also generate echoes from a scatterer hit by the ultrasound energy in the side lobes, i.e. outside the main beam.|
|Side lobes originating
from the fusion line of the aortic cusps, seen to
extend into both the LV cavity and the aortic root
||As opposed to reverberations, the side
lobes moves with the structure, and may change
with time (in this case the echo intensity of the
fusion line decreases as the valve opens, and thus
the intensity of the side lobes too) .
In M-mode imaging, the angle between the motion direction
and the ultrasound beam may lead to a distortions as
|Apical position of
the probe. The two orthogonal planes can be seen
to bisect the apex.
erroneous placement of the probe. The two
orthogonal planes can be seen to bisect the
wall, not the apex.
In one plane this will not be evident, as the intersecting wall still shows an ellipsoid shape.
|Fourier analysis of the resulting signal in native frequency (left) and second harmonic mode (left) shows that the native signal contains much more energy at all depth, while the harmonic signal contains most of the energy at a certain depth, in this case at the level of the septum, showing a much better signal-to-noise ratio.(image courtesy of Hans Torp).||Energy distribution of the signal from cavity (lower curve) and septum (upper curve), showing the same phenomenon as the middle picture. The difference between cavity signal (being mostly clutter) and tissue is small in the native frequency domain (1.7 MHz), but there is little clutter at the harmonic frequency (3.4 MHz). Thus, filtering the native signal will reduce clutter, as shown below. (image courtesy of Hans Torp).|
|The same image
in harmonic (left) and fundamental (right) mode, showing
the improved signal-to-noise ratio in harmonic
imaging, especially in rducing noise from the
cavity. (Thanks to Eirik Nestaas for
correcting my left-right confusion in this image
Stationary reverberation in harmonic (left) and fundamental (right) imaging, showing the effect of harmonic imaging on clutter.
pattern. Here is simulated two wave sources or
scatterers at the far field (white points). The
emitted or reflected waves are seen to generate a
speckle pattern (oval dots) as the amplitude is
increased where wave crests cross each other, while
the waves are neutralised where a wave crest crosses a
though. This can be seen by throwing two stones
simultaneously in still water . The speckle pattern
can be seen in front of the scatterers, towards the
interference pattern. This is generated by more
scatterers somewhat randomly distributed. The speckle
pattern is thus random too. Again there may be a
considerable distance between the speckles and the
scatterers generating the pattern.
The speckle pattern can be used to track myocardial motion due
to two facts about the speckle pattern.
|An M-mode along the septum demonstrates how the speckles is shown as motion curves. It is evident that many speckles are only visible during part of the heart cycle, but if the speckle pattern is compared from frame to frame, the changes will be small. The grainy texture of the lines is due to the limited frame rate as the M-mode on the right is reconstructed from the 2D image at the left. When the speckle pattern is followed by an M-mode in the wall, the alternating bright and dark points are seen as alternating bright and dark lines. The lines remaining to a large degree unbroken, shows the pattern to be relatively stable, the speckles moving along with the true myocardial motion, and thus myocardial motion can be tracked by the speckles.|
tracking. Real time M-mode demonstrates how the
speckle pattern follows the myocardial motion.
(Remark how this image is not grainy, due to the
high frame rate of real time M-mode).
kernel in the myocardium will define a speckle
pattern within (red). Within a defined search area
( blue), the new position of the kernel in the
next frame (green) can be recognised by finding
the same speckle pattern in a new position. The
movement of the kernel (thick blue arrow)
can then be measured.
tracking search algorithm. The kernel is defined
in the original frame at t=0 (red square). In the
next frame, at t=t, the algorithm defines a search
area (white square), and the search is conducted
in all directions for the matching kernel.
|LOngitudinal speckle tracking in apical 4 chamber view. The resulting tracking of the kernels shown in motion. As can be seen, with a drop out apicolateral, this ROI tracks less than perfect, giving too low strain both in LA and MA segments.||Speckle tracking can be applied crosswise. In this parasternal long axis view, the myocardial motion is tracked both in axial and transverse (longitudinal) direction. It is evident that the tracking is far poorer in the inferior wall, due to the poor lateral resolution at greater depth.|
(1803 - 1853)
|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 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 diasplacement of the waveform of pulse 2 relative to pulse 1, is 2d.||By sampling the two pulses simultaneously at two timepoints, as shown in the previous illustration, the phase of each pulse can be determined. The phase analysis of the relative positions of all four points is done by autocorrelation, a quick (and dirty?) method that allows online computation.|
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
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:
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, as described above.
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.
|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).
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 masured.
patient with a stationary reverberation in
the basal lateral wall (left), with the
sampling point for both pw and colour
Doppler is indicated. . The pw
spectral Doppler (middle) can be seen to be
broadened, comprising all velocities between
peak and zero. This is due to stationary
pixels due to the artifact, but a sufficient
number of pixels with normal velocity (and a
number of pixels in between).
Averaging the spectrum will result in too
low values, as the lowest values are due to
artifact. The autocorrelation values
(right) are likewise average values, thus
By reducing the number of sender beams and increasing
the MLA factor, it has proved possible to increase the
TDI frame rate substatially (172, 268). Using two
broad, unfocussed (planar) beams, each covering one
wall, as well as 16 MLA and sparse interleaved B-mode
imaging, it has been possible to increase frame rate
to 1200 FPS in 2D imaging. this is the extreme example
of exchanging spatial for temporal resolution.
measurement of velocities in the whole sector, enables
the measurement of velocity differences. This is the
prerequisite for measuring the velocity gradient or
strain rate by tissue Doppler.
Few beams give high frame rate. Image courtesy of Svein Arne Aase, modified from (172).
|Kernel displacement Displacement curve obtained by tracking through a whole heart cycle shown to the right, derived velocity curve shown below.||From two different kernels, the relative displacement and hence, strain as well as strain rate can be derived.|
|With kernels at all segmental borders, segmental motion and deformation can be tracked, as shown to the right.||And the length variations of the segments between the kernels kan be followed through the heart cycle.|
|Strain rate is displayeed as yellow to orange in systole (shortening) and cyan to blue in the two diastolic phases early and late filling (lengthening), but green in periods of no deformation. The changes are too quick to observe entirely, to make full use of the information the image has to be stopped and scrolled.||Combined
strain rate image with one systolic and
one diastolic frame displaued in B.mode,
below the CAMM from the septum and below
that the strain rate (yellow) and strain
curves from one point in septum.
| Velocity traces may be
considered the raw data. All other
modalities are integrated or derived from
this. Here normal function is shown at
normal basal velocity (6 cm/s), as well as
normal decrease from base to apex.
This decrease is evident by visual
assessment alone, as the distance between
the curves. The distance between the
curves then is a direct visual assessment
of strain rate. Strain rate curves
can be obtained by spatial derivation of
curves obtained by integration of
velocity. Temporal integration reduces
velocity to motion. In principle, strain
could be obtained by spatial derivation of
displacement, although not used:
of strain rate
strain rate curves are the spatial
derivative of velocity, showing the time
course of the velocity gradient. This is
equivalent to the local deformation rate.
The curves, however, are noisy, shown in
this unfiltered image, the increase in
random noise is a consequence of
is regional deformation. This can be
obtained both by spatial
derivation of strain (not
used at present) and
temporal integration of strain rate
(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