ShinKyoungwon1
YooByeongcheol2
ChoiHeungho3
-
(Research Institute (Attached), ASTERASYS Co., Ltd., Seoul, Korea skw123@asterasys.co.kr)
-
(Research Institute (Attached), AirLab, Gimhae, Korea bcyoo79@gmail.com)
-
(School of Biomedical Engineering, Inje University, Korea hhchoi@inje.ac.kr )
Copyright © The Institute of Electronics and Information Engineers(IEIE)
Keywords
HIFU, Microbubble, Simulation, Thermal effect
1. Introduction
Ultrasound is one of the most widely used diagnostics in the medical imaging field
because it is non-invasive and generally less expensive than other imaging techniques.
It also has developed into a very successful modality in clinical diagnosis because
it can provide real-time images of soft tissue structures and blood flow. However,
it does not have sharp contrast and is sometimes distorted due to its own artifacts.
But with the development of ultrasound contrast agents (UCAs), including microbubbles,
this problem can be partially resolved.
The interaction of microbubbles with ultrasound has been studied in the last 30
years, and it is broadly used in diagnostic medical applications. As a UCA, microbubbles
consist of 1 to 10-μm encapsulated gas-filled bubbles. One of their major characteristics
is that they oscillate strongly under an external ultrasound field. The backscattering
echo intensity is proportional to the change in acoustic impedance between the blood
and microbubbles and acts as an echo-enhancer in diagnostic ultrasound. Another aspect
that is impressive is the possibility of using microbubbles to carry various drugs
to a target region and rupturing them by localized ultrasound energy. There are numerous
results that show that they may also have therapeutic applications as drug or gene-delivery
agents and as a mechanism for bypassing the blood-brain barrier [1,2].
Therapeutic ultrasound was considered as a tool for hyperthermia or thermal ablation
of tumors. Ultrasound acoustic power induces destruction and collapse of a microbubble,
produces a high-amplitude response, and can produce strong microstreaming. The local
heat deposition is amplified when microbubbles are present as cavitation or microbubble-enhanced
heating [3].
High-intensity focused ultrasound (HIFU) is a therapeutic medical technology where
high-pressure acoustic radiation is focused on an Region Of Interest (ROI) within
the body to heat tissue. HIFU has gained interest because it is non-invasive and has
potential to treat cancers, such as those in the liver and brain [4,5]. But HIFU causes unwanted tissue damage during the treatment with long-treatment
time and damages normal tissue before the target region. To reduce damage to target
tissues, resting time has to be increased while modulating the intensity level [6].
Cavitation or microbubble-enhanced heating effects can be reduced by the acoustic
intensity or treatment time required for the HIFU effect. Since the early 2000s, research
to artificially induce the thermal enhancement effect by microbubbles and use it for
HIFU treatment has been in progress. Kajiyama studied the optimal ultrasound pulse
in a heating characteristic experiment that used HIFU after injecting a UCA [7]. Yu evaluated HIFU treatment using a UCA through the tissue necrosis rate depending
on whether the UCA was injected into a rabbit liver [8].
Recently, as the potential for the efficacy and safety of HIFU enhancement using
microbubbles has begun to draw attention, clinical trials for actual patients have
been increasing. Wang Jingqi clinically tested 102 patients with adenomyosis. When
HIFU treatment was performed after injection of ultrasound contrast medium, it was
confirmed that it was safer than the result of treatment with only HIFU with less
energy and reduced average power [9]. Cheng conducted clinical trials with 63 rabbits and 143 patients with uterine fibroids.
As a result, it was confirmed that the group injected with UCA was able to treat fibroids
in a much shorter time than the group without UCA, and there were no major complications
[10]. However, most of the studies to date are limited to the research results through
the experience that UCA can cause the thermal enhancement effect of HIFU. In order
to be used clinically in practice, a quantitative analysis of microbubbles and a quantitative
study under conditions close to reality are required.
Gnanaskandan numerically analyzed the reinforcement effect of microbubbles using
the Keller-Herring bubble dynamic equation and compared the simulation results with
actual experimental results using the microbubble distribution model. However, the
shape is different from a group of microbubbles actually formed by modeling microbubbles
in a cube-shaped cylinder [11]. Kamei analyzed the non-linear HIFU enhancement effect by microbubbles, but there
is still insufficient experimental content to verify the theory [12].
Therefore, we present a method for quantitatively analyzing and predicting the
heat enhancement effect by a microbubble cloud collected during HIFU irradiation.
To this end, the behavior of microbubbles was mathematically analyzed, and a simulation
was performed by modeling a group of microbubbles, in which are collected form similar
to the actual fluids field. In order to verify the simulation, an experiment was performed
under the same conditions as the simulation.
The paper is organized as follows. In Section 2, we introduce the background knowledge
on a microbubble and its behavior equations and theories used in this study, such
as the Rayleigh-Plesset equation, modified Herring model, and bio-heat transfer equation
(BHTE). Section 3 describes a simulation program using the proposed microbubble behavior-analyzing
method for quantitatively analyzing and predicting the heat effect. In section 4,
the simulation condition and experimental variables are introduced. In Section 5,
we describe the compared microbubble behavior results of the simulation and experiment
for the heating effect. Finally, Section 6 explains the conclusions of this study
and describes the limitations.
2. Background
In this section, theoretical studies are introduced to describe a microbubble
that as a UCA. Also, we discuss a bio-heat transfer equation for ultrasound acoustic
energy transfer to thermal energy. These discussed background studies are applied
for the microbubble-cloud heating-effect simulation and experimental tests.
2.1 Microbubble used as UCA
A microbubble UCA consists of a phospholipid coating film, which has surface
tension and viscoelasticity. Because a phospholipid film suppresses vibrations caused
by ultrasound irradiation, it acts as a basic variable in the behavior analysis. Surface
tension ($\sigma $) is measured as a force per unit length, and the definition can
be expressed by Eq. (1).
The shear viscosity changes due to the continuously changing shear speed during
the expansion of the gas bubble. The shear speed can be explained by Eq. (2), and
the shear viscosity is defined by Eq. (3).
where $p_{xy}\left(t\right)$ is the non-equilibrium average of the pressure tensor
using the dynamic equation of motion.
2.2 Microbubble behavior Equation
Since 1917, several theoretical models have been introduced to study gas bubble
dynamics in liquids, and early study on the geometric analysis of microbubble behavior
began with Rayleigh's work, which focused on the application of fluid mechanics [9]. Later, Plesset conceived a mathematical model called the Rayleigh-Plesset equation
based on Rayleigh's study [10]. This is the most commonly used formula to describe a single microbubble vibration.
But these models are the simplest models because a microbubble is driven by just a
low-amplitude sound field in an infinite fluid. This model ignores the liquid compressibility
effects and assumes that the gas pressure in a microbubble is uniform and follows
the polytropic law. The Rayleigh-Plesset equation is shown in Eq. (4):
where $R_{0}$ is the microbubble radius at equilibrium, $\dot{R}$ and $\ddot{R}$
represent the first- and second-order derivatives of the microbubble radius $R$, $p_{0}$
is the hydrostatic pressure, $p_{i}\left(t\right)$ is the incident ultrasound pressure
in the liquid at an infinite distance, $p_{g}\left(t\right)$ is the uniform gas pressure
within the microbubble, and $\rho $, $\sigma $, and $\eta $ are the density of surrounding
liquid, surface tension of the microbubble-liquid interface, and viscosity of the
bulk fluid, respectively.
In general, UCA is a form in which a film constrains a gas inside a bubble, and
characteristics such as the thickness and viscosity of the film must be considered.
De Jong and Hoff modeled encapsulated microbubbles by incorporating experimentally
determined elastic and friction parameters into the Rayleigh-Plesset model. They studied
it based on the Herring model, which considered the changes of pressure for liquid
compressibility modified by Morgan [14,15]. The modified Herring model is shown in Eq. (5):
where $\rho $ is the density of the surrounding liquid (assumed to be constant),
$R$, $\dot{R}$, and $\ddot{R}$ are the radius, change in velocity, and acceleration
of the microbubble, respectively, and $R_{0}$ is the initial radius. $p_{0}$ is the
atmospheric pressure, and $P\left(t\right)$ is the external pressure (that is, the
pressure by HIFU). $\sigma $ is the surface tension, $\chi $ is the elasticity of
the microbubble shell, $\gamma $ is the polytropic index, $\mu $ is the viscosity
of the medium, $\mu _{sh}$ is the viscosity of the microbubble shell, and $\varepsilon
$ is the thickness of the shell.
Through Eq. (5), the behavior according to the external pressure applied to a
microbubble can be analyzed. The behavior of microbubbles generates additional acoustic
pressure, and the pressure can be obtained from the pressure equation according to
the distance defined in the Rayleigh-Plesset model. Eq. (6) shows the pressure equation
according to distance:
where $r_{b}$ represents the distance away from the center of the microbubble.
2.3 Bio-heat Transfer Equation
Ultrasound acoustic power-induced destruction and collapse of a microbubble produces
high microbubble-enhanced heating in vasculature and tissue. The complex thermal interaction
between the vasculature and tissue has been a topic of interest for many researchers.
Harry H. Pennes presented the first quantitative relationship describing heat transfer
in human tissue including the effects of blood flow on tissue temperature on a continuum
basis [16]. For small volumes, the rate of heat generation is calculated by the attenuation
coefficient in the tissue and the time-average intensity of the ultrasound.
The transfer of heat takes place through mechanisms such as conduction, convection,
radiation, metabolism, evaporation, and phase change. In consideration of this, Pennes
proposed a heat transfer model called the Bio-Heat Transfer Equation (BHTE), as shown
in Eq. (7):
where $\rho $ is tissue density, $C$ and $C_{b}$ are the specific heats of tissue
and blood, $W_{b}$ is the perfusion rate of blood, $T_{a}$ is the temperature of blood
vessels, $k$ is the thermal conductivity of tissue, and $Q$ is the rate of heat production
by ultrasound destruction and collapse. In this study, we developed a modified BHTE
to understand the thermal effect propagation caused by microbubbles oscillation and
explosion, which is given by Eq. (8):
where $Q_{Hifu}$ and $Q_{\textit{Bubble}}$ are the thermal effects induced by
HIFU irradiation and microbubbles behaviors, respectively.
3. Simulation Program
The thermal effect caused by the interaction between a microbubble cloud and the
HIFU was simulated using a 3-D numerical model or structural model. This section is
organized as follows. We first present a microbubble cloud behavior model that considers
the microbubble's distribution in a cloud volume. Then, we introduce a program used
to calculate the thermal effect of interaction between a developed microbubble cloud
and the microbubble behavior model.
3.1 Microbubble Cloud Modeling
The UCA does not exist as a single microbubble but exists in the form of a large
number of microbubbles collected in an aqueous solution, so the clustered microbubble
cloud should be considered. In addition, the distance of each microbubble is a very
important variable for calculating the delivery pressure of the microbubble cloud,
so it is important to set the cloud shape and the distance between the microbubbles
in modeling the microbubble cloud. We first considered a cube-array bubble cloud suggested
by Tatsuya Moriyama et al. [17]. The cube-array microbubble cloud method has the advantage of being simple and intuitive
for modeling because all microbubbles are located at equal intervals. However, the
distance between the microbubbles is very diverse because the focal point of the HIFU
located in the center is very narrow and small. This is a major cause of slowing down
the simulation processing speed.
Therefore, in this study, a spherical array method involving spherical microbubble
clouds with multiple layers of shells was chosen. The microbubbles on each shell have
the same distance to the target. This means that the microbubbles on the same shell
transmit the same pressure to the target, so the computation speed of the simulation
can be improved. Fig. 1 illustrates the spherical microbubble cloud model.
Fig. 1. Proposed microbubble cloud model: (a) diagram; (b) 3D simulation results.
3.2 Simulation Program Algorithm
Fig. 2 shows the produced simulation program. The simulation presented in this paper was
designed as follows. First, in order to analyze the behavior of microbubbles, the
modified Herring model was solved using a 6$^{\mathrm{th}}$-order Runge-Kutta numerical
analysis. Second, single microbubbles were placed on the modeled microbubble group
model, and the distance to the target was calculated to obtain the total pressure
applied to the target. Finally, the total pressure applied to the target was converted
to thermal energy through BHTE, and then the expected temperature was calculated.
Fig. 2. Developed simulation program UI and simulation parameters.
MATLAB 2018b was used for all simulation procedures. This simulation program
was designed to control the characteristics of the microbubbles, the concentration
of the microbubble cloud, the size of the microbubble cloud, the characteristics of
the medium, and the HIFU characteristics. It was also set to display a graph of the
temperature rise over time.
However, this simulation program and procedure have some assumptions:
· First, all microbubbles are completely spherical and all have the same characteristics.
· Second, all microbubbles are evenly spread in the microbubble group.
· Third, the explosion of microbubbles all occurs within one cycle of HIFU ultrasound.
· Finally, the interaction between microbubbles is ignored.
4. Outline of the Experiment
In order to investigate the ability of UCA to increase the thermal effect of HIFU,
several systems were assembled, and an experimental procedure was established.
4.1 The Contrast Agent
To verify the UCA ability for the thermal effect, the commercially available
SonoVue$^{\mathrm{TM}}$ was selected. It is a novel ultrasound contrast medium made
of phospho-lipid-stabilized microbubbles of sulfur hexafluoride (SF$_{6}$) and a poorly
soluble and totally innocuous gas. It presents outstanding stability and resistance
to pressure. SonoVue$^{\mathrm{TM}}$ appears as a white, milky solution and is stable
for hours.
Because no sterilizing agent is present in the composition, SonoVue$^{\mathrm{TM}}$
should be used within 6 hours after reconstitution. SonoVue$^{\mathrm{TM}}$ is isotonic,
and its viscosity is similar to that of blood. It does not contain protein-based materials
(Table 1).
Table 1. SonoVue$^{\mathrm{TM}}$ Characteristics.
|
SonoVueTM Parameter
|
Characteristic
|
|
Bubble diameter
|
2.5 [μm]
|
|
Bubble volume concentration
|
~ 5 [μL/mL]
|
|
Surface tension σ
|
0.051 [N/m]
|
|
Polytropic gas exponent γ
|
1.07 [N/m]
|
|
Shell elasticity χ
|
0.26 [N/m]
|
|
Shell viscosity
μ
s
h
|
0 to 8 [N/m]
|
|
Shell thickness ε
|
$1\times 10^{-9}$ [m]
|
Table 2. Acoustic parameter of phantom and human. tissue
|
Parameter
|
NIPAM
|
Soft tissue
|
Polyolefin tube
|
Vessel
|
|
Sound velocity $\left[\mathrm{m}/\mathrm{s}\right]$
|
1,505
|
1,540
|
1,515
|
1,513
|
|
Attenuation $\left[\mathrm{dB}/\mathrm{cm}\right]$
|
0.50
|
0.70
|
1.18
|
1.45
|
|
Density [$\mathrm{kg}/m^{3}]$
|
999.32
|
1.080.00
|
0.92
|
1.07
|
|
Impedance [$\text{Mrayl}]$
|
1.520
|
1.620
|
1.394
|
1.560
|
Fig. 3. NIPAM phantom schematic and picture.
4.2 Ultrasound Phantom Parameters
A phantom was fabricated using N-isopropylacrylamide (NIPAM), which is similar
to soft tissue and has acoustic characteristics of the human body. We also used polyolefin
tubes, which have similar characteristics to blood vessels and were implanted to inject
the ultrasonic contrast agent (Fig. 3). In addition, in order to evaluate the temperature rise, a thermocouple intersected
with the tube, and a liquid mixed with a blood-mimicking material filled the tube.
Table 2 compares the acoustic parameters of human tissue and tissue-mimicking phantom materials
Fig. 4. Experiment setup schematic and picture.
4.3 Experiment Setup
To evaluate the thermal effect ability by UCA in HIFU therapy, an experimental
system was set up as shown in Fig. 4. The phantom system ($30\times 30\times 30mm$$^{3}$) includes a polyolefin tube ($d=5mm$)
for use in microbubble capture and a thermocouple implanted orthogonally to the polyolefin
tube for measurement of temperature changes. The temperature changes were continuously
monitored by Datalogger (Graphtec, Japan) from the implanted thermocouple. A set of
a commercial focused ultrasound surgical units (ASTERASYS, Korea) that includes a
HIFU transducer was used during the experiment. The HIFU transducer produces a peak
acoustic intensity of $1.2MPa$ at the main lobe. The frequency was set to $4MHz$.
For focusing point adjustment, a transducer was attached to a 3-axis jig system. The
initial temperature of the blood-like substance mixed with microbubbles and the phantom
was set to 25$^{\circ}$C
4.4 Experiment Procedure
Prior to the main experiments, to evaluate the proposed spherical microbubble
cloud model and simulation program, an experiment and comparison were conducted by
changing the mixing ratio of the UCA and the blood-mimicking material from 1:4 to
1:1. The main experiment consists of measuring the change in the thermal effect according
to the volume and the concentration of the microbubble cloud. First, a temperature
increase was simulated by increasing the radius of the microbubble cloud from 0.5
mm to 1.5 mm at 0.25-mm intervals using a 30% concentration of UCA. Second, using
a microbubble cloud with a radius of 1 mm, the temperature increase was simulated
by increasing the concentration of UCA from 10% to 50% in 10% increments. Fig. 5 shows a diagram of the experimental procedure, and Table 3 shows parameters for the simulation study.
5. Evaluation Results
5.1 Spherical Microbubble Cloud Model
To evaluate the spherical microbubble cloud model and simulation program, an
actual experiment and comparison were conducted. Table 4 shows the simulation results, and Table 5 shows the results of HIFU irradiation experiments using an ultrasonic phantom. In
the absence of microbubbles, the simulation results and experimental results increased
by 5.12$^{\circ}$C and 5.1$^{\circ}$C to 30.12$^{\circ}$C and 30.1$^{\circ}$C, respectively.
After that, in the case of mixed contrast agent, it was confirmed that both the simulation
and experiment showed additional heat increase. Also, the heat-enhancing effect increased
as the proportion of microbubbles increased. At a 1:1 ratio, a temperature increase
of 6.92$^{\circ}$C was confirmed through simulation, and a temperature increase of
7$^{\circ}$C was confirmed in the ultrasonic phantom.
Fig. 5. Flowchart of the experiment procedure.
Fig. 6. Simulation and experiment results of the maximum temperature according to
the ratio of solution.
Table 3. Simulation parameter for UCA thermal effect.
|
Simulation
No.
|
Microbubble-cloud radius [mm]
|
UCA solution ratio
[%]
|
|
1
|
0.5
|
30
|
|
0.75
|
|
1.0
|
|
1.25
|
|
1.5
|
|
2
|
1.0
|
10
|
|
20
|
|
30
|
|
40
|
|
50
|
Table 4. Temperature rise simulation result.
|
Simulation Result
|
|
Unit [℃]
|
Non-bubble
|
1:4
|
1:3
|
1:2
|
1:1
|
|
Init temp
|
25
|
|
Peak temp
|
30.12
|
30.40
|
30.56
|
30.89
|
31.92
|
|
Delta T
|
5.12
|
5.40
|
5.56
|
5.89
|
6.92
|
Table 5. Temperature rise experiment result.
|
Experiment Result
|
|
Unit [℃]
|
Non-bubble
|
1:4
|
1:3
|
1:2
|
1:1
|
|
Init temp
|
25
|
|
Peak temp
|
30.1
|
30.2
|
30.3
|
30.76
|
32
|
|
Delta T
|
5.1
|
5.2
|
5.3
|
5.7
|
7
|
Fig. 6 shows a graph of the simulation results according to the concentration of the ultrasonic
contrast agent and the temperature rise result of the experiment using the ultrasonic
phantom. Simulation results and experimental results using ultrasonic phantoms show
that the temperature rise according to each condition has error within 0.2, and the
temperature rise pattern is similar. This means that the analysis of the thermal enhancement
effect through quantitative analysis of the microbubble cloud presented in this study
is significant.
5.2 Thermal Effect of HIFU according to the Characteristics of Microbubble Cloud
The thermal enhancement effect of HIFU according to the characteristics of the
microbubble cloud was evaluated through simulation using the simulation program. The
factors that have the greatest influence on the thermal enhancement effect of HIFU
are the number of microbubbles in the medium and the distance between the focal point
and the microbubbles. This is closely related to the size of the microbubble cloud
and the concentration of the ultrasonic contrast medium. Therefore, in this study,
the temperature increase was confirmed and evaluated through simulation while changing
the volume of the microbubble cloud and the concentration of the ultrasonic contrast
medium. Fig. 7 shows a simulation result of the temperature rise with no microbubbles.
5.3 Thermal Effect according to the Volume of Microbubble Cloud
While changing the diameter of the microbubble cloud, the medium was fixed at
30%, and the microbubble group radius was increased from 0.5 mm to 1.5 mm. When the
radius of the microbubble group was 0.5 mm, it increased by 0.1025$^{\circ}$C compared
to when the microbubbles were not present. After that, it was confirmed that the temperature
exponentially increased as the size of the microbubble cloud increased.
In Section 3.2, microbubbles separated by more than 25 μm were found to have
little effect. It is considered that the increase in the total pressure due to the
increase in the number of microbubbles and the increase in the size of the microbubble
cloud has more effect than the attenuation due to the distance. Table 6 and Fig. 8 show the heat rise results with increasing radius of the microbubble cloud.
5.4 Thermal Effect according to UCA Solution Ratio
The radius of the microbubble cloud was set to 1 mm, and the temperature increase
was confirmed through a simulation while increasing the collection density of the
microbubbles (that is, the concentration of the ultrasonic contrast agent). The thermal
enhancement effect of the microbubble group increased from 10% to 50% according to
the increase in the concentration of the ultrasonic contrast agent. This confirmed
that an additional exponential temperature increase occurred. When the ultrasonic
focusing point and the microbubbles become closer, a large amount of microbubbles
can be collected in a group of microbubbles of the same size, thereby inducing a synergistic
heat effect. Table 7 and Fig. 9 show the results of thermal rise according to the microbubble concentration.
Table 6. Simulation results according to microbubble-cloud volume.
|
UCA Solution Ratio [%]
|
Microbubble cloud radius
[mm]
|
Peak Temperature
[°C]
|
|
30
|
0.5
|
29.29
|
|
0.75
|
29.3687
|
|
1.0
|
29.7257
|
|
1.25
|
30.5072
|
|
1.5
|
31.8956
|
Table 7. Simulation results according to microbubble-cloud concentration ratio.
|
Microbubble cloud radius
[mm]
|
UCA Solution Ratio
[%]
|
Peak Temperature
[°C]
|
|
1
|
10
|
29.2507
|
|
20
|
29.4282
|
|
30
|
29.7257
|
|
40
|
30.1286
|
|
50
|
30.7017
|
Fig. 7. Temperature simulation result without microbubble cloud.
Fig. 8. Simulation results according to microbubble-cloud volume.
Fig. 9. Results of thermal rise according to the microbubble concentration.
6. Conclusion
This study confirmed the effect of the characteristics of a microbubble cloud
on the thermal enhancement during HIFU irradiation and analyzed it quantitatively.
To do this, a microbubble behavior model was mathematically analyzed, and a microbubble
group was modeled to simulate the microbubble distribution in ultrasonic contrast
medium. After that, a simulation program to check the thermal enhancement effect was
made using the microbubble behavior model and the microbubble group model, and the
effect of the characteristics of the microbubble group on the thermal enhancement
effect of HIFU was confirmed.
The microbubble behavior of ultrasonic contrast medium was simulated using the
modified Herring model. At this time, since the energy change according to the sound
speed was limited, the change in the sound speed due to the HIFU pressure was not
considered in the simulation. In addition, in the actual experiment, it was expected
that there would be limitations in the collection of microbubbles and the temperature
rise due to ultrasonic physical phenomena such as scattering. Therefore, it was expected
that the accuracy of the temperature rise result could be improved if the conditions
of the temperature saturation point are confirmed through additional experiments and
applied to the simulation program.
In addition, in the simulations produced by this study, all microbubbles had the
same properties as the microbubbles spread out in the shell of the spherical microbubble
cloud model. Practical results can be obtained by applying an algorithm that randomly
applies the arrangement and characteristics of microbubbles to the extent physically
possible. The results of this study are expected to contribute to the development
of HIFU treatment research using ultrasound contrast agents.
ACKNOWLEDGMENTS
This research was supported by the National Research Foundation of Korea (NRF)
grant, which is funded by the Korean government (No. 2019R1A2C1006583)
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Author
Kyoungwon Shin is a graduate of Inje University’s Master of Engineering program.
He is a member of the Affiliated Research Institute, ASTERASYS Co., Ltd.
Byeongcheol Yoo is member of the Affiliated Research Institute, AirLab. He is a
graduate of Inje University’s Master of Medical Engineering program.
Heungho Choi is a professor of biomedical engineering at Inje University, Gimhae,
Korea. In 1993, he was an exchange professor at the University of Tokyo, Japan. He
served as an editor of the Korean Society of Medical & Biological Engineering. His
research interests include medical ultrasound, health care, rehabilitation, and biological
signal processing.