From the Departments of Surgery and Medical Physics (D.H.E.), Faculty of
Medicine, University of Leicester (England).
Correspondence to Prof David H. Evans, Department of Medical Physics, Sandringham Building, Leicester Royal Infirmary, Infirmary Square, Leicester, England LE1 5WW.
Methods—A pure source of gaseous and particulate emboli was
obtained from in vitro and in vivo studies, respectively, and
recorded onto digital audiotape for off-line analysis. In
total, 100 gaseous emboli and 215 particulate emboli were
analyzed to measure four embolic parameters,
namely, embolic duration, embolic velocity, relative signal intensity
increase (measured embolic power [MEP]), and SVL of the embolic
signal (=DurationxVelocity). Receiver operator characteristic
analysis was used to assess the optimum threshold for each
parameter to differentiate between particulate and gaseous
emboli, and levels of sensitivity and specificity were calculated.
Results—Embolic duration and velocity produced the poorest levels
of sensitivity and specificity compared with the MEP and SVL
parameters. The optimum thresholds for embolic duration and
velocity were 35 ms and 1 m/s, respectively, which produced a
sensitivity (specificity) of 85.1% (87%) and 87% (67%),
respectively. The optimum MEP and SVL thresholds were 30 dB and
12.8 mm, respectively, which produced a sensitivity (specificity)
of 86.5% (95%) and 93% (97%), respectively. The SVL and MEP
parameters were compared statistically (
Conclusions—SVL is the best parameter for
differentiating between gaseous and particulate emboli but needs to be
calculated with the use of a high-temporal-resolution spectral
analyzer to measure embolic duration and velocity.
Many studies have been published in scientific and clinical journals
concerning the differentiation between solid and gaseous matter.
Essentially there are two different embolic parameters that
previous researchers have used when attempting to classify or size
emboli: (1) embolic signal duration and (2) the ratio of embolic signal
amplitude to background blood signal amplitude (ratio of embolus to
blood [MEP]). In addition, embolic velocity has also been found to
correlate with the MEP. In 1991 and 1992, Russell et
al2 3 4 published various works concerning the
composition and size of emboli. A series of experiments was performed
by injecting emboli of known compositions and sizes into rabbits.
Doppler recordings were made from the descending aorta,
which has a diameter similar to that of the human middle cerebral
artery. All 125 injected emboli were detected with TCD and produced MEP
values greater than 15 dB, with air and fat emboli producing higher MEP
values than platelet or atheromatous emboli. A
positive correlation was found between MEP and embolic size for
platelets, blood clots, and atheroma. Markus and
Brown5 also performed an in vitro study using
different compositions of solid emboli injected into a flow rig model
with intervening skull bone between the TCD transducer and the tubing.
A positive correlation was found between MEP and embolic size; for
emboli of the same composition, a positive correlation was also found
between embolic size and embolic signal duration. A similar correlation
was found when different sizes and compositions of emboli were injected
into an in vivo sheep model.6 Grosset et
al7 investigated the difference between the
embolic signal characteristics from cardiac and carotid origins using
TCD and found that embolic signals of cardiac origin produced greater
MEP values than those of carotid origin, concluding that emboli of
cardiac origin were either greater in size or of different composition
than carotid emboli. It was also noted that the embolic signals with
higher MEP values had significantly longer embolic signal durations.
Georgiadis et al8 injected various compositions
and sizes of emboli into an in vitro flow model and found a positive
correlation between MEP and embolic size, with MEP values being
comparable to those seen in vivo. Droste et al9
found a distinct inverse relationship between embolic velocity and
embolic signal duration in both in vivo and in vitro studies for all
sizes and compositions of emboli. The reason given for this
relationship was that the product of velocity and duration is equal
to the length of the sample volume, which was assumed constant for all
embolic signals. A slight positive correlation was also found between
embolic velocity and MEP in vivo but was not corroborated in vitro.
Bunegin et al10 described a method for estimating
the volume of air in the middle cerebral artery using TCD. This was
achieved by injecting controlled volumes of air (0.5 to 40 µL) into
the carotid arteries of monkeys. A linear correlation was found between
MEP and air volume; however, all the embolic signals produced amplitude
saturation of the TCD system, and therefore Bunegin et al actually
correlated the duration of the saturated embolic signal with
the volume of air.
The common thread that has hindered all of the above studies in their
analysis of TCD-detected emboli is that of an inadequate
dynamic range and poor time resolution of conventional spectral
analyzers. To overcome these two limitations, TCD systems need
to have a dynamic range of at least 60 dB and a spectral
analyzer with a time resolution of at least 1 ms. Although TCD
manufacturers have begun to address the problem of dynamic range, the
inadequate time resolution, inherent with FFT spectral
analyzers, still remains. One alternative to an FFT is the
Wigner distribution function, which is simply an alternative processing
algorithm with the advantage of a high time resolution (0.08 ms)
without the disadvantage of sacrificing frequency
resolution.11 The Wigner analyzer is able
to measure embolic duration and velocity far more accurately than
conventional FFT analyzers, enabling the SVL, which is the
product of duration and velocity, to be calculated with up to 250
times greater accuracy. The SVL has been proposed as a
parameter to characterize emboli, the underlying hypothesis
being that air reflects more of the incident ultrasound and will
therefore be detected over a greater SVL than particulate emboli.
Although this is effectively an alternative method of measuring the
MEP, it was thought to be better because of its ability to smooth out
any local "hot spots" that may be present in the amplitude of
the embolic signal as a result of distortion and attenuation of the
ultrasound beam by the skull and intervening tissues. The SVL
parameter was shown to be a good predictor of embolic
composition but has not previously been compared with the more
conventional methods described above.
The aim of this study was to calculate the SVL measurements of a
known group of both air and particulate embolic signals and to compare
this method with three other embolic parameters, namely
embolic velocity, embolic duration, and MEP.
A pure source of gaseous bubbles was acquired in vitro with the use of
a pulsatile flow rig model. Silicon tubing was used except at the
insonation site, where a 30-cm length of 3-mm internal diameter
heat-shrunk tubing was inserted to provide a more realistic model of
arterial wall. Fresh human whole blood was used as the flow
medium in the flow rig and was left to circulate for 2 to 3 hours to
expel any small air bubbles before the experiment was started. Once the
Doppler signal was free of any inadvertently
introduced air, controlled volumes of air (1 µL) were introduced
through a rubber-sealed side port with a 50-µL Hamilton syringe and
repeating dispenser that delivered 2% of the syringe capacity at the
press of a button. The emboli were injected at a distance of 20 cm
proximal to the insonation site, a distance similar to that between the
carotid bifurcation and the middle cerebral artery. Our aim was to
generate a "worse case scenario" by analyzing the smallest volume
of air possible to inject (1 µL), since larger volumes generate
larger MEP and SVL values, thus increasing the separation between air
and particulate data. The angle of insonation of the transducer with
respect to the axial direction of flow was set to 0°. One hundred air
emboli were injected into the system, and the backscattered Doppler
signal was recorded onto digital audiotape for off-line
analysis.
The duration of each embolic signal was measured with the Wigner
analyzer in its fine-resolution mode to the nearest 80 µs.
The embolic signal duration was defined as the period of time that
the amplitude of the backscattered signal was 10 dB or
greater than the background blood signal. The Wigner analyzer
displays the instantaneous mean velocity, and the mean embolic velocity
was defined as the point at which there is maximum deviation (within
the embolic signal duration) from the mean blood velocity (outside the
embolic duration). The ratio of embolus to blood (MEP) was calculated
for each embolic signal in this study with the use of the Wigner
analyzer in its fine-resolution mode. The backscattered power
of the background Doppler blood signal was measured over one
complete cardiac cycle immediately preceding the cardiac cycle
containing the embolic signal. This background signal was also taken as
the reference when the 10-dB threshold limit for calculating embolic
duration was defined. The peak value of the instantaneous power within
the embolic signal was then taken as the embolic signal power. The MEP
was then calculated as follows:
Statistical Analysis
To choose the optimum threshold for each embolic parameter,
an ROC curve was plotted. This was achieved by plotting a graph of
sensitivity against the false-positive rate for a series of different
threshold values. Four ROC curves were generated, one for each embolic
parameter (SVL, MEP, duration, and velocity) and plotted on
the graph shown in Figure 2
Defining the optimum SVL threshold to predict embolic composition is
dependent on a variety of factors. The ROC curve shows that an SVL
threshold that identifies all particulate emboli correctly (ie, 100%
sensitivity) would result in a poor specificity of only 34%. However,
an SVL threshold that always identified gaseous emboli correctly (100%
specificity) would produce a relatively good sensitivity of 87.4%. The
best threshold for this study is one that correctly identifies the
highest number of particulate and gaseous emboli combined, which occurs
when the combined sensitivity and specificity are greatest. This can be
deduced from the ROC curve by selecting the point closest to the top
left corner of the ROC space.
The Table
Two previous studies have shown that embolic signal duration correlates
well with MEP and therefore can be used to determine embolic size or
composition.5 10 For this reason it was decided
to test the measurement of embolic duration as a parameter
to help determine embolic composition, but in this study, despite
improved temporal resolution, it was not the best parameter
to classify emboli. The effect that embolic velocity had on signal
duration was minimized in the previous in vitro studies because the
mean velocity of the flow medium was kept constant and emboli were
injected at the same site, causing them to travel in similar axial
planes at similar velocities. It is likely that the variation in
embolic velocity observed in the in vitro studies was extremely small
compared with the wider range observed during surgical procedures, when
blood flow velocities can change significantly. For example, if an
embolus travelling at 1.0 m/s has a signal duration of 100 ms, then a
similarly sized embolus travelling at 0.5 m/s would take 200 ms to
traverse the sample volume, no longer producing a correlation between
embolic duration and MEP. Furthermore, it is probable that any small
deviations in the relationship between signal duration and MEP were
masked because of the poor temporal resolution (10 to 20 ms) used in
the previous studies.
Droste and colleagues9 were able to demonstrate a
significant positive linear correlation between embolic velocity and
MEP. This was a result of finding a significant inverse relationship
between embolic velocity and embolic duration due to a relatively
constant sample volume length for all embolic signals studied. Our
study has shown that the measured SVL of embolic signals is not
constant for all compositions and sizes of emboli. However, the
experiments of Droste et al were performed using a relatively small
size range (105 to 150 µm) of emboli all having the same
composition, which would not be expected to produce any appreciable
variation in the SVL measurement. If a much larger size range of emboli
or different compositions of similarly sized emboli had been used,
there would not have been such a significant correlation between
embolic velocity and MEP. Consequently, our study showed that embolic
velocity performs poorly as a predictor of embolic composition,
yielding the lowest combined sensitivity and specificity of all four
parameters studied.
The MEP method performed relatively well in this study, with the
effective dynamic range of the TCD system increased to 60 dB. If the
dynamic range was kept at 20 dB, then all the air bubbles detected in
vitro would have been amplitude saturated, resulting in a much larger
overlap in MEP values between the particulate and gaseous emboli,
yielding a very poor sensitivity and specificity for classifying
emboli. Despite increasing the dynamic range to 60 dB, the SVL
parameter was statistically more accurate for classifying
emboli than the MEP parameter. The optimum thresholds for
MEP and SVL were 30 dB and 12.8 mm, respectively. It is
interesting to note that the 30-dB optimum MEP threshold defined from
the ROC curve agrees exactly with the optimum embolus-to-blood ratio
threshold determined theoretically.12 One
possible reason for the SVL parameter performing better
than the MEP parameter is that the SVL measurement is able
to "smooth out" any aberrant peak values of intensity within the
signal duration. This is in contrast to the MEP measurement, which is
derived from a single peak intensity within the embolic duration and
which may result in overestimation or underestimation of the MEP value
for some embolic signals.
Ideally, both gaseous and particulate emboli would be obtained from the
same source, either in vivo or in vitro. Unfortunately, when we tried
to inject small particles of platelet thrombus into the flow rig
through the rubber-sealed side port, small volumes of air were also
introduced, which no longer provided a pure source of particulate data.
Alternatively, a pure source of in vivo generated gaseous bubbles is
desirable. This would not be possible if we used the same patient group
in which the particulate emboli were detected because of the presence
of atherosclerotic disease in the arteries of these vascular patients.
Although there are clinical situations in which pure gaseous emboli
could be detected (ie, hyperbaric decompression, cerebral arteriography
in young patients, testing for patent foramen ovale), it is unlikely
that the size range of these emboli would match with those detected in
vivo during CEA, thus providing no better model than in vitro generated
gas bubbles. To simulate more closely the type of gaseous emboli
detected during carotid endarterectomy, an
injection of gas bubbles into the carotid artery would be required;
however, because it has not been conclusively proven that air emboli
are asymptomatic, this would be both unethical and
potentially dangerous. Although this report has used both in vitro and
in vivo data, we believe that this is valid for a comparison study of
the four parameters, which were all computed using the same
data set. Use of a different data set may lead to differing degrees of
overlap between the results of gaseous and particulate emboli but would
be unlikely to change their ranking in terms of performance.
The type of particulate material (ie, atheroma,
platelets, or thrombus) detected in vivo is likely to be different
for different patient groups (ie, carotid versus cardiac patients). The
variability in MEP measurements due to unknown particulate composition
has previously been investigated by Markus and
colleagues,13 who found that there was no
significant difference between thrombus and atheroma, while
the mean MEP values of thrombi and atheroma were greater
than that of platelets. The range of mean MEP values of all three
types of particulate emboli was slightly less than 3 dB, which is
unlikely to produce a significant effect on the threshold values
presented in this study because of unknown particle composition
but, more importantly, is unlikely to affect the ranking of the four
parameters.
In conclusion, this study has shown that measuring the SVL of embolic
signals and defining a threshold of 12.8 mm is the best of the
four embolic parameters tested for classifying emboli. The
SVL is calculated as the product of embolic duration and velocity,
which are measured with the use of the high-temporal-resolution Wigner
analyzer.
Received October 6, 1997;
revision received November 18, 1997;
accepted March 17, 1998.
2.
Russell D, Madden KP, Clark WM, Sandset PM, Zivin JA.
Detection of arterial emboli using Doppler ultrasound
in rabbits. Stroke. 1991;22:253–258.
3.
Russell D, Brucher RR, Madden KP, Clark WM, Sandset
PM, Zivin JA. The intensity of the Doppler signal caused by
arterial emboli depends on embolus type and size.
Stroke. 1992;23:158.
4.
Russell D, Brucher RR, Madden KP, Clark WM, Sandset
PM, Zivin JA. The intensity of the Doppler signal caused by
cerebral embolic materials. Stroke. 1992;23:474. Abstract.
5.
Markus HS, Brown MM. Differentiation between different
pathological cerebral embolic materials using transcranial
Doppler in an in vitro model. Stroke. 1993;24:1–5.
6.
Markus H, Loh A, Brown MM. Detection of
circulating cerebral emboli using Doppler ultrasound in a sheep
model. J Neurol Sci. 1994;122:117–124.[Medline]
[Order article via Infotrieve]
7.
Grosset DG, Georgiadis D, Kelman AW, Lees KR.
Quantification of ultrasound emboli signals in patients with cardiac
and carotid disease. Stroke. 1993;24:1922–1924.
8.
Georgiadis D, Mackay TG, Kelman AW, Grosset DG,
Wheatley DJ, Lees KR. Differentiation between gaseous and formed
embolic materials in vivo: application in prosthetic heart
valve patients. Stroke. 1994;25:1559–1563.[Abstract]
9.
Droste DW, Markus HS, Nassiri D, Brown MM. The effect
of velocity on the appearance of embolic signals studied in
transcranial Doppler models. Stroke. 1994;25:986–991.[Abstract]
10.
Bunegin L, Wahl D, Albin MS. Detection and volume
estimation of embolic air in the middle cerebral artery using
transcranial Doppler sonography. Stroke. 1994;25:593–600.[Abstract]
11.
Smith JL, Evans DH, Fan L, Thrush AJ, Naylor AR.
Processing Doppler ultrasound signals from blood borne emboli.
Ultrasound Med Biol. 1994;20:455–462.
12.
Moehring MA, Spencer MP, Davis DL, Demuth RP.
Exploration of the embolus to blood power ratio model (EBR) for
characterizing microemboli detected in the middle cerebral artery. In:
Program and abstracts of the IEEE Ultrasonics Symposium; November
7–10, 1995; Seattle, Wash.
13.
Markus H, Loh A, Brown MM. Detection of circulating
cerebral emboli using Doppler ultrasound in a sheep model.
J Neurol Sci. 1994;122:117–124.
© 1998 American Heart Association, Inc.
Original Contributions
A Comparison of Four Methods for Distinguishing Doppler Signals From Gaseous and Particulate Emboli
Abstract
Top
Abstract
Introduction
Materials and Methods
Results
Discussion
References
Background and Purpose—Many reports
in the medical literature have proposed methods of differentiating
between gaseous and particulate emboli detected with the use of
transcranial Doppler ultrasound. The purpose of this
study was to compare the previously published methods with our own
sample volume length (SVL) parameter to assess the accuracy
of each method in classifying emboli. 
2)
at chosen specificity values of 90%, 95%, 97%, 99%, and 100%,
which showed that the SVL sensitivities were statistically greater than
MEP sensitivities (P<0.01).
Key Words: embolism • receiver operator characteristics • ultrasonography, Doppler
Introduction
Top
Abstract
Introduction
Materials and Methods
Results
Discussion
References
During CEA and
cardiac surgery, both air and particulate emboli can be detected with
the use of TCD. The clinical significance of each type of embolus is
relatively unknown because it has been very difficult to distinguish
between gaseous and particulate matter. However, there is
circumstantial evidence that particulate emboli are potentially far
more damaging than gaseous emboli, and therefore simply counting the
number of emboli during each surgical procedure does not necessarily
correlate with their clinical significance. A recent study showed that
persistent embolization during the dissection phase of CEA, when all
emboli are particulate by default, was associated with a significant
decline in cognitive function, while emboli detected during the early
recovery phase were associated with an increased risk of
perioperative thrombosis and cerebral
infarction.1 This study also showed that presumed
air emboli rarely cause significant morbidity.
Materials and Methods
Top
Abstract
Introduction
Materials and Methods
Results
Discussion
References
A pure source of particulate emboli was obtained during the
dissection phase of routine CEA surgery when no air was able to enter
the arterial system. This procedure was performed in the
standard manner with the use of normotensive, normocarbic general
anesthesia, systemic heparinization (5000 IU IV), and
carotid sinus nerve blockade (1 mL 1% lidocaine). One hundred sixteen
patients underwent continuous intraoperative TCD monitoring of middle
cerebral artery blood flow velocity during CEA. The TCD system used was
a Scimed PcDop 842 that had been modified to increase the effective
dynamic range to 60 dB.11 Nineteen patients
(16.4%) had evidence of more than one embolus during the initial
dissection phase of the operation before cross-clamping. All
Doppler signals were recorded onto digital audiotape for
off-line analysis. Unfortunately, one of the Doppler
channels was not functioning during two of the 19 patient
recordings in which there was evidence of particulate emboli,
and therefore these were excluded from this study since they could not
be analyzed. In total, 215 particulate embolic signals were
detected in 17 patients during dissection.
Finally, the SVL of each embolic signal was calculated from the
product of embolic duration and velocity.
Our data were not normally distributed, and accordingly all the
results in the text and figures refer to median values and their IQRs.
Nonparametric (Mann-Whitney) tests were used. Significance
was assumed at a value of P<0.05.
Results
Top
Abstract
Introduction
Materials and Methods
Results
Discussion
References
Figure 1
shows scatterplots of the
four embolic parameters for each embolic composition. The
median (IQR) embolic velocity for particulate emboli was 0.72 m/s (0.58
to 0.85) and for gaseous emboli was 1.10 m/s (0.90 to 1.32)
(P<0.001). The median (IQR) embolic duration for
particulate emboli was 14.32 ms (5.76 to 27.52) and for gaseous emboli
was 48.72 ms (39.12 to 65.04) (P<0.001). The median (IQR)
MEP for particulate emboli was 19.9 dB (16.3 to 24.9) and for gaseous
emboli was 36.8 dB (34.5 to 39.4) (P<0.001). The median
(IQR) SVL for particulate emboli was 4.1 mm (1.8 to 6.9) and for
gaseous emboli was 20.0 mm (16.4 to 24.8) (P<0.001).
It is not clear from these results which parameter is the
best to differentiate particulate from gaseous emboli since there is a
significant difference between the two types of emboli for all four
parameters. All four scatterplots in Figure 1 show an
obvious overlap of the two data groups, and therefore any arbitrary
threshold could be chosen that does not necessarily provide optimum
specificity and sensitivity.
View larger version (17K):
[in a new window]
Figure 1. Distribution of mean embolic velocity, embolic
duration, MEP, and SVL for both gaseous and particulate emboli. . The ROC
curve that produces the best performance of separating
particulate from gaseous emboli is the one that is the highest and lies
farthest to the left of the ROC space since this provides the greatest
sensitivity and specificity. Figure 2 shows that the SVL ROC curve
performs best, and therefore this embolic parameter will
correctly classify a higher percentage of emboli than the other three
parameters. To test whether the SVL parameter
was statistically different from the MEP parameter, a
series of 2 tests was performed at chosen
specificity values of 90%, 95%, 97%, 99%, and 100%. At each
specificity value the sensitivity values for SVL and MEP were
statistically different (P<0.05), and when we used the
additive property of the 2 test by combining
the results of all five tests, even greater statistical significance
was achieved (P<0.001).
View larger version (22K):
[in a new window]
Figure 2. ROC curves plotted for each of the four embolic
parameters used to attempt to classify emboli. The best
performance is achieved by the SVL curve. EBR indicates
embolic-to-blood ratio. shows some of the possible combinations of sensitivity and
specificity for different threshold values that can be achieved with
each of the four embolic parameters used to attempt to
classify emboli. The optimum SVL threshold that yields the highest
combined sensitivity and specificity for defining gaseous and
particulate matter was 12.8 mm, which yielded a sensitivity of
93% and a specificity of 97%. The optimum MEP threshold was 30 dB,
which yielded a sensitivity of 86.5% and a specificity of 95%.
View this table:
[in a new window]
Table 1. Sensitivity and Threshold Values for a Given Specificity for
All Four Emboli Parameters
Discussion
Top
Abstract
Introduction
Materials and Methods
Results
Discussion
References
The aim of this study was to compare three existing embolic signal
parameters (MEP, embolic duration, and embolic velocity)
with the more recently defined SVL parameter without the
restrictions of temporal resolution and amplitude saturated embolic
signals to find the optimum method of classifying emboli.
Selected Abbreviations and Acronyms
CEA
=
carotid endarterectomy
FFT
=
fast Fourier transform
IQR
=
interquartile range
MEP
=
measured embolic power
ROC
=
receiver operator characteristic
SVL
=
sample volume length
TCD
=
transcranial Doppler ultrasonography
Acknowledgments
This study was supported by the UK Stroke Association. J.L.
Smith is a clinical research associate funded solely by the Stroke
Association.
References
Top
Abstract
Introduction
Materials and Methods
Results
Discussion
References
1.
Gaunt ME, Martin PJ, Smith JL, Rimmer T, Cherryman
G, Ratliff DA, Bell PR, Naylor AR. Clinical relevance of intraoperative
embolization detected by transcranial Doppler
ultrasonography during carotid endarterectomy: a
prospective study of 100 patients. Br J Surg. 1994;81:1435–1439.[Medline]
[Order article via Infotrieve]
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