Reprinted from

REGULAR PAPER

Detection of Arterial Wall Boundaries Using an Echo Model
Composed of Multiple Ultrasonic Pulses
Nabilah Ibrahim, Hideyuki Hasegawa, and Hiroshi Kanai

Jpn. J. Appl. Phys. 52 (2013) 07HF03# 2013 The Japan Society of Applied Physics

Person-to-person distribution (up to 10 persons) by the author only. Not permitted for publication for institutional repositories or on personal Web sites.

REGULAR PAPER

Japanese Journal of Applied Physics 52 (2013) 07HF03
http://dx.doi.org/10.7567/JJAP.52.07HF03

Detection of Arterial Wall Boundaries Using an Echo Model
Composed of Multiple Ultrasonic Pulses
Nabilah Ibrahim1 , Hideyuki Hasegawa2;1 , and Hiroshi Kanai1;2
1Graduate School of Engineering, Tohoku University, Sendai 980-8579, Japan
Graduate School of Biomedical Engineering, Tohoku University, Sendai 980-8579, Japan
E-mail: kanai@ecei.tohoku.ac.jp
2

Received November 20, 2012; accepted March 27, 2013; published online June 20, 2013
The assessment of the intima-media thickness (IMT) of the carotid arterial wall, which is the most frequently used indicator to diagnose
atherosclerosis by ultrasound, involves the measurement of the lumen-intima boundary (LIB) and media-adventitia boundary (MAB). In this study,
using the mean squared error (MSE) method and by applying the template matching technique, an adaptive model of an ultrasonic echo, which is
obtained from an ultrasonic pulse measured with a hydrophone, was fitted with the measured in vivo RF echo to estimate the boundaries of the
carotid arterial wall. In the present study, the frequency and phase of the adaptive model were considered to improve the accuracy in thedetermination of the LIB and MAB. For a 7.5-mm-long short segment of the carotid artery in the longitudinal direction, the average IMTs estimatedby the improved technique and the previous method were 502  61 and 558  120 m, respectively, showing a decrease in the standard deviationby the proposed method. Moreover, the result obtained by the improved technique presented only 0.4% difference between the automaticallydetected boundary and the manually detected boundary, which is smaller than that obtained by the previous method (10.7% difference). These
results verified that the boundary detected by the improved technique was more accurate than that detected by the previous method.
# 2013 The Japan Society of Applied Physics

1. Introduction

LUMEN

monocytes

Cardiovascular diseases remain the biggest cause of deathworldwide. It is reported that such diseases are responsible
for over 17.3 million deaths yearly and are the leading
causes of death in the world.1) Moreover, in 2008, out of
17.3 million cardiovascular deaths, 7.3 million deaths werecaused by heart attacks and 6.2 million deaths were causedby strokes or atherosclerosis. The rise in the prevalence ofatherosclerosis is considered to be the main cause of
cardiovascular diseases.2–4) Hence, assessment of the riskof atherosclerosis5,6) and its diagnosis at an early stage havebecome very important. Various noninvasive markers for
detection of atherosclerosis or arterial wall alteration havebeen identiﬁed, such as arterial wall thickening [i.e., intimamedia thickness (IMT)],7) endothelial dysfunction,8) andcoronary artery calciﬁcation.9) Additionally, the increase of
the IMT of the carotid artery, which is the region between
the lumen-intima boundary (LIB) and the media-adventitia
boundary (MAB), correlates very well with pathohistologic
measurements for assessing cardiovascular risk.10) A lesionof atherosclerosis occurs when the endothelial cell lining on
the luminal surface of the arterial wall is dysfunctions.11–13)
When the cholesterol level in blood is abnormally high andthe endothelium cells are injured, red blood cells become
procoagulant and the adhesiveness of the blood cells
increases. These responses lead to the migration of
monocytes in the lumen as macrophages and the smoothmuscle cells in the media layer into the intima layer.
Moreover, the accumulated macrophages swell into fat-foamcells. Consequently, those conditions increase the arterial
wall thickness and narrow the blood vessel. This phenomenon is illustrated in Fig. 1. Hence, the increase in the IMTof the carotid artery is a valid surrogate marker ofatherosclerosis.14–18) Additionally, Davis et al.19) have reported that the IMT measurement in the carotid artery ismore reproducible than the measurement of the IMT in theabdominal aorta because there is less tissue between theultrasonic probe and the carotid arterial wall. Furthermore,injuredendothelium celllipid coremacrophages
foamy cellint. elastic membranesmooth muscles
ext. elastic membrane
bundles ofcollagen fibrils

thicknessincreasemigration of
the smoothmuscles

intimamedia

adventitia

surrounded by loose connecting tissue

Fig. 1. (Color online) Illustration of atherosclerosis lesions in the arterial

wall.

the IMT measurement is only assessed at the posterior wallof the carotid artery because of the clear double-line patternand consistent appearance.20–22)
A number of methods are capable of detecting the LIB
and MAB of the posterior carotid arterial wall. The manualtechnique for boundary detection is not a desirable method
because it lacks reproducibility.23,24) Thus, automatedtechniques are necessary.29–33) Using the standard medical
imaging tool, computed tomography (CT), cardiovascularrisk factors such as hypertension and atherosclerosis havebeen studied,25) and the reproducibility of good agreement of
the IMT measurement using CT with that using ultrasoundhas been reported.26) Boussel et al.27) and Yanai et al.28)
compared the measurement of the IMT and thickening of
the carotid arterial wall by MRI with those measured withultrasound and found a very good agreement between thesetechniques. However, the method using MRI and CT is time
consuming and cost ineﬀective compared with ultrasound.
Savithri and Purushothaman29) estimated the arterial wallboundaries using the intensity gradient method. However,this method was signiﬁcantly inﬂuenced by ultrasonicscattering in the in vivo measurement even though image
enhancement, thresholding, and denoising processes were
proposed. For the measurement of the IMT using the

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N. Ibrahim et al.

dynamic programming method reported by Liang et al.,30)
the boundaries were detected precisely. However, themethod required retraining when the image characteristics
change, and is thus time consuming. Loizou et al.31) used
an active contour method for the segmentation of the carotid
artery and IMT measurement in an ultrasonic image. The
disadvantage of this method is that the solution often
falls into the local minima rather than the global minima.
This method has been reported by Rocha et al.32) for themeasurement of the boundaries using the combination of
dynamic programming, intensity gradient, and active contour methods. Golemati et al.33) used another approach byusing the segmentation of a speciﬁc shape in an ultrasonicimage so that the lines of the LIB and MAB could be
detected. However, the method can be applied to only
straight carotid arterial walls. Therefore, there is a limitationand inaccuracy in detecting random shapes or curved
vessels. Hence, the accurate detection of the LIB and
MAB positions is essential for the estimation of the IMT.
For the accurate assessment of the LIB and MAB positions,a technique using template matching between the echomodel and the measured echo is used.34–37)
Previous eﬀorts have been carried out to automaticallyassess the LIB and MAB positions using template matching
between the echo models (LIB and MAB echo models) andthe measured in vivo RF echoes.34–37) The positions of themodels, which give the minimum diﬀerence of the measured
echo from the echo model, are determined as the LIB and
MAB. The echo model is created by imitating the reference
RF signal. Kaneko et al.34) and Ikeshita et al.35) considered
the reference RF signals which is an echo from a point
scatterer (wire diameter, 35 m). However, the diﬀerence
between the echo model and the measured in vivo RF echo
and even that between the echo model and the reference RF
signal are signiﬁcant. To obtain better ﬁtting between theecho model and the reference RF signal, the improved echomodel was considered by taking the envelope signal into
account and by using a Gaussian window as an envelope
function of the echo model.36) Even though the diﬀerence
between the improved echo model and the reference RF
signal showed a better result than the previous one,34,35) the
approach of measuring the diﬀerences between the improved
echo model and the measured in vivo RF echoes did notconsider the phase information and, thus, produced inadequate results. The template matching method using theenvelope signal was also reported for the accurate estimationof boundaries, but it only detects the MAB.37) Instead ofcreating an echo model by imitating the reference RF signal,
we omit implemented the echo model by considering theestimated echo signal, which is obtained from the transmitted wave measured using a hydrophone. However, theresults obtained are insuﬃcient since the center frequenciesof the echo models that need to be ﬁtted with the measured
in vivo RF echoes are ﬁxed. A model with a ﬁxed center
frequency is not suitable because the center frequency ofthe echo signal from an arterial wall diﬀers from person toperson due to frequency dependent attenuation. To overcome this drawback, in the present study, the center
frequency and the phase of the echo model are changed
gradually to ﬁnd the best ﬁt between the echo models and themeasured in vivo RF echoes. Moreover, for the accurate

lumen
intima

LIB

media
MAB
adventitia

Fig. 2. (Color online) Schematic of longitudinal section of arterial wall(left) and ultrasound echo (right).

estimation of the MAB, the technique is improved so thatthe echo model could ﬁt to the echo from the MAB insteadof those from the scatterers below the adventitia layer. In
addition, we showed the comparative results of detectedboundaries for a few beams between our improved automated method and the previous and manual methods. In thistechnique, the assessment of the boundary positions isanalyzed in the arterial longitudinal section of the common
carotid artery because it provides a clearer description of theborder between the lumen and the arterial wall. Moreover, inthe longitudinal section, the averaging of the IMT over ashort segment of the artery is applicable.36)
2. Principle and Methodology

Morphologically, the structure of the arterial wall consists ofthree layers, i.e., the intima, media, and adventitia, whichenable arteries to maintain their function and strength. Thevery thin intima layer consists of a single layer of endothelial
cells lining the luminal surface of the arterial wall, and the
media layer, which is the middle layer of the artery, consistsof a complex network of smooth muscle cells, elastin, andcollagen ﬁbrils. Meanwhile, the adventitia layer, which is themechanically strong outer layer, consists mainly of ﬁbroblasts, ﬁbrocytes, and bundles of collagen ﬁbrils.38) These
diﬀerent characteristics of the respective layers produce aparticular ultrasonic echo composed of reﬂected echoes fromthe LIB and MAB, as shown in the schematic view of theposterior wall in the longitudinal section illustrated in Fig. 2.
In an echo signal from the posterior wall, an ultrasonic echo
reﬂected from the LIB is found, followed by a slightly low
amplitude region in the media, and another echo is foundaround the MAB. Since the distance between the LIB andthe MAB, which is deﬁned as the IMT, is considered as avalid surrogate marker of atherosclerosis, the positions of the
LIB and MAB should be determined accurately. Subsequently, in the present study, using our proposed templatematching, the LIB and MAB are determined automatically.
First, we created the echo models for the LIB and MAB
using a transmitted wave measured using a hydrophone.
2.1 Outline of proposed method

The proposed template matching technique for automatic
assessment of the LIB and MAB provides the LIB and MAB
echo models as the template to be matched with themeasured signal from the posterior carotid arterial wall.
Figure 3 shows the ﬂow chart of the outline of the proposed

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N. Ibrahim et al.

Consider the received echo model zˆ ( f 0 , Td , a; nTs ) as thereceived wave y ′( f 0 , Td′ , a; nTs′)[ f s = 40 MHz, f s′ = 1 GHz, Td = mTs′ , Td′ = τ + Td ]ultrasonic diagnostic
equipment SSD-6500

oscilloscope

linear probe
10 MHzMeasure the in vivo RF echo x(nTs )of the posterior carotid arterial wall

clamp
Change the delay time (phase) Td 1 , Td 2 of echo modelz ( f 0 , Td 1 , Td 2 , a1 , a 2 ; nTs ) from 0 to 24 ns

d
clamp

Change the frequency f 0 of echo modelz ( f 0 , Td 1 , Td 2 , a1 , a 2 ; nTs ) from 6 to 8 MHzhydrophone(a)Calculate the MSE α ( f 0 , Td 1 , Td 2 , a1 , a 2 ;τ 1 ,τ 2 )and measured in vivo RF echo x(nTs )noIs f 0 = 8 MHz?
yesno

Is Td1,Td2 = 24 ns?
yes

Search for min {α ( f 0 , Td 1 , Td 2 , a1 , a 2 ;τ 1 ,τ 2 )}(b)

f 0 ,Td1 ,Td 2

normalized amplitude

between the echo model z ( f 0 , Td 1 , Td 2 , a1 , a 2 ; nTs )1

g T (t )

0.5
0
-0.5
-1
0

500

1000

1500

2000

2500

time [ns]Determine the LIB and the MAB of posterior carotid arterial wallFig. 3. (Color online) Flow chart of the outline of the proposed method toaccurately detect the boundaries of the carotid arterial wall.

method. In the beginning, a model z^ð f0 ; Td ; a; nTs Þ of areceived ultrasonic pulse was implemented using a received
ultrasonic pulse y0 ð f0 ; Td0 ; a; nTs0 Þ estimated from the transmitted wave measured using a hydrophone and sampled
at a much higher sampling frequency, where f0 is thecenter frequency, a is the amplitude coeﬃcient, Ts and Ts0 are
the sampling periods (Ts > Ts0 ), and Td and Td0 correspondto the initial phases of z^ð f0 ; Td ; a; nTs Þ and y0 ð f0 ; Td0 ; a; nTs0 Þ,
respectively. Since the LIB and MAB echo models,z^1 ð f0 ; Td1 ; a1 ; nTs Þ and z^2 ð f0 ; Td2 ; a2 ; nTs Þ, are being utilized
to determine the boundaries, the optimum echo models thatﬁt well with the measured signal xðnTs Þ are desired. The
echo model zð f0 ; Td1 ; Td2 ; a1 ; a2 ; nTs Þ is deﬁned as the sumof z^1 ð f0 ; Td1 ; a1 ; nTs Þ and z^2 ð f0 ; Td2 ; a2 ; nTs Þ. To obtain the
optimum echo model, the center frequency f0 of the echomodel zð f0 ; Td1 ; Td2 ; a1 ; a2 ; nTs Þ is changed gradually from 6
to 8 MHz and the root-mean-squared diﬀerences ð f0 ; Td1 ;Td2 ; a1 ; a2 ; 1 ; 2 Þ between the echo model zð f0 ; Td1 ; Td2 ;a1 ; a2 ; nTs Þ and the measured in vivo RF echo xðnTs Þ areestimated using the mean squared error (MSE) method,where i and ai are the time delay and amplitude coeﬃcientof the LIB and MAB echo models, respectively (i ¼ 1: LIB,
2: MAB). The phases of the echo models are also changed
by changing time delay Td1 ; Td2 at a pitch of Ts0 [¼ 1=fs0 ; fs0 :sampling frequency of estimated received pulse y0 ð f0 ; Td0 ; a;nTs0 Þ]. Afterwards, the minimum diﬀerence minfð f0 ; Td1 ;Td2 ; a1 ; a2 ; 1 ; 2 Þg is searched to accomplish the best ﬁt
between the echo models and the measured in vivo RF echo.
The positions of the models for LIB and MAB echoes are
determined as the boundaries of LIB and MAB of theposterior carotid arterial wall.

Fig. 4. (Color online) (a) Experimental setup of measuring transmitted
wave using a hydrophone. (b) Transmitted wave measured using ahydrophone.

2.2 Measurement of received ultrasonic pulse used for
creation of echo model

In this study, an echo model zð f0 ; Td1 ; Td2 ; a1 ; a2 ; nTs Þ ismatched with the measured in vivo RF echo xðnTs Þ from theposterior carotid arterial wall by evaluating the diﬀerencesð f0 ; Td1 ; Td2 ; a1 ; a2 ;  1 ;  2 Þ between them using the MSE
method. To reduce the diﬀerences between the echo modeland the measured in vivo RF echo, we implemented areceived echo model z^ð f0 ; Td ; a; nTs Þ, which considered a
received ultrasonic pulse y0 ð f0 ; Td0 ; a; nTs0 Þ calculated froma transmitted wave measured using a hydrophone. A schematic of the experimental setup for the measurement of atransmitted wave using a hydrophone and the measured
transmitted wave are shown in Figs. 4(a) and 4(b), respectively. The transmitted wave gT ðtÞ was emitted in a watertank with a linear-array transducer at 10 MHz (ALOKA
UST-5410), which is connected to a modiﬁed ultrasonicdiagnosis system (ALOKA SSD-6500). The wave wasmeasured with a hydrophone (FORCE MHA9-150) placedat the distance d of 13.7 mm, associated with the focal
distance. The wave was averaged 64 times, and recorded bya digital oscilloscope (Tektronix TDS 2014) at a sampling
frequency of 1 GHz at 8-bit resolution. Figures 5(a) and 5(b)show a schematic of the propagation of the transmitted andreﬂected waves and the block diagram of the process that
motivates the creation of a model of a received pulse using
a transmitted wave, respectively. In the present study, thetransfer function from an excitation signal to ultrasound intransmission and that from ultrasound to a received signal inreceiving were assumed to be the same. The transmitted
wave is assumed as a one-way propagation wave with

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N. Ibrahim et al.

Transmitted wave = oneway propagation wave
gT(t) = g(t)Transmitted wave measured using a hydrophone
Estimated received ultrasonic pulse

g(t)hydrophone≈ impulse

1

y'(t)

0.5

round-trip propagation
time 2τ

0

-0.5

-1
0

Output signal
500

1000

1500

2000

1

one-way propagation
probe time τreflector or scatterer

2500

g(t)*g(t)Reflected wave = roundtrip propagation wavegR(t) = g(t)time [ns](a)Convert X( f ) to transmit
response G T ( f )Electrical pulse(input signal X( f ) =impulse)Measured with hydrophone
X( f )G T( f )Convert US reflected wave to
receive response GR( f )Probe(Piezo elements)

US transmitted wave
(one-way propagation time τ )Normalized amplitude

normalized amplitude

Input signal

0.5

0

-0.5

-1
0

500

1000

1500

2000

2500

time [ns]Receive wave
(output signal Y( f ))Fig. 6. (Color online) Transmitted wave measured gT ðtÞ using ahydrophone and the estimated received ultrasonic pulse y0 ðtÞ.

US reflected wave
(round-trip propagation time 2τ )Reflection factor of
scatterer S( f ) = A

Y 0 ð f Þ ¼ fGT ð f ÞXð f ÞGR ð f ÞSð f Þge j2f ¼ AfGð f Þg2 e j2f  :

(b)

ð2Þ

0

Fig. 5. (Color online) (a) Schematic propagation of transmitted andreﬂected waves. (b) Frequency-domain block diagram of the process that
occurs when the transmitted wave encounters a scatterer.

the delay  and the reﬂected wave is assumed as a roundtrip propagation wave with the propagation time of 2
[Fig. 5(a)]. From the frequency domain block diagram
shown in Fig. 5(b), the process starts with the electricalsignal xðtÞ that excites the piezo elements. In the ultrasonicdiagnosis equipment used in the present study, the excitation
signal is a rectangular pulse with a width of half the
wavelength. However, the excitation signal was assumed to
be an impulse for simplicity. The piezo elements transform it
to the transmitted wave gT ðtÞ. At this stage, the transmitted
wave is measured using the hydrophone. Note that the wave
encounters the scatterer and returns to the transducer asa reﬂected wave AgT ðtÞ with a reﬂection factor Sð f Þ ¼ A(A: real constant) when a scatterer is placed alongpropagation path of the transmitted wave. In this process,the reﬂection factor Sð f Þ is assumed not to change withfrequency. The reﬂected wave AgT ðtÞ that returns to thesame transducer is transformed again by piezo elements,
whose response is gR ðtÞ, to the received wave yðtÞ ¼
AgT ðtÞ  gR ðtÞ, where  denotes convolution. Since thetransmitting and receiving elements are the same, gT ðtÞ ¼
gR ðtÞ is appropriate. Thus, the received wave (output) yðtÞ ofthe overall process can be written as AfGð f Þg2 ;Yð f Þ ¼ fGT ð f ÞXð f ÞGR ð f ÞSð f Þg ¼

ð1Þ

where Yð f Þ, GR ð f Þ, GT ð f Þ, and Xð f Þ are the frequency
spectra of yðtÞ, gR ðtÞ, gT ðtÞ, and xðtÞ, respectively. In thiswork, the time delay due to the round-trip propagation of thereceived wave is modiﬁed to be the same as the time delay
due to the one-way propagation of the transmitted wave of ,
so as to observe the transmitted wave and the received wave
at the same time. Therefore, the received wave, namely thereceived ultrasonic pulse y0 ðtÞ, is deﬁned as

To account for the received ultrasonic pulse y ðtÞ in the time
domain, the inverse Fourier transform =1 ½ is applied toY 0 ð f Þ. Figure 6 shows the transmitted wave gT ðtÞ measured
using a hydrophone and the estimated received ultrasonicpulse y0 ðtÞ. The echo model that was created by referring toan echo from a point scatterer (wire diameter, 35 m)34,35)
and a glass plate36) is not desired because such echoes
depend on the reﬂector, and thus the echo from the carotid
arterial wall is not suitable to be ﬁtted with. Therefore, in thepresent study, the received ultrasonic pulse was estimatedusing the transmitted wave measured using a hydrophone.
2.3 Implementation of optimum received echo model

In the subsequent sections, we have elaborated the drawbackof the previous method, i.e., the results obtained are
insuﬃcient since the center frequency of the echo model,
which needs to be ﬁtted with the measured in vivo RF
echoes, is ﬁxed. Thus, in the present study, to ﬁnd the best ﬁt
between the echo model zð f0 ; Td1 ; Td2 ; a1 ; a2 ; nTs Þ and themeasured in vivo RF echo xðnTs Þ, the optimum echo modelis implemented by changing the center frequency f0 andthe phases Td1 , and Td2 of the echo model gradually. As
noted, an echo model zð f0 ; Td1 ; Td2 ; a1 ; a2 ; nTs Þ is deﬁned as
the sum of LIB and MAB echo models, z^1 ð f0 ; Td1 ; a1 ; nTs Þand z^2 ð f0 ; Td2 ; a2 ; nTs Þ, and the received echo modelz^ð f0 ; Td ; a; nTs Þ is determined from the received ultrasonicpulse y0 ð f0 ; Td0 ; a; nTs0 Þ. As shown in Fig. 7, the leadingedge of z^ð f0 ; Td ; a; nTs Þ is determined at the zero phase ofy0 ð f0 ; Td0 ; a; nTs0 Þ with the prior length of . In addition, thevariables Td and Td0 are expressed as Td ¼ mTs0 and Td0 ¼
 þ Td . The received echo model z^ð f0 ; Td ; a; nTs Þ, which is
the received ultrasonic pulse y0 ð f0 ; Td0 ; a; nTs0 Þ calculated
from the transmitted wave gT ðtÞ measured using a hydrophone, is sampled at fs0 ¼ 1 GHz (Ts0 ¼ 1=fs0 ¼ 1 ns). Meanwhile, the measured in vivo RF echo (details of theacquisition are described in Sect. 3) is sampled at fs ¼
40 MHz (sample spacing: 25 ns). The center frequency fc
of the received echo model is 7.4 MHz, determined at thepeak frequency of the power spectrum of the received

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we are able to obtain the best ﬁt with the measured in vivo
RF echo.

leading edge

normalized amplitude1(a)2.3.1 Echo model composed of multiple received echomodels obtained from received ultrasonic pulse

0.5

The optimum received echo model z^ð f0 ; Td ; a; nTs Þ is
implemented by changing the center frequency and initial
phase of the received echo model y0 ð f0 ; Td0 ; a; nTs0 Þ. Since theultrasonic echo signal from the posterior wall includes
mainly echoes from the LIB and MAB, as shown in Fig. 2,
the echo model zð f0 ; Td1 ; Td2 ; a1 ; a2 ; nTs Þ from the arterial
wall is composed of two received echo models; the LIB and
MAB echo models z^1 ð f0 ; Td1 ; a1 ; nTs Þ and z^2 ð f0 ; Td2 ; a2 ; nTs Þ,
which can be expressed as

0

-0.5

-1
0

100

200

300

400

500

600

700

time [ns]normalized amplitude

1(b)z^ð f0 ; Td1 ; Td1 ; a1 ; a2 ; nTs Þ¼ z^1 ð f0 ; Td1 ; a1 ; nTs   1 Þ þ z^2 ð f0 ; Td2 ; a2 ; nTs  2 Þ; ð3Þ

0.5
τ

0

-0.5

-1
0

500

1000

1500

2000

2500

time [ns]Fig. 7. (Color online) (a) Received echo model z^ð f0 ; Td ; a; nTs Þ obtained
from (b) the received ultrasonic pulse y0 ðTd0 ; a; Dn Ts0 Þ.

where f0 is the center frequency, Td1 and Td2 correspond toinitial phases, a1 and a2 are amplitude coeﬃcients, and 1and  2 are the time delays of the respective echoes. The
parameters f0 , fTdi g, fai g, and fi g (i ¼ 1: LIB, 2: MAB)
are determined so that the diﬀerence between the echomodel zð f0 ; Td1 ; Td2 ; a1 ; a2 ; nTs Þ and measured in vivo RFecho xðnTs Þ is minimum. Figure 9 shows the echo modelzð f0 ; Td1 ; Td2 ; a1 ; a2 ; nTs Þ composed of z^1 ð f0 ; Td1 ; a1 ; nTs Þ andz^2 ð f0 ; Td2 ; a2 ; nTs Þ, at the center frequency f0 of 7.4 MHz and
the initial phases Td1 , and Td2 of 0 ns.
2.4 Procedure of fitting echo models to measured in vivo
RF echoecho model. Firstly, to obtain the same sampling interval(1=fs ¼ 25 ns) of these echo signals, even though the center
frequency f0 of the received echo model z^ð f0 ; Td ; a; nTs Þ ischanged, the sampling rate Ts of the downsampled received
echo model is set to be the same as the sampling rate of themeasured in vivo RF echo xðnTs Þ of 40 MHz. By performing
this procedure, the center frequency f0 of the received echomodel is changed gradually from 6 to 8 MHz at intervalsof 0.3 MHz, which corresponds to one sampling interval
Ts ¼ 1 ns of a cycle of fc ¼ 7:4 MHz (Ts =Ts0  fc ). The
downsampling factor D is deﬁned as D ¼ f0 =fc  Ts0 =Ts .
Figures 8(b)–8(e) show the received echo models at center
frequencies of 6.2, 6.5, 6.8, and 7.4 MHz at downsampling
factors D of 21, 22, 23, and 25, respectively. Secondly,by changing the beginning point Td (corresponding to theinitial phase) of the received echo model z^ð f0 ; Td ; a; nTs Þ
by a pitch of 1 ns, the delay time of the received echomodel is changed gradually, as shown in Figs. 8(f )–8(i)for Td ¼ 0, 5, 10, and 15 ns, respectively. The diﬀerenceð f0 ; Td1 ; Td2 ; a1 ; a2 ;  1 ;  2 Þ between the echo model zð f0 ; Td1 ;Td2 ; a1 ; a2 ; nTs Þ and the measured in vivo RF echo xðnTs Þ is
calculated at the preassigned lags fi g (i ¼ 1: LIB, 2: MAB)
for the received echo models at diﬀerent delay times fTd g.
When the received echo model reaches the delay time of
Td ¼ 25 ns, the calculation of the diﬀerence ð f0 ; Td1 ; Td2 ;a1 ; a2 ; 1 ; 2 Þ is repeated by incrementing the delay time fi g
by 25 ns. Therefore, the delay time Td is changed from 0 ns
to the maximum of 24 ns. Consequently, the combinationsof all the center frequencies f f0 g and the phases fTd g of
the received echo model z^ð f0 ; Td ; a; nTs Þ could be consideredto implement the optimum received echo model, and thus

Our proposed automated template matching method for thedetection of the carotid arterial wall boundaries is realizedby calculating the MSE ð f0 ; Td1 ; Td2 ; a1 ; a2 ; 1 ; 2 Þ between
the measured in vivo RF echoes xðnTs Þ and the echo modelzð f0 ; Td1 ; Td2 ; a1 ; a2 ; nTs Þ expressed asð f0 ; Td1 ; Td2 ; a1 ; a2 ;  1 ;  2 Þ
¼

1
X
1N
jxðnTs Þ  zð f0 ; Td1 ; Td2 ; a1 ; a2 ; nTs ; 1 ; 2 Þj2 þ ;
N n¼0

¼

X
1 N1
jxðnTs Þ  f^z1 ð f0 ; Td1 ; a1 ; nTs   1 Þ
N n¼0

þ z^2 ð f0 ; Td2 ; a2 ; nTs  2 Þgj2 þ ;
 T2
1 X
¼
jxðnTs Þ  z^1 ð f0 ; Td1 ; a1 ; nTs  1 Þj2
N nTs ¼T1
þT4XjxðnTs Þ  z^2 ð f0 ; Td2 ; a2 ; nTs  2 Þj2

nTs ¼T3þ

T3
XjxðnTs Þ  f^z1 ð f0 ; Td1 ; a1 ; nTs  1 Þ

nTs ¼T2þ z^2 ð f0 ; Td2 ; a2 ; nTs  2 Þgj2 þ ;

ð4Þ

where fTm g (m ¼ 1; 2; 3; 4) indicates the periods illustrated
in Fig. 10(a) and the variance  is deﬁned as
¼

var ½xðnTs Þ;nTs 2L1 ;L3

ð5Þ

where L1 ¼  1  T0 and L3 ¼ 2  1  L2 . Figure 10(a)shows an example of echo models when z^1 ð f0 ; Td1 ; a1 ; nTs Þ

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(a)leading edge

normalized amplitude

1

0.5

0

-0.5

-1
0

100

200

300

400

500

600

700

time [ns]10.06

amplitude

amplitude

0

-0.5

(b)

0.2

0.4

time [ μ s]

0.6

Td = 0 ns
0

10

(f)
20

30

40

50

40

50

40

50

40

50

time [ns]
0.06
0.04

amplitude

0.5

amplitude

0
-0.02

-0.06

0.8

1

0

-0.5

0.02
0
-0.02
-0.04

f 0 = 6.5 MHz

-1
0

0.2

0.4

Td = 5 ns

-0.06

0.6

0

time [ μ s]

10

20

(g)
30

time [ns]

1

0.06
0.04

amplitude

0.5

amplitude

0.02

-0.04

f 0 = 6.2 MHz

-1
0

(c)

beginning point( = initial phase)

0.04

0.5

0

-0.5

0.02
0
-0.02
-0.04

(d)

f 0 = 6.8 MHz

-1
0

0.2

0.4

time [ μ s]

Td = 10 ns

-0.06

0.6

0

10

20

(h)
30

time [ns]

1
0.06
0.04

amplitude

amplitude

0.5

0

-0.5

0.02
0
-0.02
-0.04

(e)

f 0 = 7.4 MHz

-1
0

0.1

0.2

0.3

0.4

time [ μs]0.5

Td = 15 ns

-0.06

0.6

0

10

20

30

(i)

time [ns]Fig. 8. (Color online) (a) Received echo model z^ð f0 ; Td ; a; nTs Þ calculated from the transmitted wave measured using a hydrophone. z^ð f0 ; Td ; a; nTs Þ at(b) f0 ¼ 6:2 MHz, (c) f0 ¼ 6:5 MHz, (d) f0 ¼ 6:8 MHz, and (e) f0 ¼ 7:4 MHz. Enlarged partial waveforms of echo model indicating the initial point ofz^ð f0 ; Td ; a; nTs Þ at the delay times of (f ) Td ¼ 0, (g) Td ¼ 5, (h) Td ¼ 10, and (i) Td ¼ 15 ns.

1
LIB echo model
MAB echo model

amplitude

0.5

0

-0.5

-1

0

0.5

1

1.5

2

2.5

time [ μs]Fig. 9. (Color online) Echo model composed of LIB and MAB echomodels at the frequency f0 of 7.4 MHz and the initial phase Td of 0 ns.

and z^2 ð f0 ; Td2 ; a2 ; nTs Þ do not overlap, which is describedby the ﬁrst and second subterms on the right-hand side ofEq. (4). Figure 10(b) shows an example when z^1 ð f0 ; Td1 ; a1 ;nTs Þ and z^2 ð f0 ; Td2 ; a2 ; nTs Þ overlap (echo was measured
at beam position i in Fig. 11). The overlapped echoes fromthe LIB and MAB are automatically taken into consideration
because the MSE ð f0 ; Td1 ; Td2 ; a1 ; a2 ; 1 ;  2 Þ is between themeasured in vivo RF echoes xðnTs Þ and the echo modelzð f0 ; Td1 ; Td2 ; a1 ; a2 ; nTs Þ obtained using the sum of z^1 ð f0 ; Td1 ;a1 ; nTs Þ and z^2 ð f0 ; Td2 ; a2 ; nTs Þ. The evaluation of thediﬀerence between the measured in vivo RF echoes xðnTs Þand the sum of the echo models z^1 ð f0 ; Td1 ; a1 ; nTs Þ andz^2 ð f0 ; Td2 ; a2 ; nTs Þ is shown by the third subterm in Eq. (4).
For the robust detection of LIB, the variance ðnTs 2 L1 Þ inthe region above the LIB echo (from T0 to T1 ) is calculated
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15

L2

0.3

0.1
0
-0.1

T4

-0.2

(a)

15.5

L3

L1

depth [mm]amplitude

0.2

L4

-0.3

T2 T3

T0 T1
0

Measured echo
LIB echo model
MAB echo model

0.5

1

1.5

2

0.3

i

k

16.5
17
17.5
18
1.5

2.5

3.0

4.5

6.0

lateral length [mm]time [μs]L2 ∩ L2 ∩ L4

16

Fig. 11. B-mode image of posterior carotid arterial wall of healthy 23year-old male.

L4 ∩ L2 ∩ L4

L2 ∩ L4

amplitude

0.2
0.1
0
-0.1
-0.2

(b)

-0.3 T0
0

T1
0.5

T2 T3
1

T4
1.5

Measured echo
LIB echo model
MAB echo model

2

2.5

time [ μ s]
Fig. 10. (Color online) Measured signal at beam position i labeled inFig. 11 and echo model in cases of (a) separated and (b) overlappedz^1 ð f0 ; Td1 ; a1 ; nTs Þ and z^2 ð f0 ; Td2 ; a2 ; nTs Þ.

as shown by the second term on the right-hand side ofEq. (4), deﬁned in Eq. (5). Note that T0 is the starting pointof the ﬁtting process that is manually set. Since themagnitude of the echoes from the lumen is small, ðnTs 2
L1 Þ is nearly zero. On the other hand, ðnTs 2 L1 Þ increases
when the LIB echo model is ﬁtted to the MAB echo.
Additionally, the variance ðnTs 2 L3 Þ of the measured
signals from T2 to T3 is added [Eq. (5)] when the echomodels do not overlap. The variance ðnTs 2 L3 Þ increases
when the echo models are ﬁtted with the echo from the tissue
below the adventitia layer that is falsely detected. Therefore,
the estimation of ðnTs 2 L3 Þ could prevent the echo frombelow the adventitia layer from being detected. Overall, themisdetection of the LIB and MAB can be reduced by
calculating such variances. Thereafter, the positions of the
LIB and MAB echo models, z^1 ð f0 ; Td1 ; a1 ; nTs Þ and z^2 ð f0 ;Td2 ; a2 ; nTs Þ, which give the minimum diﬀerence minfð f0 ;Td1 ; Td2 ; a1 ; a2 ;  1 ;  2 Þg between the measured in vivo RFecho xðnTs Þ and echo model zð f0 ; Td1 ; Td2 ; a1 ; a2 ; nTs Þ aredetermined as the optimum positions of the LIB and MAB.
3. Boundary Detection for In vivo Ultrasonic Data

In the in vivo measurement, the modiﬁed ultrasonicdiagnostic equipment (ALOKA SSD-6500) was used. The
right carotid artery of a healthy male at the age of 23 wasscanned by a 10-MHz linear array transducer (ALOKA
UST-5410), and the RF signals were sampled at 40 MHz at a
16-bit resolution. The electronic focus was set at 13.7 mm
and the acoustic focus was at 20 mm. By applying the MSE
method, as shown in Eq. (4), two restrictions were set in thisdetection procedure. First, the amplitude of detected MAB

should be larger than the amplitude of the detected LIB.
Second, since the average IMT ranges from 0.39 to 0.62 mm
for a healthy male aged between 17 and 63,39) and the IMT
varies from person to person, in this work, the range of2   1 was scaled from 0.35 to 1.0 mm. Figure 11 shows
the B-mode image of the posterior carotid arterial wall for a
7.5-mm-long short segment in the arterial longitudinal
direction (with 150 m beam spacing). The results of theevaluated MSE ð f0 ; Td1 ; Td2 ; a1 ; a2 ;  1 ;  2 Þ between themeasured in vivo RF echoes xðnTs Þ and the echo modelz^ð f0 ; Td1 ; Td2 ; a1 ; a2 ; nTs Þ for the beam position k labeledin the B-mode image of the posterior carotid arterial
wall (Fig. 11) are shown in Fig. 12. Figure 12(a) represents
the distribution of the minimum MSE minf10 log10 ð f0 ; Td1 ;Td2 ; a1 ; a2 ; 1 ; 2 Þg evaluated at the center frequencies off0 ¼ 7:4 MHz for LIB and MAB echo models, for which thelowest minimum MSE minfð f0 ; Td1 ; Td2 ; a1 ; a2 ; 1 ; 2 Þg isdetermined at the initial phase Td1 ¼ 4 ns of LIB andTd2 ¼ 24 ns of MAB echo models. Focusing on the lowest
minf10 log10 ð f0 ; Td1 ; Td2 ; a1 ; a2 ; 1 ; 2 Þg (with optimum
phases and frequency of LIB and MAB echo models) of
21.2%, the positions of the LIB and MAB are indicated atpoint R, as shown in Fig. 12(b) of the MSE distribution
f10 log10 ð f0 ; Td1 ; Td2 ; a1 ; a2 ; 1 ; 2 Þg. Note that the estimated minf10 log10 ð f0 ; Td1 ; Td2 ; a1 ; a2 ;  1 ;  2 Þg is between
the normalized measured in vivo RF echo xðnTs Þ and the
LIB and MAB echo models, z^1 ð f0 ; Td1 ; a1 ; nTs Þ and z^2 ð f0 ;Td2 ; a2 ; nTs Þ. The positions of the LIB and MAB, which are
the positions of the LIB and MAB echo models ﬁtted withthe measured in vivo RF echo at the minimum MSE
minfð f0 ; Td1 ; Td2 ; a1 ; a2 ; 1 ; 2 Þg, are shown in Fig. 12(c).
The MSE distributions f10 log10 ð f0 ; Td1 ; Td2 ; a1 ; a2 ; 1 ;
2 Þg, which are evaluated for the measured signal and theecho model without and with the variance ðnTs 2 L1 Þ, arepresented in Figs. 13 and 14, respectively. To conﬁrm
the robustness of LIB detection by adding the varianceðnTs 2 L1 Þ, the distributions do not account for the varianceðnTs 2 L3 Þ. As shown in Fig. 13(a), the MSE withoutðnTs 2 L1 Þ appears to have more than one local minimum
MSE other than region P. The local minimum in region R
is similar to the global minimum in region P. Thus, amisdetection boundary would occur [Fig. 13(b)]. In contrast,
as shown in Fig. 14(a), the MSE with ðnTs 2 L1 Þ reduces
the local minimum and focus into the LIB region.
Considering the variance ðnTs 2 L3 Þ in the MSE evalua-

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10log10α [dB]10log10α [dB]

25

-27.4

Td1

-20

τ 2 = τ MAB

P

-27.6

20

-22

MAB

-27.8
15

R

-28

-24
10

5

-28.2

Td1 = 4 ns

-28.4

Td2 = 24 ns

(a)
5

10

15

20

-26

-28.6
25

200 μ mTd2

10log10α [dB]LIB
350 μ m-20

τ 2 = τ MAB

-28

(a)
0 μm

200 μ m

τ 1 = τ LIB

-30

-22

0.15
-24

amplitude

0.1-26
200 μ m

MAB

(b)

-28

R

0
-0.05
-0.1

-30

τ = τ LIB

0 μm

200 μ m 1

0.15

MAB

LIB

(b)
0

Measured echo
LIB echo model
MAB echo model

0.1

amplitude

0.05

LIB

350 μ m(c)

Measured echo
LIB echo model
MAB echo model

0.5

1

1.5

2

2.5

time [μ s]0.05

Fig. 13. (Color online) (a) MSE distributions f10 log10 ð f0 ; Td ; 1 ; 2 Þg
evaluated for measured signal and echo model without the varianceðnTs 2 L1 Þ. (b) Result of ﬁtting of measured signal and echo model without
the variance ðnTs 2 L1 Þ.

0
-0.05
-0.1
0

LIB

MAB

0.5

1

1.5

2

2.5

time [ μ s]

Fig. 12. (Color online) (a) Distribution of minimum MSE minfð f0 ; Td1 ;Td2 ; a1 ; a2 ;  1 ;  2 Þg at the frequency f0 ¼ 7:4 MHz of LIB and MAB echomodels. (b) MSE distribution f10 log10 ð f0 ; Td1 ; Td2 ; a1 ; a2 ; 1 ; 2 Þg
indicating the positions of LIB and MAB at point R. (c) Estimatedminimum MSE minfð f0 ; Td1 ; Td2 ; a1 ; a2 ;  1 ;  2 Þg of 21.2% between themeasured in vivo RF echo at beam position k and the LIB and MAB echomodels.

10log10α [dB]-20

τ 2 = τ MAB

-22

R
-24

MAB

-26
200 μ mtion, the MAB echo model is ﬁtted closer to the LIB region,
as shown in Fig. 12(c). Meanwhile, the MAB echo model isﬁtted at the position with the distance of L3 from the LIB
echo model [Fig. 14(b)] when the variance ðnTs 2 L3 Þ isnot considered in the MSE evaluation. These results veriﬁedthat the estimation of the ðnTs 2 L3 Þ could prevent theecho from the tissue below the adventitia layer from being
detected. Therefore, the misdetection of the LIB and MAB
can be reduced by calculating such variances.

-28

(a)

350 μ m-30

τ = τ LIB

0 μm

200 μ m 1

0.15

Measured echo
LIB echo model
MAB echo model

amplitude

0.1

4. Discussion

In the present study, the template matching technique ofour proposed automated method has been improved to detect
the boundaries of the intima-media complex (IMC) moreaccurately. We also considered the echo model using thereceived pulse estimated from the transmitted wave
measured using a hydrophone, instead of referring the echo
from a point scatterrer. As explained in Sect. 2.2, theexcitation signal xðtÞ was assumed to be an impulse.
However, the actual signal was a rectangular pulse with awidth of half the wavelength. In such a case, the frequency

LIB

0.05
0
-0.05
-0.1

LIB
L1

(b)

0

MAB

L2
0.5

L3
1

L4
1.5

2

2.5

time [ μ s]

Fig. 14. (Color online) (a) MSE distributions f10 log10 ð f0 ; Td ; 1 ; 2 Þg
evaluated for measured signal and echo model with the varianceðnTs 2 L1 Þ. (b) Result of ﬁtting of measured signal and echo model withthe variance ðnTs 2 L1 Þ.

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1

15
LIB
MAB

15.5

depth [mm]

Power

0.8

0.6

0.4

16
16.5
17
17.5
18

(a)

0.2

1.5

(a)
0

0

5

10

15

4.5

6.0

20

Frequency [MHz]

15
LIB
MAB

15.5

1

depth [mm]|Y( f )|
|Y'( f )|

0.8

Power

3.0

lateral length [mm]

0.6

16
16.5
17
17.5
18

0.4

(b)
1.5

3.0

4.5

6.0

lateral length [mm]

0.2

(b)
0

0

5

10

15

Fig. 16. (Color online) B-mode image of detected boundaries of theposterior carotid arterial wall by (a) the improved template matching
technique of the proposed method and (b) the previous method.

20

Frequency [MHz]Fig. 15. (Color online) (a) Power spectrum jXð f Þj2 of a rectangular pulse
with a pulse length of half the wavelength. (b) Power spectra jYð f Þj2 andjY 0 ð f Þj2 .

spectra Yð f Þ and Hð f Þ of the transmitted ultrasonic pulsehðtÞ measured with a hydrophone and received pulse yðtÞ areexpressed as follows:Hð f Þ ¼ Gð f ÞXð f Þ;

ð6Þ2

fHð f Þg:ð7Þ
Xð f Þ
In the present study, the estimated Y 0 ð f Þ of the received
pulse Yð f Þ was deﬁned as fHð f Þg2 . Therefore, the bandwidthof Y 0 ð f Þ is narrower than Yð f Þ owing to the overconsideration of Xð f Þ. Let us explain the eﬀect of the overconsideration of Xð f Þ below. Figure 15(a) shows the power
spectrum [corresponding to jXð f Þj2 ¼ f02 ½1  cosðf =f0 Þ=ð2 f 2 Þ] of a rectangular pulse with a length of half the
wavelength at a center frequency f0 ¼ 10 MHz, simulating
the excitation signal xðtÞ. For simplicity, in this explanation,
the amplitude spectrum jHð f Þj of the transmitted wave hðtÞwas assumed to be Gaussian (¼ exp½ð f  f0 Þ2 = 2 ,  ¼
3 MHz in Fig. 15). Figure 15(b) shows jYð f Þj2 ¼ jfHð f Þg2 =Xð f Þj and jY 0 ð f Þj2 ¼ jfHð f Þg2 j. As can be seen in
Fig. 15(b), the over consideration of Xð f Þ shifted the center
frequency and decreased the bandwidth (width at half
maximum) by 2.6%. The shift of the center frequency can becompensated by stretching or compressing the model signal
in the proposed method. There should be a change inwaveform owing to the decrease in the bandwidth. However,the change in bandwidth would be a few percent (2.6%) as
described above and it can be assumed to be negligible.
For the evaluation of the results, we compared theboundaries in a few beams obtained by our improved
technique of the proposed automated method, the previous
method, and the manual method. Figures 16(a) and 16(b)Yð f Þ ¼ fGð f Þg2 Xð f Þ ¼

show boundaries detected by the improved templatematching technique of the proposed method and those by
the previous method, respectively. From these results, atsome beam positions [marked by arrows with yellow lines inFig. 16(b)], the boundaries estimated by the improved
technique diﬀer from those estimated by the previous
one. For all the analyzed beams, only (4.8–27.5%) ofminfð f0 ; Td1 ; Td2 ; a1 ; a2 ;  1 ;  2 Þg convinces us that the echomodel used in this study is in very good agreement with themeasured in vivo RF echo. Moreover, the average IMTs fora 7.5-mm-long short segment in the arterial longitudinal
direction calculated by the improved technique and that by
the previous one are 502  61 and 558  120 m, respectively, showing a decrease in the standard deviation. Thus,
the proposed method yields better results than the previous
method and leads to more accurate boundary detection.
From the manual method of boundary detection, as shownin Fig. 17, that was carried out by observing the B-mode
image, the average IMT for a 7.5-mm-long short segment in
the arterial longitudinal direction was 504  65 m. This
result shows that the improved technique achieve a smaller
diﬀerence in the estimated average IMT from that by manual
method (only 0.4% diﬀerence) compared with the average
IMT obtained by the previous method (10.7% diﬀerence).
However, since the manual method is lacks reproducibility
and depends on the operator, the comparison between the
result obtained by the automated method and that by the
manual method is not satisfactory for evaluation. We foundthat it is diﬃcult to evaluate the results obtained in thepresent study since the true values are not provided in thein vivo measurement. Hence, the accuracy evaluation of
the improved technique of our proposed method should
be further investigated. Furthermore, at a few beams, small
distortions of MAB are observed. This could be due to the

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1) S. Mendis, P. Puska, and B. Norrving: Global Atlas on Cardiovascular

15
LIB
MAB

depth [mm]

15.5

2)
3)

16
16.5

4)

17
17.5

5)

18
6)

1.5

3.0

4.5

6.0

lateral length [mm]7)Fig. 17. (Color online) B-mode image with detected boundaries of theposterior carotid arterial wall obtained by the manual method.

8)
9)
10)

interference of ultrasound echoes caused by tissue scatterers
spaced closer together than the duration of ultrasonic pulses.
In this study, the wavelength of an ultrasound wave was
150 m at 10 MHz. The adventitia layer, which consists ofmostly of tissues such as bundles of collagen ﬁbers,38) issmaller than the wavelength of ultrasound (tissue cells are
10–20 m thick and 15–50 m high).40) The ultrasound wavethat is emitted to such small structures scatters and returns
to the transducer through multiple pathways. Thus, the echothat returns to the transducer is no longer coherent. It is
instead the sum of a number of echoes that produces acomplex pattern of constructive and destructive interference.
Boundary detection would be inﬂuenced by such interference. Furthermore, more than one scatterers would exist
in the adventitia layer owing to the bundles of collagenﬁbers, compared with the intima layer, which consists of asingle layer of endothelium cells. Thus, the adventitia layer
produces multiple interfaces that could be misdetected as
MAB. In a future work, to improve the detection of MAB byconsidering the multiple interfaces produced in the adventitia layer, multiple echo models should be employed whenﬁtting with the echo from the MAB instead of an echomodel. However, the waveform of the measured echo in thiscase should also be further analyzed to obtain a better result.
In summary, the results obtained in the present study using
the improved proposed method showed an advantage for the
automatic estimation of the IMT of the carotid arterial wall,and it would be useful for the diagnosis of early-stage
atherosclerosis.

11)
12)
13)
14)
15)
16)
17)

18)
19)
20)

21)
22)
23)
24)
25)
26)
27)

28)
29)
30)

5. Conclusions

In this study, using the proposed template matching
technique, we automatically estimated the boundary positions of IMT in the posterior wall of the carotid artery of
a 7.5-mm-long short segment in the arterial longitudinal
direction. The technique realized more accurate MAB
detection. Moreover, for the accurate detection of the LIB
and MAB, the frequency and phase of the echo model wereconsidered when ﬁtting with the measured echo. It wasobserved that the average IMT obtained by the improved
technique in the present study was 502  61 m, showing adecrease in standard deviation compared with the average
IMT of 558  120 m obtained by the previous method.
Thus, the method employed in the present study yields moreaccurate results than the previous method and leads toaccurate boundary detection.

31)
32)
33)
34)
35)
36)
37)
38)
39)
40)

07HF03-10

Disease Prevention and Control (World Health Organization, Geneva,2011) p. 3.
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Kashiwagi, and J. Hayashi: Atherosclerosis 172 (2004) 337.
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Atherosclerosis 154 (2001) 497.
J. Gariepy, J. Salomon, N. Denarie, F. Laskri, J. L. Megnien, J. Levenson,
and A. Simon: Arterioscler. Thromb. Vasc. Biol. 18 (1998) 584.
J. Gariepy, M. Massonneau, J. Levenson, D. Heudes, and A. Simon:
Hypertension 22 (1993) 111.
E. de Groot, G. K. Hovingh, A. Wiegman, P. Duriez, A. J. Smit, J.-C.
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