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Echo motion imaging with adaptive clutter filter for assessment of cardiac blood flow

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2015 Jpn. J. Appl. Phys. 54 07HF09(http://iopscience.iop.org/1347-4065/54/7S1/07HF09)
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REGULAR PAPER

Japanese Journal of Applied Physics 54, 07HF09 (2015)
http://dx.doi.org/10.7567/JJAP.54.07HF09

Echo motion imaging with adaptive clutter ﬁlter for assessment of cardiac blood ﬂow
Hiroki Takahashi1*, Hideyuki Hasegawa1,2, and Hiroshi Kanai2,1
1

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

Received December 10, 2014; accepted March 24, 2015; published online June 18, 2015
Visualization of the vortex blood ﬂow in the cardiac chamber is a potential diagnostic tool for the evaluation of cardiac function. In the present study,
a method for automatic selection of the desirable cutoff frequency of a moving target indicator ﬁlter, namely, a clutter ﬁlter, was proposed in order tovisualize complex blood ﬂows by the ultrahigh-frame-rate imaging of echoes from blood particles while suppressing clutter echoes. In this method,
the cutoff frequency was adaptively changed as a function of the velocity of the heart wall (clutter source) in each frame. The feasibility of the
proposed method was examined through the measurement of a healthy volunteer using parallel receive beamforming with a single transmission ofa non-steered diverging beam. Using the moving target indicator ﬁlter as above with the cutoff frequency determined by the proposed method, thevortex-like blood ﬂow in the cardiac chamber was visualized as movements of echoes from blood particles at a very high frame rate of 6024 Hz
while suppressing clutter echoes. © 2015 The Japan Society of Applied Physics

1.

Introduction

In recent years, the understanding of the behavior of the bloodﬂow in the cardiac chamber has progressed because themethod for imaging the cardiac ﬂow has been increasingly
improved, for instance, color Doppler ﬂow imaging, echocardiographic particle image velocimetry (E-PIV), and phasecontrast magnetic resonance imaging (PC-MRI). In particular,
the role of the vortex-like blood ﬂow in the cardiac chamber
has been strongly focused on. It is assumed that vortices
contribute to blood smoothly ﬂowing out to the aorta by
preserving the momentum of blood in the diastolic phase.1)
Such ﬂow behavior would enhance the cardiac pumping
eﬃciency owing to the ejection of blood in the chamber with asmaller energy provided by the myocardium. Hence, visualization of the vortex blood ﬂow would be a new diagnostic
tool for the evaluation of cardiac function. However, a PCMRI scanner is expensive and unsuitable for screening tests
due to the long time required for scanning. On the other hand,echocardiography provides cross-sectional images noninvasively in real time; therefore, it is useful valuable if a complex
blood ﬂow such as the vortex ﬂow can be easily visualizedusing echocardiography. In medical ultrasound, the color
Doppler method is a major diagnostic implement for visualizing the cardiac blood ﬂow. The true direction of the blood
ﬂow, however, cannot be shown because the Doppler
measurement only reveals the axial velocity.
Various techniques have been developed and studied toreveal the direction of blood ﬂows.2) A technique with twocrossed transmit=receive beams utilizing the Doppler spectral
broadening eﬀect has been proposed to estimate the transverse velocity component (perpendicular to the ultrasonicbeam) at a speciﬁc point.3,4) The velocity vector of the blood
ﬂow was estimated by combining Doppler measurement fromtwo (or more) directions.5,6) Recently, the imaging of blood
velocity vectors estimated by the vector Doppler techniquewith transmissions of crossed plane waves has beenachieved.7,8) A method for imaging blood velocity vectors
using multiple transmissions of steered plane waves and aleast squares ﬁtting with velocities in diﬀerent directions
was also developed.9) These techniques, however, requirea relatively large beam steering angle and, thus, theirapplication is limited to blood ﬂows in a shallow region,such like the carotid blood ﬂow. Hence, the vector Doppler
technique is not suitable for normal cardiac measurementwith a phased-array transducer. As a result, a linear-arraytransducer with a large aperture is used in ongoing studies.
A method for estimation of the direction of a blood ﬂow
was developed that traces the interframe movement of echoes
between successive frames by searching for the maximal
correlation between echoes in the kernels in successiveframes (called the speckle tracking technique).10) The speckle
tracking technique has been also used for estimation of
myocardial velocity.11) Løvstakken et al. proposed a method
for contrast-free imaging of the blood ﬂow direction from the
movements of echoes from blood particles obtained by thecustomized sweeping of a focused beam.12) This method,
however, has the critical issue of an insuﬃcient frame rate
because multiple transmissions in the same direction areneeded for visualization of the echoes from blood particles.
As a result, the two-dimensional (2D) blood velocity vectors
could not be estimated by the speckle tracking technique dueto the low continuity of the movement of echoes between
consecutive frames as described in Ref. 12. Recently, theimaging of 2D blood velocity vectors estimated by the
speckle tracking technique with high-frame-rate measurement by plane wave transmission and parallel receive
beamforming has been shown to be feasible.13) Morerecently, the imaging of 2D blood velocity vectors estimatedin the frequency domain by plane wave imaging was also
demonstrated.14) The application of fast ultrasound imaging
with parallel beamforming has been broadened to themonitoring of areas treated by ultrasonic therapy.15) The
width of the region illuminated by a plane wave is limitedby the aperture size of the transducer. In echocardiography,
a phased-array transducer with a small aperture is usedbecause the heart must be insoniﬁed from a narrow acoustic
window between ribs. This means that the use of thetransmission of the plane wave requires many transmissions
per frame and limits the frame rate in echocardiography.
Plane wave imaging, therefore, would be not suitable forvisualizations of the cardiac blood ﬂow requiring a highframe-rate measurement.
As a diﬀerent approach to estimating the transverse
component in the blood ﬂow velocity, vector ﬂow mapping
has been developed.16) In vector ﬂow mapping, the transverse

07HF09-1© 2015 The Japan Society of Applied Physics

Jpn. J. Appl. Phys. 54, 07HF09 (2015)

H. Takahashi et al.

ﬂow velocity is mathematically calculated from the 2D
distribution of the axial velocity measured by the color
Doppler method on the basis of the hydrokinetic assumption.
In another method, the velocity of the surrounding heart wall
estimated by the speckle tracking method has been utilized toobtain a more accurate boundary condition in the continuity
equation.17,18) Although these calculation techniques can
depict the intraventricular ﬂow behavior, the radical ﬂow ofblood cannot be visualized.
In our previous study, it was shown that the direction of
the blood ﬂow in the cardiac lumen could be visualized by
the very high frame rate (kHz order) measurement of echoesfrom blood particles using parallel beamforming coupledwith transmissions of diverging waves.19) High-pass ﬁlteringin the direction of the frame [called the moving target
indicator (MTI) ﬁltering] has been used to suppress theundesirable echoes from ribs and the heart wall. The cutoﬀ
frequency for the MTI ﬁlter was set to match the typicalmaximal velocity of the heart wall and was ﬁxed throughout
the cardiac cycle. However, the velocity of the heart wallchanges over time during the entire cardiac cycle; therefore,
the cutoﬀ frequency should ﬂuctuate depending on the heart
wall velocity to increase the detectivity of echoes from blood
particles in a slow ﬂow.
In the present study, a method for automatic selection of
the cutoﬀ frequency in MTI ﬁltering was proposed. In thismethod, the cutoﬀ frequency of the MTI ﬁlter was adaptively
changed depending on the velocity of the heart wall in eachframe. The feasibility of the proposed method was examinedby comparison between blood echo images obtained by MTI
ﬁltering with static and automatically selected cutoﬀ
frequencies through an in vivo experiment. In the ﬁrst partof this paper, a strategy for the ultrahigh-frame-rate imaging
of movements of echoes from blood particles is described in
Sect. 2.1. The ultrahigh-frame-rate imaging was implemented using parallel receive beamforming with the transmission of a single diverging beam. In Sect. 2.1, a procedure
to suppress sidelobe levels in echoes from blood particles
using the phase coherence factor with sub-beamformers(PCF-SB)20) is also presented. In Sect. 2.2, the proposed
adaptive MTI ﬁltering is presented. Our in vivo experimental
result used to examine the feasibility of the proposed MTI
ﬁltering is reported in Sect. 3 and discussed in Sect. 4.
2.

Principles

2.1 Ultrahigh-frame-rate echocardiography with improved
image quality

A modiﬁed diagnostic ultrasound system (Hitachi-Aloka
Medical Alpha10) with a commercial 3.75 MHz phased arraytransducer was used to acquire ultrasonic RF echo signals
from 96 transducer elements. RF signals received byindividual elements were sampled at 15 MHz for oﬀ-line
processing by a personal computer. Using individual RF
signals obtained by the transmission of a single diverging
wave, high-frame-rate echocardiography was realized byparallel receive beamforming.21–23) The acquisition frame rate
decreases with increasing number of transmissions per frame.
A strategy for the high-frame-rate imaging of echoes fromblood particles is described in this section.
In high-frame-rate echocardiography, the lateral spatial
resolution and the sidelobe characteristics can be improved

virtual point source

35.5

30 mm

phased arraytransducer
19.2 mmtransducer
element

receive
focal point
incident wave front
Fig. 1. (Color online) Illustration of transmission of spherically diverging
beam and parallel receive beams.

by compounding (averaging) the echo signals acquired bytransmissions at diﬀerent steering angles. Although the image
quality is improved by this compounding technique, a higher
frame rate, i.e., fewer transmissions, is desirable for visualization of the cardiac blood ﬂow in order to avoid the signal
decorrelation caused by the fast (maximal velocity of over
1 m=s) and complex blood ﬂow in the cardiac lumen.
The frame rate required for visualization of the cardiac
blood ﬂow was examined through a steady-ﬂow experiment.
Echoes from a blood-mimicking ﬂuid (BMF) steadily ﬂowing
in a cylindrical ﬂow path (diameter of 8 mm) surrounded bytissue-mimicking rubber were measured. The angle between
the transducer surface and the ﬂow axis was set at 56°. The
transducer surface was placed at a distance of 7 cm fromthe outer surface of the ﬂow path. The ﬂow rate, whichcorresponds to the mean ﬂow velocity, was measured by amass ﬂow meter connected to the ﬂow path. The echo data
were acquired at a frame rate of 6024 Hz by the single
transmission of a non-steered diverging beam per frame ata ﬂow rate of 0.6 L=min (mean ﬂow velocity: 0.2 m=s,
Reynolds number: 960). An illustration of the beamforming
is shown in Fig. 1. The transmit wave diverged from a virtual
point source placed 30 mm behind the array surface. In thismeasurement, the accuracy of velocity estimation was
evaluated for diﬀerent frame rates at a constant Reynolds
number of 960. RF echo signals for diﬀerent frame rates(1506, 2008, 3012, and 6024 Hz) were simulated by changing
the frame interval of the acquired echo data. A higher ﬂow
rate of 1.8 L=min (mean ﬂow velocity of 0.6 m=s) was also
simulated for each set of echo data with further changes in
the frame interval. Figure 2 shows the mean ﬂow velocities
obtained from the velocity vectors estimated by the speckle
tracking technique with the temporal averaging of 2D
correlation functions. On the assumption that the distributionof the ﬂow velocity follows the Hagen–Poiseuille equation,24)
the mean ﬂow velocity was computed as follows:Nj 1XV ¼ 1ðrj rÞvj  n;
ð1Þ
A j¼0
where vj, rj, Δr, A, and n are the velocity vector estimated at
the jth spatial point, the distance from the center of the ﬂow
path to the jth spatial point, the interval between the jth and( j + 1)th spatial points, the cross-sectional area of the ﬂow

07HF09-2© 2015 The Japan Society of Applied Physics

H. Takahashi et al.

mean velocity [cm/s]

80

(a)

1 cm

ideal

60

LV lumen

40

septumfree wall

normalized magnitude [dB]

Jpn. J. Appl. Phys. 54, 07HF09 (2015)

0

−30

20
0

0

2

4
6
frame rate [kHz]8

enlarged

1 cm

Fig. 2. (Color online) Mean ﬂow velocities estimated at diﬀerent frame
rates in steady-ﬂow measurements. The standard deviations were computedfrom 50 frames.

(b)

96 elements

1 cm(n+1)−thframeflow of RF data
n−th
frame

high−pass filteringin frame direction
at specific position
beamforming and
PCF weighting

B−modeimage

automatic selection of
cut−off frequency

LV lumenbeamforming and
PCF weighting

free wall

septumnormalized magnitude [dB]scan direction
phased arraytransducer

0−30

Blood echo
image
n

n+1

frame directionnn+1

frame direction

1 cm

enlarged

weighting byframe coherence

overlap

Fig. 3. (Color online) Schematic of proposed sequence of procedures forimaging of echoes from blood particles.

path, and the unit vector parallel to the ﬂow direction,
respectively. The interval between the jth and ( j + 1)th
spatial points was set at 0.4 mm. As shown in Fig. 2, it was
found that a high frame rate of 6 kHz was required to measure
the fast blood ﬂow. Therefore, the ultrahigh-frame-rate
measurement was performed with the single transmission of
the diverging beam per frame.
The contrast of the B-mode and blood echo images was
low due to high sidelobe levels caused by the transmission of
unfocused ultrasound. To improve the image quality without
decreasing the frame rate, we employed special beamformingwith the PCF-SB. This technique improves the sidelobe
characteristics and the spatial resolution by utilizing thediversity of echo signals received by transducer elements.
Through phantom and in vivo experiments, the strategy of
the PCF-SB was shown to be eﬀective for improving thequality of B-mode images.20) It has also been found that the
improvement of the sidelobe characteristics in blood echo
imaging using the PCF-SB is feasible.25)
The proposed sequence of procedures is shown in Fig. 3.
B-mode images were created using beamformed RF signals
weighted by the PCF-SB. On the other hand, MTI ﬁltering
was applied to individual echo signals received by transducer
elements. Then, the PCF-SB for the blood echo image wascomputed from the ﬁltered element signals. As with B-mode
imaging, beamformed signals after the MTI ﬁltering were
weighted using the PCF-SB. In our study, the brightness in a
blood echo image corresponds to the intensity of MTI-ﬁlteredecho signals. Figure 4 shows blood echo images (hot-scaled)scan direction
Fig. 4. (Color online) Enlarged blood echo images in in vivo experimenton a healthy volunteer (a) without weighting and (b) with weighting by the
PCF-SB. In the blood echo image, the intensity of MTI-ﬁltered echo signals
was color-coded according to a hot scale.

without weighting and with weighting by the PCF-SB forblood echo imaging with B-mode images (gray-scaled). As
shown in Fig. 4, speckle echoes from blood particles became
sharper as a result of improving the sidelobe characteristics of
MTI-ﬁltered echoes. Toward the imaging of blood echoeswith a high contrast, beamformed RF signals obtained with
the PCF-SB were further weighted by the coherence in time(frame) variation.19) We deﬁned this frame coherence as the
temporal-averaged power of the complex correlation of
demodulated signals between successive frames at a lag ofzero normalized by the power of the signals. The frame
coherence decreases with the variation of the temporalchange in the phase of echoes. MTI-ﬁltered and beamformed
echo signals in six neighboring frames were used forcomputation of the frame coherence.
Adaptive selection of cutoff frequency in MTI ﬁltering
The amplitude of echoes from the ribs and heart wall ismuch larger than that from blood particles. Therefore, echoesfrom blood particles in the direction of the main lobe arecontaminated even by sidelobe echoes from clutter sources
such as the ribs and the heart wall. The maximal velocity of
the heart wall is almost 10 cm=s, while that of the cardiac
blood ﬂow reaches over 1 m=s. Hence, clutter echoes from
2.2

07HF09-3© 2015 The Japan Society of Applied Physics

H. Takahashi et al.
normalized magnitude [dB]

Jpn. J. Appl. Phys. 54, 07HF09 (2015)
apical side
1 cm

LV lumen

(a)

1 cm

0

LV lumen−30

free wall

septumseptumfree wall
leafletbasal side

(b)

1 cm

Fig. 5. (Color online) Sidelobe artifact captured in early diastole inin vivo experiment on a healthy volunteer. The green arrows indicate thesidelobe artifacts. The cutoﬀ frequency of the MTI ﬁlter was set at 650 Hz.

the ribs and heart wall are suppressed by MTI ﬁltering, whichcorresponds to applying high-pass ﬁltering to echoes from a
speciﬁc position in the direction of the frame.
Regarding the blood echo image obtained with unfocusedtransmission, a serious artifact is generated by sidelobe
echoes from clutter sources due to the high sidelobe level
when the cutoﬀ frequency of the MTI ﬁlter is lower thanthe desirable value. Figure 5 shows the blood echo image(hot-scaled) with the B-mode image (gray-scaled) in early
diastole. The green arrows indicate beltlike artifacts originating from sidelobes. As shown in Fig. 5, the artifacts interferewith echoes from blood particles in the cardiac lumen. The
levels of clutter echoes are intensiﬁed at a low cutoﬀ
frequency of the MTI ﬁlter. On the other hand, thedetectability of a slow blood ﬂow is degraded when the
cutoﬀ frequency of the MTI ﬁlter is high.
The velocity of the heart wall changes over time.
Therefore, the cutoﬀ frequency of the MTI ﬁlter desirable
for the detection of a slow blood ﬂow depends on the cardiac
phase. In the present study, the cutoﬀ frequency of the MTI
ﬁlter was adaptively changed over time as a function of thevelocity of the heart wall. First, echo signals from the ribs andheart wall (clutter sources) were identiﬁed (labeled) on thebasis of the amplitude of the RF signal using the k-meansclustering technique.26) In this technique, the means offeatures belonging to diﬀerent groups are updated repeatedly
while all points are discerned so that the distance between themean feature of each group and the feature of each point issmall. Then, in regard to the echo signals from the clutter
sources, the mean frequency μ of the echo signal in the
direction of the frame and the standard deviation σ were
estimated by the autocorrelation technique.27) Because thevelocity of the heart wall ﬂuctuates spatially and temporally,
the deviation of the frequency of echoes in the direction of
the frame should be considered in the determination of thecutoﬀ frequency. We deﬁned the cutoﬀ frequency as fcut =μ + nσ, where n is an integer.
For evaluation of the diﬀerence in the myocardial
velocities between diﬀerent spatial positions, the spatial

average f and its standard deviation f were estimatedusing fcut in each position labeled as the heart wall, and the
cutoﬀ frequency in the frame was set to fcut:sp ¼ 
f þ 3f .
Note that echo signals with a very slow variation in the
direction of the frame were omitted in the following analysis.
The ribcage is almost static in the entire cardiac cycle;

Fig. 6. (Color online) (a) B-mode image and (b) distribution of points
labeled as the heart wall (red) and ribs (green) by k-means clustering.

therefore, the cutoﬀ frequency can be underestimated if
echoes from ribs are used in the calculation of f and f . fand f were computed excluding echoes from tissues with avelocity below 0.2 mm=s, which were assumed to be echoesfrom ribs. Figures 6(a) and 6(b) respectively show the Bmode image and points labeled as the heart wall (red) andribs (green) by k-means clustering. The cutoﬀ frequencies
fcut.sp were temporally averaged in the direction of the frame
to prevent a large variation of the cutoﬀ frequency in near byframes. The impulse response of the MTI ﬁlter was generated
by a 60th-order sinc function multiplied by the Kaiser
window function, and the cutoﬀ frequency was automaticallydetermined in each frame.
3.

In vivo experimental result for a human heart

RF echo signals from the left ventricle were measured at aframe rate of 6024 Hz in a transthoracic three-chamber viewof a 27-year-old healthy volunteer. Figure 7 shows the cutoﬀ
frequency automatically determined at n = 3. Figure 8 shows
blood echo images obtained at n = 1, 2, and 3 in the frame
indicated by the blue dashed line in Fig. 7 (when the cutoﬀ
frequency was minimum in diastole). In Fig. 8, the B-mode
images were not superimposed. Echoes from the heart wall
signiﬁcantly contaminated those from blood particles at n = 1
in Fig. 8(a), and echoes from the free wall surrounded by thecyan dashed line remained at n = 2 in Fig. 8(b). Therefore,
the cutoﬀ frequency was deﬁned as fcut = μ + 3σ.
Figure 9(1) shows the blood echo images with the B-mode
images in the frame in early diastole, which was indicated as(1) in Fig. 7. In Figs. 9(a-1), 9(b-1), and 9(c-1), blood echo
images were obtained using MTI ﬁlters with diﬀerent cutoﬀ
frequencies of 300 and 650, which were constant in the entire
cardiac cycle, and 1059 Hz, which was determined by the
proposed method, respectively. Figure 9(2) shows the bloodecho images with the B-mode images obtained in the framein mid diastole, which was indicated as (2) in Fig. 7, at
diﬀerent cutoﬀ frequencies of 300, 650, and 410 Hz (theproposed method).

07HF09-4© 2015 The Japan Society of Applied Physics

Jpn. J. Appl. Phys. 54, 07HF09 (2015)
systoleH. Takahashi et al.
systolediastole

(1)

1500

31

apical side(a−1)

cutoff frequency [Hz]velocity [cm/s]

12

1 cm

900

600

basal side

6

300

0

0

(1)
0

0.2

(b−1)

1 cm

(b−2)

1 cm

(c−1)

1 cm

(c−2)

1 cm

(2)

0.4
transit time [s]

0.6

0.8

Fig. 7. (Color online) Automatically determined cutoﬀ frequencies inentire cardiac cycle. The blue dashed line and blue solid lines indicate thetime at which the blood echo images shown in Fig. 8 and Figs. 9(1), and 9(2)were obtained, respectively.

(a)

(a−2)

1 cm

1200

24

18

(2)

1 cm

−30

0

normalized magnitude [dB]

1 cm

normalized magnitude [dB]

(b)

(c)

Fig. 9. (Color online) Blood echo images obtained from frames in(1) early diastole and (2) mid-diastole (a) by MTI ﬁltering with a cutoﬀ
frequency of 300 Hz, (b) by MTI ﬁltering with a cutoﬀ frequency of 650 Hz,
and (c) by MTI ﬁltering with cutoﬀ frequencies of 1059 Hz in (1) and 410 Hzin (2) determined by our proposed method.
0

−30

1 cm

Fig. 8. (Color online) Blood echo images obtained by determining the
cutoﬀ frequency fcut as (a) fcut = μ + σ, (b) μ + 2σ, and (c) μ + 3σ in the frame
indicated by the blue dashed line in Fig. 7.

As shown in Figs. 9(a-1) and 9(a-2), echoes from blood
particles are visualized in a wider region in mid-diastole
using the low cutoﬀ frequency of 300 Hz, but more clutter
echoes are seen in early diastole. Using the MTI ﬁlter withthe cutoﬀ frequency of 650 Hz, echoes from blood particles
ﬂowing into the cardiac lumen are visible with suppressed

clutter echoes in early diastole as shown in Fig. 9(b-1).
However, as shown in Fig. 9(b-2), the echoes from blood
particles have lower intensities than those in Fig. 9(a-2). As
shown in Fig. 9(c-2) obtained by our proposed method,
although visible echoes from blood particles are slightly lessintense than those in Fig. 9(a-2), echoes from blood particles
in the vortex ﬂow can be seen over a wider ﬁeld in middiastole, while clutter echoes are suﬃciently suppressed inearly diastole.
Figure 10 shows 2D velocity vectors of the blood ﬂow
estimated by the speckle tracking technique10) at cutoﬀ
frequencies of 650, 300, and 410 Hz (the proposed method)in the same frame as in Fig. 9. The spatial points at which 2D
velocity vectors were estimated were manually assigned at aregular interval inside the cardiac lumen. Vector magnitudes
at all pixels were obtained by reconstructive interpolation28)of the distribution of the 2D vector velocity. The size of thespatial window for the computation of the 2D correlation
function in the speckle tracking technique was set to 9° and8.2 mm in the lateral and axial directions, respectively. 2D
correlation functions were temporally averaged over 12
frames corresponding to almost 2 ms.29) The averaged 2D
correlation function was up-sampled by reconstructive
interpolation28) in order to measure the small movement of
echoes between successive frames. In Fig. 10(a), a discontinuous distribution of velocity vectors can be observed in thegreen circle due to the low detectivity of echoes from blood

07HF09-5© 2015 The Japan Society of Applied Physics

Jpn. J. Appl. Phys. 54, 07HF09 (2015)

H. Takahashi et al.

(a)

(a)

1 cm

1 cm
coherence

1

free
wall

0.8
0.6
0.4
0.2
0

septum

(b)

1 cm

1 cm

velocity magnitude [m/s]

(b)
0.6
0.4
0.2
0

Fig. 11. (Color online) (a) Distribution of coherence in direction of frameand (b) distribution of points labeled as the heart wall (red) and ribs (green)by k-means clustering in the same frame as in Fig. 6. The coherence wascoded according to the right color bar.

(c)single diverging beamplane wave compounding0
echo magnitude [dB]

1 cm

Fig. 10. (Color online) 2D velocity vectors of blood ﬂow estimated by the
speckle tracking technique with temporally averaged 2D correlation function
at ﬁxed cutoﬀ frequencies of (a) 650 Hz and (b) 300 Hz and at the cutoﬀ
frequency of (c) 410 Hz determined by our proposed method in mid-diastole.
The cyan arrows and the hot color-coded intensity indicate the direction andmagnitude of the 2D velocity vectors, respectively.

particles in the slow ﬂow. The peak values of the 2D
correlation functions averaged for all velocity vectors were0.77 in Fig. 10(a), 0.86 in Fig. 10(b), and 0.85 in Fig. 10(c).
In Fig. 10(c), the rotation of the ﬂux in the clockwise
direction was revealed by the distribution of the 2D velocity
vectors. The proposed adaptive MTI ﬁltering was eﬀective
for improving the accuracy of velocity vector estimation byenhancing the detectivity of echoes from blood particles.
4.

Discussion

To determine the cutoﬀ frequency of the MTI ﬁlter, only the
amplitude of echoes was used for the identiﬁcation of echoesfrom the heart wall by the k-means clustering method. An
alternative method for the identiﬁcation of echoes from theheart wall using the coherence in the direction of the frame
has also been proposed by our group.30,31) Figures 11(a) and11(b) show the distribution of the frame coherence and theregion identiﬁed as the heart wall by the k-means clustering
method using the frame coherence in the same frame as in

−20
−40
−60
−80

−100

−40 −30 −20 −10 0 10 20
lateral angle [deg]3040Fig. 12. (Color online) Lateral proﬁles of amplitude of an echo from awire placed in water at a distance of 7 cm. Amplitude proﬁles were obtained(a) by no compounding with the single diverging transmission and (b) bycompounding with transmissions of the plane wave in seven directions.

Fig. 6. In contrast to Fig. 6(b), it can be seen that all theregions except for the septum and the free wall weremisclassiﬁed as the heart wall in Fig. 11(b) because thecoherence in such regions is high.
The reason for the high coherence in the cardiac lumen isconsidered to be that echoes from blood particles inside
the cardiac lumen were contaminated by sidelobe echoes
originating from the surrounding heart wall. In the previous
studies of our group,30,31) parallel receive beamforming withthe transmission of plane waves in ﬁve or seven directions
was performed, and the coherence in the direction of theframe at each spatial point was estimated using echo signals
compounded in diﬀerent transmissions. Figure 12 shows
lateral proﬁles of the amplitude of an echo from a wire
obtained by the single diverging beam transmission used in
the present study and by compounding seven transmissionsof the plane wave with an angular interval of 6°. The wire
was placed in water at a distance of 7 cm from the transducer

07HF09-6© 2015 The Japan Society of Applied Physics

Jpn. J. Appl. Phys. 54, 07HF09 (2015)

H. Takahashi et al.

(a)

1 cm

septumleaflet of
mitral valve

(b)

(c)

1 cm

1 cm

normalized magnitude [dB]surface. The ratios of the echo magnitude averaged in lateralangle θ ranges of −45° < θ < −4.5° and 4.5° < θ < 45° to thepeak magnitude were evaluated to be −38.8 dB for thetransmission of the single diverging beam and −45.3 dB for
the transmission of seven plane waves. Hence, the echoes
originating from sidelobes in the case of the compounding of
echoes obtained by plane wave transmissions in sevendirections were smaller than those in the case of the single
diverging beam transmission. The number of transmissions
per frame should be one in order to prioritize increasing theframe rate for visualization of the fast cardiac blood ﬂow.
Under such conditions, the use of the frame coherence isconsidered to be unsuitable for identiﬁcation of the heart wallin ultrahigh-frame-rate measurement.
Echoes from blood particles on the apical side were moreclearly visualized in the blood echo image in Fig. 9(b-1) with
a ﬁxed cutoﬀ frequency of 650 Hz than those in Fig. 9(c-1)
obtained by our proposed method. This means that the blood
ﬂow on the apical side of the cardiac lumen was slow relative
to the basal side, and echoes from such regions were
suppressed by the MTI ﬁltering with the determined cutoﬀ
frequency of 1059 Hz. In early diastole, the leaﬂets ofthe mitral valve, which are one of the clutter sources,
independently move very rapidly due to the inertial force ofthe blood ﬂowing into the left ventricular chamber. In the
proposed method, the spatial variation of the mean frequency
in the direction of the frame was used for determination of thedesirable cutoﬀ frequency. Therefore, in early diastole, it wasconsidered that the determined cutoﬀ frequency was higher
than the desirable value due to the large diversity of velocities
in the clutter sources.
A radical solution to this problem is to determine the cutoﬀ
frequency of the MTI ﬁlter at each location by expanding our
proposed method. As shown in Fig. 5, in high-frame-rateechocardiography with diverging beam transmission, theartifact echoes originating from sidelobes are generated in thelateral direction along the wave front of the diverging beamacross a clutter source. Here, the cutoﬀ frequency of the
MTI ﬁlter was overestimated due to the large diversity ofvelocities in the clutter sources. Hence, it may be eﬀective toadaptively change the cutoﬀ frequency of the MTI ﬁlter at
each location according to the velocity of the clutter sourcesuch as the ribs, heart wall, and cardiac valve. To consider thefeasibility of this method, the mean frequencies of echoesfrom the heart wall and the leaﬂet in the direction of theframe were obtained using echoes from the corresponding
regions in the same frame as in Fig. 9(1). Echoes from theregions ﬁlled in red in Fig. 13(a) were used for evaluation of
the mean frequencies. Using the autocorrelation technique,the mean frequencies μ of the regions on the apical side of the
septum and the leaﬂet of the mitral valve were evaluated to be
230 and 409 Hz, respectively. Then, to evaluate the eﬀect ofadaptively changing the MTI ﬁltering depending on thelocation, the cutoﬀ frequency of the MTI ﬁlter over the whole
region was determined as fcut.sp = μ + 3σ using the meanfrequency μ and the standard deviation σ in the direction of
the frame obtained with echoes from either the septum or the
leaﬂet. Figures 13(b) and 13(c) show the blood echo images
obtained by the cutoﬀ frequencies of 665 and 548 Hz
determined using echoes from the regions in the leaﬂet andthe septum indicated in Fig. 13(a), respectively. The cyan

0−30

Fig. 13. (Color online) Regions assigned for evaluation of meanfrequencies and for determination of cutoﬀ frequencies. (a) Echoes in theregions in the septum and the leaﬂet ﬁlled in red were used. Blood echo
images obtained by the cutoﬀ frequencies determined using echoes from(b) the leaﬂet and (c) the septum.

lines indicate a region on the apical side of the left ventricularlumen. Echoes from blood particles in this apical region in
Fig. 13(c) were stronger than those in Fig. 13(b). Therefore,
adaptively changing the MTI ﬁltering depending on thelocation would be eﬀective to increase the detectivity ofblood ﬂows.
5.

Conclusions

A method for automatic selection of the cutoﬀ frequency in
the MTI ﬁlter has been proposed that increases the detectivityof echoes from a slow blood ﬂow while suppressing clutter
echoes. Ultrahigh-frame-rate measurement by parallel beamforming with the single transmission of a non-steered
diverging beam was implemented to follow the fast bloodﬂow in the cardiac lumen. The feasibility of the proposed
method was examined in terms of the visibility of echoesfrom blood particles and the suppression of clutter echoes
through an in vivo experiment on a 27-year-old volunteer.
Although echoes from blood particles on the apical side were
suppressed in early diastole, the detectivity of the blood ﬂow
was improved within the vortex-like blood ﬂow of the
cardiac lumen in mid diastole. Furthermore, it was conﬁrmedthat the proposed adaptive MTI ﬁlter was eﬀective forincreasing the accuracy of estimation of the blood velocity
vector by the speckle tracking technique.

07HF09-7© 2015 The Japan Society of Applied Physics

Jpn. J. Appl. Phys. 54, 07HF09 (2015)

H. Takahashi et al.

Acknowledgments

This work was supported by JSPS Research Fellowships for
Young Scientists (DC1) and the International Advanced
Research and Education Organization of Tohoku University.

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