A18:ULTRASOUND

PROCEEDINGS of the24th International Congress on Acoustics
October 24 to 28, 2022, in Gyeongju, Korea
Extended abstractABS-0170

Influence of the mechanical conditions applied to ultrasoniccoupling agents on the acoustic transmission for high frequency
imaging
Thibaut DEVAUX1; Arthur ANGERMULLER1; Latifa ATTAR1; François VANDER MEULEN1;
Séverine BOUCAUD GAUCHET1
1

GREMAN UMR 7347, Université de Tours, CNRS, INSA Centre Val de Loire, 41000 Blois, France

ABSTRACTIn medical context or for nondestructive evaluation (NDE), the ultrasonic image quality is related to theacoustic frequency excitation and to the acoustic energy level transmitted from the transducer to the studiedmaterial. To guarantee the image quality, ultrasound coupling agents are used to insure the mechanical contact
between the ultrasonic transducer and the scanned object. During a medical diagnose, the transmission level
depends on the mechanical conditions applied to the coupling agent by the operator (static pressure, angle)
as well as on the agent acoustic properties. In high frequency regime (from 10 MHz to 30 MHz), theseparameters are critical to ensure the imaging quality. In our knowledge, the impact of these parameters has
never been quantified in the literature. In this work, we are proposing an experimental bench to identify andquantify the impact of the mechanical properties applied to the coupling agent on the acoustic transmission.
Different commercial ultrasound gels are tested and experimentally characterized in high frequency regime
for variable mechanical properties. This study should lead to the optimization of ultrasound gels for highresolution images.

Keywords: Acoustic imaging, Coupling agent, Transmission

1. INTRODUCTION
Ultrasound medical imaging requires a high acoustic energy transmission level between thetransducer and the human body which is ensured by a coupling agent. The coupling agent mimics theacoustic properties of water (celerity and impedance) to adapt the acoustic mismatch between theultrasonic probe and the human body [1-4]. During the medical diagnosis, the contact between theprobe and the human body is dependent of the practician. The static pressure as well as the angle haveinfluence on the acoustic energy transmission [5-6]. In another hand, to increase the lateral resolutionof ultrasound-based images, new class of ultrasonic transducers and probes working in high frequency
regime, from 30 MHz to 50 MHz is developed. As the frequency is higher than the usually usedtechnics, new challenges should be overcome to adapt the coupling agent to high frequency regime.
At these frequencies, the quality of the contact is critical to ensure the ultrasonic image quality.
Here, we propose an experimental set-up to measure the influence of the mechanical properties
applied on coupling agent on the level of acoustic energy transmission.

2. METHODSThe experimental setup is set for acoustic piezoelectric immersion transducer (Olympus
Panametric 50MHz 0.25” V358-SU) of 15mm diameter. The acoustic excitation is set at the bottomof the setup as shown on Fig.1. An AVTECH pulser (AVG-3-C) delivers a 5ns pulse width with a rateof 2kHz. The signal transmitted is measured by the piezoelectric sensor at the top and acquired by a1

thibaut.devaux@univ-tours.fr

Rohde & Schwartz oscilloscope (RTB2004) connected to a computer through LAN-wire. An example
of received signal through the water is shown on Fig.1(b). The acoustic setup allows to characterize
the sample from 8 to 80 MHz within a 20 dBV dynamic scale. Three alignment rods are set to ensure
the alignment of acoustic transducers during the measurement. The static force is applied through amanual screw located on the top of the experimental setup. The associated static force is measured by
the S2M-50N force transducer (from HBM company). The signal is post-processed by a clipX signal
conditioner (from HBM company) and then acquired by the oscilloscope. The sample acoustic
transmission properties are evaluated through the measurement of the maximum of the envelop of thefirst pulsed received. The static force applied is varying from 0 to 20 N to correspond to the practician
habits [4-5]. Two samples are studied: a commercial ultrasound gel pad (Aquaflex, Parker
Laboratories) and a thin polymer disk film (polyurethane resin mixed with 50% of Tetraglym,
thickness of 800 microns, 3 cm diameter). The gel pad is cut to a cube of dimensions 30 x 30 x 17 mm3
to fit into the sample holder. The static force applied to the gel pad is limited to 10 N to avoid damages
on the sample as cracks. In this study, the angle between the sample and the piezoelectric transducer
is set to have a normal incidence (α=0°).
(a)

(b)

Verticaldisplacement
screw

-20
-30

Acoustic
receiver

Sampleholder

Amplitude (dBV)

-40

Force
transducer

-50
-60
-70
-80

Water

-90
-100

0

50

100

150

Frequency (MHz)Acoustic
emitter

Fig1: Experimental setup for high frequency characterization of the static force influence on
the acoustic energy transmission. (a) Picture of the experimental setup. (b) Spectrum of thereceived signal without sample in function of the frequency.

3. RESULTS
Results are shown on Fig.2. The amplitude transmitted through the sample is reported in functionof the static force applied.
(a)

(b)
1500

60
Loading
Unloading

1400

Loading
Unloading

50

1200

Amplitude (mV)

Amplitude (mV)

1300

1100
1000
900
800
700

40

30

20

10

600
500

0

2

4

6

Static Force (N)

8

10

0

0

5

10

15

20

Static Force (N)Fig2: Amplitude transmitted by the coupling agents in function of the applied static force:(a) for a commercial gel pad (b) for a thin polymer film.

As shown on Fig.2(a), the received amplitude tends to be constant (±20%) for a commercial
ultrasound gel pad. For an increasing static force as well as for a decreasing static force, the amplitude
behavior is similar. The commercial pad ensures the acoustic transmission during the clinicaldiagnosis where the practician force varies.
On Fig(b), the transmitted amplitude by the polymer film varies in function of the applied load.
When the static force is increasing, the amplitude level increases. From 0 N to 7 N, the leveltransmitted is close to zero (noise measurement) due to the presence of air between the transducer andthe polymer. From 7 N to 12N, the amplitude is increasing in function of the static force: the air ispulled away from the transducer-polymer interface. From 12N to 20N, the compression loading
modifies the acoustic properties leading to a better transmission. When the static force is decreasing,these three behaviors are recovered for lower static pressures. The acoustic transmission is allowed
for lower static pressure (4 N instead of 7N) due to the adhesion of the polymer to the transducer
surface without air.
Around 10N, the average-pressure level commonly used by the practicians, the received amplitude
by the 50 MHz receiver through the polymer layer is around 6% of the value obtained through thecommercial gel. However, the polymer film does not dry over the time, comparing to the gel pad,paving the way for the development of a new class of coupling agent.

ACKNOWLEDGEMENTS
Authors acknowledge the Région Centre-Val-Loire for its financial support through the APR-IR
project “POLYSONS” (2019-2022). The authors would like to thank Moduleus, Protavic International
and the PCM2E laboratory for the preparation of polymer samples.

REFERENCES
1. Poltawski, Leon, and Tim Watson. Relative transmissivity of ultrasound coupling agents commonly usedby therapists in the UK. Ultrasound in Medicine & Biology 33.1 (2007): 120-128
2. Casarotto, Raquel Aparecida, et al. Coupling agents in therapeutic ultrasound: acoustic and thermal
behavior. Archives of physical medicine and rehabilitation 85.1 (2004): 162-165.
3. Benson, Heather AE, and James C. McElnay. Transmission of ultrasound energy through topicalpharmaceutical products. Physiotherapy 74.11 (1988): 587-589.
4. Klucinec, Brian, et al. Transmissivity of coupling agents used to deliver ultrasound through indirect
methods. Journal of Orthopaedic & Sports Physical Therapy 30.5 (2000): 263-269.
5. Klucinec, Brian, Craig Denegar, and Rizwan Mahmood. The transducer pressure variable: its influence on
acoustic energy transmission. Journal of Sport Rehabilitation 6.1 (1997): 47-53.
5. Warren, G.C., J.N. Koblanski, and R.A. Sigelmann. Ultrasound coupling media: Their relativetransmissivity. Arch. Phys. Med. Rehabil. 57:218-222, 1976.

PROCEEDINGS of the24th International Congress on Acoustics
October 24 to 28, 2022, in Gyeongju, Korea
Extended abstractABS-0260

High Resolution Ultrasound Image Reconstruction for Plane-Wave
Imaging Using Deep Learning
Leang Sim NGUON1; Jungwung SEO2; Kangwon SEO2; Suhyun PARK1
1

Department of Electronic and Electric Engineering, Ewha Womans University, Korea
2

School of Electrical and Electronics Engineering, Chung-ang University, Korea

ABSTRACTThe plane-wave imaging (PWI) has attracted researchers' attention owing to its ability to obtain high frame
rate ultrasound imaging, which is crucial for cardiac applications. However, degraded image quality remains
a drawback in PWI. Coherent plane-wave compounding (CPWC) using multiple plane-waves steered at
different angles can be employed but it compensates the framerate. Thus, an imaging method that can provide
both high image quality and frame rate is required. In this study, we propose a method to reconstruct highresolution ultrasound images from plane-wave raw channel data (pre-beamform RF data) by adapting themodified U-Net as the deep learning network architecture. The training and test data consist of the
combination of simulation and experimental data conducted on phantom and in-vivo study. The performance
evaluation result shows improvement in terms of the structure of similarity, peak signal to noise ratio, andfull width at half maximum compared to the conventional delay and sum (DAS) method. In conclusion, theproposed ultrasound image reconstruction method using the deep learning beamformer is capable ofreconstructing a high-resolution image of the carotid artery for a single plane-wave ultrasound imaging.
Keywords: ultrasound, plane-wave, deep learning beamformer

1. INTRODUCTION
High frame rate ultrasound imaging can be realized by transmitting plane-waves (1, 2). Whilearchiving a high frame rate, ultrasound images acquired from PWI suffer from low image quality (1,2). Previous studies proposed CPWC and adaptive beamforming on the received signal to improve theimage contrast and resolution (3-7). Due to the multi-angle data acquisition time required for CPWC
and the computational complexity of adaptive apodization, frame-rate for the reconstruction can bedegraded significantly. In this study, we propose a deep learning beamformer (3-8) designed to
improve the quality of high frame-rate PWI for the carotid artery by utilizing a single plane wave.

2. METHODSIn this study, there are two training phases. The first training phase consists of only simulation
data (8), while the second phase consists of experiment data collected from vascular phantom (fine tune phase). For the first training phase, the data were simulated using Field-II (9, 10) with L7-4 probe
configuration (transmit frequency = 5.208 MHz). The simulated phantom consists of a random numberof scatterers with random amplitude. To further generalize the train data, the exponential decreasing
gain and randomly fluctuating gain augmentations were applied the random to the pre-beamformedplane-wave data. The training targets were obtained from the envelop detected signal of the CPWC
beamforming (8) For the second phase training, we acquired 200 data from the ultrasound imaging
system (Vantage 256, Verasonics, Inc., Kirkland, WA, USA) with the L7-4 transducer on vascularphantom (0707, VATA Inc., USA). The target was generated using the same technique as the firstphase data, with the number of steering angles for CPWC set to 33.
The test data were acquired from the experimental data of the tissue-mimicking phantom model(040GSE, (CIRS Tissue Simulation and Phantom Technology, VA, USA) and in vivo studies of the
carotid artery.
In this study, the deep learning beamformer consists of the encoder (EfficientNet B5) (11), decoder(U-Net) (12), and layer concatenation (12). This structure was inspired by (13), we replaced theencoder part and modified the network by increasing the network depth and replacing the activation
function at the decoder part by Leaky ReLU. The illustration of the proposed deep learning
beamformer is shown in Figure 1. The deep learning network was trained from scratch using the firstphase data for 160 epochs (batch size: 4, learning rate: 0.0008, optimizer: Adam, loss function:
structural similarity). Then, the weights from the first training phase were used as pre-trained weights
for the second phase training for 20 epochs. The proposed network was trained and tested using an
AMD Ryzen Threadripper PRO 3975WX @ 3.5 GHz processor, a CUDA-enabled Nvidia RTX A6000
graphical processing unit (GPU), and Python 3.7.6 and PyTorch 1.8.2.
To evaluate the performance of the proposed beamformer, multiscale structural similarity (MS SSIM) (14) and peak signal-to-noise ratio (PSNR) were used to compare the similarity to the image
generated from the CPWC beamformer. The full width at half maximum (FWHM) was used for the
resolution evaluation on the point target. Lastly, contrast ratio (CR) and contrast to noise ratio (CNR)
are used for contrast evaluation. DAS beamformer with turkey apodization and CPWC on 33 steering
angles are used to compare with the proposed beamformer.

Figure 1 – The proposed deep learning beamformer. (A) input data, (B) deep learning network, (C) postprocessing techniques, and (D) output B-mode image.

3. RESULTS AND DISCUSSION

Figure 2 – B-mode images from different beamformers using multi-purpose phantoms with (A) anechoic
cyst and (B) hyperechoic cysts and point targets.

Figure 3 – FWHMs for (A) axial and (B) lateral directions on a point target marked in Fig. 2(B).
The B-mode images from the multi-purpose phantom using three different beamformers (DAS,CPWC, and proposed) are shown in Figure 2. Figure 3 shows the axial and lateral FWHM on a point
target in Figure 2(B). There is no significant difference in terms of MS -SSIM (0.63 for DAS and 0.64
for proposed method) and axial FWHM (0.42 for DAS and 0.40 for proposed method). However, theproposed beamformer shows superior PSNR (20.8 for DAS and 21.2 for proposed method), CR, CNR,
and lateral resolution compared to DAS as shown in Figure 2 and Figure 3.
Figure 4 shows the B-mode images generated using three different beamformers (DAS, CPWC,
and proposed) on the data from carotid artery in vivo studies. Similar to the phantom study, there isno significant difference of MS-SSIM (0.45 for both DAS and proposed). The proposed method shows
improved PSNR (15.8 for DAS and 16.8 for proposed method), CR, and CNR as shown in Figure 4.

Figure 4 – B-mode images of carotid artery in vivo studies from different beamformers. (A) cross-sessionalview and (B) longitudinal view (dynamic range 60dB).

4. CONCLUSION
In this study, a deep learning beamformer is proposed to reconstruct a high -quality B-mode image
for the carotid artery in-vivo data from a single plane-wave pre-beamform RF data. The performanceof the proposed beamformer was evaluated using the data acquired from phantom and in vivo study.
The proposed beamformer shows image quality improvement in terms of lateral resolution andcontrast compared to DAS. It is shown that the proposed deep learning beamformer can be designed
for the carotid artery imaging using high-frame rate ultrasound imaging. Also, this study proves thefeasibility that the proposed deep learning beamformer can be further optimiz ed for other imaging
targets.

ACKNOWLEDGEMENTS
This work was supported by Basic Science Research Program through the National Research
Foundation of Korea (NRF) funded by the Ministry of Science and ICT under grant number NRF 2020R1A2C1011889.

REFERENCES
1. Zhou Z, Wang Y, Yu J, Guo Y, Guo W, Qi Y. High spatial–temporal resolution reconstruction of planewave ultrasound images with a multichannel multiscale convolutional neural network. IEEE transactions
on ultrasonics, ferroelectrics, and frequency control. 2018;65(11):1983-96.
2. Alomari Z, Harput S, Hyder S, Freear S, editors. The effect of the transducer parameters on spatial
resolution in plane-wave imaging. 2015 IEEE International Ultrasonics Symposium (IUS); 2015: IEEE.
3. Hollman K, Rigby K, O'donnell M, editors. Coherence factor of speckle from a multi-row probe. 1999
IEEE Ultrasonics Symposium Proceedings International Symposium (Cat No 99CH37027); 1999: IEEE.
4. Wang S-L, Li P-C. MVDR-based coherence weighting for high-frame-rate adaptive imaging. IEEE
transactions on ultrasonics, ferroelectrics, and frequency control. 2009;56(10):2097-110.
5. Kim K, Park S, Park S-C, Kang J, Kim Y-T, Kim J-H, editors. Fast transform-based adaptive beamformer
for medical ultrasound imaging. 2013 IEEE International Conference on Image Processing; 2013: IEEE.
6. Synnevag J-F, Austeng A, Holm S. Benefits of minimum-variance beamforming in medical ultrasoundimaging. IEEE transactions on ultrasonics, ferroelectrics, and frequency control. 2009;56(9):1868-79.
7. Montaldo G, Tanter M, Bercoff J, Benech N, Fink M. Coherent plane-wave compounding for very highframe rate ultrasonography and transient elastography. IEEE transactions on ultrasonics, ferroelectrics,
and frequency control. 2009;56(3):489-506.
8. Nguon LS, Seo J, Seo K, Han Y, Park S. Reconstruction for plane-wave ultrasound imaging using
modified U-Net-based beamformer. Computerized Medical Imaging and Graphics. 2022;98:102073.
9. Jensen JA, Svendsen NB. Calculation of pressure fields from arbitrarily shaped, apodized, and excitedultrasound transducers. IEEE transactions on ultrasonics, ferroelectrics, and frequency control.

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10. Jensen JA, editor Field: A program for simulating ultrasound systems. 10TH NORDICBALTICCONFERENCE ON BIOMEDICAL IMAGING, VOL 4, SUPPLEMENT 1, PART 1: 351--353; 1996:
Citeseer.
11. Tan M, Le Q, editors. Efficientnet: Rethinking model scaling for convolutional neural networks.
International Conference on Machine Learning; 2019: PMLR.
12. Ronneberger O, Fischer P, Brox T, editors. U-net: Convolutional networks for biomedical image
segmentation. International Conference on Medical image computing and computer-assisted intervention;
2015: Springer.
13. Baheti B, Innani S, Gajre S, Talbar S, editors. Eff-unet: A novel architecture for semantic segmentation in
unstructured environment. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern
Recognition Workshops; 2020.
14. Wang Z, Simoncelli EP, Bovik AC, editors. Multiscale structural similarity for image quality assessment.
The Thrity-Seventh Asilomar Conference on Signals, Systems & Computers, 2003; 2003: Ieee.

PROCEEDINGS of the24th International Congress on Acoustics
October 24 to 28, 2022, in Gyeongju, Korea
Extended abstractABS-0856

Vessel navigation algorithm for single beam ultrasound Doppler
Hwanseung YU1; Jinhyoung PARK2
1, 2

Sungkyunkwan University, Republic of Korea

ABSTRACT
Pulsed wave ultrasound Doppler with single element transducer has a potential for being developed as
portable forms due to having simple imaging systems. Since the single element transducer does not provide
ultrasound images, manual adjustment of the Doppler gate is required for locating the Doppler gate in atargeted vessel. Therefore, optimal vessel navigation is required in using a single element transducer forpulsed wave Doppler. In this research, we developed a vessel navigator integrating an optically transparent
single element transducer with photoplethysmography, and a three-dimensional vessel navigation algorithm.
An optically transparent 5 MHz transducer was fabricated by loading indium-tin-oxide on a lead magnesium
niobate-lead titanate and attached to a reflective pulse oximetry sensor. The offset and pulsation amplitudeof the optical signal were analyzed for lateral navigation. Furthermore, Doppler velocity and power Doppler
signal calculated from acoustic signal were employed for axial navigation. The proposed vessel navigatorand navigation algorithm were evaluated by flow phantom, successfully detected the vessel, and acquired
Doppler signals successfully.
Keywords: Ultrasound Doppler, vessel navigation, pulsed Doppler gating

1. INTRODUCTION
Pulsed wave Doppler (PWD) has been widely used for assessing blood flow and diagnosing diverse
cardiovascular diseases in blood vessels and organs (1). Since locating a Doppler gate in a proper
vessel position is critical to acquire desired Doppler signal, array ultrasound imaging systems are
typically utilized to provide anatomical information from ultrasound images. However, array systems
have complexity limitations in being developed as portable forms. In order to overcome the limitationsof array imaging systems, single beam PWD has been proposed. Single beam PWD requires much
simpler electronics and a quite small size transducers than PWD with array systems. However, single
beam PWD does not provide ultrasound image guidance unless mechanical scanning is conducted.
Most recently developed PWD devices with single element transducer adjust the Doppler gate
manually, which is highly time-consuming and operator-dependent (2, 3). Therefore, an optimal
Doppler gate positioning without ultrasound images is necessary.
In this paper, vessel navigator and three-dimensional vessel navigation algorithm were introducedfor optimal Doppler gating in single beam PWD. An optically transparent single element transducer
was fabricated and integrated with photoplethysmography as a vessel navigator. Both acoustic andoptic signals were employed for the vessel navigation algorithm. To demonstrate the developed device
and algorithm, a flow phantom study was conducted.

2. METHODS
2.1 Vessel Navigator
The vessel navigator was developed to detect the vessel with a single element transducer as shownin Figs. 1(A) and (B). In order to avoid the opacity of conventional transducers, indium-tin-oxide(ITO) and lead magnesium niobate-lead titanate (PMN-PT) were chosen for electrode andpiezoelectric material, respectively. An optically transparent 5 MHz ultrasound transducer was
fabricated by depositing ITO on both sides of a 450 𝜇𝑚 thick PMN-PT. A transparent epoxy (EPO12

yhs1903@g.skku.edu
jin.park@skku.edu

TEK 301, Epoxy Technology Inc., MA, USA) was also utilized as a backing layer while maintaining
optical transparency. Furthermore, an acoustic lens made of polydimethylsiloxane was attached to the
PMN-PT to focus the acoustic beam tightly. A fully stacked transducer was finally poled by alternating
current poling to improve the transparency (4). The fabricated transducer was attached on a top ofreflective pulse oximetry sensor to acquire acoustic and optic signals from identical position
simultaneously.
2.2 Vessel Navigation Algorithm
The optical signal was applied for detecting the vessel laterally. The optical absorption coefficientsof biological tissues vary depending on the tissue types and the optical wavelength. Typically, theoptical absorption coefficients of the blood and the blood vessels are greater than the absorption
coefficients of the surrounding tissues. Additionally, the optic signal in the presence of the vessel andthe blood has a large pulsation due to the heartbeat. Therefore, distinguishing blood and vessel fromthe surrounding tissues is possible where the local minima of the offset level and local maxima of thepulsation in the optical waveform are found.
The autocorrelation algorithm, which is commonly used for color Doppler imaging, was utilized
to find the lateral vessel position as shown in Fig. 1(D) (5). The Doppler ensemble consisting of six
echo scanlines from a potential range including the targeted vessel was converted into in-phase (I)and quadrature (Q) signals through the Hilbert transform. Both I and Q signals were filtered to remove
clutter signals and autocorrelated to calculate a velocity and Doppler power consecutively. The
velocity and Doppler power signals in each ensemble were accumulated for a specific time andaverage, and thresholds were applied to identify the location of the vessel along the beamline.

Figure 1(A) Fabricated optically transparent transducer. (B) Developed vessel navigator integrating transducer withpulse oximetry sensor. (C) Pulse-echo response and frequency spectrum of the transducer. (D) Block
diagram of vessel navigation algorithm in the axial direction.

3. RESULTS
Fig. 2(A) presents a diagram of the phantom experiment for evaluating the performance of thevessel navigator and the algorithm. The navigator was scanned across the vessel along theperpendicular direction on a surface of a flow phantom for 10 mm while reddish blood mimicking
fluid circulated. The offset signal and the pulsation amplitude of the optical signal around the vessel
decreased by 1% and increase by 6% compared to the signals from the surrounding tissue, respectively,
as shown in Figs. 2(B) and (C). From the navigated vessel position, velocity and Doppler powersignals were calculated. As shown in Figs. 2(D) and (E), the vessel in axial direction was explored at
a location where Doppler was larger than the background noise by 6 dB. Furthermore, axial navigation
was reached where the velocity profile was greater than its half maximum. By merging acquired
information, the Doppler signal from the navigated position was successfully displayed as shown in
Fig. 2(F).

Figure 2(A) Diagram of flow phantom experiments. (B)(C) The offset and the pulsation amplitude of the opticalsignal, respectively. (D)(E) Doppler power signal and the velocity from the acoustic signal, respectively. (F)Doppler image from the flow phantom.

4. DISCUSSION
Despite the possibility that PWD with a single element transducer can be miniaturized, the absenceof adequate Doppler gating to replace the guidance from ultrasound images has limited thedevelopment of miniaturization. However, the conventional limitations were overcome in this researchby merging optics and acoustics, and the location of the targeted vessel was successfully found three dimensionally without image guidance.

ACKNOWLEDGEMENTS
This work was supported in part by the National Research Foundation of Korea (NRF) grants
funded by the Korea government (MIST) NRF-2020R1A2C2011808 (J.P.) and NRF2021R1A4A1028713 (J.P.).

REFERENCES
1. KK Shung. Diagnostic ultrasound: Imaging and blood flow measurements. Boca Raton, Florida, USA:
CRC Press; 2006.
2. I Song, J Yoon, J Kang, M Kim, WS Jang, N Shin, Y Yoo. Design and implementation of a new wirelesscarotid neckband Doppler system with wearable ultrasound sensor: preliminary results. Appl Sci.
2019;9(11):2202.
3. C Huang, P Lee, P Chen, T Liu. Design and implementation of a smartphone-based portable ultrasoundpulsed-wave Doppler device for flow measurement. IEEE Trans Ultrason Ferroelect Freq Control.
2012;59(1):182-188.
4. C Qiu, B Wang, N Zhang, S Zhang, J Liu, D Walker, Y Wang, H Tian, TR Shrout, Z Xu, L Chen, F Li.
Transparent ferroelectric crystals with ultrahigh piezoelectricity. Nature. 2020;577:350-354.
5. C Kasai, K Namekawa, A Koyano, R Omato. Real-time two-dimensional blood flow imaging using an
autocorrelation technique. IEEE Trans Son Ultrason. 1985;32(3):458-464.

PROCEEDINGS of the24th International Congress on Acoustics
October 24 to 28, 2022, in Gyeongju, Korea
Extended abstractABS-0213

A deep learning based vascular wall motion tracking
for ultrasound imaging
Jeongwung SEO1; Leang Sim NGUON2; Suhyun PARK2
1
2

Department of Electrical and Electronics Engineering, Chung-ang University, Seoul, Korea.

Department of Electronic and Electrical Engineering, Ewha Womans University, Seoul, Korea.

ABSTRACT
Cardiovascular diseases (CVDs) can be found in early stage by discovering unhealthy vascularcharacteristics such as arterial stiffness and aging. Vascular wall motion is related to thesecharacteristics. A deep learning model is proposed to track the vascular wall motion in the ultrasoundimage. Ultrasound data were acquired using ultrasound imaging system. To train the deep learningmodel, data were generated from simulation by modeling the ultrasound data reflecting the geometryof the carotid artery, its motion (e.g., cardiac, respirational, and body movement), and noise on thesignal. The trained deep learning model is tested on the data acquired from sim ulation and tissuemimicking phantom experiment. The performance of the proposed method on the vascular wall motion
tracking is compared with the traditional motion tracking algorithm using cross -correlation. The
proposed model yielded better performance than the cross-correlation based method in simulation dataand comparable performance in tissue-mimicking phantom experiment. This study presents thefeasibility of the deep learning approach applied for the ultrasound vascular imaging application.
Keywords: vascular wall motion tracking, RNN-based deep learning model, ultrasound vascular imaging

1. INTRODUCTION
Cardiovascular diseases (CVDs) are one of the main causes of death in the world (1). Evaluating
mechanical properties of blood vessel may help early diagnosis of CVDs as well as proper preventive
treatment. Previous study showed that the elevational wall motion of carotid artery is affected by themechanical properties of arteries by comparing the wall motion and the arterial stiffness between
unhealthy and healthy arteries (2).
Ultrasound imaging has been widely used in clinical practice owing to its portability, easy access,non-invasion, and reasonable price. Multiple studies (3-5) were performed on tracking vascular wallmotion, but reliable autonomous system is not available yet.
Recently, deep learning has been widely used for medical image processing. Recurrent neuralnetwork (RNN) is generally applied for sequential data. As a variation of RNN, long short-term
memory (LSTM) can be employed to avoid vanishing gradient problem (6). In this study, a deeplearning model mainly composed of LSTM is proposed to estimate the vascular wall motion using
radiofrequency (RF) data. The performance of the proposed model is compared with the traditionalcross-correlation (XCorr) method using data acquired from simulation and phantom experiments.

2. Material and Methods
Training data were generated in the form of RF data through simulation using Field-II (7, 8). The
parameters of the probe were set using the same parameters as L11-5 linear probe. The center
frequency was 7.8MHz and the sampling frequency was four times of the center frequency. The widthof the scanned image was 33.255 mm.
Phantom for simulation data had a size of 40 х 10 х 5 mm3 for lateral, axial, and elevational
directions, respectively. The start depth of the phantom was 20 mm. The density of the backgroundscatter was 12.5 units per 1 mm3 . The background scatterers were uniformly distributed and theiramplitudes were in the range of [-0.5, 0.5]. Scatterers representing vascular wall had a density of fivetimes higher than that of the background scatterers and had an amplitude range from 0.4 to 1. The
thickness of the vascular wall was 0.3 ~ 0.5 mm (9).
To train the deep learning model, the RF signal was cropped with M (M = 121 in this study) samples,
which included the vascular wall. RF signals were paired with various displacements. The
displacement between each paired signals was used as ground truth in training and test the deeplearning model. Total 4464 and 1000 data were generated with uniformly distributed displacements
for training and test, respectively.

Figure 1 – Experimental setup to scan a vascular phantom.
To test the trained deep learning model, experimental data acquired from a vascular phantom wasused. Experiment setting for scanning tissue-mimicking vascular phantom is shown in Figure 1. The
vascular phantom was scanned using an ultrasound imaging system (Vantage 256 TM , Verasonics Inc,
Kirkland, USA) and linear array transducer (ATL L11-5, ATL Ultrasound Inc., Bothell, USA). Other
imaging parameters were the same as the settings for simulation. Probe scanned one spot duringobtaining a sequence. The vascular wall motion was controlled by manual pumping. The signal fromthe first frame was exploited as reference signal and all others were used as comparative signals. Total
number of frames was 100. The dimension of input data to the deep learning model was 100 х 121 х
2.

Figure 2 – Block diagram of deep learning model for vascular wall motion estimation.
To estimate the vascular wall motion using deep learning model, LSTM is adopted to learn thelong-term contextual dependencies (10). The architecture of the whole process is shown in Figure 2.
Two signals, reference signal and comparative signal, having length M were handled as input signals
to the model. The number of LSTM layer was N (N = 4) and the number of output features was H (H= 16). Linear activation function generates the output of the LSTM layers, which is the estimatedvascular wall motion.
To compare the performance of the proposed deep learning model, XCorr was chosen to estimate
displacement between two signals.

3. Results and Discussion

Figure 3 – Bland-Altman plots of estimated wall motion from simulated data using (a) proposed deeplearning model and (b) XCorr method.
Figure 3 illustrates the Bland-Altman (BA) plots using the estimated and ground truth vascularwall motion from the proposed deep learning model (Fig. 3(a)) and XCorr method (Fig. 3(b)). The
95% confidence interval of the wall motion using the proposed deep learning model (i.e., 2.70) was
76.49% smaller than that using the XCorr method (i.e., 3.53). It represents more outliers for the XCorr
method than the proposed method. Furthermore, absolute mean difference between the estimatedvalues and the ground truth in BA plot was 0.1 and 0.52 for the proposed deep learning model and
XCorr method, respectively. This result indicates that the proposed deep learning model was moreaccurate than the XCorr method for the vascular wall motion estimation. As shown in the BA plot of
proposed deep learning model (Fig. 3(a)), however, it is observed that most errors are near ±15 mm
wall motion due to the limited range of the training data. By utilizing training data with increased
motion range, the performance of the model can be further improved.

Figure 4 – Estimated wall motion from vascular phantom using (a) proposed deep learning model and (b)XCorr method.
Figure 4 presents the estimated wall motion from tissue-mimicking vascular phantom using the
proposed deep learning method (Fig. 4(a), solid line) and XCorr method (Fig. 4(b), dotted line). Fig.
4(a) and Fig. 4(b) show that the proposed deep learning model can stably track the motion of thevascular wall from the phantom experiment.

4. Conclusion
In order to improve the blood vascular wall motion track of ultrasound images, this study proposed

a deep learning model for ultrasonic displacement measurements by exploiting RNN-based deeplearning model. The proposed deep learning model yields better performance in the simulated
experiment and comparable performance in the tissue-mimicking phantom experiment with the XCorr
approach. As a future work, the proposed deep learning model will be further improved for the in-vivo
test.

ACKNOWLEDGEMENTS
This work was supported by Basic Science Research Program through the National Research
Foundation of Korea (NRF) funded by the Ministry of Science and ICT under grant number NRF 2020R1A2C1011889.

REFERENCES
1. Allan S, Olaiya R, Burhan R. Reviewing the use and quality of machine learning in developing clinicalprediction models for cardiovascular disease. Postgraduate Medical Journal. 2022;98(1161):551-8.
2. Yli-Ollila H, Tarvainen MP, Laitinen TP, Laitinen TM. Principal component analysis of the longitudinalcarotid wall motion in association with vascular stiffness: a pilot study. Ultrasound in Medicine & Biology.
2016;42(12):2873-86.
3. Bharadwaj S, Almekkawy M, editors. Deep learning based motion tracking of ultrasound image
sequences. 2020 IEEE International Ultrasonics Symposium (IUS); 2020: IEEE.
4. Singh S, Sahani AK, editors. A machine learning approach to carotid wall localization in a-mode
ultrasound. 2020 IEEE International Symposium on Medical Measurements and Applications (MeMeA);
2020: IEEE.
5. Rabben SI, Bjærum S, Sørhus V, Torp H. Ultrasound-based vessel wall tracking: an auto-correlation
technique with RF center frequency estimation. Ultrasound in medicine & biology. 2002;28(4):507-17.
6. Sherstinsky A. Fundamentals of recurrent neural network (RNN) and long short-term memory (LSTM)
network. Physica D: Nonlinear Phenomena. 2020;404:132306.
7. Jensen JA, editor Field: A program for simulating ultrasound systems. 10TH NORDICBALTICCONFERENCE ON BIOMEDICAL IMAGING, VOL 4, SUPPLEMENT 1, PART 1: 351--353; 1996:
Citeseer.
8. Jensen JA, Svendsen NB. Calculation of pressure fields from arbitrarily shaped, apodized, and excitedultrasound transducers. IEEE transactions on ultrasonics, ferroelectrics, and frequency control.
1992;39(2):262-7.
9. Xiao C, Li Z, Lu J, Wang J, Zheng H, Bi Z, et al. A new deep learning method for displacement tracking
from ultrasound RF signals of vascular walls. Computerized Medical Imaging and Graphics.
2021;87:101819.
10. Nguyen THL, Park S. Intelligent ultrasonic flow measurement using linear array transducer with recurrent
neural networks. IEEE Access. 2020;8:137564-73.

PROCEEDINGS of the24th International Congress on Acoustics
October 24 to 28, 2022, in Gyeongju, Korea
Extended abstractABS-0252

Multimodal Ultrasound and Optical Imaging Systems
Towards Intelligent Cancer Diagnosis
Jae Youn Hwang1
1

DGIST, South Korea

ABSTRACT
To achieve the bench-to-bedside goal of translational research in the biomedical imaging field, thedevelopment of new approaches, allowing deployment into a clinical setting, will be needed. We have beendeveloping novel multimodal imaging techniques by combining various high-frequency ultrasound andoptical imaging modalities for the achievement of better outcomes in the detection and characterization of
diseased regions within the human body, especially quantitatively without contrast agents. Particularly, forthe early diagnosis of malignant diseases, new tools are needed, with likely new outcomes. For the last
decades, we have proposed and implemented multimodal approaches to biomedical imaging at all levels,
optimized for earlier, more quantitative, and reproducible detection of abnormalities. We could improvedisease management through addressing major areas of unmet need in the clinical realm with these new
approaches. In this talk, our work on cancer detection and treatment will be described with emphasis on the
new technologies needed to achieve the desired imaging performance. Particularly, novel multimodal
imaging techniques by combining high-frequency ultrasound and optical imaging will be demonstrated forquantitative examinations of human colorectal tumor biopsies in situ. After that, multimodal endoscopic
imaging and analysis with high-frequency ultrasound and light will be highlighted for precise tumor detection.
Keywords: High-frequency ultrasound imaging, spectral imaging, multimodal imaging, endoscope, cancer

1. INTRODUCTION
Cancer is known as the second most disease causing a death in the United States [1]. Despite
significant improvement of medical imaging systems for the diagnosis and treatment of cancer, theirperformance is still limited to reduce the survival rate through precise diagnosis [2].
In this talk, we demonstrate multimodal biomicroscopic and endoscopic imaging systems to
overcome the aforementioned issues in the diagnosis and treatment of cancer [3 -4]. The systems werebased on high-frequency ultrasound and optical spectral imaging modalities for detection o f tumor
distribution on the surface and tumor invasion depth in the target organ. They offered multiple but
different information on target tumors and then allowed to segment tumor regions on the surface andon the plane orthogonal to the surface in vivo and ex vivo, thus demonstrating its potential to detect
extracted and in situ tumors regions precisely.

2. METHODS
A multimodal biomicroscopic and endoscopic imaging systems were developed. The multimodal
biomicroscopic imaging system includes multispectral, high-frequency B-mode, and acoustic
radiation force impulse (ARFI) imaging m modalities whereas the multimodal endoscopic imaging
system includes white-light, autofluorescence multispectral, high-frequency B-mode, ultrasoundintegrated backscattering coefficient (IBC) imaging. Figure 1 illustrates key functionalities of themultimodal endoscopic imaging system. For the evaluation of the performance of the systems, we
performed multimodal biomicroscopic and endoscopic imaging of tumors extracted from color ectal
cancer patients. Spectral classification, ARFI, and IBC processing techniques were applied to thecorresponding images and then spectra classified, ARFI, and IBC images were obtained in addition to
the high-frequency B-mode images.

Figure 1 – Multimodal imaging based on optical and high-frequency ultrasound imaging

3. RESULTS and DISCUSSION
3.1 Multimodal Imaging of Colorectal Tumors
Multimodal endoscopic imaging of colon tissues from cancer patients was carried ou t. The
representative multimodal images of tumors ex vivo are shown in Figure 2. Using the white -light
imaging, the colon tissue of interest are found and then sequentially multispectral and high -frequency
ultrasound imaging were performed at that location. Spectral classification of multispectral images
was conducted to determine the distribution of tumors on the surface. The red color represents thetumor regions whereas the green color represents the normal regions in the spectral classified image(Figure 2, left-middle). High-frequency ultrasound imaging was conducted along the red arrow linein the spectral classified image, In the IBC image overlaid with the high -frequency ultrasound image(Figure 2, right-middle), the tumor regions exhibited a more hypo-echogenicity and lower IBC thannormal tissues. Furthermore, the co-registered multimodal image including color, MS, HFUS B-mode,and IBC images is constructed (Figure 2, right-most) [4].
In this study, we developed multimodal systems for tumor detection in situ. The systems
simultaneously offer different but synergetic/complementary information on tumor regions of interst.
The results showed that the multimodal systems could allow us to detect the tumor distribution bothon the surface and deep tissue, thus demonstrating that the systems have the potential to become an
innovative tool for precise diagnosis and treatment of various cancer.

Figure 2 – Multimodal imaging of colorectal tumors. Spectral classified and ICC images are obtained and then a
3D overlaid image with the images was registered. The images were modified the images published in theprevious study [4].

ACKNOWLEDGEMENTS
This work was supported by the Korea Medical Device Development Fund Grant funded by the
Korea government (the Ministry of Science and ICT, the Ministry of Trade, Industry and Energy, theMinistry of Health & Welfare, the Ministry of Food and Drug Safety) (RS -2020-KD000125,
9991006798) and by the Bio & Medical Technology Development Program of the National Research
Foundation (NRF) funded by the Korean government (MSIT) (2017M3A9G8084463)

REFERENCES
1. Siegel R. L., Miller K. D., and Jemal A., Cancer statistics, 201. CA: a cancer journal for clinicians, 2016;
66(1): 7-30.
2. Partridge M. et al., Detection of minimal residual cancer to investigate why oral tumors recur despite
seemingly adequate treatment, Clin Cancer Res, 2000; 6 (7): 2718-25.
3. Kim J. et al., A Multimodal Biomicroscopic System based on High-frequency Acoustic Radiation Force
Impulse and Multispectral Imaging Techniques for Tumor Characterization Ex vivo, Scientific reports,
2017; 7 (1): 1-12.
4. Kim J. et al., Forward-looking Multimodal Endoscopic System based on Optical Multispectral and Highfrequency Ultrasound Imaging techniques for Tumor Detection, IEEE Trans. Medical Imaging, 2021;
40 (2): 594 – 606.

PROCEEDINGS of the24th International Congress on Acoustics
October 24 to 28, 2022, in Gyeongju, Korea
Extended abstractABS-0351

Impact of data quantization on ultrasound imaging
Doyoung JANG 1; Woojin OH1; and Heechul YOON1*
1

School of Electronics and Electrical Engineering, Dankook University, Korea

ABSTRACT
Recent imaging approaches relying on ultrafast ultrasound often desire the faster frame rate, the higher
sampling rate, and the greater number of channels. Thus, the array-based beamforming methods suffer fromthe high computational complexity and large memory space. To overcome this challenge, we investigate theimpact of channel RF data quantization on the image quality and propose an adaptive quantization method
based on the regional dynamic range of the data. Through numerical simulation, this study compares
structural similarity and contrast-to-noise ratio of the adaptive quantization over the fixed traditional
quantization as a function of quantization levels with various beamforming methods, including delay-andsum, minimum variance, coherence factor, and delay-multiply-and-sum. Consequently, we demonstrated
that our adaptive quantization approach effectively reduces quantization levels, which can be useful in
ultrafast ultrasound-based imaging systems
Keywords: Ultrafast ultrasound, Quantization, Adaptive beamforming

1. INTRODUCTION
Ultrasound diagnostic imaging is widely used in the clinic because of its non-ionizing, real-time, costeffective characteristics. To further improve the diagnostic capability of ultrasound imaging, ultrafast
functional imaging methods, including shear-wave elastography, super-resolution imaging, and ultrafast
Doppler, have been introduced, which enables the visualization of microvasculature, detection of microflow,
and quantification of tissue stiffness. However, the computational complexity and large memory spacerequired in ultrafast ultrasound are still challenges in real-time imaging applications (1). To alleviate thischallenge, we introduce a new quantization approach that effectively reduces the data rate with minimal
image quality degradation.

2. MATERIALS AND METHODS
2.1 Adaptive quantization
During the digitization process in the ultrasound analog front-end, time-sampled RF data isquantized with the pre-determined level (typically 10~14 bits). As the quantization bits increase, thequantized value offers the higher dynamic range at the expense of the data rate increase. This
conventional quantization method depends on the global maximum and minimum values of the input.
However, the dynamic range of the input vary over the depth because of many factors including
wave diffraction and tissue attenuation. Thus, this study suggests an adaptive quantization approachthat re-normalizes the RF data based on local minima and maxima to fully utilize the regional
dynamic range during quantization.
2.2 Numerical simulation and evaluation metrics
Simulated phantom images with and without adaptive quantization were compared with four
beamforming methods (delay-and-sum (DAS), coherence factor (CF), minimum variance (MV),delay-multiply-and-sum (DMAS)) with the library available at http://www.USTB.no (2). The
simulated data was generated with a linear array probe centered at 5 MHz with 128 elements and apitch of 300 μm. Plane-wave compounded imaging using 11 angles with a 2° step was used. (3). For
1doyoung.Jang@dankook.ac.kr

evaluation, this paper compared the contrast-to-noise ratio (CNR) and structural similarity index
measure (SSIM) as a function of quantization levels.

3. RESULTS AND DISCUSSION

Figure 1 – Processed images and quantitative evaluation results with DAS, CF, MV, DMAS
beamforming methods. (a) Original images quantized with 14 bits and images using
adaptive and fixed quantization with 3 bits. (b) CNR and (c) SSIM comparison.
Figure 1(a) presents original images with full 14-bit quantization and images with adaptive andconventional 3-bit quantization by DAS, MV, CF, DMAS beamforming methods. As a result, the CNR and
SSIM values of images quantized with adaptive and fixed quantization over the full-bit images are reducedby 84.44% and 60.81%, respectively, with 3-bit fixed quantization, whereas 51.70% and 30.43% for theadaptive quantization, as shown in Figure 1(b), (C).

4. CONCLUSIONSThis study has demonstrated that our adaptive quantization approach can preserve the image quality
better in ultrasound systems with limited hardware resources.

ACKNOWLEDGEMENTS
This work was supported by the Institute of Information & communications Technology Planning &Evaluation (IITP) grant funded by the Korea government (MSIT) (No. 2022-0-00101)

REFERENCES
1. M. Tanter and M. Fink., “Ultrafast imaging in biomedical ultrasound”, IEEE Trans. Ultrason.
Ferroelectr. Freq. Control, vol. 61, no. 1, January. 2014. pp.102-119
2. O. M. H. Rindal, A. Fatemi, A. Rodriquez-Molares, “The Effect of Dynamic Range Alterations in the
Estimation of Contrast”, IEEE Trans. Ultrason., Ferroelectr., Freq. Control, vol. 66, no. 7, July. 2019. pp.
1198-1208.
3. G. Montaldo, M. Tanter, J. Bercoff, N. Benech, M. Fink, Coherent plane-wave compounding for very
high frame rate ultrasonography and transient elastography, IEEE Trans. Ultrason. Ferroelectr. Freq.
Control, vol. 56, no. 3, March. 2009. pp. 489-506.

PROCEEDINGS of the24th International Congress on Acoustics
October 24 to 28, 2022, in Gyeongju, Korea
Extended abstractABS-0371

BLADDER VOLUME ESTIMATION DEEP LEARNING ALGORITHM
USING DEPTH DEPENDENT COEFFICIENTS OF ULTRASOUND
SIGNALS
Minji KANG1; Moonhwan LEE2; and Jae Youn HWANG31

DGIST, Rep. of Korea

2

DGIST, Rep. of Korea

3

DGIST, Rep. of Korea

ABSTRACT
Bladder volume estimation in patients with dysuria is performed through ultrasound imaging.
Estimation of bladder volume with bladder ultrasound images differs from the actual volume by anaverage of 18% when the bladder is assumed to have a spherical shape without considering thedifference in a bladder shape along a bladder volume. To overcome this issue, we demonstrate a deeplearning-based bladder volume estimation network that is capable of reducing volume estimation
errors as the shape of the bladder changes. The proposed network synthesizes a few scanline images
into an ultrasound image with a large number of scanlines using the combination of GAN(Pix2Pix)
and U-Net architectures. The network shows an accuracy of 93% in terms of IoU, demonstrating theapplicability of the bladder ultrasound wearable system for the segmentation of bladder regions witha few scanlines.
Keywords: Ultrasound, Wearable, Segmentation

1. INTRODUCTION
An ultrasound imaging system has been used as a diagnostic device in medicine (1-4). Comparedto other medical imaging devices, the ultrasound imaging system is less harmful to the human bodyand has the advantage of being capable of imaging the human body in real-time, non-invasively. In
particular, ultrasound imaging has already shown its potential for bladder monitoring (5-8).
Furthermore, a wearable ultrasound imaging device for bladder monitoring has been recently requiredas the number of elderly patients with bladder function disorders is increasing due to the advent of anaging society. However, for the wearable ultrasound imaging device, the mobility of a patient must be
ensured, and an ultrasound probe must be self-adhesive, flexible, and portable (5). Therefore, such
constraints in the wearable ultrasound imaging device inevitably result in a limited number ofscanlines (9). Note that in the case of such a small number of scanlines, precise ultrasound imaging
and monitoring of full bladder regions is not possible.
In this paper, we therefore propose a novel deep learning network for the wearable ultrasoundimaging device with a small number of scanlines so that it allows the accurate segmentation of bladderregions.

1
2
3

mangoinjuice@dgist.ac.kr
moon2019@dgist.ac.kr

jyhwang@dgist.ac.kr

2. METHODS
2.1

Network Structure

The overall deep learning network is shown in Figure 1. Raw RF data is acquired using commercial
ultrasound medical imaging equipment. The RF data is processed into ultrasound wearable systemsimulation images to make few scanline images. The few scanline images and the entire scanline
images obtained in this way are used as the input of the Pix2Pix model. The few scanline images arethen synthesized into images with a large number of scanlines as an output. Afterward, this synthesizedimage is input to the pretrained U-Net for the segmentation of bladder regions.

Figure 1 – Overall deep learning architecture
2.2 Data Preparation
1289 Ultrasound B-mode images and RF data from human bladders were acquired from 78 patientsat Seoul National University Hospital. Since the annotation of the bladder image was drawn in a scanconversion state, reverse scan-conversion was performed to use it for the segmentation task. 1289
images were divided into train, validation, and test sets a t a ratio of 6:2:2 (train 773, validation 258,
test 258 images), and the images were randomly shuffled. The overall data construction is shown in
Figure 2.

Figure 2 – Data construction

3. RESULTS
In the Pix2Pix model, MSE loss and L1 loss were used for training, and we generated an output
image using a test set for each epoch and checked the results. As a result of Pix2 Pix, the images at 90,
100, and 110 epochs were similar to the B mode image constructed with 128 scanlines, and therefore,
the output images from the constructed GAN model at 90 epoch were used as an input to the pretrained
U-Net model. The segmentation performance of the model was compared when the GAN generatedand 128 scanline B-mode images were used as an input.
Table 1 – The result for 30 and 50 epochs of the segmentation model.
Segmentation
GAN
IoU
Precision
Recall
epoch
epoch

50

90
original(B)0.93070.95930.78860.92740.73750.806

Figure 3 – 128 scanline B-mode and Pix2Pix generated images and their corresponding segmentation
output images
As shown in Table 1, IoU(Intersection Over Union) (12), precision, and recall were used asindicators for the evaluation of the segmentation performance of the model. Only about a 3%
difference between IoU values in the segmentation of bladder regions with 5 scanline generated and128 scanline raw ultrasound images was observed. Figure 3 shows the representative images for themodel.

4. CONCLUSIONSIn this study, we developed a novel deep learning network for the wearable ultrasound imaging
device with a small number of scanlines. The deep learning network has the potential to do the accuratesegmentation of bladder regions with a few ultrasound scanlines. The results show that thesegmentation outputs from the GAN-generated ultrasound image with 5 scanlines and 128 scanline
ultrasound image were comparable. The difference between IoU values of the synthesized and original
images was only 3%, but it needs to be further evaluated for implementation of the network into thewearable ultrasound imaging device for portable bladder monitoring.

ACKNOWLEDGEMENTS
This work was supported by the National Research Foundation of Korea (NRF)(2020R1A2B5B01002786) and by the Bio & Medical Technology Development Program of the
National Research Foundation (NRF) funded by the Korean government (MSIT)(2017M3A9G8084463)

REFERENCES
1. Lapostolle, Frédéric, et al. "Usefulness of hand-held ultrasound devices in out-of-hospital diagnosis
performed by emergency physicians." The American journal of emergency medicine, 24.2, 2006. p. 237242.
2. Busch, M. "Portable ultrasound in pre‐hospital emergencies: a feasibility study." Acta anaesthesiologicalscandinavica, 50.6, 2006. p. 754-758.
3. Coombes, Graham M., and Richard J. Millard. "The accuracy of portable ultrasound scanning in themeasurement of residual urine volume." The Journal of urology, 152.6, 1994. p. 2083-2085.
4. Choe, Jin Ho, Ji Yeon Lee, and Kyu-Sung Lee. "Accuracy and precision of a new portable ultrasoundscanner, the BME-150A, in residual urine volume measurement: a comparison with the BladderScan BVI
3000." International Urogynecology Journal, 18.6, 2007. p. 641-644.
5. Fournelle, Marc, et al. "Portable ultrasound research system for use in automated bladder monitoring withmachine-learning-based segmentation." Sensors 21, 6481, 2021.
6. Brouwer, Tammo A., et al. "Non-invasive bladder volume measurement for the prevention of postoperative
urinary retention: validation of two ultrasound devices in a clinical setting." Journal of clinical monitoring
and computing 32.6, 2018. p. 1117-1126.
7. Rosseland, L. A., A. Stubhaug, and H. Breivik. "Detecting postoperative urinary retention with anultrasound scanner." Acta Anaesthesiologica Scandinavica 46.3, 2002. p. 279-282.
8. Daurat, Aurélien, et al. "Diagnosis of postoperative urinary retention using a simplified ultrasound bladdermeasurement." Anesthesia & Analgesia 120.5, 2015. p. 1033-1038.

9. Sultan, Nabil. "Reflective thoughts on the potential and challenges of wearable technology for healthcare
provision and medical education." International Journal of Information Management 35.5, 2015. p. 521526
10. Isola, Phillip, et al. "Image-to-image translation with conditional adversarial networks." Proceedings ofthe IEEE conference on computer vision and pattern recognition. 2017.
11. Ronneberger, Olaf, Philipp Fischer, and Thomas Brox. "U-net: Convolutional networks for biomedical
image segmentation." International Conference on Medical image computing and computer-assisted
intervention. Springer, Cham, 2015.
12. Jaccard, Paul. "Étude comparative de la distribution florale dans une portion des Alpes et des Jura." Bull
Soc Vaudoise Sci Nat 37, 1901. p. 547-579.

PROCEEDINGS of the24th International Congress on Acoustics
October 24 to 28, 2022, in Gyeongju, Korea
Extended abstractABS-0541

Development of Silicon Photonic Opto-mechanical UltrasoundSensor with Ultra-thin Membrane: Proof-of-concept study
Jaesok YU1
1

DGIST, South Korea

ABSTRACTThe optical ultrasound sensing technique typically provides higher sensitivity and wider bandwidth, even though
its application is limited due to the bulky optical system size. Silicon photonics has a great potential to fabricate
large-scale optical systems on tiny silicon chips with sub-micron-sized components. In this study, we report on asilicon photonic-based new opto-mechanical ultrasonic sensor and its design scheme that allows easy fabrication
while allowing to have a very thin membrane structure. Compared to previous work, which has a 2 μm-thickmembrane defined by a wafer bonding technique, we have a much broader design space as the dimension andshape of the membrane are defined by lithography and etching. As a proof of concept, we implemented a sensor
with only a 70 nm-thick membrane having acoustic resonance at 6.5 MHz. The sensitivity and fractional bandwidthof the device are 0.41μV/Pa and 62%, respectively.

Keywords: Ultrasound, Opto-mechanical, Silicon Photonics, MEMS, Photoacoustic

1. INTRODUCTION
An optical ultrasound sensor has received great attention while it outperforms in sensitivity andbandwidth compared to a traditional piezoelectric ultrasound sensor1–4. The major limitation of the
optical ultrasound sensor is the integration of the array level. Recently, photonics integrated circuits(PICs) have been introduced as a promising technology to fabricate large-scale optical systems into asmall single silicon chip. Therefore, the PICs-based acoustic sensor can be considered a great candidate
for the next-generation optical ultrasound sensor array. In this talk, the prototype of the optomechanicalsensor on the PICs is designed, fabricated, and evaluated its sensor performance.

2. METHODS
Silicon photonic MEMS platform (SPMP) was employed to fabricate the PICs opto-mechanical sensor(Fig. (a)). The sensor was made of a bus waveguide, an optical ring resonator, and an acousto-opticalmembrane, which works as an optical phase shifter. The sensor array was fabricated in a matrix form of
8 × 8 sensors, with a corresponding footprint of 2.4 mm × 2.4 mm. The optical ring resonator couplingwith the bus waveguide detected the mechanical resonance frequency shift of the resonator during theacoustically sensitive membrane, the phase shifter, vibrated in a transverse direction (Fig. (b, c)). Thatis, the Lorentzian resonance of the sensor would be shifted when the distance between the phase shifterand the ring resonator was short enough to be coupled mutually. As a result, the acoustic pressure1

jaesok.yu@dgist.ac.kr

modulated the Lorentzian resonance, resulting in the modulation of the optical intensity transmitted
through the bus waveguide. Therefore, the acoustic pressure can be measured by the measurement of
optical intensity at the output of the bus waveguide. As our design scheme was based on the CMOS
technology, providing great flexibility achieved an unprecedented thin membrane of 70 nm. This
thickness was 30 times thinner than the previously reported using a wafer-bonded acousto-optic
membrane5. To evaluate the feasibility of the fabricated sensor, a grating coupler array coupled with thelaser to the input of the bus waveguide. Photodiode measured the optical intensity change at the outputof the bus waveguide. A single element ultrasound transducer as a sound source (f0 = 5 MHz, Fractional
bandwidth of 52%, A309S, Olympus-NDT, PA, USA) was connected to the ultrasound pulser (5072PR,
Olympus-NDT, PA, USA) for insonification. The optical intensity was recorded by an oscilloscope(MDO 3, Tektronix, OR, USA). For coupling between the sound source and sensor, they were immersedin the water tank (Fig. (d)).

3. RESULTS & DISCUSSION
Fig. (e) shows the time series of the measured optical intensity, indicating the sensed acoustic pressure,and Fig. (f) shows the corresponding frequency spectrum of the measured signal. According tomeasurement results, the sensor sensitivity and fractional bandwidth are 0.41 μV/Pa and 62%,
respectively. The center frequency is 6.5 MHz, with the bandwidth at 3 dB of 3.1 MHz. While thefractional bandwidth of the acoustic source is limited to 52%, the broader bandwidth of the designed
sensor is expected when using the broader bandwidth sound source, such as a photoacoustic source.

4. CONCLUSIONSIn this study, we demonstrate a novel optical ultrasound sensing technique and its feasibility. Our single
silicon layer-based opto-mechanical ultrasound sensor with a 70 nm-thick membrane shows greater
sensitivity of 0.41V/Pa and fractional bandwidth of the sensor of 62%, respectively. Our promising
proposed design scheme will further advance the scalability and acoustical sensing performance.

ACKNOWLEDGEMENTS
This work is supported by Samsung Research Funding & Incubation center for Future Technology of
Samsung Electronics under project number of SRFC-IT2102-02 and by Ministry of SMEs and Startups
under project number of S3248096.

REFERENCES
1.

Dong B, Sun C, Li H, Zhang HF, Shu X. Quantifying melanin concentration in retinal pigment
epithelium using broadband photoacoustic microscopy. Biomed Opt Express, Vol 8, Issue 6, pp
2851-2865. 2017;8(6):2851-2865. doi:10.1364/BOE.8.002851

2.

Basiri-Esfahani S, Armin A, Forstner S, Bowen WP. Precision ultrasound sensing on a chip. Nat
Commun 2019 101. 2019;10(1):1-9. doi:10.1038/s41467-018-08038-4

3.

Shnaiderman R, Wissmeyer G, Ülgen O, Mustafa Q, Chmyrov A, Ntziachristos V. A
submicrometre silicon-on-insulator resonator for ultrasound detection. Nat 2020 5857825.
2020;585(7825):372-378. doi:10.1038/s41586-020-2685-y

4.

Kim KH, Luo W, Zhang C, et al. Air-coupled ultrasound detection using capillary-based opticalring resonators. Sci Reports 2017 71. 2017;7(1):1-11. doi:10.1038/s41598-017-00134-7

5.

Westerveld WJ, Mahmud-Ul-Hasan M, Shnaiderman R, et al. Sensitive, small, broadband andscalable optomechanical ultrasound sensor in silicon photonics. Nat Photonics 2021 155.
2021;15(5):341-345. doi:10.1038/s41566-021-00776-0

PROCEEDINGS of the24th International Congress on Acoustics
October 24 to 28, 2022, in Gyeongju, Korea
Extended abstractABS-0707

Diagnosis of Incomplete Kawasaki Disease using Deep learning
Techniques with Ultrasound Images of Coronary Artery Lesions
Haeyun Lee1; Moon Hwan Lee1; Lucy Youngmin2; Yongsoon Eun1; Jae Youn Hwang1
1

Department of Electrical Engineering and Computer Science, Daegu Gyeongbuk Institute of Science andTechnology (DGIST), South Korea
2

Department of Pediatrics, College of Medicine, Yonsei University, South Korea

ABSTRACT
Kawasaki disease (KD) is the most common cause of acquired heart disease in young children and can leadto sudden death. Incomplete KD lacks clinical characteristics of KD and is thus difficult to distinguish fromother diseases presenting similar symptoms. Although ultrasound imaging is useful to identify one of themost fatal complications, coronary aneurysms, the diagnosis of incomplete KD is still difficult due to its
similar symptoms to other diseases. We here demonstrated the feasibility of the deep learning algorithms forthe diagnosis of incomplete KD. Various deep learning networks were trained, and their accuracy wascompared. Although the accuracy is lower than the experienced specialist, the experimental results suggestthat deep learning algorithms may assist clinicians to diagnose KD.
Keywords: Kawasaki disease, echocardiography, computer-aided diagnosis, deep learning

1. INTRODUCTION
Kawasaki disease (KD) is the most common cause of acquired heart disease in young children [1].
KD causes coronary aneurysms and can lead to myocardial infarction or even sudden death in absenceof appropriate diagnosis and treatment [2]. Although KD can be diagnosed with its representative
main symptoms, incomplete KD, which lacks manifestation of the full clinical criteria, is difficult to
identify and causes misdiagnosis [3]. Particularly, incomplete KD shows similar fever patterns toother acute febrile diseases in children. In addition, coronavirus disease 2019 (COVID -19) presented
similar symptoms [4]. Therefore, the more accurate diagnosis of KD is required to timely diagnose
the disease and reduce the risks of coronary aneurysms.
Given the most important goal is the prevention and treatment of coronary aneurysms, ultrasoundimaging is being used to investigate the coronary arteries and diagnose KD, along with blood tests.
However, diagnosing incomplete KD is still challenging without a specialist since it is difficult todistinguish from other acute febrile diseases and COVID-19 due to the aforementioned reasons.
In this study, we applied deep learning algorithms to KD diagnosis based on ultrasound images
and assess the feasibility of deep learning-based diagnosis for timely diagnosis of KD, especially
incomplete KD.

2. MATERIALS AND METHODS
2.1 Dataset
Ultrasound images were acquired from Yonsei University Gangnam Severance Hospital. There are
203 images: incomplete KD (138 patients) and pneumonia (65 patients). Pneumonia was selected andused as a representative disease of other acute febrile diseases that show s imilar symptoms to KD.
The images were labeled as KD and non-KD by a specialist and cropped to 512 × 512 pixels fortraining and evaluation of the deep learning algorithms.

1jyhwang@dgist.ac.kr

2.2 Deep learning algorithms
To comprehensively assess whether the deep learning algorithms can classify between incomplete
KD and non-KD, various deep learning architectures were implemented and evaluated: VGG -19 which
is baseline architecture but widely used architecture, Xception which demonstrates further
improvement with depth-wise separable convolution based on Inception network architecture,
ResNet-50 which uses summation based skip connections to promote information flow, ResNext -50
which shows enhanced performance than baseline ResNet due to increased cardinality using grouped
convolution, and SE-ResNet-50 and SE-ResNext-50 which are modified to have squeeze-andexcitation blocks based on ResNet and ResNext, respectively.
The networks were implemented using PyTorch. We trained the networks using Adam optimizer
with parameter 𝛽1 = 0.9 , 𝛽2 = 0.999 , and 𝜖 = 10−8 . The learning rate was initially provided as
1𝑒 −3 , and decreased by 1/10 every 30 epochs. Total training epoch was 120. Batch size was set to 32.
By minimizing binary cross-entropy loss function, the networks were trained. Note that the networks
were pretrained on ImageNet to achieve better performance. The networks were trained and tested
using a 2-GHz Intel Xeon E5-3620 processor and an NVIDIA TITAN RTX graphics card (24GB).

Figure 1. Examples of the echocardiographic image dataset: (a) incomplete KD, (b) pneumonia.

3. RESULTS
Since the dataset in our study is relatively small, we conducted 10-fold cross validations. The
dataset was divided into 182 or 183 images for training and 21 or 20 images for testing. In detail, thetest dataset was set to include 14 or 15 images of KD and 6 or 7 images of non-KD. We compared andassessed the performance of the deep learning algorithms in terms of accuracy, F1 score, sensitivity,
specificity, positive predictive value (PPV) and negative predictive value (NPV).
As shown in Table 1., SE-ResNext50 achieves the best performance: 75.9% of accuracy, 82.6% of
F1 score, 84.1% of sensitivity, 58.5% of specificity, 81.1% of PPV, and 63.3% of NPV. Xception
shows the second performance: 73.4% of accuracy, 81.9% of F1 score, 88.4% of sensitivity, 41.5% of
specificity, 76.2% of PPV, and 62.8% of NPV. Other networks demonstrate similar modest
performance. Among the metrics, specificity and NPV is respectively lower than other metrics i n all
the networks. Still, SE-ResNext50 shows the highest values.

Table 1. Comparison of the performance of the deep learning algorithms. The best and the second performance isin bold, and underlined, respectively.
Networks
Accuracy
F1 score
Sensitivity
Specificity
PPV
NPV

VGG19
67.5
77.5
82.6
35.4
73.1
48.9

Xception
73.4
81.9
88.4
41.5
76.2
62.8

ResNet50
65.0
75.4
79.0
35.4
72.2
44.2

ResNext50
64.0
74.0
75.4
40.0
72.7
43.3

SE-ResNet50
67.0
75.8
76.1
47.7
75.5
48.4

SE-ResNext50
75.9
82.6
84.1
58.5
81.1
63.3

4. DISCUSSION AND CONCLUSION
In this study, we applied deep learning algorithms to distinguish KD from non -KD, which is adifficult task without specialists. Various well-known deep learning algorithms from the simple
VGG19 to complex SE-ResNext50 were evaluated. As a representative disease of non-KD, pneumonia
was selected, and its ultrasound images were used for the training and validation. The experimental
results demonstrate that the deep learning algorithm can be used to assist clinicians in practice for thetimely diagnosis of KD.
Among the various networks, SE-ResNext50 and Xception achieved the best and second
performance, respectively. With the addition of the squeeze-and-excitation blocks to ResNext50, SEResNext50 showed significantly improved performance. On the contrary, SE-ResNet50 did not
significantly improve the performance compared to SE-ResNext50. This implies that the improvement
was not due to only the squeeze-and-excitation blocks which adaptively weight channels. Interestingly,
both SE-ResNext50 and Xception have different weights between channels. SE-ResNext50 achieved
it with grouped convolutions and the squeeze-and-excitation blocks. Xception accomplished it withdepth-wise separable convolutions. Therefore, it is suggested that the adaptively weighting or linea r
combination of channel-wise extracted features is effective for the classification of KD and non -KD
in ultrasound images.
The deep learning algorithms all show relatively low performance in terms of specificity and NPV.
Therefore, the deep learning algorithms are successful for distinguishing true KD but relatively
disappointing for recognizing true non-KD. Nevertheless, since the goal of the diagnosis is on findingtrue KD and preventing the development of coronary aneurysms, the deep learning algorithms are
sufficient to assist clinicians in avoiding misdiagnosis. With further improvement based on ourfindings, the deep learning algorithm would be a promising assistant tool for the diagnosis of KD. We
will address it in the future study.

ACKNOWLEDGEMENTS
This work was supported by the Technology Innovation Program (No. 2001424) funded By the
Ministry of Trade, Industry & Energy (MOTIE, Korea).

REFERENCES
1. KUO, Ho-Chang. Preventing coronary artery lesions in Kawasaki disease. Biomedical journal, 2017,
40.3: 141-146.
2. ZHU, Frank H.; ANG, Jocelyn Y. The clinical diagnosis and management of Kawasaki disease: a review
and update. Current infectious disease reports, 2016, 18.10: 1-10.
3. CIMAZ, R.; SUNDEL, R. Atypical and incomplete Kawasaki disease. Best Practice & Research ClinicalRheumatology, 2009, 23.5: 689-697.
4. VINER, Russell M.; WHITTAKER, Elizabeth. Kawasaki-like disease: emerging complication during the
COVID-19 pandemic. The Lancet, 2020, 395.10239: 1741-1743.

PROCEEDINGS of the24th International Congress on Acoustics
October 24 to 28, 2022, in Gyeongju, Korea
Extended abstractABS-0832

HIFU thermal lesion difference between simulated human phantom
and tissue-mimicking phantom
Seong Chan Kim1 and Min Joo Choi2*

Jeju National University, Interdisciplinary Postgraduate Program of Biomedical Engineering, Korea

1

Jeju National University, School of Medicine, Korea

2

ABSTRACT
Most HIFU procedures use the thermal effect of ultrasound, and information about temperature is directly
related to the effect and side effects of the procedure. In the case of the human body, there are complex
structures including bones around the target tissue to be irradiated with ultrasound. Thermal lesions in humantissue differ from thermal lesions in ideal conditions due to the standing waves generated by the target tissue
and surrounding structures. If a high temperature rise is induced at a location other than the focal point of the
HIFU, side effects of the procedure and patient safety may be threatened.
In this study, two conditions were simulated, a tissue-mimicking phantom and a human body, in order tocompare differences in thermal lesions. In the simulation results, the shape of the thermal lesion generated in
the two conditions was not the same, and there was a difference in the location where the temperature wasincreased.
Keywords: HIFU, Thermal lesion, Tissue mimicking phantom, Human phantom.

1. INTRODUCTION
Focused Ultrasound (FU) uses the thermal effect of ultrasound to induce an increase in thetemperature of the target tissue. In the thermal effect caused by focused ultrasound, temperature
information is particularly highly related to side effects caused by FU. Tissue Mimicking Phantom(TM Phantom) is a representative material used as a practical method to obtain temperature
information(Lafon et al, 2005; Guntur and Choi, 2014; Eranki et al. 2019;).Through the thermal
lesion formed in the TM Phantom, the performance of focused ultrasound can be visualized with asimple technique. Since the human body consists of various tissues and includes complex structures
around the target tissues, it is different from TM Pahntom, which consists of a single material.
Therefore, there is a possibility that the sound field of the focused ultrasound may be changed in thehuman body due to the reflection by the bone or the standing wave. If a non-target tissue is thermallyaffected, the effectiveness of FU is reduced, and there is a problem with side effects and patient
safety. In this study, a thermal lesion generated by focused ultrasound in a human phantom wassimulated.

2. MATERIALS AND METHODSIn this study, a human phantom (Duke Model, ver. 3.1b02) and a transducer were simulated with
Sim4Life (Swiss, Zurich MedTech AG, ver. 6.0.0.3176) to simulate the heat distribution inside thehuman body by focused ultrasound.

3. RESULTS
As a result of the simulation, although there is a difference depending on the depth of focus,
ultrasound reflection by the bone is observed near the focal point (Fig. 1). Such a result may cause atemperature increase in the tissue other than the target point, and in particular may damage the tissue
around the bone. In previous studies, thermal lesions in the TM phantom appeared in the form ofCigar, Tadpole, and Egg (Lafon et al, 2005). The human body phantom is not an ideal condition likethe TM phantom of previous studies. In complex conditions such as the human body, thermal lesions
may appear differently from previous studies.

Fig 1. Simulation result of intensity in the human body phantom.

4. CONCLUSION
Since the human body phantom is not an ideal condition like the TM phantom, the simulation
results are different depending on the conditions. The results of this study are expected to be used as
basic data for improving the performance of focused ultrasound and patient safety.

REFERENCES
1. Eranki, A., Mikhail, A. S., Negussie, A. H., Katti, P. S., Wood, B. J., & Partanen, A. Tissue-mimicking
thermochromic phantom for characterization of HIFU devices and applications. International Journal ofHyperthermia, 36(1), 2019, 517-528.
2. Lafon, C., Zderic, V., Noble, M. L., Yuen, J. C., Kaczkowski, P. J., Sapozhnikov, O. A., ... & Vaezy, S.
(2005). Gel phantom for use in high-intensity focused ultrasound dosimetry. Ultrasound in medicine &biology, 31(10), 1383-1389.
3. Guntur, S. R., & Choi, M. J. (2014). An improved tissue-mimicking polyacrylamide hydrogel phantom
for visualizing thermal lesions with high-intensity focused ultrasound. Ultrasound in medicine &biology, 40(11), 2680-2691.

PROCEEDINGS of the24th International Congress on Acoustics
October 24 to 28, 2022 in Gyeongju, Korea

ABS-0868

Measurement of single-cell viscoelastic properties using acoustic
tweezers
Hee Yeon Park1, Sangyeon Youn1, Jae Seong Kim, Moon Hwan Lee1, and Jae Youn Hwang1
1

DGIST, Korea

ABSTRACTThe mechanical property of a cell is a key indicator that shows the pathological characteristics of cells. Cellviscoelasticity is one of the mechanical properties of the cell that exhibits both viscous and elastic
characteristics when cell deformation occurs, and a changing acoustic pressure is required when analyzing
them. In the measurement of the mechanical properties of cells, acoustic tweezers have been utilized in a
non-invasive manner since they have several advantages such as inducing less cell damage and generating
high-trapping force compared to optical tweezers. To quantify the viscoelasticity of a cell, a press-focused
single-element ultrasound transducer was fabricated. It was used to trap a target cell and then change thedeformation by varying trapping forces. The deformation of breast cancer cells at different trapping forces
was recorded in a high-speed camera for quantification of cell deformability. Acoustic pressure at differentpressures was applied to the cell for quantification of deformation and comparison of metastability. Also, it
was found that the amplitude modulated force resulted in a phase delay between applied forces and cell
deformation while cell trapping, suggesting that by analyzing the phase delay, the viscosity of the trappedcell can be quantified.
Keywords : Acoustic trapping, high-speed camera, viscoelasticity, cell deformation

1. INTRODUCTION
The mechanical properties of cells are one of the key indicators of the pathological properties of cells [1].
The mechanical properties of a cell alter depending on the phenotypes of the cell [2]. Cancer and normal cells
typically exhibit different mechanical properties. Deformation of cancer cells in response to external exerted
force occurs more easily than normal cells due to a decrease in the density of cell skeletal fibers such as actin.
Also, since cancer cells are softer than normal cells, cancer cells can invade into surrounding vessels from a
primary tumor and then form secondary tumors in other organs [3].
In the mechanical properties of cancer cells, viscoelasticity affects invasive and metastatic properties of
the cancer cells as cancer cells progress. The cancer cells have lower density of cytoskeletons such as actins
compared to that normal cells. Moreover, cancers show different structural arrangements due to thephenotypes of cancer cells, thus resulting in different viscoelasticity of the cells with different phenotypes.
Therefore, the particular characteristics of cancer cells may be identified and diagnosed by quantification of
the viscoelasticity of cancer cells [4 -7].

In this study, we developed an acoustic tweezer system that can selectively trap and manipulate suspended
single cell under different pressures, which can analyze the mechanical properties of cells and diagnose themetastability of two cells with different biological properties.

Figure 1 – A cell is trapped in focused beam and its deformation varies differently along with the types ofapplied acoustic forces from an ultrasound transducer.

2. METHOD
In this study, an acoustic tweezer system was constructed for monitoring the deformation of cancer cells
due to applied trapping force. High-frequency ultrasound transducers (center frequencies: 29.6 MHz and
70MHz) were fabricated using a lithium niobate piezoelectric element. The high-frequency ultrasoundtransducer was attached to three-axis motorized stages so that the focus of the ultrasound transducer can be
moved in the desired direction. In addition, a high-speed camera was used to monitor the deformation of cells
while the cells were trapped. Cells were trapped by applying amplitude-modulated and non-modulated
acoustic trapping forces to target cells, and then their cell deformations were monitored for quantification ofviscoelasticity of cancer cells (Figure 2).

Figure 2 - Acoustic tweezer system based on an high-speed camera

3. RESULTS and DISCUSSION
3.1

Deformation of cancer cells by trapping forces with different types

We quantified the deformation of breast cancer cells in an acoustic trap at different acoustic pressures withmodulated and non-modulated amplitudes. A high-speed camera at 80000 fps was used to monitor changes
in the deformation of trapped cells (MCF-7 and MDA-MB-231) at different pressure (1.85, 2.00, 2.20, and
2.40MPa). The deformation rates of the cells were compared As a result, the deformation of MDA-MB-231
cells was quantified to a larger value, and it was confirmed that cell deformation occurred under variouspressure. MDA-MB-231 cells exhibited a higher deformation rate than MCF-7 cells, thus indicating that
MDA-MB-231cells have higher elasticity than MCF-7 cells (Figure 3).

Figure 3 – Change in the deformation of trapped cells at different pressure (1.85, 2.00, 2.20, and 2.40 MPa)
In addition, we compared the deformation of trapped cells by constant trapping and amplitude modulated
trapping forces. The constant force induced a creep-like cell deformation while the cell was trapped whereas
the amplitude modulated force resulted in a phase delay between applied signals and cell deformation whilecell trapping trapping (Figure 4). By analyzing the phase delay, the viscosity of the trapped cell can bequantified.

Figure 4 – Changes in the deformation of trapped cells at (a) constant and (b) amplitude modulated trapping
forces.

In this study, we confirmed that the acoustic tweezer system can be utilized to quantify both the elasticity
and viscosity of breast cancer cells. By analyzing the viscoelastic properties of breast cancer cells, thephenotypes of breast cancer cells such as their invasiveness may be elucidated with more high accuracy.

ACKNOWLEDGEMENTS
This work was supported by the National Research Foundation of Korea (NRF) (2020R1A2B5B01002786)
and by the Bio & Medical Technology Development Program of the National Research Foundation (NRF)
funded by the Korean government (MSIT) (2017M3A9G8084463)

REFERENCES
1. Ketene AN, Schmelz EM, Roberts PC, Agah M. “The effects of cancer progression on the viscoelasticityof ovarian cell cytoskeleton structures.” Nanomedicine : nanotechnology, biology, and medicine, vol. 8,
no. 1, pp.83-102, (2012)
2. Jae Youn Hwang, Jihun Kim, Jin Man Park, Changyang Lee, Hayong Jung, Jungwoo Lee & K. Kirk shung.
"Cell Deformation by single-beam Acoustic Trapping : A Promising Tool for Measurements of Cell
Mechanics." Scientific Reports, Article number: 27238, (2016)
3. Małgorzata Lekka. "Discrimination Between Normal and Cancerous Cells Using AFM" BioNanoScience,
vol. 6, no.1, pp.65-80, (2016)
4. Y. Abidine, V. M. Laurent, R. Michel, A. Duperray, C. Verdier. "Local mechanical properties of bladdercancer cells measured by AFM as a signature of metastatic potential" The European Physical Journal Plus,vol. 130, no. 10, pp.1-13, (2015)
5. Marita L. Rodriguez, Patrick J. McGarry, Nathan J. Sniadecki. “Review on Cell Mechanics: Experimental
and Modeling Approaches.” Applied Mechanics Reviews, vol. 65, no. 6, (2013)
6. A. Link, T. Franke. “Acoustic erythrocytometer for mechanically probing cell viscoelasticity.” Lab on a
Chip, vol. 20, no. 11, pp.1991-1998, (2020)
7. Evgeny V. Lyubin, Maria D. Khokhlova, Maria N. Skryabina, Andrey A. Fedyanin. “Cellular
viscoelasticity probed by active rheology in optical tweezers.” Journal of Biomedical Optics, vol. 17, no.
10, pp.1-4, (2012)

ABS-0890

Towards an alternative methodology for leaf hydraulic analysis using
contactless ultrasound-based methods.
Jose L. Castaño-Bernal(1) , Joao L. Ealo Cuello(2) , Maria S. Mejía(3) 1(1) Universidad del Valle, Colombia, jose.luis.castano@correounivalle.edu.co(2) Universidad del Valle, Colombia, joao.ealo@correounivalle.edu.co(3) Universidad Nacional de Colombia, Colombia, msmejiat@unal.edu.co

ABSTRACT
Hydric states in plants are essential to making irrigation decisions in crops, determining their plasticity, anddefining physiological status in foliar tissues. Techniques such as Schölander pumps and desiccation chambers
are destructive, and observation of living tissues in real-time is limited. Non-destructive ultrasonic spectroscopy(NDUS) is a versatile technique adapted to evaluate hydric states in-vivo foliar tissues. This work presents amethodology to evaluate the physiological statuses of leaves during rehydration, through a case study in Coffea
arabica by NDUS in two media: water and air. The methodology for both cases used two broad-band transducers
facing each other at 250kHz in air and 5MHz in water. The samples were monitored in real-time with continuous
measurements for 10h. Then we analyzed changes in spectra, transmitted energy and time-of-flight while leaf
samples were undergoing rehydration over time. We determined that methodology underwater allows locating
results of changing spectrum and time-of-flight. However, tissues suffer hypoxia from continuous underwatermeasurement exposure. We found that air ultrasonic spectroscopy is suitable for evaluating the integral dynamics
of transpiration and rehydration in foliar tissues. This experimental setup promotes new sensing possibilities for
non-invasive studies on hydration processes in plant tissues.

Keywords: Non-destructive ultrasonic spectroscopy (NDUS), leaf tissues, Transmission, rehydration

1

INTRODUCTION

Water distribution in leaves is a complex process determined by causes such as heterogeneously distributed vascular ducts, exchanges of xylem and phloem for nervations and petioles, number of stomata per unit leaf area,
and gases exchange that cause water vapor to flow through leaves stomata [5, 6]. Each plant, under its phenotypic, ecotype and plasticity characteristics, contains vascular networks connected to foliar tissues that spreadbiosubstances and nutrients through water dilutions [11, 12]. Thus, the leaf water content evaluation determines
plant hydric states, foliar tissues physiology, and its relationship with water mechanisms distribution on leaves.
Furthermore, leaves must withstand transitory changes of water loss and will result on immediate effects on themorphological and mechanical characteristics on the leaves that could affect plant or crop production. Thus, thewater content evaluation in leaves is a determining element for efficient water use, especially on crop irrigation.
Various techniques exist to evaluate the water content state in leaves and can be divided into destructive andnon-destructive. Among the destructive techniques are the Schölander pump, desiccation chambers, and psychrometers [11, 12]. These technologies present problems such as leaf sample destruction, inhibiting the observation of in situ water transport processes, or being time-consuming [10, 9]. Among the non-destructive techniques are magnetic resonance methods [13], electrical impedance measurements [14, 15], and optical methods[16, 17]. However, these techniques are expensive, difficult to implement to open-field or an early stage of technological improvement. One non-destructive technique, the Non-destructive Ultrasonic Spectroscopy (NDUS), isa promising method for mechanical parameters measurement in foliar tissues that is economical, easy to imple-

ment in open-field, and achieve accurate measurements without destroying the biological sample [1, 2].

In NDUS, a broadband ultrasonic wave pulse propagates through a solid plate, and the plate filters thefrequency response of waves transmitted. The propagated wave will induce resonance peaks at frequencies
where the thickness of the plate is a half wavelength integer number [2]. Specifically, the spectral response inleaves has shown to be suitable for measuring mechanical parameter of the entire leaf thickness or specific leaf
tissues, such as chlorenchyma and spongy mesophyll. In addition, the spectral response can evaluate the mechanical properties of foliar tissues and their correlation with their physiological properties in drying processes.
NDUS has established an accurate relationship between physical and mechanical state of different tissue layers,
water content in the foliar tissues, and leaf resonant thickness modes [1, 3]. In addition, NDUS has studied indifferent plant species, through contactless air-coupled ultrasonic transmission [3, 4].
Despite the developments in ultrasonic spectroscopy in plant tissues and its interaction with hydration mechanisms in leaves [7, 4, 3, 1], there are no experiments developed to date that combine rehydration processes
and non-contact ultrasonic spectroscopy. Therefore, this research focuses on changes in contactless spectroscopy
technique under two coupled media (water and air), to observe changes in the transmitted signals on rehydrated
leaves over prolonged exposure. We analyzed leaves samples states of a coffee tree under different rehydration
conditions in dry and underwater conditions. Also, we compared transmitted energy, time-of-flight and transmitted spectrum results for both media conditions in NDUS. Finally, we considered both media NDUS feasibility
to evaluate rehydration in leaves.

2

METHODOLOGY

2.1 Plant and leaves conditions
Coffee leaves used were taken from a plant of 20cm height and rise at 500m above sea level in Cali (Colombia).
The leaf samples were exposed to constant solar radiation and controlled irrigation levels. Also, to maintainhomogeneity in the samples, we collected mature leaves, from the same branch, and taken from the upper thirdof the plant.
2.2 Underwater Ultrasonic Spectroscopy (UUS).
Immediately upon collecting the coffee leaves, the samples were attached to a frame-holder with magnets (figure1a). A broadband emitter transducer (Krauthammer type l5k) centered at 5 MHz, and a hydrophone (Ondamodel HNR-0500) separated 5 cm was used and the immersion in water was made at 10h and 24h. The
emitter transducer connected to a square wave pulser-receiver (Olympus 5077PR) at an excitation frequency of
5 MHz and the hydrophone was connected to a pre-amplifier (Onda AH-2010). The transducer and hydrophone
were placed in front of each other underwater and a reference wave was acquired. After this, the coffee leaves
samples were placed between the transducer and the hydrophone, changing the transmitted waves as the water
diffusion on samples increased hydration levels over time. The transmitted waves were monitored every 8
min during all measurement period with an oscilloscope (GW-Instek GDS 2104A). The mean water temperatureduring leaves immersion was 22±0.3C and gauged with a thermocouple temperature sensor (digital thermometer
fluke 52II). Then, changes in spectra, signal amplitudes, time-of-flight, transmitted energy and transfer functions
were analyzed while the leaf samples were undergoing hydration over time. Signals pre-processing and postprocessing were performed with Matlab ® software.
2.3 Air-Coupled Ultrasonic Spectroscopy (ACUS).
In order to observe the hydration behavior of the coffee leaves in dry environment, the samples were measured under dry conditions
by ACUS. In the experimental setup, the petiole was inmmersed in a 2ml container with distilled water in order to stimulate thehydration of the coffee leaves. A pair of air-coupled ultrasonic transducers (US-BioMat lab; ITEFi-CSIC, Madrid), with center
frequency 250kHZ, bandwidth 150 kHz-350 kHz, and 20 mm diameters were mounted to opposite side of the leaf sample separated
0.6 m. A voltage amplitude of 300 V was used, connected to the same pulse/receiver system centered on 250 kHz and the received
signal was amplified 30 dB before entering the oscilloscope. The capture of the signals was carried out every 0.5 min for 10 h. The
received signal was averaged over 32 samples and digitized to 10MS/s. Signal pre-processing and post-processing were performed

in the same way as underwater ultrasonic spectroscopy. Every leaf tested were probed in 22 ± 0.9C of temperature and relative
humidity 60 ± 0.8%

Water
depositHydrophone

Receiver

Water
tank

Transmitter

Transmitter

Ultrasonic
Pulse
Generator

Oscilloscope

Ultrasonic
Pulse
Generator

ComputerPre-Amplifier
LeafsampleLeafholder(a) Underwater transmission methodology

OscilloscopeComputerLeafsampleLeafholder(b) Air-dry transmission methodology

Figure 1. Experimental implementation schemes non-contact ultrasonic spectroscopy for 1a water and 1b air

2.4 Experimental Results
2.4.1 ACUS Transmitted Energy.
Changes in coffee leaf hydration alters the cells osmotic pressure, that modifies tissue structure and its associated mechanical parameters. The changes in the mechanical parameters modify the transmitted ultrasonic wave. Figure 2a shows ACUS normalizedenergy for leaf drying during 10h at ambient laboratory conditions (24 ± 1.8C and relative humidy 66 − 70%). The leaf response
to dehydration presents an ascending curve (blue dots) until the completed time measured. The curve shows that leaf tissues, when
dehydrated under controlled environmental conditions, transmited a higher energy radiated to the ultrasonic transducer receiverand increase throughout the exposure period. A linear model fit was plot to present the ascending slope in transmitted energy.
Also, the transmitted energy has a changing slope compared to the linear fit, that indicate that the water loss rate is non-linear bypossible activity on stomata closure.
Figure 2 shows two different rehydration conditions. Figure 2b presents the continuous rehydration of a leaf dehydrated for
24h under ambient laboratory conditions. The measurement started with addition of distilled water from the beginning of themeasurement. The leaf uptake the distilled water through the petiole ducts until the water container is dried up. This process isvisible in the slope changed in the transmitted energy indicated in figure 2b. After the complete water uptake in 6.8h (verticaldotted red line), the leaf shows dehydration behavior with a positive slope similar described in figure 2a. During this time, theleaf loses water, its thickness is reduced and the energy transmitted increases. Figure 2c shows a drying-rehydration of a fresh
leaf test. Initially the sample was dried for 2.7h. Subsequently, distilled water was added to petiole leaf sample until the end of
measurement time. The dotted line shows the instant when the transmitted energy curve changes when the distilled water is added.
By absorbing water, the foliar tissues increase their thickness, the turgor of the leaf increases and the energy transmitted during theentire measurement is reduced.
The transmitted energy curves 2b and 2c show differentiable behavior with respect to the drying process described in figure
2a. Although the results obtained in the transmitted energy curves of figure 2b and 2c show a behavior with a single trend in therehydration period, the frequency of the transducers is also a parameter to be evaluated with respect to changes in rehydration. If
the frequency of the transducer is increased, the interaction with the tissue will increase by diffraction and internal reflections in theleaf tissue. These associated phenomena potentially might allow to extract more information from the interaction of the transmitted
energy in the receiving transducer. Nevertheless, we ought to confirm this hypothesis by models and additional experiments toverify this phenomena.
2.4.2 ACUS Time-of-Flight.
Under hydric stress, leaves change their thickness and turgidity that could generate affectations in the Time-Of-Flight (TOF) of
the transmitted waves. TOF determination can be obtained from several methods such as cross-correlation, phase spectrum, andamplitude threshold [4]. Figure 3 presents the calculated TOF from cross-correlation for dehydration, rehydration and dryingdehydration tests.

Transmitted Energy
linear model fit

0.7

E/EmaxE/Emax

0.8

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1

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4

t [h]

t[h]

(a) Transmitted energy dehydration

6

8

10

t[h]

(b) Transmitted energy rehydration(c) Transmitted energy dehydration-rehydration

Figure 2. Experimental results of transmitted energy of leaf sample of 2b an uptake hydration at the measurement begining, 2c uptake hydration added added at 2.7h

Initially, the high variability observed in TOF should be mentioned, explained by the high heterogeneity of leaf tissues related totheir compositional layers and vascular tissues. Figure 3 are divided into TOF measurements for leaf samples previously pointed
out in section : continuos drying (3a), hydration from the beginning of the experiment until the water supply was exhausted (3b),and hydration added after 2.7h of drying (3c). Each figure presents the standard deviation (blue) and smoothed TOF (red line).

0.6

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8

t[h]

(b) Time-of-flight continous rehydration

10

0

2

4

6

8

10

t[h]

(c) Time-of-flight dehydration-rehydration

Figure 3. Time-of-flight result for drying process during at the experiment (3a). Rehydration process starting
the measurement, also dotted line shows the time when water supplied was dried up (3b). Drying-rehydratation
at time 2.7h. Dotted line shows the distilled water uptake (3c).
Figure 3a presents a positive slope until 2h of measurement time, to decay continuously with a negative slope until the measurement end. Tissue shrinkage is a natural process during drying by reducing the amount of water in leaf tissues while gas
exchange occurs. Furthermore, since the density remains constant, the loss of water is compensated by the reduction in tissue
volume [7]. This is demonstrated by the TOF progressive reduction until the measurement end.
On the one hand, rehydration processes and their respective TOF are shown in figures 3b and 3c. Figure 3b presents the rehydration
throughout the experiment until the end of the distilled water (dotted red line), showing a constant TOF until 4h. Subsequently,
TOF changes with a positive slope until 6.7h; instant in which the distilled water is exhausted and the dehydration process begins,
showing a negative slope. Figure 3c shows the change in TOF for the drying-rehydration shift process in 2.7h. We observed apositive slope change at the time of adding the distilled water (dotted red line). The process ended up with constant slope around3.2h of time measurement and remains invariant with constant slope.
TOF comparisons in the water states in the leaves establish changes in the propagation velocity of the transmitted wave. Uponloss of water, the tissues shrink and keep the leaf density constant [?]. Finally, given the alteration of the water content in therehydration and dehydration processes, the changes in bulk modulus of foliar tissues will affect TOF results [4].

2.4.3 ACUS Transmitted Energy Spectrum.
Figure 4 presents the spectra for the three experimental conditions exposed in the leaf samples: drying, continuous rehydration anddrying-rehydration. In each B-Scans are shown the frequency bands centered at 175 kHz, 240 kHz and 300 kHz, corresponding to
the spectrum of specific air-coupled ultrasonic transducer frequency response.

(a) Spectra dehydration(b) Spectra Continuous Rehydration(c) Spectra Dehydration and rehydration

Figure 4. B-Scans results of leaf in drying process (4a), an uptake hydration at the measurement begining (4b),and uptake hydration added added at 2.7h (4c)
In drying process (figure 4a), the leaf tissues stiffness decrease in the frequency bands centered at 175 kHz and 300 kHz
throughout the dehydration process. While the leaf breathes, it loses water, which generates a reduction in osmotic pressure,which generates a stiffness decrease. The previous results demonstrate what was stated by Gomez et al. and Fariñas et al., that
demonstrated different water content dehydration in plant leaves related to frequency responses [1, 3, 4]. During the rehydration
process (figure 4b), there is a slight increase in the amplitude of the 300 kHz band at the measurement time 4h. At the end of thewater contained in the container, the 300 kHz band disappears. This demonstrates an increase in osmotic pressure due to water
uptake which increases stiffness. Finally, the leaf under drying and rehydration conditions exhibits two separate changes (figure4c). During drying, the leaf loses stiffness and intensity decreases in the band centered at 300 kHz. Subsequently, by adding
distilled water at 2.7 h, the amplitude of the band increases and the stiffness increases. The same event occurs for the 175 kHz
frequency band.

Based on the obtained B-Scan results we confirmed that the stiffness increase after the leaf rehydration update. Also, theresults may be attributed to changes in the bulk modulus of the cell wall, elastic constant and thickness of the leaf as hydrationlevels increases. Finally, no thickness mode resonance frequencies were found in the frequency bandwith under study. This might
be due the radiated ultrasonic zone in the leaf that contained the main leaf nervation. This tissues are rigid and inhibit the free leaf
resonant response.
2.4.4 Underwater Ultrasonic Spectroscopy.
For leaf samples examinated, we extracted TOF, transmitted energy and spectrum transmitted during 10h and 24h of measurement
time. However no clear trends was extracted from the ultrasonic parameters under study. At the begining, we hypothesize an
explanation that the changes in the leaf samples were due to an exchange of internal pressures of the leaf tissues with the externalpressure of the water until pressure stability is gained in the leaf. Also, although TOF and the transmitted energy showed an
exponential trend, the forced diffusion of water and its long exposure do not declare a clear trend from TOF, transmitted energy
and transmitted spectrum.
Figure 5 show four examples of different leaves with two different underwater exposition times. Figure 5a and 5b present thefrequency transmitted response on leaves hydrated for 10h. Figure 5c and figure 5d show the transmitted frequency response
during 24h.
Every spectrum in figure5 showed changes during the wave underwater propagation. However, these measured result irregular given the forced diffusion in every leaf sample. Comparison between figure 5a and figure 5b for two different leaf samples
measurements show, despite the same measurement conditions, changes in the spectrum for changes in the spectra by time 2hin figure 5b. Figures 5c and figure 5d show similar behavior. The frequency spectrum changes at the measurement time given
the inhomogeneity in the measurements, with the same conditions despite changing the periods of exposure under water. Leaves,when exposed to prolonged measurements, alters its metabolic cycles of respiration and transpiration so that heterogeneous results

(a) Transmitted spectra 10h
measurement time(b) Transmitted spectra 10h
measurement time(c) Transmitted energy 24h
measurement time(d) Transmitted energy 24h
measurement time

Figure 5. Frequency response over measured time at 10h and 24h of underwater spectroscopy measurement

are presented that do not show a plausible regularity. The spectrum results reject the hypothesis of a pressure balance, due to theeffects of prolonged exposure of the leaf to immersion.
Underwater ultrasonic spectroscopy showed to be highly invasive in plant tissues. Between 5h − 8h of measurement, the planttissue was damaged by water inmersion. Figure 6 shows dark spots in the leaf in certain zones that present tissue destruction.
The damage generated by plasmolysis occurs due to the forced diffusion of water through the foliar tissues. The tissues do notsupport excess water for so long (except in aquatic leaves). The structure of the leaf tissue disintegrates, and air in leaf tissues isdisplaced by water, generating a lack of oxygen (hypoxia), which inhibits the metabolic processes of the leaf and accelerates its
senescence. The leaf, unable to exchange CO2 and O2 (anoxia), begins a collapse process where, despite the closure of the stomata,the water will continue to enter by diffusion. This damage is observed in figure 5b and 5d where jumps and drastic changes in leaf
spectra transmission measurement were described before. Also, the spectrum measurment show that, after forced hydration, water
penetrates the spongy mesophyll tissue.

Figure 6. Leaf sample measured during 8h. White circles point out tissue anoxia damage

3

CONCLUSIONS

Air-coupled ultrasonic spectroscopy enables accurate, non-contact inspection of ultrasonic parameters that determine hydric statesin plant tissues by measuring transmitted energy, time-of-flight and transmission spectrum. The measurements performed showedchanges in the transmitted energy, time of flight and transmitted spectrum to determine dehydration and rehydration water changes
in coffee leaf tissues. In addition, the measurements carried out confirm similar results by Fariñas [1, 4] and Gomez[3].
Transmitted energy and time-of-flight on ACUS differentiated trends between dehydration and rehydration processes. However,mathematical models are required to establish the relationship between the transmitted energy and water changes in plant tissues.
We will explore the models in future experiments, to establish relationships between water uptake by the plant leaf and its connection with its foliar tissues.
Given the high irregularity in the results on times-of-flight, transmitted spectrum and transmitted energy, we do not endorse
underwater ultrasonic spectroscopy as a technique for measuring water states of terrestrial leaves. The prolonged exposure timesof underwater ultrasonic spectroscopy altered metabolic and respiration processes in the leaves. In addition, Underwater UltrasonicSpectroscopy generated anoxia and tissue destruction in coffee leaves. However, the technique could be used in aquatic plants, but
further research needs to be done to determine its potential use in water uptake by this plant species.

ACKNOWLEDGEMENTS
J.L.Castaño aknowledges financial support from MinCiencias (Colombia) through the programme Becas Bicentenario

REFERENCES[1] Fariñas, M. D., and TE Gómez Álvarez-Arenas. Ultrasonic assessment of the elastic functional design of component tissuesof Phormium tenax leaves. Journal of the Mechanical Behavior of Biomedical Materials 39. 2014. p 304-315.
[2] Waag, Grunde, Lars Hoff, and Petter Norli. Air-coupled ultrasonic through-transmission thickness measurements of steel
plates. Ultrasonics. 56. 2015. p. 332-339.
[3] Gómez Álvarez-Arenas, Tomas and Gil-Pelegrin, Eustaquio and Ealo Cuello, Joao and Fariñas, Maria Dolores and SanchoKnapik, Domingo and Collazos Burbano, David Alejandro and Peguero-Pina, Jose Javier. Ultrasonic sensing of plant waterneeds for agriculture. Sensors. 16(7), 2016. p. 1089.
[4] Fariñas, María Dolores and Sancho-Knapik, Domingo and Peguero-Pina, José Javier and Gil-Pelegrín, Eustaquio and ÁlvarezArenas, Tomás E Gómez. Contact-less, non-resonant and high-frequency ultrasonic technique: Towards a universal tool forplant leaf study. Computers and Electronics in Agriculture. 199. 2022. p 107160.
[5] Rockwell, Fulton E., N. Michele Holbrook, and Abraham D. Stroock. Leaf hydraulics I: Scaling transport properties fromsingle cells to tissues, Journal of Theoretical Biology, Volume 340, 2014, p. 251-266,[6] Rockwell, Fulton E., N. Michele Holbrook, and Abraham D. Stroock. Leaf hydraulics II: vascularized tissues. Journal of
Theoretical Biology. 340. 2014. p 267-284.
[7] Sancho-Knapik D, Alvarez-Arenas TG, Peguero-Pina JJ, Fernández V, Gil-Pelegrín E. Relationship between ultrasonic properties and structural changes in the mesophyll during leaf dehydration. J Exp Bot. 62(10). 2011[8] Zwieniecki MA, Brodribb TJ, Holbrook NM. Hydraulic design of leaves: insights from rehydration kinetics. Plant Cell Environ. 30(8). 2007.
[9] Mustafa Aktaş, Ataollah Khanlari, Burak Aktekeli, Ali Amini, Analysis of a new drying chamber for heat pump mint leaves
dryer, International Journal of Hydrogen Energy. 42(28). 2017. p. 18034-18044.
[10] Ding, Yiting, et al. Pressure–volume curves: revisiting the impact of negative turgor during cell collapse by literature review
and simulations of cell micromechanics. New Phytologist. 203(2). 2014. p 378-387.
[11] Jones, Hamlyn G. Plants and microclimate: a quantitative approach to environmental plant physiology. Cambridge university
press, 2013.
[12] Azcón-Bieto, Joaquín, and Manuel Talón. Fundamentos de fisiología vegetal. Edicions Universitat de Barcelona, 2000.
[13] McCain DC, Markley JL. A nuclear magnetic resonance imaging technique designed for studies of water in plant leaves. J
Struct Biol. 108(3). 1992. p 195-201.
[14] Tushar Kanti Bera, Sampa Bera, Kalyan Kar, Shubha Mondal. Studying the Variations of Complex Electrical Bio-Impedanceof Plant Tissues During Boiling. Procedia Technology. 23. 2016 p248-255.
[15] Díaz-Arango, Félix Octavio, Andrea Osorio-Alturo, and Julio César Caicedo-Eraso. Espectroscopia de impedancia eléctrica
aplicada al control de la calidad en la industria alimentaria. Ciencia y Tecnología Agropecuaria. 21(1). 2020 p 951.
[16] Collazos-Burbano, D. A., JL Ealo Cuello, and M. Villagrán-Muniz. Propagation of ultrasonic guided waves in plant leaves.
Journal of Physics D: Applied Physics. 54(9). 2020. p095401.
[17] Ansari, M. Z., A. Mujeeb, and A. K. Nirala. Assessment of biological leaf tissue using biospeckle laser imaging technique.
Laser Physics. 28(6). 2018. p 065608.

PROCEEDINGS of the24th International Congress on Acoustics
October 24 to 28, 2022 in Gyeongju, Korea

ABS-0479

Blood–brain barrier opening across polyolefin plate, US
characterization and in vitro results
Giovanni DURANDO1; Fabio SABA1; Laura LIBRIZZI2; Laura UVA2; Luca RASPAGLIESI3,4;
Matteo GIONSO3,5; Nicoletta CORRADINO3,6; Riccardo CIOCCA3,6; Marco DE CURTIS2;
Massimiliano DEL BENE7,8; Francesco DIMECO6,7,10; Francesco PRADA3,7,11,121
2
3

Istituto Nazionale di Ricerca Metrologica I.N.Ri.M., Turin, Italy

Epilepsy Unit, Fondazione IRCCS Istituto Neurologico Carlo Besta, Milan, Italy.

Acoustic Neuroimaging and Therapy Laboratory, Fondazione IRCCS Istituto Neurologico Carlo
Besta, Milan, Italy
45

Department of Neurosurgery, Humanitas Clinical and Research Center, Milan, Italy

Department of Biomedical Sciences and Humanitas Clinical and Research Center, IRCCS, Milan,
Italy
6
7

Department of Health Sciences, University of Milan, Milan, Italy.

Department of Neurosurgery, Fondazione IRCCS Istituto Neurologico Carlo Besta, Milan, Italy

8

Department of Experimental Oncology, IEO, European Institute of Oncology IRCCS, Milan, Italy
9

Department of Pathophysiology and Transplantation, University of Milan, Milan, Italy
10

Johns Hopkins Medical School, Baltimore, MD, United States

11

Department of Neurological Surgery, University of Virginia, Charlottesville, VA, United States
12

Focused Ultrasound Foundation, Charlottesville, VA, United States

ABSTRACTThe blood–brain barrier (BBB) represents a major obstacle to the delivery of drugs to the central nervous
system. The combined use of low intensity pulsed ultrasound waves (LIPUS) and intravascular microbubbles(MB) represents a promising solution to this issue, allowing reversible disruption of the barrier. In this study,
we evaluate the feasibility of BBB opening through a biocompatible, polyolefin plate in an in vitro wholebrain model. Preliminarily to the BBB permeabilization experiments, the passage of the ultrasound (US)waves through the polyolefin material was characterized in laboratory. The polyolefin plate, the ultrasonicinstrumentation and the insonation protocol under examination were identical to those employed for theexperiments on the in vitro brains. Circulating MBs were visualized with an ultrasonographic device with alinear probe. BBB permeabilization was assessed by quantifying at confocal microscopy the extravasation offluorescein isothiocyanate conjugate (FITC) albumin perfused after each treatment. US treated brains
displayed BBB permeabilization exclusively in the volume under the US beam; no significant differenceswere observed between brains insonated with or without the polyolefin plate.
Keywords: Ultrasounds, Blood Brain Barrier, Polyolefin Plate

1. INTRODUCTION
The blood–brain barrier (BBB) represents the interface between cerebral vessels and the central
nervous system that maintains cerebral homeostasis and protects the brain from circulating agents.
1

g.durando@inrim.it

BBB is composed of a layered structure of endothelial cells, basal membrane, mural cells, pericytes
and astrocytic glial cells; its mechanical selectivity depends greatly on the lack of capillary fenestrae
and on the tight junctions between adjacent endothelial cells, which provide a high -resistance andsize-selective filter preventing most of the systemically applied pharmacological agents from having
therapeutic effects in the brain.
It is essential to overcome this relevant barrier to deliver therapies for neurological diseases. In
this regard, BBB permeabilization obtained through low-frequency, low-intensity ultrasound waves
combined with intravenously injected MBs has gained particular interest, as it is delivered in a
localized, reversible, and non-invasive fashion compared to other procedures. MBs are high-molecularweight gasses encapsulated in either lipid, protein or polymeric shells, approximately the size of an
average red blood cell; they were originally designed for contrast enhanced ultrasound (CEUS)
imaging, as they are purely intravascular, highly echogenic and allow excellent macro and microvasculature visualization.
This work describes the use of a 4 mm-thick polyolefin-based cranial prosthesis that was developed
and patented to allow transcranial US imaging and therapy. The prosthesis has been tested, afterlaboratory characterization, by performing in vitro and in vivo tests: (i) to demonstrate its competence
to minimize attenuation and deviation of the US waves and (ii) to validate its use for trans-prosthesis
imaging and treatments.

2. ACOUSTIC CHARACTERIZATION
2.1 Ultrasound Transducer and Transparent Polyolefin Plate Characterization
Preliminarily to the BBB-permeabilization experiments, the passage of the US waves through thepolyolefin material was characterized in INRiM Ultrasound laboratory, Turin, Italy.
For the in vivo test it has been used a plane wave ultrasound transducer, TRA08, INRIM madebased on lithium-niobate piezoelectric transducer, with a center frequency of 0.985 MHz, connectedwith signal generator via power amplifier. The ultrasonic instrumentation and the insonation protocol
tested at INRiM were identical to those employed for the in vivo test.
2.2 Ultrasound Power Plane Wave Transducer Characterization
The most important parameter necessary to define the ultrasonic field is the output ultrasonic power
P emitted by the transducer. The standardized method is based on the radiation force balance method,which is defined as the time-averaged force exerted by an ultrasonic field on a target that intercepts
the ultrasonic beam. Under the assumption that the target is much larger than the lateral dimensionsof the ultrasonic beam, P can be related to the resultant radiation force F or, in other terms, thevariation of mass induced by the ultrasound field, by means of the simple equation:
𝑃 = 𝑢(𝑡)𝐹 = 𝑢(𝑡)𝑔 ∆𝑚(1)
where u(T) is the speed of sound in a medium (ultra-pure water), g is the gravitational acceleration,and Δm is the mass variation.
Equation (1) is valid for a plane-wave assumption, plane wave ultrasonic beam with a power of
1 W exerts a radiation force of 0.68 N that corresponds to 69 milligram force (1); very sensitive andaccurate balances are therefore required. The radiation force method is the internationally accepted
method generally used to characterize ultrasonic power. It specifies a procedure to determin e the total
emitted acoustic power of ultrasonic transducers based on the use of a radiation force balance (2) toevaluate the ultrasonic parameters that are related to physiotherapy systems. This procedure can beapplied to ultrasonic equipment designed for physiotherapy that has an ultrasonic transducer, which
generates continuous wave ultrasounds in the 0.5 MHz to 5 MHz frequency range (3).
Table 1 shows the measured ultrasonic power values obtained with and without polyolefin plate
placed at z = 40 mm from the transducer surface.
Table 1 – Ultrasound Power Measurements on TRA08 (f = 0.985 MHz)With polyolefin plate placed atz = 40 mm from the transducer

Without polyolefin platesurface
Ultrasound Power, P

205 mW

230 mW

The results obtained at with and without polyolefin plate placed at z = 40 mm from the transducer
showed differences no higher than 10% in terms of ultrasound power, P.
2.3 Ultrasound Pressure Plane Wave Transducer Characterization
The ultrasound pressure measurements took place in a plexiglass tank filled with distilled waterusing scanning tank system, ONDA corporation model AIMS III. Tank dimensions were: length 1.0m, width 0.5 m, height 0.6 m. Measurements were conducted by a needle hydrophone connected withpre-amplifier; the hydrophone was mounted on an adjustable support linked with three linear actuators.
The actuators were driven by a stepper motor giving a positional resolution of 20 μm. Generator
trigger pulse output, synchronized with the signal excitation of the transducer, was connected to theoscilloscope of the scanning tank system.
The definition of the ultrasound field parameters, pressure and energy, is based on hydrophone
voltage waveform, V(t), acquired by a digital oscilloscope and post-elaborated on the PC.
Planar scans were performed around the transducer with and without an interposition of a 4 mm
polyolefin plate placed at z = 40 mm from the transducer surface, see Figure 1.

Figure 1 – Measurements setup: plane wave transducer, polyolefin plate, hydrophone
After transducer mounting in fixed point, the alignment of the hydrophone in the direction of
maximum sensitivity was carried out. Automatic search to locate the point of maximum spatial peak
acoustic pressure was performed by scanning the volume in which the ultrasonic field is produced.
The hydrophone was placed on the plane, which was 70 mm away from the transducer surface. The
hydrophone moves in the x–y-axis (lateral directions) with step of 1 mm. For each point of the plane(20 mm × 20 mm) the oscilloscope acquired voltage waveforms, V(t), with a sampling frequency of
2 GSample/s for a time period of 10 μs, and one acoustic pressure was measured.
The US pressure were evaluated on hydrophone voltage waveform acquired by a digital
oscilloscope and post-elaborated on the PC. Planar scans (20 mm × 20 mm), at 70 mm away from thetransducer surface, were performed with, Figure 2 left, and without, Figure 2 right, an interpositionof a 4 mm polyolefin plate. The results obtained at 20 kPa showed differences no higher than 12% interms of pressure levels between the scans performed with the interposition of the polyolefin plate
and the ones acquired without. No significant planar shifts in x or y of the US beam could be observed
between the same groups (4).

Figure 2 – Measured array pressure with polyolefin plate placed at 40 mm from the transducer
surface (left), without polyolefin plate (right)

3. in vivo TEST
3.1 Methods
Brains were isolated from young adult female Hartley guinea pigs according to the standard techniquedescribed in detail elsewhere (5, 6).
The brain isolation procedure was conducted in accordance with the guidelines defined by the European
Communities Council directive (2010/63/EU), ARRIVE guidelines. Efforts were made to limit the numberof animals used. The experimental protocol was reviewed and approved by Committee on Animal Care andUse and by the Ethics Committee of the Fondazione IRCCS Istituto Neurologico Carlo Besta (DO-01-2020)and by the Italian Ministry of Health (Aut. Min. No. 271-2012/A).
Microbubbles provided as a sulphur hexafluoride powder were reconstituted in 10 ml of 0.9 % NaClsolution and were perfused into the isolated guinea pig brain at the rate of 1ml/min via a syringe infuserconnected to the main arterial perfusion line, Figure 3 left. Sonication of MBs for BBB opening was
performed using a planar transducer with nominal frequency of 0.985 MHz placed over the targeted
hemisphere, sonicating a region that included one temporal lobe, Figure 3 right, for 2 consecutive minuteswith intensity 50 mW/cm2, pulsed for 100 ms ON, 900 ms OFF duty cycle, 1 kHz pulse repetition rate.

Figure 3 – Schemes of the experimental setup for trans polyolefin plate MBs + transcranial
ultrasound (TUS) mediated BBB opening.
MBs circulation within the in vitro brain was assessed in a subset of experiments using a portable US
system equipped with a dedicate algorithm for CEUS. A 3-11 MHz linear array diagnostic probe was placedon a holder perpendicular to the brain axial plane; the whole brain was scanned in coronal sections. Imaging
was performed both with and without the polyolefin plate in place.
In five experiments a polyolefin plate was interposed between the transducer and the isolated brain. The
BBB opening sonication protocol started 1 min after MBs infusion. At the end of sonication protocol, theMBs infusion was interrupted and a 5 minutes washout with standard solution followed.
Quantitative results were analysed using one way analysis of variance ANOVA followed by Tukey post

hoc test. The normal distribution of samples was checked with Shapiro–Wilks test. All statistical tests were
performed with Origin Pro2016.

4. RESULTS
4.1 Evaluation and quantification of BBB permeability changes
The presence of BBB breakdown in isolated brains was assessed by perfusing fluorescein isothiocyanate(FITC)–albumin for 4 min at the end of the sonication experiment.
Coronal sections (50 μm) were cut by vibratome throughout the extension of the hippocampus. Sections
collected on gelatin-coated slides were mounted in Fluorosave and cover slipped. Two representative sections
corresponding to coronal rostrocaudal level A5.40 and A6.00 of the Luparello atlas were collected for eachbrain and used for the subsequent quantification. Slide-mounted sections were examined with a laser
scanning confocal microscope equipped with three laser lines, mounted on a light microscope using
excitation light of 488 nm. Laser intensity was set at 30–35% power. Gain and photomultiplier were kept
constant during the acquisition of all images. Quantification of FITC albumin fluorescent signal was
performed in the neocortex, in the hippocampal formation and in the thalamus. In each brain, five high power
non-overlapping fields per section were acquired bilaterally at 10x magnification, image size 512x512 pixels.
The percentage of FITC albumin signal was estimated and quantified using the Image-Pro Premier 9.1
software. An average of coefficients obtained from the examined fields was calculated and the statisticalanalysis was performed. The data measured in the five non overlapping fields per slice were averaged,
providing a single value for each hemisphere that was used for statistical analysis of data.
4.2 US‑mediated BBB disruptions
The infusion of MBs or the exclusive application of therapeutic ultrasound, TUS, without MBs did notalter the BBB permeability in the in vitro brain preparation. Figure 5 shows the fluorescent signal generated
by FITC albumin in neocortex, hippocampus and thalamus areas in the four tested conditions.

Figure 4 – Fluorescent signal generated by FITC-albumin in neocortex, hippocampus andthalamus areas
These findings demonstrate that TUS mediated enhancement of BBB permeability can be achieved only
by coupling MBs infusion and TUS. Measurements of BBB changes confirmed the BBB permeability
increase when MBs and TUS were co-applied with or without the polyolefin plate, compared to control
conditions characterized by both separate infusion of MBs and exclusive TUS stimulation, see Figure 5. No
significant difference in the area of FITC albumin extravasation was observed between cerebral areas thatreceived BBB opening protocol with or without the interposition of the polyolefin plate. These findings provethat TUS is effective through a 4 mm plate of artificial acoustic permeable material.

Figure 5 – Quantification of FITC-albumin signal in the four experimental conditions

5. CONCLUSION
The present work demonstrates the feasibility of opening the BBB across 4 mm-thick polyolefin-based
cranial prosthesis using an US insonation system, specifically designed. Brain areas exposed to concomitantsonication and MBs administration displayed a fluorescent pattern compatible with FITC extravasation, withno significant differences dependent on the interposition of the polyolefin plate; on the other hand, brains
separately exposed to either MBs or US waves only did not show BBB disruption. No signs of acute tissue
damage attributable to the treatment could be found in the brain specimens. Interestingly, brain anatomy andMBs circulation were easily visualized through the ultrasonography equipment; furthermore, it was possible
to observe real time changes in CEUS pattern during insonation.
The presented findings represent the necessary preliminary step to propose polyolefin-based material as
US permeable skull prostheses to be used for repetitive BBB openings in a clinical setting. The concept of
implanting sonolucent cranioplasties for imaging purposes has already been fostered by various authors(7, 8, 9), however, only one article from 1987 had theorized the implantation of US permeable cranioplasty
materials for TUS purposes: Tobias and collaborators already proposed US based ablative treatments to be
performed across a sonolucent window to preserve the mechanical protection of the skull while allowing full
penetration of the US waves (10).

ACKNOWLEDGEMENTS
The results here presented have been developed in the framework of the EMPIR Project
18HLT06 RaCHy. The project 18HLT06 RaCHy has received funding from the EMPIR programmeco-financed by the Participating States and from the European Union’s Horizon 2020 research andinnovation programme.

REFERENCES
1. B. Zeqiri, Metrology for ultrasonic applications, Progress in Biophysics and Molecular Biology, vol.
93, issues 1-3, pp. 138-152, 2007.
2. Ultrasonic-physiotherapy system-field specifications and methods of measurement in the frequency
range 0.5 to 5 MHz, International Electrotechnical Committee (IEC), IEC 61689 ed.3.0, 2013.
[Online]. Available: https://webstore.iec.ch/ publication/5722.
3. Ultrasonics – Power Measurement –Radiation force balances and performance requirements,
International Electrotechnical Committee (IEC), IEC 61161 ed.3.0, 2013. [Online]. Available:
https://webstore.iec.ch/publication/4708.
4. Prada, F. et al. In vitro and in vivo characterization of a cranial window prosthesis for diagnostic andtherapeutic cerebral ultrasound. J. Neurosurg. https://doi.org/10.3171/2019.10.JNS191674, 2020.
5. Muhlethaler, M., de Curtis, M., Walton, K. & Llinas, R. The isolated and perfused brain of theguinea-pig in vitro. Eur. J. Neurosci.5, 915–926, 1993.
6. de Curtis, M., Librizzi, L. & Uva, L. The in vitro isolated whole guinea pig brain as a model to study
epileptiform activity patterns. J. Neurosci. Methods 260, 83–90, 2016.
7. Belzberg, M. et al. Transcranioplasty ultrasound through a sonolucent cranial implant made ofpolymethyl methacrylate: Phantom study comparing ultrasound, computed tomography, andmagnetic resonance imaging. J. Craniofac. Surg. 30, e626–e62, 2019.
8. Belzberg, M. et al. Sonolucent cranial implants: Cadaveric study and clinical findings supporting
diagnostic and therapeutic transcranioplasty ultrasound. J. Craniofac. Surg. 30, 1456–1461, 2019.
9. Spena, G., Guerrini, F., Grimod, G., Salmaggi, A. & Mazzeo, L. A. Polymethyl methacrylate
cranioplasty is an effective ultrasound window to explore intracranial structures: Preliminary
experience and future perspectives. World Neurosurg. 127, e1013–e1019, 2019.
10. Tobias, J. et al. An ultrasound window to perform scanned, focused ultrasound hyperthermia
treatments of brain tumors. Med. Phys. 14, 228–234, 1987.

PROCEEDINGS of the24th International Congress on Acoustics
October 24 to 28, 2022, in Gyeongju, Korea
Extended abstractABS-0689

Ultrasonocoverslip: A High Throughput In-Vitro Platform for
Investigating Neuron-Glia Response to Direct Stimulations using
Low Intensity (<1W/cm2) UltrasoundKeunhyung Lee1, Jung Moo Lee3,4, Joo Min Park3, C. Justin Lee3,4, and Jinhyoung Park1,2
1

Department of Intelligent Precision Healthcare Convergence, Sungkyunkwan University, Suwon, Republic ofKorea.
2

Department of Biomedical Engineering, Sungkyunkwan University, Suwon, Republic of Korea.
3

Center for Cognition and Sociality, Institute for Basic Science, Daejeon, Republic of Korea.

4

KU-KIST Graduate School of Converging Science and Technology, Korea University, Seoul, Republic ofKorea.

ABSTRACT
Molecular mechanistic studies on low intensity ultrasound brain stimulations have been spotlighted to haveinsights on reliable acoustic neuronal stimulations. While recent studies report different ultrasound effects
showing both astrocytic TRPA1 mediated neuronal excitation and direct neuronal excitations throughcalcium-permeable mechanosensitive channels, further reliable in-vitro platforms to acquire the efficient
stimulation parameters are lacking. To address the needs, ultrasonocoverslip, an ultrasound transducer
integrated glass coverslip, for culturing and stimulating brain cells directly on the transducer is developed.
Ultrasonocoverslip can deliver uniform ultrasonic longitudinal pressure to the cultured brain cells, andsimultaneously allows to observe cellular signaling from neurons and astrocytes. The ‘U’ shaped electrodeof the ultrasonic transducer separates sonicated and non-sonicated regions, respectively, the artifactual
stimulations other than the longitudinal wave can be identified as a signal in the non-sonicated region. Due
to direct culture on the transducer, the acoustic absorber could be loaded in the back side of the transducer,
and reverberations causing elongated stimulation could be substantially reduced. With the reliable in-vitro
platform, the minimum ultrasound pressure level and stimulation length which can solely activate astrocytic
TRPA1 channel mediated-neuronal excitation with low intensity ultrasound stimulation can be acquired
through pharmacologic studies made in the novel in-vitro platform.
Keywords: ultrasonocoverslip, low intensity ultrasound stimulation, neuron-glia response, glutamate
mediated neuronal excitation, cover glass-based ultrasound transducer, TRPA1

1. INTRODUCTION
Transcranial ultrasound stimulations are known for activating brain cells and ultimately
modulating brain functions for treating neuronal disorders or improving cognitive functions 1. Despiteof the successful demonstrations of ultrasound stimulations on animals and primates 2 , theireffectiveness relies on various stimuli parameters discovered by empirical trials3 . To achieve reliable
performance in the stimulation, understanding molecular mechanisms of brain cell responses toultrasound stimulations needs to be supported by robust in-vitro platforms allowing of investigating
fluorescence changes to uniform ultrasound stimulations over statistically significant number of cells.
In recent studies, low-intensity ultrasound less than 1 W/cm2 demonstrated better performance inmodulating brain functions by forming non-thermal stimulation than the use of higher intensity 4 andCa 2+ -dependent glutamate release from astrocyte was reported 5 . Also, an experimental setup
sonicating the neurons on a coverslip from the other side of the cultured surface with external
ultrasound transducers was proposed 6. Nevertheless, this experimental setup would inherently induce
reverberations between the transducer and the glass coverslip causing unintentionally elongated andnon-uniform stimulations over the cell groups which hindered in identifying the minimum stimulationintensity or efficient stimulant parameters. Therefore, a further reliable experimental in-vitro platform
which can deliver uniform stimulations with minimized mechanical artifacts is essential to detect
minimum stimulatory conditions inducing reliable brain cell responses. In this report, we developed
Ultrasonocoverslip, an optimized acoustic coverslip platform, consisting of an ultrasound transducer
embedded on top of a glass coverslip. This new platform enabled us to co-culture neuronal cells
together with astrocytes directly on the transducer to acquire cellular responses of fluorescence signals
upon ultrasound stimulation. With this Ultrasonocoverslip, the glutamatergic neuronal excitations
followed by ultrasound-induced activation of astrocytic TRPA1 were investigated.

2. Methods
2.1 Fabrication protocol of ultrasonocoverslip
The glass coverslip was cleaned swapping the surface with acetone, alcohol, 1 % water dissolved
detergent and deionized water on the glass coverslip surface. After covering the glass coverslip witha kapton tape having a ‘U’ pattern of the transducer for the designated shape, Au/Cr electrode was
loaded on the coverslip by using a DC magnetron sputtering system. After removing the Kapton tapeon the cover glass, the PVDF-TrFE solution was spin-coated on the electrode deposited coverslip at
the velocity of 500 rpm to obtain consistent layer of 16 μm in 30 seconds. And then, the PVDF-TrFE
loaded coverslip was cured in a dry oven at 70 ℃ for 30 minutes and further baked at 135 ℃ for 2
hours. Top electrode was formed on top of the PVDF-TrFE layer with the patterned kapton tape usinga DC magnetron sputtering. To obtain the piezoelectricity of PVDF-TrFE, high voltage of 100 V/μm
was applied to the transducer after micro coaxial cable (38 AWG micro coax cable, Samtec) wasconnected to the transducer. A parylene layer in thickness of 12 μm was loaded for acoustic impedance
matching between water and PVDF-TrFE by a parylene coater.
2.2 Measurement of acoustic characteristics of ultrasonocoverslip
The pulse-echo measurement was performed for measuring center frequency and bandwidth byusing a pulse generator (UT-340, UTEX Scientific) which set the transmit pulse length, voltage level
and receive gain of 2 ns, 100 V pp and 20 dB, respectively. During the test, ultrasonocoverslip wasplaced in the bottom of a water tank and a flat crystal reflector was hold by a clamp at 36 mm abovefrom the transducer where the natural focus depth was. The uniformity of the surface pressure wasmeasured by a needle hydrophone (NH0200, Precision Acoustics) hooked up to XYZ linear stage. The
tip of the needle hydrophone was located on 1 mm above from the surface of the transducer and thepressure level was scanned by using the translational step size of 40 μm Although the nearest depth
in the experiment was 1 mm, the similarity of between the surface pressure above 50 μm and 1 mm
was estimated by a numerical simulation with a computer simulator (PZflex 1.25.3.0, Onscale)utilizing finite element method.
The output pressure level was calibrated for every transducer due to the deviations in the acoustic
output, and the final input voltage level was defined for each transducer to make a targeted acoustic
output. The acoustic output was measured by a needle hydrophone placed at 1 mm above thetransducer in a custom-made zig which mimics the actual experiment system. To mimic theexperimental system, the zig and the shape of objective lens was 3D printed to simulate thereverberation artifacts, and the hydrophone was placed through the central hole in the mockup of thelens. The measured acoustic output without absorber (Aptflex F28, Precision Acoustics) was
compared with the one with absorber which was laid on those boundaries as show in figure 2a.
2.3 Ultrasound stimulation and calcium imaging
Intracellular calcium concentration of astrocytes and neurons was monitored by a confocal
microscopy (LSM880, Carl Zeiss). To visualize calcium channel activations of astrocytes cultured on
the ultrasonocoverslip, astrocytes were labeled by Fluo4-AM dye (F14217, Thermo Fisher Scientific)
which was widely used in monitoring intracellular calcium concentration with fluorescent m icroscopy.
Following the Fluo4-AM loading, the excitation of cultured brain cells was visualized by confocal
microscopy with 488 nm excitation laser and 543 nm emission through a 10× water-immersion
objective lens. To suppress the ultrasound reverberation from the transducer and objective lens, anultrasound absorber (Aptflex F28, Precision Acoustics) was wrapped around the objective lens by acustom-made 3D printed holder shown in figure 2a. Another layer of the ultrasound absorber wasplaced under the transducer to reduce the reverberation made between transducer and the surface of

the acrylic well shown in figure 2a. The overall system configuration was shown in figure 2a. A
programmable trigger control system (Master8, A.M.P.I) was employed to synchronize the stimulation
with the image acquisition from microscopy. Once the trigger signal was sent to a function generator,
the arbitrary waveform generator transmitted the 335 μs long 6 MHz bursts repeated by 1.5 kHz pulse
repetition frequency (PRF). To have 500 ms long transmission, 750 bursts were transmitted.
2.4 Primary co-culture of neurons and astrocytes
Primary mouse cortical neurons were prepared from embryos (E14 -16) dissociated from pregnant
C57BL/6J mice. Embryos were decapitated and cortex weas washed with HBSS for 3 times after
dissecting it from embryos in HBSS. The cortex was treated by 0.1 % trypsin (15090-046, Gibco)
prior to incubating it for 5 minutes at 37 ℃. Heat-inactivated FBS (10082-147, Gibco) was
supplemented into the cortex and segregated it by mild trituration. It was centrifuged for 3 minuteswith 1000 rpm and resuspended in neurobasal media (10888022, Gibco) supplemented with 5 % Heat inactivated FBS (10082-147, Gibco), 2 % B27 supplement (17504-044, Gibco), 2 m M Glutamax-I(35050-061, ThermoFisher) and 10 units/ml penicillin-streptomycin (15140-122, Gibco) after filteringwith 40 μm cell strainer. Next, neurons (cell density: 1×105 ) and astrocytes (cell density: 5×10 4 ) were
simultaneously plated on the ultrasonocoverslip coated with 0.1 mg/ml poly-D-lysine (P6407, SigmaAldrich). Media listed above was used to culturing cells with incubation in 37 ℃ at 5 % humidified
CO2 incubator for 7~10 days before experiment. Half amount of the media was replaced every 2~3days.

3. Results
The surface pressure values measured by both the experiment and the simulation were shown infigure 1a-b, respectively. In the experimental results, the standard deviation of the measured surfacepressure was 0.005 MPa, less than 6 % of the mean value of 0.086 MPa. To confirm the uniformity of
the acoustic output on the surface, the simulation was performed on 50 μm above the surface where
neuronal cells were supposed to be located 7. The mean value and the standard deviation of acousticpressure at the location were 0.078 MPa and 0.002 MPa, respectively, similar with the values at 1 mmabove the surface.
Brain cells could be successfully cultured on the ultrasonocoverslip. The co -cultured neurons andastrocytes were labeled with anti-Tuj1 (neuron-specific class III bet-tubulin, red) and anti-GFAP (glial
fibrillary acidic protein, green), respectively, and the live cells could be identified by DAPI (4’,6diamidino-2-phenylindole for nuclear acid, blue) with fluorescence microscopy as shown in figure 2b.
As the figures showed, both neurons and astrocytes could be co-cultured and maintain healthy stateson the ultrasonocoverslips. With the decent performance of ultrasonocoverslip in high specificity
showing brain cell responses mostly from the longitudinal stimulations, the minimum pulse length totrigger astrocytic calcium elevations were investigated. Figure 2c showed the responses for thedifferent pulse length in ultrasound to find out the minimum pulse length that induced a sharp andtransient rise in calcium. Considering the area-under-curve (AUC) of each trace, the pulse length of
0.5 s would be the closest value to the minimum pulse length that could induce reproducible calcium
responses. Also, pAAV-GFAP-iGluSnFR and pAAV-GFAP-jRGECO were transfected into primarycultured astrocytes for monitoring the glutamate excretion accompanied with calcium excitations.
While the stimulation period of 0.25 and 0.5 s did not show significant differences in the calcium
excitations as shown in figure 2d, the peak glutamate release with 0.5 s of ultrasound stimulation was
2.7 times more than 0.25 s of ultrasound stimulation.
The neuronal excitations through astrocytic glutamate release triggered by mechanosensitive
TRPA1 channel by ultrasound stimulation was confirmed by using ultrasonocoverslip. Dose responses
to the different acoustic pressure and stimulation time into co-cultured neuron-astrocyte were plottedin figure 3a. Under the buffer without magnesium ions (Mg 2+ ) with TTX, the pressure level wasinvestigated by increasing the value from 0.05 to 0.15 MPa corresponding to from 0.02 to 0.186 W/cm2
as shown in figure 3a. While the astrocytic calcium responses were increased from 0.07 MPa, theneuronal calcium response showing sharp increase could be found from 0.15 MPa. Also, as increasing
the stimulation time from 0.05 s to 0.5 s, astrocytic and neuronal calcium responses were graduallyaugmented. As a result, the minimum pulse length and pressure level of low intensity ultrasound that
could evoke neuronal calcium response are 0.5 s and 0.15 MPa, respectively.
Over the pharmacologic experiments on the co-cultured neuron-astrocyte cells, glutamatergicresponses were confirmed by loading NMDAR and AMPAR antagonists of D-AP5 (D-2-amino-5phophonopentanoate) and CNQX (6-cyano-7-nitroqunoxaline-2,3-dione), respectively, and the results
were compared with the post stimulatory responses with the selective TRPA antagonist HC-030031 aspharmacologic strategies. With the treatment of sole D-AP5 (50 μ M ) and combination of D-AP5 andCNQX (50 μ M, 20 μ M), the neuronal responses were partly suppressed with the single treatment of DAP5, and they were significantly blocked by treating a mixture of AP5 and CNQX. The neuronal
responses were significantly reduced while the astrocytic response was barely affected by those drugs(figure 3b). Because the cultured astrocytes were known for having smaller number of those glutamate
receptors (NMDAR, AMPAR) compared to neurons 25 , the results indicated that the calcium response
was mostly induced by astrocytic glutamate release. Also, the selective TRPA antagonist, HC-030031(40 μM ), showed significantly reduced neuronal and astrocytic calcium responses at the same time.
Those results meant that astrocyte was the major source for glutamate release with low-intensity
ultrasound stimulations.

Figure 1. (a) (left) Measured pressure distribution at the depth of 1mm above from the surface. (right) Line plotof the measured pressure at line A. (b) Simulated pressure distribution at the depth of 1mm and 50um above fromthe surface.

Figure 2. (a) Overall experiment scheme. (b) Immunostained neurons and astrocytes labeled by Tuj1, GFAP, DAPI.
(c) Astrocytic calcium response induced by different pulse length of the ultrasound. (d) Virally transfected
iGluSNFR, jRGECO indicator and their responses as a function of pulse length of the ultrasound.

Figure 3. (a) Averaged neuronal, astrocytic calcium responses and quantified area under the curve (AUC) as afunction of acoustic pressure and burst period. (b) Blocking glutamate released from astrocyte withpharmacological treatment induced by ultrasound stimulation. (NMDAR antagonist : D-AP5 (50μM), AMPAR :CNQX (20 μM), TRPA1 : HC030031 (40 μM).

4. Conclusion
Ultrasonocoverslip was a reliable high-throughput in-vitro ultrasound stimulation platform to
discover the optimal non-thermal low intensity acoustic stimulant parameters. Unlike the previously
utilized experimental setup for ultrasound cell stimulations, ultrasonocoverslip was capable ofdelivering uniform acoustic stimulations over the hundreds of brain cells cultured on the transducer’s
surface simultaneously and visualizing fluorescence signal changes upon the cellular responses. Also,
ultrasonocoverslip allowed to measure the cell responses to a planned longitudinal acoustic waveform
because the platform could minimize acoustic reverberations causing unintentional stimulatory
elongation by loading an absorbing stack in the back side of the transducer. In addition, the patterned‘U’ shaped electrode separated the cellular responses within the active from the inactive transducer
region to identify artifacts in cellular responses made by surface wave which could not be separated
in the conventional setups using external ultrasound transducers. With the ultrasonoco verslip, theastrocytic TRPA1 mediated neuronal excitation was recapitulated, and the minimum stimulationpressure and length to trigger neuronal excitation in response to the low intensity ultrasound was
successfully discovered.

ACKNOWLEDGEMENTS
This work was supported by grants from the National Research Foundation of Korea funded by the
Ministry of Science and ICT (grant numbers: 2020R1A2C2011808, 2021R1A4A1028713).

References
1. Sin-Guang Chen, Chih-Hung Tsai, Chia-Jung Lin, Lee Cheng-Chia, Hsiang-Yu Yu, Tusng-Hsun Hsieh,
Hao-Li Liu. Transcranial focused ultrasound pulsation suppresses pentylenetetrazol induced epilepsy invivo. Brain stimulation, 13(1), 35-46, 2020.
2. Wonhye Lee, Hyun-Chul Kim, Yujin Jung, Yong An Chung, In-Uk Song, Jong-Hwan Lee, Seung-SchikYoo. Transcranial focused ultrasound stimulation of human primary visual cortex. Scientific Reports, 6,34026, 2016.
3. Sungho Lee, Keunhyung Lee, Myunghwan Choi, Jinhyoung Park. Implantable acousto-optic window formonitoring ultrasound-mediated neuromodulatin in vivo. Neurophotonics, 9(3), 032203, 2022.
4. William J. Tyler, Yusuf Tufail, Michael Finsterwald, Monica L. Tauchmann, Emily J. Olson, Cassondra
Majestic. Remote excitation of neuronal circuits using low-intensity, low-frequency ultrasound. PLoS
ONE, 3(10), e3511, 2008.
5. Soo-Jin Oh, Jung Moo Lee, Hyun-Bum Kim, Jungpyo Lee, Sungmin Han, Jin Young Bae, Gyu-Sang
Hong, Wuhyung Koh, Jea Kwon, Eun-Sang Hwang, Dong Ho Woo, Inchan Youn, Il-Joo Cho, Yong Chul
Bae, Sungon Lee, Jae Wan Shim, Ji-Ho Park, C. Justin Lee. Ultrasonic neuromodulation via astrocytic
TRPA1. Current Biology, 30(5), 948, 2020.

6. Marc Duque, Corinne A. Lee-Kubli, Yusuf Tufail, Uri Magaram, Janki Patel, Ahana Chakraborty, Jose
Mendoza Lopez, Eric Edsinger, Aditya Vasan, Rani Shiao, Connor Weiss, James Friend, Sreekanth H.
Chalasani. Sonogenetic control of mammalian cells using exogenous transient receptor potential A1
channels. Nature communications, 13, 600, 2022.
7. Aditya Vasan, Jeremy Orosco, Uri Magaram, Marc Duque, Connor Weiss, Yusuf Tufail, Sreekanth H
Chalasani, James Friend. Ultrasound mediated cellular deflection results in cellular depolarization.
Advanced Science, 9(2), 2101950, 2021.

PROCEEDINGS of the24th International Congress on Acoustics
October 24 to 28, 2022, in Gyeongju, Korea
Extended abstractABS-0724

Rebound Excitation of Epileptic Brain Activities
After Transcranial Focused Ultrasound Stimulation
Minseok KOO1; Taewon CHOI2; Jinhyoung PARK3
1,2,3

Sungkyunkwan University, Republic of Korea

ABSTRACTTranscranial focused ultrasound stimulation has recently shown its feasibility in inducing anticonvulsant
effects. However, its association with rebound excitation is yet unknown although adverse effects caused bypost-inhibitory rebound excitations have been reported for conventional antiepileptic therapies. In this study,
rebound excitations with transcranial focused ultrasound were firstly demonstrated, and the modulation of
epileptiform activities towards both suppressive and excitatory responses were investigated following
ultrasound stimulations. For the experiment, ultrasound was applied on the thalamus of a chemically induced
acute epilepsy rat model. Sonication effects were monitored via electroencephalography (n=38),
immunohistochemical analysis (n=24), and optical measurement of cerebral blood volume changes (n=18).
While ultrasonic stimuli patterns with long intervals showed antiepileptic effects on electroencephalography,
those with short intervals showed rebound excitatory responses followed by inhibitory activities. Additionally,
suppressive states induced by inhibitory stimulations were transformed into excitatory states by consecutively
applying short acoustic bursts with higher pressure. Cerebral blood volume changes demonstrated consistent
results with the electroencephalography. Immunohistochemistry revealed that both inhibitory and excitatoryneuronal cells were activated to generate rebound excitatory conditions, while inhibitory cells were activatedfor suppressive conditions. Our first discovery of the rebound excitations would provide safety guidelines inepilepsy patients with ultrasound stimulation.
Keywords: Transcranial Focused Ultrasound, Epilepsy, Antiepileptic Therapy, Neuromodulation, ReboundExcitation

1. INTRODUCTION
Epilepsy, one of the most common neurological disorders, is caused by the failure of the inhibition
that could trigger the dispersion of epileptic responses over broad brain regions. Thus, main featuresof antiepileptic therapies, such as those using antiepileptic drugs (AEDs), vagus nerve stimulation(VNS), deep brain stimulation (DBS), transcranial magnetic stimulation (TMS), and transcranial
direct/alternating current stimulation (tDCS/tACS), involve alleviation of pathologicalhyperexcitability via reduction of neuronal excitation or enhancement of inhibition and are partiallyeffective in suppressing epileptiform activities. Meanwhile, the use of antiepileptic methods can causeside effects such as rebound excitations. Acute rebound effects followed by the application of
inhibitory stimuli have been reported in AEDs, VNS, DBS, and TMS, and the effects even lasted fora longer period. The stimulus frequency and length are known to be significant parameters causing
the rebound excitatory effects; in electrical stimulations, brief stimuli can cause temporary reboundeffects after a short-time inhibition. Moreover, fast successive stimulations could exhaust GABA andreinforce excitatory effects, ultimately breaking inhibitory functions.
Recently, low-intensity focused ultrasound has demonstrated antiepileptic effects in drug -induced
small animal temporal lobe epilepsy models after stimulating seizure -triggering specific regions ofanterior thalamic nuclei (ATN) and hippocampus. Despite the successful induction of suppressive
1

minseokkoo@g.skku.edu
tw_93@naver.com
3
jin.skku.edu@skku.edu
2effects, functional adverse effects have not been reported. Because ultrasound stimulation is known
to work on inhibitory pathways by activating GABAergic neurons or blocking ionic channels likeother conventional stimulant methods, the rebound excitation could be induced after transcranial
focused ultrasound (tFUS) stimulation similar to other stimulation approaches.

2. OBJECTIVE
In this study, the ultrasonic stimulus sequences that could upregulate or downregulate theepileptiform activities were firstly discovered on an acute epilepsy model. Referring to the rebound excitation-triggering-electrical-stimulations, pulse length, frequency, and intensity were varied toinduce the paradoxical excitatory responses. The changes in epileptic activities were monitored withelectroencephalography (EEG) and optical measurement of cerebral blood volume (CBV) changes.
After each trial, immunohistochemistry (IHC) was performed to visualize the imm une responses
related to excitatory and inhibitory activations.

3. METHODS
Fig. 1(A) depicts the overall experimental platform for delivering ultrasonic energy to the target
region and recording the EEG signals. For the acoustic stimuli, a 690 kHz single -element ringtransducer was fabricated. The beam was focused 5 mm ahead of the tip of the collimator with −3 dB
beam sizes of 4.7 mm and 2.2 mm along the axial and lateral directions. By placing the tip of thetransducer above the parietal bone, the acoustic beam was delivered to the right ATN. The beam
pathway was confirmed by overlaying the beam profile on a rat brain atlas, as shown in Fig. 1(B).
A

B

CH 4

MC

Trigger
SignalReference

Transmit
SignalPosition Relative toInteraural Line mm

CH
CH 2
CH

8

SC

CgC
cc
AD vhc AD
AV

4

Rt

sm
PT PT AV
AM

AM

Re

2

CPu

Rt

SC2

IsC

PaAP
f

02

LH

AHA BSTMPL

0
4

2

0

2

4

Position Relative to Midline mm

Fig. 1. (A) Overall experimental platform (B) Superimposed image with the measured beamprofile and Paxinos brain atlas
To simplify the ultrasonic conditions and mimic electrical stimulations generating reboundexcitations, the stimuli were classified into two types: one with low pressure (0.25 MPa) consecutive
burst train (TX1-TX4) and the other with high pressure (1.0 MPa) with shorter bursts (TX5) with a
50% duty cycle of 100 Hz pulse repetition frequency (PRF). TX6, a back-to-back transmission of TX3
and TX5, was generated with the assumption that the rebound excitation could be artificially induced
by applying a brief excitation followed by inhibitory stimulation. The experimental procedure
involved a total of 59 male Sprague-Dawley (SD) rats. The animals were anesthetized via urethane,and pentylenetetrazol was administered acutely and intraperitoneally to induce epileptic seizures. The
sonication effects were monitored by EEG (N=38), immunohistochemical analysis (N=24), and opticalmeasurement of cerebral blood volume changes (N=18).

4. RESULTS
Fig. 2(A) compares the severity of epilepsy by counting the number of discharging peaks in the
EEG signals. Over 20 min of the post-stimulation period, the number of epileptic spikes for the PTZcontrol was increased by more than two times compared to the baseline level. Although the levelslightly decreased from 5AS to 20AS for the PTZ-control, the change was not statistically significant(p-value: 0.764). As the stimulation length was elongated from 10 s to 40 s (from TX1 to TX3), thenumber of epileptic spikes decreased (p-values: 0.003, 0.005, and 0.624 at 20AS for TX1, TX2, andTX3 compared to baseline, respectively) and ultimately became comparable to the baseline with TX3(p-values: 0.515, 0.995, 0.478, and 0.624 at 5AS, 10AS, 15AS, and 20AS, respectively). In contrast,
TX4 having shortened burst intervals generated a comparable number of epileptic spikes with thePTZ-control (p-values: 0.467, 0.186, 0.232, and 0.061 at 5AS, 10AS, 15AS, and 20AS, respectively).
Moreover, the increase in the number of epileptic spikes with TX1 was statistically significant
compared with the number with PTZ-control at the post-stimulation periods from 5AS to 20AS (pvalues: 0.083, 0.031, 0.061, and 0.014 at 5AS, 10AS, 15AS, and 20AS, respectively). With TX6
consecutively transmitting TX3 and TX5, the number of peaks was increased by 12.4% more than thevalue with the sole use of TX1 at 5AS (p-value: 0.0014), but the level afterward became similar to
those with TX1 (p-values: 0.009, 0.002, and 0.403 at 10AS, 15AS, and 20AS, respectively). In
addition, the number of epileptic spikes with TX6 was always higher than that for PTZ-control overall post-stimulation periods at 10AS, 15AS, and 20AS although the differences were not statisticallysignificant (p-values: 0.145, 0.285, and 0.083, respectively). Additionally, as shown in Fig. 2(B), thespectral power of EEG for the TX1 condition increased higher than that of the PTZ -control (p-value:
0.153 at 10AS), while the level with TX3 was lower than that of the control group ( p-value: 0.014 at
10AS). Interestingly, the elevation of the power spectral level with TX6 was noticeable because themean value was 1.914 times higher than that of the control and 5.884 times higher than the baseline
level at 20AS.
In Fig. 2(C) showing the time courses of the number of epileptic spikes in the post -drug injection
periods, the inhibitory and excitatory responses induced by different transmit conditions could be
identified. Interestingly, TX1 exhibiting rebound excitatory effects in the post-stimulus period showed
suppressive effects presenting a lower level than PTZ-control during the stimulus period from 0 to 5
min in Fig. 5B. With TX4 shown in Fig. 5C, rebound excitatory effects in the post -stimulus period
were identified while suppressive effects were seen during the stimulus period from 0 to 5 min. With
TX3, the prominent suppressive effects lasted during and after the stimulation.

Fig. 2. (A) Number of epileptic spikes ratio after tFUS (B) Delta power amplitude after tFUS
(C) Time courses of the spike ratio
Fig. 3 shows the immunohistochemistry study results. For the c-Fos analysis, TX1 group showed
higher c-Fos-expressed neuronal cells (Mean=80.72%) than the PTZ group (Mean =71.73%).
Additionally, TX6 presented even higher value of about 87.27%. However, the expression ratio was
reduced to 24.22% in TX3 condition. In the GAD65 analysis, both normal and PTZ control cases
barely showed GAD65-positive cells while the other conditions all presented more expressed cells.
The GAD65-positive cell density in the suppressive TX3 group (Mean=13.58 cells/mm 2 ) was about
5.8 times higher than the PTZ control (Mean=2.33 cells/mm 2). For the Iba1 analysis, increase in theIba1-positive cell density in TX1 and TX6 compared to the PTZ control were observed.
A

B

C

Fig. 3. (A) c-Fos-positive cell ratio represents neuronal activity (B) GAD65-positive celldensity represents GABAergic network (C) Iba1-positive cell density represents neuronaldamageFig. 4 shows the cranial window for the optical measurement and the changes in CBV, which is
known to be coupled with neuronal activities. With TX1, Δ[HbO] sharply increased +19% comparedto the baseline and was maintained at a higher level than the Δ[HbO] of PTZ-control. Meanwhile, theincrease in Δ[HbO] was suppressed using TX3, and Δ[HbO] level was maintained lower than the level
shown in PTZ-control. With TX6, the increase in Δ[HbO] level shown in PTZ-control was minimized
to approximately +7.5% from the baseline, and the suppressive response was translated to theexcitatory state after the completion of sonication with TX5.
A

B

Normal Control

T

T

PT Control

T

T

Fig. 4. (A) Cranial window and the visualized cortical surface of the rat brain (B) Changes of
the cerebral blood volume level

5. CONCLUSION
The modulation of epileptiform brain activity was first demonstrated by purely changing the pulse
duration, intervals, and amplitude. The rebound effect worsening the epileptiform activities could be
induced by applying brief stimulations or decreasing the stimuli interval which might make the amountof glutamate higher than GABA. In addition, the rebound effect could be amplified by using back-toback transmissions of the low-pressure elongated and high-pressure shortened stimuli, which resulted
in worsening of the epileptiform activities followed by fully suppressive effects.

ACKNOWLEDGEMENTS
This work was supported in part by the National Research Foundation of Korea (NRF) grants
funded by the Korea government (MIST) NRF-2020R1A2C2011808 (J.P.) and NRF-

2021R1A4A1028713 (J.P.).

REFERENCES
1. Choi T, Koo M, Joo J, Kim T, Shon Y-M, Park J. Rebound Excitation of Epileptiform Activities by
Transcranial Focused Ultrasound Stimulation. bioRxiv 2022;2022.04.30.490021.
2. Chen S, Tsai C, Lin C, Chen-Chia L, Yu H, Hsieh T, Liu H. Transcranial focused ultrasound pulsation
suppresses pentylenetetrazol induced epilepsy in vivo. Brain Stimul. 2020;13:35-46.
3. Min B, Bystritsky A, Jung K, Fischer K, Zhang Y, Maeng L, Park S, Chung Y, Jolesz FA, Yoo S. Focusedultrasound-mediated suppression of chemically-induced acute epileptic EEG activity. BMC Neurosci.
2011;12:1-12.

PROCEEDINGS of the24th International Congress on Acoustics
October 24 to 28, 2022, in Gyeongju, Korea
Extended abstractABS-0530

Influence of RSSI Due To Cavitation-Induced Scattering In The
Ballistic ESWT Device
Ohbin Kwon1; Min Joo Choi2
1

Interdisciplinary Postgraduate Program in Biomedical Eng, Korea
2

Dept. of Medicine, Jeju National University, Jeju, Korea

ABSTRACTA laser Doppler method(LDM) is suffering from severe noises involved with cavitation in the ballistic
extracorporeal shock wave therapy(bESWT) device. It is difficult to distinguish between a shielded signal
due to cavitation or noise due to cavitation collapse. Although not accurate, you can determine the reliabilityof a signal from the signal level of the Receive Signal Strength Indicator (RSSI). The scattering due tocavitation of the shock wave transmitter surface of the bESWT, the RSSI signal, and the impulse oscillation
relationship measured by LDM was observed. it uses a synchronized high-speed camera. The bubbles grewwith time, and some of them were fused into larger bubbles and formed gradually an air layer on the surfaceof the shock wave transmitter. The interrogating laser toward the center of the vibrating circular surface was
likely scattered and the laser returning to the sensor is largely reduced in power(i.e. RSSI). The red light
captured by a high-speed camera exhibits strong scattering, consistent with the strong, dense noise of theimpulse pulses. It was confirmed through RSSI that cavitation lowered the light intensity. The Red light isfaster than RSSI for reaction rate. It has the potential to be proposed as a new evaluation method.
Keywords: extracorporeal shock wave therapy, scattering light beam, cavitation

1. INTRODUCTION
The shock wave therapeutic device is based on the mechanical effect of ultrasound, and cavitation
generated by the shock wave is known as an important treatment mechanism.[1-4] The lower thefrequency, the lower the P- (peak negative pressure, MPa), and the higher the intensity of thecavitation. Among them, the radial ESWT mainly generates shock waves in a ballistic type. A ballistic
launched by the force of high-pressure compressed air collides with the back of the shock wave
transmitter (SWT), and the vibration of the surface of the SWT is radiated into the adjacent humantissue in the form of a pressure wave (shock wave). Compared with the focused shock wave, themaximum pressure (~10 MPa) of the radial shock wave is low, the duration of the shock wave (0.2~5ms) is very long, and the frequency characteristics are also different. [5,6]The ballistic shock wave is accompanied by severe noise signals related to cavitation due to thelong duration of the shock wave. Recently, a method using the laser Dopper Method (LDM) has been
proposed as an alternative for ballistic shock wave measurement. [7] However, there is still noise
signals related to cavitation. In this paper, we observed the relationship between laser signal shielding,
RSSI, and cavitation in LDM.

2. M&MWe save the data in an oscilloscope(HDO6104A, LeCroy, USA), its diameter of 15 mm flat type
SWT surface vibration and RSSI from laser vibrometer(OFV-5000, Polytec, Germany) in ballistic
shock wave(Zeus Wave, Weverinstruments, Rep. Korea) output setting of 4 bar. In addition, scattered
light due to cavitation was observed using a synchronized high-speed camera (VEO 710, visionresearch inc, USA). The camera captures at a rate of 100,000 images at a resolution of 128 x 128
pixels.
1
2

ohbin@jejunu.ac.kr
mjchoi@jejunu.ac.kr

3. Result & Discussion
Fig. 1 shows the signal shielding due to cavitation and the RSSI signal at that time. It can be seenthat the decrease in the level of the RSSI signal in the time domain coincides with the distortion sign aldue to cavitation. It responds more slowly than rapidly changing vibration signals. At the time
indicated by A, it was observed that the LDM signal was darkly masked in black (23.12 ms). It took
126 us for the RSSI to drop 90% from its maximum value.
Figure 2(a) shows the growth ofcavitation bubbles at 50 µs intervals.
Cavitation bubbles generated by theshock wave largely grow and collapse on
the surface of the SWT. A cavitation
bubble is created in the laser path and it
is observed to scatter the laser signal.
Figure 2 (b) is a laser scattering image
taken simultaneously with the signal
shown in Figure 1. It shows the light due
to scattering well and the consistency of
the RSSI signal. Also, it responds fasterthan RSSI.
It is possible to check the shielding
due to the presence or absence of
Figure 1. LDM signal and RSSI signal measured at 4 bar,cavitation to some extent from RSSI, but
is it a transient signal? It is difficult to
A: cavitation-related noise.
distinguish whether it is a cavitation
signal. In addition, since the laser is focused on the center of the surface of the SWT, if there is nobubble in the center of the SWT, the RSSI signal may be greatly reduced and there may be no distortion
signal due to cavitation.

Figure 2. Cavitation and laser scattering took with a high-speed camera. (a) Cavitation images (b) Laser
scattering.
The laser scattering signal responds immediately to bubble formation and disappears with thedisappearance of the cavitation bubble. This suggests that laser scattering due to cavitation bubbles
can be an evaluation method that can quantify cavitation without expensive equipment. Future studies
on SWT of other shapes and power settings are required.

4. conclusion
In this study, RSSI and LDM signals were observed during the cavitation cloud, and the lightscattering at cavitation was confirmed. The bubbles grew with time, and some of them were fused intolarger bubbles and formed gradually an air layer on the surface of the shock wave transmitter. Theinterrogating laser toward the center of the vibrating circular surface was likely scattered and the laser
returning to the sensor is largely reduced in power(i.e. RSSI).
The red light captured by a high-speed camera exhibits strong scattering, consistent with the strong,dense noise of the impulse pulses. It was confirmed through RSSI that cavitation lowered the lightintensity. The Red light is faster than RSSI for reaction rate. It has the potential to be proposed as a
new evaluation method.

ACKNOWLEDGEMENTS
This work was supported by the research grants from National Research Foundation of Korea(Grant No. 2017R1A2B3007907) and the Korean Medical Device Development Fund grant funded by
the Korean government (the Ministry of Science and ICT, the Ministry of Trade, Industry and Energy,
the Ministry of Health & Welfare, the Ministry of Food and Drug Safety) ( KD0000103G0003057).

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