電気学会論文誌 C（電子・情報・システム部門誌）
IEEJ Transactions on Electronics, Information and Systems
Vol.137 No.4 pp.573-578
DOI: 10.1541/ieejeiss.137.573

Paper

Near Real-time Measurement of Myocardial Contraction andRelaxation Based on High-frame-rate Ultrasound with aGraphical Processing Unit System
Takuma Asai＊ Non-member,
Hirofumi Taki＊ Member
Hiroshi Kanai＊＊a) Non-member(Manuscript received June 20, 2016, revised Sep. 13, 2016)
Several speckle tracking methods have been proposed for noninvasive and quantitative evaluation of tissue motion. Since thelow temporal resolution causes a large myocardial motion in the elevational direction and a large deformation, two-dimensional(2D) speckle tracking at a high frame rate is desirable for accurate estimation of myocardial contraction and relaxation. 2D speckle
tracking at a high frame rate requires a high computational load, and the large suppression of calculation time is, therefore,
essential for clinical use. In the present study, we investigate the minimum frame rate required for the estimation of myocardial
contraction and relaxation. Furthermore, we employ a parallel computing principle using a graphical processing unit (GPU)system with 2,496 streaming processors to decrease the calculation time effectively. The employment of a parallel computing
principle with a GPU system successfully decreased the calculation time to 1/50 of that using a desktop PC with a CPU. Whenthe number of tracking points is 64, the calculation time was decreased to 28.7 s for the estimation during 1 s at a frame rate of
287 Hz, indicating that the proposed method with a GPU system has a potential to realize a near real-time estimation of myocardial
contraction and relaxation.
Keywords : speckle tracking, myocardial contraction/relaxation property, two-dimensional (2D) displacement estimation, graphicalprocessing unit (GPU)two-dimensional (2D) speckle tracking at a high frame rate isdesirable for accurate estimation of myocardial contraction andrelaxation(28)(29). In our previous study, 2D displacements of theheart wall were estimated at a frame rate of 860 Hz and over usinga wide transmit beam(30)-(32). The high temporal resolution of themethod is suitable for the accurate estimation of myocardial
contraction and relaxation; however, a 2D speckle tracking method
at a high frame rate requires a high computational load, and thelarge suppression of calculation time is, therefore, essential forclinical use.
Recently, graphics processing unit (GPU) systems have drawn
large interest as a solution to the high computational load(33)-(35).
In the present study, we investigate the potential of the 2D speckle
tracking method at a high frame rate using a GPU system in themeasurement of myocardial contraction and relaxation. Ourcontributions are considered to be two-fold. First, we examine theminimum frame rate required for the estimation of myocardial
contraction and relaxation. Second, we evaluate the potential of a
GPU system in the high-frame-rate measurement of myocardial
contraction and relaxation using a 2D speckle tracking method,where this application has not been considered in GPU
investigations.

1. Introduction
Cardiovascular disease is a major cause of mortality, and early
diagnosis of the disease has become increasingly important becauseof the progress of the aging society. Ultrasonic diagnosis is anindispensable modality for diagnosis of cardiac diseases because it
is noninvasive, cost effective, and easy to use. Diagnostic ultrasoundis, therefore, widely used for diagnosis of the circulatory system,
such as arteries(1)-(3) and the heart(4)-(6). For the diagnosis ofarteriosclerosis, the roughness of the blood vessels has been
estimated using a highly accurate method for imaging the surface
of a vessel(7)-(9). Techniques for measuring the elasticity of a bloodvessel using echoes from the radial artery have been studied(10)-(12).
For the evaluation of cardiac function, methods for visualizationof hemodynamics(13)-(15) and tissue Doppler methods(16)-(18) havebeen reported. Two-dimensional myocardial displacement
measurement(19)(20) and regional myocardial thickness variation
rate measured by speckle tracking(19)(21) have been reported to be
useful techniques; however, investigation of myocardial change
from a contracted state to a relaxed state during one cardiac cycle
is still challenging(22)-(24).
Since the low temporal resolution causes a large myocardial
motion in the elevational direction and a large deformation(25)-(27),2. Materials and Methodsa) Correspondence to: Hiroshi Kanai. E-mail: kanai@ecei.tohoku.
ac.jp＊
Graduate School of Biomedical Engineering, Tohoku University
Sendai 980-8579, Japan＊＊
Graduate School of Engineering, Tohoku University
Sendai 980-8579, Japan© 2017 The Institute of Electrical Engineers of Japan.

2.1 Parallel Beamforming for the Signal Acquisition at a
High Frame Rate
Parallel beamforming is a technique thatrealizes the acquisition of RF signals at a high frame rate(28)(29).
This technique employs few wide transmit beams in a frame, andmany narrow receive beams are generated for each transmit beam,573

Near Real-time Ultrasound Measurement of Myocardium with GPU (Takuma Asai et al.)(a) Tracking points at the first frameFig. 1. Illustration of one transmit-receive procedurein parallel beamforming using plane wave transmission.
as shown in Fig. 1. Typically, the frame rate, f FR , is determined by
the pulse repetition frequency, f PRF , and the number of transmit
beams in a frame, NT.

f FR  f PRF / N T . ........................................................... (1)Therefore, the employment of few transmit beams widely improves
temporal resolution that suppresses the effects of a myocardial
motion in the elevational direction and a deformation on motion
estimation.
2.2 Calculation of 2D Tissue Motion using Normalized
Cross-correlation Function The normalized cross-correlation
between ultrasound RF signals is one of the most common speckle
tracking techniques for the estimation of tissue motion. The
normalized cross-correlation function between a tracking point atl-th lateral position and d-th axial position in the n-th frame, Pl,d,n ,and a calculation point in a search area in the (n +n)-th frame,
Ql+m,d+k,n+1 , was given by l , d ( n , m, k ) (b) Tracking points at the n-th frameFig. 2. Estimation of myocardial contraction andrelaxation from the distance change between two
tracking points.
correlation kernel function was given by  i2j 2  .................................
(5)w(i, j )  exp   ,2
2 D 2   2 L L  BL / l , ................................................................. (6)1  N WL 1 / 2  N WD 1 / 2 {w(i, j ) 2 r (n, i  l , j  d )A i 1 N WL  / 2 j 1 N WD  / 2

 D  BD / a, ............................................................... (7)where l and a are the lateral and axial intervals of ultrasoundRF data, respectively.
2.3 Estimation of Myocardial Contraction and Relaxation
Figure 2 shows the proposed process to estimate the myocardial
contraction and relaxation from the change in distance between
two tracking points. First, the tracking points were set along a scanline in the IVS at the time of the Q-wave in the electrocardiogram.
Second, the position of each tracking point was tracked using the
2D speckle tracking method. The myocardial expansion Si (n)around the i-th tracking point at the n-th frame is given byr(n  n, i  l  m, j  d  k )}, ............... (2)A   ( n, i, j ) ( n  n, i  m, j  k ), .............................. (3) ( n, i , j ) N WL 1 / 2 N WD 1 / 2

{w(i, j )r(n, i  l , j  d )} ,i  1 N WL  / 2 j  1 N WD  / 2............................................... (4)where m and k are respectively the lateral and axial shifts of
Ql+m,d+k,n+1 from Pl,d,n , NWL and NWD are respectively thecorrelation kernel widths in the lateral and axial directions, w(i, j)
is the correlation kernel function, and r(n, i, j) is the RF signal ati-th lateral position and j-th axial position in the n-th frame.
The correlation kernel should be designed based on theultrasound speckle size that depends on the ultrasound beam widthand pulse length(30)(31). In the present study, the correlation kernel
were defined using the − 20 dB width of the lateral ultrasoundbeam, BL , and that of the envelope in the axial direction, BD . The

Si (n)  d i (n)  d i (0), .............................................. (8)d i ( n )  d i W ( n )  d i W ( n ) , ....................................... (9)where di (n) denotes the position vector of the i-th tracking point,di (n) denotes the distance between the two position vectors, andW is a constant. In the present study, the obtained myocardial
expansion Si (n) was averaged temporally to suppress
high-frequency noise components by
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IEEJ Trans. EIS, Vol.137, No.4, 2017

Near Real-time Ultrasound Measurement of Myocardium with GPU (Takuma Asai et al.)

S i (n) calculation time depends on the computational load of a thread. In
contrast, the number of streaming processors is smaller than thatof threads, the number of threads calculated simultaneously is thesame as that of the streaming processors, and the threads arecalculated in turn. Therefore, the computational load for 2D
speckle tracking per one frame using a GPU system is given by

1 ( N C 1) / 2
 S i (n  k ), ..................................... (10)N C k  ( N C 1) / 2

where NC is an odd number that denotes the number of frames
used for the temporal averaging.
2.4 Reduction of Calculation Time by Employing
For the estimation of myocardial
Appropriate Frame Rate
contraction and relaxation using 2D speckle tracking, a highframe-rate data acquisition can suppress the effect of myocardial
motion in the elevational direction and a large deformation.
Therefore, a high frame rate acquisition is desirable for accurate
estimation. However, the computational load in a cardiac cycle is
proportional to the frame rate, and a low frame rate acquisition isdesirable to reduce the calculation time.
2.5 Reduction of Calculation Time using the GPU System
Since the two-dimensional speckle tracking required massive
calculation time, a parallel signal processing algorithm using a
GPU system was employed. The computational load fortwo-dimensional speckle tracking per one frame using a CPU isgiven by

OGPU ( N WL N WD )if N Thr  N Str

,

O
N
N
N
N
(
/
)
else
 GPU WL WD Thr Str........... (14)where NStr is the number of streaming processors used in a GPU
system.

3. Results
3.1 Experimental Setting
In the present study, planewaves were transmitted to seven different directions using a
3.75-MHz sector probe with 96 elements equipped to a diagnostic
ultrasound system (Alpha10, Hitachi-Aloka Medical, Japan),where the angular interval of transmit directions was 6°. Since
the number of transmit beams in a frame, NT, is 7 and the pulse
repetition frequency f PRF was 6,020 Hz, the frame rate, f FR , was
860 Hz. 16 narrow receive beams were generated for each plane
wave transmission. Therefore, the total number of receive beams
was 112 and their angular interval was 0.375°. As described above,
the ultrasound measurement using parallel beamforming achieved
a high frame rate measurement with sufficient spatial resolution.
The − 20 dB widths in the lateral and axial directions at a depthof 40 mm, BL and BD , were 2.48 and 1.96 mm, respectively. Since
the sampling frequency was 15 MHz and the sound velocity was
set to 1,540 m/s, the axial interval of RF data, a, was 51.3 m.
The lateral interval of RF data at the interventricular septum (IVS)
was 330 m, because the angular interval was 0.375° and a typical
depth of IVS was about 50 mm.
The initial distance of adjacent tracking points was set to 257 m,
as shown in Fig. 2. Since the myocardial expansion was calculated
by Eq. (9) and W was set to 3, the initial distance of two tracking
points, di (0), was 1.54 mm. The temporal averaging parameter
NC in Eq. (10) was set to 7. We investigated the estimation accuracyof the proposed method under the conditions of frame rate f FR =860, 430, 287, and 172 Hz. This investigation was conducted by
employing n = 1, 2, 3 and 5 in Eq. (2). The calculation time was
evaluated by changing the number of tracking points N = NL ND(N = 2, 4, 8, 16, 32, 64, 128, 256).
3.2 Myocardial Contraction and Relaxation in IVS
The proposed method was applied to the heart of a 23-year-old
healthy male. Figure 4 shows a B-mode image in the longitudinal
section plane of the left ventricle of the heart. Figure 5 shows the
myocardial contraction and relaxation in IVS. As shown in Fig. 5,
myocardial contraction of the apex side was followed by that of thebasal side in the IVS. This result was consistent with those in theprevious study(32). Furthermore, as shown in Fig. 5, the myocardial
expansion on the left ventricular side in the IVS was significantly
larger than that on the right ventricular side. This phenomenon
may originate from the fact that the left ventricle ejects blood into
the aorta with large pulse pressure.
3.3 Appropriate Frame Rate for the Estimation of 2D
In theMyocardial Contraction and Relaxation in IVS
present study, we investigated the estimation accuracy of the
proposed method under the conditions that the frame rate f FR = 860,
430, 287, and 172 Hz. Figure 6 shows the lateral displacement of a
tracking point estimated by the proposed method. The estimated

OCPU ( N WL N WD N SL N SD N L N D ), ..................................(11)where NWL NWD is the number of points in a correlation window
required to calculate a cross-correlation value, NSL NSD is thenumber of calculation points in the search area, and NL ND is thetotal number of tracking points in the heart wall.
The huge calculation time was reduced using the graphicalprocessing unit (GPU) system with 2,496 streaming processors(K20c, NVIDIA Tesla; OS: 6.4, 64 bits, CPU: Intel core i7-3770).
Programming languages used in the present study were C and
CUDA, where CUDA is one of the compute unified device
architectures. As shown in Fig. 3, each block in GPU system was
assigned to the respective tracking points and each thread was
assigned to the respective calculation point in the search area.
Since each thread calculates a cross-correlation value given by
Eq. (2), the theoretical computational load per thread is given by

OThr ( N WL N WD ). ......................................................... (12)In the present study, the number of threads is given by

N Thr  N SL N SD N L N D . ................................................ (13)When the number of streaming processors used in a GPU system islarger than that of threads, all threads are calculated simultaneously
by the streaming processors of the same number. Therefore, theFig. 3. Parallelization of the processing for calculation of
the two-dimensional correlation function. Each block in
GPU system was assigned to the respective tracking points.
Each thread in a block calculates a cross-correlation value
between a tracking point and a calculation point in a search
area.
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IEEJ Trans. EIS, Vol.137, No.4, 2017

Near Real-time Ultrasound Measurement of Myocardium with GPU (Takuma Asai et al.)Fig. 6. Lateral displacement of a tracking point in the
IVS during a cardiac cycle at the frame rates of 860,
430, 287, and 172 Hz.
Table 1. Comparison of the calculation time per frame.
Calculation time ratio denotes the ratio of the calculation
time using a GPU system to that using a desktop PC witha single CPU.

Fig. 4. B-mode image of the heart wall of a 23-year-old
healthy male. The tracking points are set along the 5 yellow
lines in the IVS.

The numberof tracking
points

2

4

8

16

32

64

128

256

Calculation
time using a
CPU [ms]

141

263

517

1119

2277

4468

8188

16769

Calculation
time using a
GPU system
[ms]

49

49

51

56

67

100

162

326

Calculation
time ratio
(%)

35

19

10

5

3

2

2

2

single CPU was almost proportional to the number of tracking
points N. On the other hand, the efficiency of a GPU systemincreased significantly as the number of tracking points increased,
and when N was equal or larger than 128, the employment of a
GPU system successfully decreased the calculation time to 1/50 ofthat using a desktop PC with a single CPU. When the number oftracking points N was set to 64, the GPU system acquired 10
frames/s. In contrast, a desktop PC with a CPU acquired only 0.22
frames/s. These results show that the GPU system employed in thepresent study is suitable for a near real-time processing.

Fig. 5. Myocardial expansion in the IVS estimated at
the frame rate of 860 Hz. Color bar shows the distancechange of two tracking points, where the initial distanceis set to 1.54 mm.

4. Discussions
As shown in Fig. 6, the lateral displacement of a tracking point
is much larger than the ultrasound beam width of 2.48 mm.
Therefore, the 2D speckle tracking should be desirable for themeasurement of myocardial contraction and relaxation comparedwith one-dimensional tracking techniques.
The calculation time of a GPU system was substantially constant
up to N = 16, and monotonically increased for N > 16. When thenumber of tracking points N was 16, the number of threads in thepresent study was 2640. Since the number of threads was 2496,
the number of the threads exceeded the number of the streaming
processors, and 2496 threads were processed at the same time andthe remaining 144 threads remained to be processed. Thus, theGPU system became sequential processing for N > 16. These
results support the investigation described in Section 2.5.
When the frame rate was 287 Hz and the number of tracking

lateral displacements under the conditions of f RF = 860, 430, and287 Hz were similar. In contrast, the estimated displacement off RF = 172 Hz was about 1 mm different from those of f RF ≥ 287 Hz
during the latter half of a cardiac cycle. This result shows that theappropriate frame rate was about 287 Hz for the accurate and nearreal-time estimation of 2D myocardial contraction and relaxationin IVS.
3.4 Efficiency of a GPU System in the Measurement ofIn order to evaluate
Myocardial Contraction and Relaxation
the effect of the parallelization using a GPU system, we compared
the calculation time by changing the number of tracking points.
Table 1 shows the calculation time for estimation of the myocardial
contraction and relaxation using a desktop PC with a single CPU
and the GPU system. The calculation time of a desktop PC with a
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Near Real-time Ultrasound Measurement of Myocardium with GPU (Takuma Asai et al.)points was 64, the proposed method with a GPU system required
28.7 s to estimate 2D myocardial contraction and relaxation during
1 s. In contrast, a desktop PC with a single CPU required morethan 20 min. Previous work has realized a six-GPU platform(35).
The employment of a six-GPU platform is supposed to decrease
the calculation time to about 5 s, indicating the high potential of
the proposed method with a GPU system in a near real-time
estimation of myocardial contraction and relaxation.

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5. Conclusion
In the present study, we proposed a near real-time method forthe estimation of myocardial contraction and relaxation based onhigh-frame-rate ultrasound with a GPU system. From an in vivo
experimental study of a healthy male, the high frame rate acquisitionof 287 Hz and more was required for a 2D tracking technique tosuppress the effects of myocardial motion in the elevational
direction and deformation on motion estimation. The parallel
computing principle using a GPU system with 2,496 streaming
processors succeeded to reduce the calculation time to 1/50 of thatusing a desktop PC with a single CPU. When the frame rate was287 Hz and the number of tracking points was 64, the proposed
method required 28.7 s to estimate 2D myocardial contraction andrelaxation during 1 s. These results show a high potential of the
proposed method with a GPU system in a near real-time estimationof myocardial contraction and relaxation.

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Takuma Asai

Hiroshi Kanai(Non-member) received a B.E. degree from Tohoku
University, Japan in 1981, and M.E. and the Ph. D.
degrees, also from Tohoku University, in 1983 andin 1986, both in Electrical Engineering. From1986 to 1988 he was with the Education Center for
Information Processing, Tohoku University, as a
research associate. From 1990 to 1992 he was alecturer in the Department of Electrical Engineering,Tohoku University. From 1992 to 2001 he was an associate professor in
the same Department. Since 2001 he has been a professor in the
Department of Electronic Engineering, Graduate School of Engineering,Tohoku University. Since 2008 he has been also a professor in the
Graduate School of Biomedical Engineering, Tohoku University. From2012 to 2015 he was a dean of Graduate School of Engineering, Tohoku
University. From 2015 he has been a vice president (for ResearchConsolidation and University Reform), Tohoku University. His presentinterests are in transcutaneous measurement of the heart wall vibrations
and myocardial response to propagation of electrical potential andcross-sectional imaging of elasticity around atherosclerotic plaque withtranscutaneous ultrasound for tissue characterization of the arterial wall.
He is a member of the Acoustical Society of Japan, a fellow of the Instituteof Electronics Information and Communication Engineering of Japan, amember of the Japan Society of Ultrasonics in Medicine, Japan Society of
Medical Electronics and Biological Engineering, and the Japanese
Circulation Society. Since 1998, he has been a member of Technical
Program Committee of the IEEE Ultrasonic Symposium. Since 2008, hehas been an International Advisory Board of International Acoustical
Imaging Symposium. Since 2011, he has been a Board Member ofInternational Congress on Ultrasonics. Since 2012, he has been an editorof Journal of Medical Ultrasonics and Japanese Journal of Medical
Ultrasonics. Since 2013, he has been an associate editor of the IEEE
Transaction on UFFC.

(Non-member) received his B.E. and M.E. degrees
from Tohoku University, Sendai, Japan in 2014 and2016, respectively. He is currently an employee ofSony Corporation, Tokyo, Japan.

Hirofumi Taki

(Member) received a M.D. degree from Kyoto
University in 2000, a Ph.D. degree in informatics
from Kyoto University in 2007. He is currently asenior assistant professor of Graduate School of
Biomedical Engineering at Tohoku University. His
research interests include medical ultrasound,
adaptive beamforming, and super-resolution
imaging. He is a Engineering Fellow of Japan
Society of Ultrasonics in Medicine, and a member of the IEEE, Acoustic
Society of Japan, The Institute of Electronics, Information andCommunication Engineers, Japan Society for Medical and BiologicalEngineering.

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