Photoacoustic medical imaging demonstration using a pulsed LED
Time: 8:20 pm
Author: Leah Burge
Abstract ID: 1919
This demonstration of photoacoustics involves a focused light-emitting diode (LED) pulse (620 nm wavelength) which illuminates an optically absorbing target. The rapid expansion generates an ultrasonic pulse detected by an immersion transducer. An LED is a cost-effective alternative to the traditional neodymium-doped Yttrium-Aluminum Garnet (Nd:YAG) laser and laser diode- that is most effective in near-infrared. The LED is driven by a home-made MOSFET driver capable of 100 A pulses. Focused pulses illuminate a horizontal 1.2 mm capillary tube filled with Fast Green Dye. A highly-diffuse Teflon cylindrical cavity (9 cm tall, 6 cm diam) contains the mounted capillary tube. A 2.25 MHz immersion transducer with four low-noise amplifier gain blocks (combined 86 decibel gain, 0.5- 30 MHz bandwidth), detects a time-averaged signal from over 1000 trials. Comparisons are made using India ink. Earlier, T. J. Allen and P. C. Beard used 35 percent hematocrit blood in a capillary tube at a 620 nm wavelength demonstrating the feasibility of photoacoustic medical imaging of vascular systems under the skin or shallow-tissue cancerous tumors (using tomography) as an alternative to radioactive medical imaging. Our work precedes a photoacoustic tomography demonstration using three targets in an open acrylic tank.
Moving object detection and tracking based on Doppler ultrasound
Time: 7:00 pm
Author: Hyeong Geun Jo
Abstract ID: 2745
Fetal health monitoring during pregnancy has become a necessary procedure. Fetal heart rate (FHR) monitoring can determine fetal development or presence of heart disease and evaluate fetal well-being. The FHR measurement uses typically an acoustic probe-based Doppler ultrasound. Doppler ultrasound method transmits a continuous wave signal to the abdomen of a pregnant woman to receive a reflected signal from the fetal heart. Periodic displacement of the heart tissue produces the Doppler effect and the phase change of the reflected wave is proportional to the velocity of the fetal heart. The reflected signal is modulated into a phase signal and the received signal is demodulated to detect the heart rate. The current clinician system consists of a single probe and requires the probe to be manipulated to the optimal position to measure FHR. The system is highly dependent on trained diagnostic experts. The movement of the pregnant woman and the fetus leads to the misaligned acoustic beam which degrades the reliability of the measurement. This work presents a detection and tracking system using a Doppler signal to compensate for the target's movement. The system is implemented by integrating multi-channel probes interfaced to a Doppler signal converter with a 2-degree of freedom (DOF) motor device. This work describes the characteristics of two key components: Doppler signals of multi-channel probes according to the direction of the acoustic beam and the algorithm with a 2-DOF tracking system.
Cough monitoring and pneumonia diagnosis algorithm through analysis of respiratory system-based vibro-acoustic signals and AI technology
Time: 7:20 pm
Author: Youngbeen Chung
Abstract ID: 3046
In case of pneumonia often accompanied by serious complications, sometimes lead to death, early diagnosis and continuous monitoring can greatly reduce the dangerousness. Moreover, the COVID-19 pandemic has demonstrated the need for new diagnostic tools that can minimize medical personnel engagement while avoiding equipment being exposed to afflicted patients. In this study, we developed cough monitoring algorithm by detecting the vibrations of human body. The acceleration response at each part of body was measured to determine propagation characteristics of vibration when cough occurs. And it was confirmed that the monitoring accuracy was improved when use the vibration signal compared to the case of using only acoustic signal. After that, we analyzed the perceived cough in terms of psych-acoustical and sound-energy aspects. For the characteristic features derived by quantifying the results of analysis, the data augmentation process was applied, and finally AI-based pneumonia diagnosis algorithm was constructed. To estimate the performance of algorithm, the accuracy of pneumonia determination in new cough cases was verified. It showed the higher value than the accuracy of pulmonologists with only cough sounds. Therefore, developed algorithm that perform continuous cough monitoring and reliable pneumonia diagnosis can be used as an effective supplementary tool for early diagnosis and prognosis of pneumonia.
Non-invasive Fetal heartbeat detection using vibration sensing system
Time: 8:40 pm
Author: Wanseung Kim
Abstract ID: 3134
Monitoring the fetal heartbeat is essential for obtaining information about fetal condition during pregnancy. By measuring the maternal uterine contractions, the movement of the fetus, and the fetal heart rate, the state of the fetus is detected and taking appropriate measures prevent fetal health abnormality. The NST method is used to obtain the fetal heart rate. The ECG method is useful to observe heart rhythm. In this study, a system that can detect fetal heartbeat signals using vibration sensors is proposed. For vibration measurement, a band-type device with a sensor was proposed. The band was wrapped around the mother's stomach to sensing signals generated by the mothers body. In order to extract the fetal heartbeat signal, appropriate signal processing for denoising is performed. Processed signal was divided into several IMFs using the CEEMD method. The maternal heartbeat signal and fetal heartbeat signal were separated from the IMF. Through frequency analysis, the characteristics of each signal make clear
Study for the interaction between the medium in the middle ear and vibro-acoustic transmission
Time: 7:40 pm
Author: Jeon Jonghoon
Abstract ID: 3138
This study presented a quantitative evaluation index related to sound response for diagnosis of middle ear condition. The signal transmission paths for human perception of sound are divided into bone conduction and air conduction, respectively, depending on the path through which vibration and sound are transmitted. The components of auditory system that can affect the sound signal variability include temporal bone, ear canal, eardrum, and middle ear cavity. The specific acoustic impedances were obtained through simple geometric model of the auditory components, and the sound transmission mechanism was implemented through the outer-middle ear circuit model. The frequency range corresponding to the resonance characteristics of each components were calculated. The response difference for the medium of middle ear was confirmed by deriving frequency response function between the input sound and the output sound in the frequency domain through the transfer function method. The reliability of the algorithm was confirmed through the ROC curve, and individual evaluation indexes were derived according to the priority factor between classification accuracy and error rate.
Intraocular Pressure Estimation Method Based on Vibration Propagation Characteristics According to Structure Contact
Time: 8:00 pm
Author: KIM Deukha
Abstract ID: 3144
Intraocular pressure (IOP) measurement is one of the basic tests performed in ophthalmology and is known to be an important risk factor for the development and progression of glaucoma. Measurement of IOP is important for assessing response to treatment and monitoring the progression of the disease in glaucoma. In this study, we investigate a method for measuring IOP using the characteristics of vibration propagation generated when the structure is in contact with the eyeball. The response was measured using an accelerometer and a force sensitive resistor to determine the correlation between the IOP. Experiment was performed using ex-vivo porcine eyes. To control the IOP, a needle of the infusion line connected with the water bottle was inserted into the porcine eyes through the limbus. A cross correlation analysis between the accelerometer and the force sensitive resistor was performed to derive a vibration factor that indicate the change in IOP. In order to analyze the degree of influence of biological tissues such as the eyelid, silicon was placed between the structure and the eyeball. The Long Short-Term Memory (LSTM) deep learning algorithm was used to predict IOP based on the vibration factor.