Reprinted from

REGULAR PAPER

Improvement of Automated Identification of the Heart Wallin Echocardiography by Suppressing Clutter Component
Hiroki Takahashi, Hideyuki Hasegawa, and Hiroshi Kanai

Jpn. J. Appl. Phys. 52 (2013) 07HF17# 2013 The Japan Society of Applied Physics

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REGULAR PAPER

Japanese Journal of Applied Physics 52 (2013) 07HF17
http://dx.doi.org/10.7567/JJAP.52.07HF17

Improvement of Automated Identification of the Heart Wallin Echocardiography by Suppressing Clutter Component
Hiroki Takahashi1 , Hideyuki Hasegawa1;2 , and Hiroshi Kanai2;11

Graduate School of Biomedical Engineering, Tohoku University, Sendai 980-8579, Japan
Graduate School of Engineering, Tohoku University, Sendai 980-8579, Japan
E-mail: htaka@us.ecei.tohoku.ac.jp
2

Received November 21, 2012; accepted March 27, 2013; published online July 22, 2013
For the facilitation of analysis and elimination of the operator dependence in estimating the myocardial function in echocardiography, we havepreviously developed a method for automated identification of the heart wall. However, there are misclassified regions because the magnitudesquared coherence (MSC) function of echo signals, which is one of the features in the previous method, is sensitively affected by the clutter
components such as multiple reflection and off-axis echo from external tissue or the nearby myocardium. The objective of the present study is toimprove the performance of automated identification of the heart wall. For this purpose, we proposed a method to suppress the effect of the clutter
components on the MSC of echo signals by applying an adaptive moving target indicator (MTI) filter to echo signals. In vivo experimental results
showed that the misclassified regions were significantly reduced using our proposed method in the longitudinal axis view of the heart.
# 2013 The Japan Society of Applied Physics

1. Introduction

Echocardiography is the predominant modality for the early
detection of cardiac diseases because of its low cost,noninvasiveness, and high temporal resolution. Myocardialischemia, which occurs upon a mismatch between thecoronary blood ﬂow and myocardial metabolism, decreases
myocardial contraction and relaxation. Methods for measuring of myocardial motion and deformation (i.e., myocardial
strain) using echocardiography are very useful to ﬁnd out
regional myocardial abnormalities, such as dysfunction incontraction and relaxation, caused by myocardial ischemia.1,2) The myocardial motion and strain rate, which isequivalent to the shortening velocity per initial muscle
length, can be quantitatively evaluated in echocardiography
by the ultrasonic Doppler shift or speckle motion.3–7) The
systolic velocity of an ischemic myocardium tends to belower than that of a normal myocardium.8,9) The systolicstrain rate in myocardial ischemia is also reduced, and, also,
the myocardial strain and strain rate have the advantage ofbeing less inﬂuenced by the tethering eﬀect from adjacenttissue.10) By estimating the myocardial strain rate, it ispossible to observe post-systolic shortening, which issensitive to myocardial ischemia under dobutamine stress.11)
On the other hand, it is reported that the temporal change
in the amplitude of an echo signal backscattered from the
myocardium is in good agreement with the rate of change inthe thickness of the myocardium and has the possibility of
being used to estimate of myocardial deformation resulting
from myocardial contraction and relaxation.12) Furthermore,
the propagation of electrical excitation and mechanicalvibration in the cardiac wall has been noninvasively
measured in echocardiography to detect regional tissue
damage, which is involved in ischemia and arrhythmia.13–15)
In most estimates of the myocardial function as presented
above, the myocardium, which is the object to be analyzed,
needs to be manually identiﬁed by an operator. However,this task is very time-consuming and suﬀers from inter- andintraobserver variability. For the facilitation of analysis
and the elimination of operator dependence, automatedidentiﬁcation of the heart wall needs to be realized.

Automated (or semi-automated) methods for segmenting
the left ventricle (LV) in echocardiographic images havebeen proposed by extracting the boundary of the LV
boundary between the left ventricular walls and thelumen.16–19) Segmentation methods for the LV can be used
to calculate diagnostic indexes, such as the cardiac chamber
size and ejection fraction, related to the global cardiac
function. These methods, however, cannot be applied toobtain parameters of myocardial functions, such as the
myocardial strain rate, because the myocardia in the LV
chamber are not identiﬁed. Identiﬁcation of the myocardia
composing heart walls is a challenging task because of thelow echogenicity in the myocardium, the low contrast, anddropout.
We have developed a method for automated identiﬁcationof the heart wall regions, which include the myocardia,
throughout an entire cardiac cycle by tracking the points
identiﬁed as the heart wall in an initial frame.20) In our
method, multiple features, such as echogenicity and temporalchanges in the phases of echo signals, are extracted toaccurately identify the heart wall in the initial frame. Echo
signals in transthoracic echocardiography contain undesirable stationary clutter which is caused by the echoes fromexternal tissue such as the ribs.21,22) In our previous study,20)
to reduce this stationary clutter, high-pass ﬁltering, corresponding to moving target indicator (MTI) ﬁltering,23) wasapplied to RF signals before extracting features. However,
the magnitude-squared coherence (MSC) function of echo
signals,20,22) which is one of the features, is still aﬀectedby undesirable echoes. This remaining clutter increases the
MSC in the cardiac lumen (essentially, the MSC in the
cardiac lumen is expected to be low) and causes misclassiﬁed
regions. In the present study, we investigated the sourceof clutter, which increases the MSC in the cardiac lumen,
and suppressed the inﬂuence of this clutter by adaptively
determining the frequency response of an MTI ﬁlter.
2. Estimation of Magnitude-Squared Coherence
Function with Tracking Method

Using a feature such as the echogenicity, which depends
only on the echo amplitude, the heart wall is not completely

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Fig. 1. (Color online) Illustration of changes in waveforms of echo signals in kernel.

distinguished from the lumen because there is also a region
with low echo amplitude inside the heart wall region. Thus,
additional second and third features, which can evaluate
diﬀerences in echo signals between the heart wall and thelumen regardless of the echo amplitude, were adopted toaccurately classify the region with low echo amplitude
inside the heart wall region. In this study, the MSC function
estimated by the phased tracking method24) and the temporalphase shift of echo signals are used as second and thirdfeatures, respectively.20)
In the ultrasonic echo signal measured at a high frame rate
the motion of the heart wall can be tracked accurately by thephased tracking method. On the other hand, as shown in
Fig. 1, blood cells in the cardiac lumen are diﬃcult to track
because they slip away from the focal area of the ultrasonicbeam owing to blood ﬂow. The temporal changes in phasesof echo signals in the kernel caused by the global motion of
the heart wall are compensated because the echo signal is
only time-shifted while the waveform of the echo from thejð f ; x; nÞj2 ¼

lumen changes signiﬁcantly owing to the large change inthe relative locations of scatterers. Therefore, there is asigniﬁcant diﬀerence in the temporal changes in the
waveforms of ultrasonic echoes in an assigned kernel, in
which the motion of the tissue is estimated using the phased
tracking method, between the heart wall and lumen regions.
The displacement ux ðn þ kÞ between the nth and ðn þ kÞthframes of tissue in a kernel assigned around depth x in thenth frame is estimated by the phased tracking method. The^ þ kÞ; ðn þ kÞÞ, of the RF echo
complex spectrum, Yð f ; xðn^ þ kÞ ½¼ x þ ux ðn þ kÞsignal in the kernel at the position xðnin the ðn þ kÞth frame is obtained by applying the discrete
Fourier transform (DFT) to the RF echo signal in the kernel.
^ þ kÞ; n þ kÞg, theUsing the complex spectra fYð f ; xðncharacteristics of phase changes in echoes in the kernel
between two consecutive frames, which depend on the
motion and the deformation of an object, are evaluated by
the MSC function,20,22) jð f ; x; nÞj2 , at a scan line in the nthframe as follows:^ þ kÞ; n þ kÞ Yð f ; xðn^ þ k þ 1Þ; n þ k þ 1Þj2
jEk ½Yð f ; xðn;^ þ kÞ; n þ kÞj2 Ek ½jYð f ; xðn^ þ k þ 1Þ; n þ k þ 1Þj2 
Ek ½jYð f ; xðn

where Ek ½ and  denote the time averaging (for 50 frames)
and complex conjugate, respectively. The numerator of
Eq. (1) denotes the power of time-averaged complex crossspectra, which evaluate the variation of temporal changes
in phases of the complex spectra between two frames. The
denominator of Eq. (1) denotes the average power of thecomplex spectra. Thus, the value of Eq. (1) does not dependon the echo amplitude. In this study, the MSC at afrequency, f , of 4.4 MHz, which was higher than thetransmission frequency of 3.75 MHz, was used to obtaina high contrast between the heart wall and lumen.22)
Conventionally, the MSC and the echogenicity are extracted
from the echo signal ﬁltered with a high-pass ﬁlter whose
cutoﬀ frequency is 10 Hz (around DC) to reduce the eﬀects
of echoes from stationary structures.
3. Investigation of Source of Clutter

A modiﬁed diagnostic ultrasound system (Aloka -10) witha 3.75 MHz phased array probe was used for the experimentsin this study. This system was modiﬁed to acquire individual
RF echo signals received by 96 elements. Parallel beamforming25,26) and spatial compounding were applied in oﬀ-

ð1Þ

line processing using RF echo signals measured by elements.
Plane waves were transmitted in seven directions at angle
intervals of 6 , and 16 receiving beams were formed atangle intervals of 0.375 in each transmission by parallel
beamforming.27) Beamformed RF echo signals sampled at
15 MHz were spatially compounded to reduce discontinuities in the ultrasound image. The number of compoundings
was determined by the transmission aperture width projectedin the transmission direction.27) Figure 2(a) illustrates therelationship between the projected transmission aperture
width lw and the angular width of the projected transmission
aperture, w ; in the present study, lw was equal to L cos ,
where L and  are the width of the transmission apertureand the transmission beam steering angle, respectively, inplane wave transmission. Figure 2(b) illustrates an example
in the case of three compounding at a point of interest p.
A beamformed RF signal was compounded to obtain acompounded RF signal at p when the angular diﬀerence
between the direction of transmission, from which the
beamformed RF signal was obtained, and that of a pointof interest p (the origin corresponds to the center of thetransmission aperture) was less than twice the angular width

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(b)

Fig. 2. (Color online) Illustrations of (a) projected transmit aperture
width lw and (b) compounding procedure at a point of interest p in the caseof the number of compounding of 3 with plane wave transmission (black,
red, and blue dashed lines show angular width w at p in three successive
transmission).

Fig. 4. (Color online) Motion of RV-AW, and clutter in RV with high
MSC estimated by the phased tracking method. (a) Regions assigned in RVAW and in RV. (b) Waveforms of spatial means of displacements in theseregions measured by the phased tracking method.

Fig. 3. (Color online) (a) Acquisition area of beamformed RF echo
signals, (b) B-mode image, (c) spatial distribution of MSCs obtained withpreviously determined MTI ﬁlter whose cutoﬀ frequency was set at 10 Hz,
and (d) region-identiﬁed image obtained by feature classiﬁcation using EM
algorithm.20)of the projected transmission aperture, 2w .27) As shown in
Fig. 2(a), the angular width w varied with the distance
between the point of interest p and the center of thetransmission aperture r and the projected aperture width lw .
Therefore, the number of compoundings was also changed
depending on the position of p. The total number oftransmissions was from one (no compounding) to seven inthis in vivo experiment. The heart of a 23-year-old male wasmeasured in the longitudinal axis view at a very high frame
rate of 860 Hz using parallel beamforming with plane wave
transmission. Figures 3(a) and 3(b) respectively show theacquisition area of the beamformed RF echo signals andthe B-mode image, which was constructed by detecting
envelopes in beamformed RF signals sampled at 15 MHz.
Figure 3(c) shows the spatial distribution of the MSC
obtained with a previously determined MTI ﬁlter, whose
cutoﬀ frequency is 10 Hz (around DC). As shown in

Fig. 3(c), the MSC in the right ventricle (RV) is as high asthat in the heart wall regions, while the echogenicity in the
RV is low compared with that in the heart wall regions.
Figure 3(d) shows a region-identiﬁed image by featureclassiﬁcation obtained using the expectation-maximization(EM) algorithm,28) in which there are misclassiﬁed regions
caused by the high MSC in the RV.
We investigated the source of this clutter, which increases
the MSC in the cardiac lumen, to propose a method forsuppressing it. Figure 4 shows the motion of the rightventricular anterior wall (RV-AW) and the clutter in the RVwith the high MSC estimated with the phased tracking
method.24) The displacement waveforms shown in Fig. 4(b)were spatially averaged inside the regions surrounded by redlines in Fig. 4(a). As shown in Fig. 4(b), the clutter in the RV
exhibits the motion similar to that in the RV-AW. Lediju
et al. also reported that clutter in the ventricle has motion
similar to that in the nearby myocardium.29) The MSC in the
cardiac lumen can be increased by multiple reﬂection and oﬀaxis echo (echo from an oﬀ-axis object caused by a mainlobe
or the sidelobe) from the nearby myocardium in addition to
extra cardiac structures, such as ribs and lungs. Therefore, theclutter in the cardiac lumen is found not to be suppressed
completely by the previously determined MTI ﬁlter which
suppresses the eﬀect of echoes from stationary structures.
4. Principle
4.1 Clutter reduction in coherence estimation

Well-suitable MTI ﬁltering has the potential to suppress theincrease in the MSC caused by clutter in the cardiac lumen
and to preserve the MSC in the heart wall in the case when
the power of the clutter component in the cardiac lumen islower than the power of the echo from the heart wall. Thatis the reason why the MSC in the cardiac lumen does not

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Fig. 5. (Color online) (a) Spatial distributions of MSCs, (b) distributions of features in feature space (echogenicity vs MSC) indicated by red points (heartwall), cyan points (RV), and blue points (LV) at diﬀerent cutoﬀ frequencies of MTI ﬁlter. (c) Frequency responses of MTI ﬁlters used in (a) and (b).

depend on the powers (amplitudes) but depends on thetemporal changes in the phases of echo signals.
Figures 5(a) and 5(b) show the spatial distributions of the
MSCs and the distributions of the features in the feature
space (echogenicity vs MSC), respectively, in manuallyassigned regions at several cutoﬀ frequencies of the MTI
ﬁlter (FIR high-pass ﬁlter) at the beginning of the slow
ﬁlling phase.20) The distributions of the features of themanually assigned region in the heart wall [interventricular
septum (IVS) and left ventricular posterior wall (LV-PW)],
RV, and LV are shown as red, cyan, and blue points in
Fig. 4(b), respectively. Figure 5(c) shows the frequency
responses of the MTI ﬁlters used in Figs. 5(a) and 5(b),where the ﬁlters’ impulse responses have the same length.
The sinc function multiplied by the Kaiser window was usedas the impulse response of the MTI ﬁlter. As shown in
Fig. 5(a), the regions with the high MSC in the RV weredecreased in intensity and the MSCs in the heart walls werestill high when the cutoﬀ frequency of the MTI ﬁlter was set
to 50 Hz. On the other hand, as shown in Fig. 5(b), it was
found that the region with the clutter in the RV cannot be
distinguished from the region with the low echogenicity inthe heart walls using the previously determined MTI ﬁlter
which suppresses the clutter caused by echoes from only
stationary structures. The result shows that the increase in
the MSC caused by clutter can be suppressed while
preserving a high MSC in the heart wall by adjusting the
cutoﬀ frequency of the MTI ﬁlter.

which the MSCs in the heart wall region start to be criticallydecreased, to suppress the eﬀect of the clutter component in
the lumen while preserving a high MSC in the heart wall.
First, candidate points in the heart wall region are extracted
on the basis of the echo amplitudes in manually assigned
areas. The candidate points have echo amplitudes between
e1 and e2 . The upper limit amplitude, e2 , is set to theamplitude at which the cumulative probability of theRayleigh distribution ﬁtted to the measured histogram [theempirical probability density function (PDF)] of the echo
amplitudes in the assigned area in the heart wall is 95%. The
lower limit amplitude, e1 , is set to the higher value of either
the expectation amplitude, eexp,1 , of the Rayleigh distribution
ﬁtted to the empirical PDF of the echo amplitudes in theheart wall or the amplitude, ecum,1 , at which the cumulative
probability of the Rayleigh distribution ﬁtted to theempirical PDF of the echo amplitudes in the lumen is
95%. This is why it is undesirable for the candidate points of
the heart wall region to be selected from the lumen region
in the case when the expectation amplitude in the assigned
area inside the heart wall, eexp,1 , is very low. Next, the spatial
mean ð fp ðiÞÞ and the standard error of the mean "ð fp ðiÞÞ,which corresponds to the standard deviation divided by thesquare root of the number of points, of the MSCs ofcandidate points are calculated while the cutoﬀ frequency,
fp ðiÞ, of the MTI ﬁlter is gradually increased from fs (aroundDC) at a pitch of dfp as follows:

4.2 Adaptive determination of MTI filter response

In the present study, both fs and dfp are set to 10 Hz. Next,
the spatial mean ð fp ðiÞÞ and the standard error of themean "ð fp ðiÞÞ are divided into group 1 with f fp ðmÞg (m ¼0; 1; . . . ; Nt ) and group 2 with f fp ðnÞg (n ¼ Nt þ 1; . . . ; N)by a threshold fp ðNt Þ (0 < Nt < N). After that, the slope a j

In the present study, we proposed a method for suppressingof the eﬀect of clutter in the MSC estimation to improveautomated identiﬁcation of the heart wall. This proposed
method estimates the cutoﬀ frequency of the MTI ﬁlter at

fp ðiÞ ¼ fs þ i  dfp

07HF17-4

ði ¼ 0; 1; . . . ; NÞ:

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Fig. 6. (Color online) (a) Regions manually assigned for determination of frequency response of MTI ﬁlter and initial parameters of EM algorithm.
Histograms of echo amplitudes and ﬁtted Rayleigh distributions in regions assigned in (b) heart wall and (c) lumen regions shown in (a). (d) Candidate points
for the heart wall extracted on the basis of echo amplitudes in the assigned regions.

and intercept b j of the regression line with respect to eachgroup j ð2 f0; 1gÞ are estimated using the weighted leastsquares method, which minimizes the weighted residual sumof squares E j . The weighted residual sum of squares, E j , foreach group j is deﬁned as follows:
E1 ¼

Nt
1 X
ðð fp ðiÞÞ  a1 fp ðiÞ  b1 Þ2;
Nt þ 1 i¼0"2 ð fp ðiÞÞ

ð3Þ

E2 ¼

N
X
1
ðð fp ðiÞÞ  a2 fp ðiÞ  b2 Þ2:
N  Nt i¼N þ1"2 ð fp ðiÞÞð4Þt

The minimum of E j , which is estimated by the weighted
least-squares method for each group j, is deﬁned as
Emin;j ðNt Þ at diﬀerent thresholds fp ðNt Þ. In group 1, theintercept of the regression line, b1 , is estimated by theweighted least-squares method where the slope, a1 , is zero
because the MSCs in the heart wall are expected not tochange in the case when the cutoﬀ frequency of the MTI
ﬁlter is in group 1 (low cutoﬀ frequency). The total of theresidual sum of squares for the two groups, EðNt Þ, is deﬁned
as follows:EðNt Þ ¼ Emin,1 ðNt Þ þ Emin,2 ðNt Þ:ð5ÞThe total errors fEðNt Þg are calculated at diﬀerent thresholds
fp ðNt Þ. Finally, the cutoﬀ frequency of the MTI ﬁlter isdetermined as the frequency where two regression lines
at the minimum total error intersect. The MSC, which is
extracted from the RF echo signal ﬁltered with thedetermined cutoﬀ frequency, is used as the feature forclassiﬁcation by the EM algorithm.
5. In vivo Experimental Results

Figure 6(a) shows the manually assigned regions used foradaptive determination of the frequency response of the MTI
ﬁlter based on echo amplitudes in the assigned regions.
These regions were also used to determine the initial
parameters of the EM algorithm for segmentation.20)
Figures 6(b) and 6(c) respectively show histograms of theecho amplitude in the regions assigned in the heart wall andFig. 7. (Color online) Spatial means of MSCs at candidate points shownin Fig. 6(d) plotted as a function of cutoﬀ frequency of MTI ﬁlter and two
regression lines at the minimum total error.

lumen shown in Fig. 6(a). As shown in Figs. 6(b) and 6(c),the amplitudes of echoes from the lumen were much lessthan that of echoes from the heart wall. Thus, the lower limit
amplitude e1 was set to eexp,1 and, as shown in Fig. 6(d), thecandidate points (colored in red) of the heart wall weresuccessfully extracted from the heart wall.
Figure 7 shows the spatial means of the MSC at thecandidate points shown in Fig. 6(d) at diﬀerent cutoﬀ
frequencies of the MTI ﬁlter and the two regression lines
with the minimum total error E. The plots and vertical bars
show the means ð fp ðiÞÞ and twice the values of the standard
errors of the means "ð fp ðiÞÞ, respectively. The cutoﬀ
frequency of the MTI ﬁlter was adaptively set at 55 Hz at
which the two regression lines intersect. Figures 8(a)–8(c)show the MSC image obtained with the previously
determined MTI ﬁlter, whose cutoﬀ frequency is set at
10 Hz, region-identiﬁed image, and the region-identiﬁed
image overlaid on the corresponding B-mode image,respectively. Figures 8(d)–8(f ) show the MSC image
with the adaptive MTI ﬁlter, whose cutoﬀ frequency is

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Fig. 10. (Color online) (a) MSC image obtained with adaptive MTI ﬁlter
whose cutoﬀ frequency was automatically set at 62 Hz, (b) region-identiﬁed
image, and (c) region-identiﬁed image overlaid on the corresponding
B-mode image using candidate points extracted on the basis of echo
amplitudes in LV-PW.
Fig. 8. (Color online) (a) MSC image obtained with previously
determined MTI ﬁlter whose cutoﬀ frequency is set at 10 Hz, (b) regionidentiﬁed image, and (c) region-identiﬁed image overlaid on thecorresponding B-mode image. (d) MSC image obtained with adaptive MTI
ﬁlter whose cutoﬀ frequency was automatically set at 55 Hz, (e) regionidentiﬁed image, and (f ) region-identiﬁed image overlaid on thecorresponding B-mode image.

automatically set at 55 Hz, the region-identiﬁed image, andthe region-identiﬁed image overlaid on the corresponding
B-mode image, respectively. By comparing Figs. 8(a) and8(b) with Figs. 8(d) and 8(e), the number of points in the RV
showing high MSCs was signiﬁcantly reduced and thesepoints were correctly classiﬁed as the lumen using theadaptive MTI ﬁlter.
6. Discussion

As shown in Sect. 5, the amplitude of the echo signal fromthe IVS was used to assign candidate points of the heart walland determine the cutoﬀ frequency of the MTI ﬁlter for the
MSC. In the LV in the longitudinal axis view, the amplitudeof the echo from the LV-PW, should be lower than that fromthe IVS owing to the larger propagation loss. Thus, theselected candidate points depend on the location of theregion which is assigned as the heart wall. We investigatedthe eﬀect of a diﬀerence in assigned regions. Figures 9(a)and 9(b) show manually assigned regions and histograms of
the echo amplitudes in the region assigned in the LV-PW,
respectively. As shown by the ﬁtted Rayleigh distributions in
Fig. 9(b), the amplitudes of echoes from the LV-PW werelower than that of echoes from the IVS. Figure 9(c) shows
the candidate points of the heart wall extracted on the basis(a)of the echo amplitudes in the region assigned in the LV-PW.
By comparing Fig. 6(d) with Fig. 9(c), the selected candidate points for the heart wall are found to be in the region
assigned as the heart wall. Most of the candidate points forthe heart wall, however, were assigned in the heart wallregion for these two diﬀerent assignments. Figures 10(a)–10(c) show the MSC image obtained with the adaptive MTI
ﬁlter whose cutoﬀ frequency is adaptively set to 62 Hz using
candidate points for the heart wall shown in Fig. 9(c), theregion-identiﬁed image, and the region-identiﬁed image
overlaid on the corresponding B-mode image, respectively.
As shown in Fig. 10, the number of points with high MSCs
in the RV was reduced and these points were correctlyclassiﬁed as the lumen using the proposed adaptive MTI
ﬁlter. This result indicates that the eﬀect of the proposed
adaptive MTI ﬁlter hardly depends on the location of themanually assigned region.
In the present study, high-frame-rate echocardiographic
measurement by parallel beamforming with plane wave
transmission was used to increase the MSCs of RF echo
signals in kernels, which traced the motion of tissue using
the phased tracking method, in the heart wall. Furthermore,
the high-frame-rate measurement is needed to selectively
suppress the clutter component without decreasing theecho from the myocardium as a result of MTI ﬁltering.
Figure 11(b) shows spatially averaged power spectra whichwere obtained by applying a DFT with respect to thedirection of the frame to the RF echo signals from the IVS
and LV-PW assigned in Fig. 11(a) at the beginning of theslow ﬁlling phase. The RF echo signals were obtained at a

(c)

(b)

Fig. 9. (Color online) (a) Regions manually assigned for determination of frequency response of MTI ﬁlter and initial parameters of EM algorithm.
(b) Histograms of echo amplitudes in the regions assigned in LV-PW shown in (a), ﬁtted Rayleigh distribution (blue line) and that shown in Fig. 6(b) (greenline). (c) Candidate points for the heart wall extracted on the basis of echo amplitudes in the assigned regions.

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frequency response of an MTI ﬁlter applied to the echo
signals before estimating the MSCs. According to the resultsof our investigation shown in Fig. 4, the source of theclutter, which increases the MSC in cardiac lumens, isreverberation and oﬀ-axis echo from adjacent myocardia
in addition to extra cardiac structures such as ribs and lungs.
As compared to with a previous method for automatedidentiﬁcation of the heart wall,20) the heart wall regions weremore accurately identiﬁed using our proposed method.
Furthermore, the result shows the feasibility of identiﬁcationof the RV-AW which should be observed to diagnose
pulmonary embolism and pulmonary hypertension.

1) G. R. Sutherland, M. J. Stewart, K. W. Groundstroem, C. M. Moran, A.

2)
3)
4)
5)

Fig. 11. (Color online) (a) Regions manually assigned in IVS and LV-

PW at the beginning of the slow ﬁlling phase and (b) power spectra spatiallyaveraged in regions assigned in (a).

6)

7)
8)

very high frame rate of 860 Hz by parallel beamforming. As
shown in Fig. 11(b), RF echoes from the myocardia
contained the dominant frequency component, which was
up to at least 100 Hz. The frame rate, however, is about
100 Hz by conventional beamforming and, thus, aliasingof the estimated myocardial velocities occurs for velocity
components above the Nyquist limit corresponding to halfof the frame rate. The MSCs of echoes from the myocardia
should be decreased by MTI ﬁltering because MTI ﬁltering
reduces not only the stationary clutter components but
echoes from the myocardia, which contain aliased velocity
components. Therefore, high-frame-rate measurement isdesirable to suppress the clutter component without
decreasing the myocardium component by MTI ﬁltering.
On the other hand, the sidelobe level in parallel beamforming with plane wave transmission is higher than that inconventional beamforming because the transmitted ultrasound wave is unfocused for high-frame-rate measurementwith parallel beamforming. Therefore, as compared toconventional beamforming, parallel beamforming with plane
wave transmission increases the clutter component, which
includes echoes reﬂected from the ribcage and the nearby
myocardium caused by the sidelobe. The proposed method
reduces the eﬀect of the clutter component, which isincreased by parallel beamforming with the transmissionof an unfocused wide beam.

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7. Conclusions

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In this study, we investigated the source of clutter, which
increases the MSC in the cardiac lumen, and proposed amethod for suppressing the increase in the MSC in the lumen
caused by the clutter by adaptive determination of the

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07HF17-7

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