https://scholar.dgist.ac.kr/handle/20.500.11750/191 Sat, 04 Apr 2026 12:35:54 GMT 2026-04-04T12:35:54Z https://scholar.dgist.ac.kr/handle/20.500.11750/59945 Title: Compact forward-viewing multimodal fluorescent and optical coherence tomography endomicroscopic probe Author(s): Im, Jintaek; Cho, Gichan; Song, Cheol Abstract: We present a compact multimodal endomicroscope that enables simultaneous fluorescence (FL) and optical coherence tomography (OCT) imaging. While current endoscopy techniques are effective for wide-area and rapid inspection, there is a growing demand for real-time precise diagnostics, including detailed tissue morphology and tumor invasion depth. Histological analysis through biopsy remains the diagnostic standard but involves a time-consuming process that can delay treatment decisions. Our approach integrates two complementary imaging modalities-FL for visualizing tissue morphology and OCT for cross-sectional imaging-within a single probe compatible with standard gastrointestinal endoscopic channels. The system employs a Lissajous scanning mechanism to achieve forward-viewing, uniform illumination, and high-speed imaging. A compact imaging probe is fabricated by assembling a composite fiber, piezoelectric tube actuator, and asymmetrically attached polymer stiffener in parallel, enabling combined fluorescence and optical coherence imaging with complementary performance characteristics. Real-time image reconstruction is implemented using parallel computing to support high-throughput data processing. Imaging experiments on phantom targets and ex-vivo animal tissues confirm the system's capability to produce detailed, co-registered images of tissue morphology and structure. This technology offers a promising platform for enhancing diagnostic accuracy and enabling real-time decision-making in gastrointestinal endoscopy. Sun, 30 Nov 2025 15:00:00 GMT https://scholar.dgist.ac.kr/handle/20.500.11750/59945 2025-11-30T15:00:00Z https://scholar.dgist.ac.kr/handle/20.500.11750/59116 Title: Compact Fiber-Optic Sensor for Simultaneous Force Measurement and Depth Profiling Author(s): Im, Jintaek; Cho, Gichan; Song, Cheol Abstract: This study presents a compact sensor for simultaneous force measurement and depth profiling. We have developed a fiber-optic sensor structure capable of integrating common-path optical coherence tomography (CP-OCT) and Fabry–Pérot Interferometry (FPI) within a single unit. The proposed FPI-OCT sensor, with an outer diameter of 0.25 mm, is encased in a 0.41 mm hypodermic tube. Analytical models were developed for efficient beam path optimization and signal interpretation, where multiple common-path references yielded distinct interferometric signals. The sensor has a sufficiently high sampling rate of 780 Hz for both CP-OCT and FPI signal processing using parallel computing. The fabricated sensor achieved a measurable force range of approximately 5 N with a resolution of 0.119 mN, and a depth sensing range of 3.6 mm with an axial resolution of 6.0 μm. Furthermore, injection experiments with multilayered phantoms and ex vivo porcine eyes demonstrated that the sensor could simultaneously measure force and depth profiles, providing enriched information during the tasks. Sat, 31 Jan 2026 15:00:00 GMT https://scholar.dgist.ac.kr/handle/20.500.11750/59116 2026-01-31T15:00:00Z https://scholar.dgist.ac.kr/handle/20.500.11750/57317 Title: Machine-learning-based diabetes classification method using blood flow oscillations and Pearson correlation analysis of feature importance Author(s): Jung, Hanbeen; Yeo, Chaebeom; Jang, Eunsil; Chang, Yeonhee; Song, Cheol Abstract: Diabetes is a global health issue affecting millions of people and is related to high morbidity and mortality rates. Current diagnostic methods are primarily invasive, involving blood sampling, which can lead to infection and increased patient stress. As a result, there is a growing need for noninvasive diabetes diagnostic methods that are both accurate and fast. High measurement accuracy and fast measurement time are essential for effective noninvasive diabetes diagnosis; these can be achieved using diffuse speckle contrast analysis (DSCA) systems and artificial intelligence algorithms. In this study, we use a machine learning algorithm to analyze rat blood flow signals measured using a DSCA system with simple operation, easy fabrication, and fast measurement for helping diagnose diabetes. The results confirmed that the machine learning algorithm for analyzing blood flow oscillation data shows good potential for diabetes classification. Furthermore, analyzing the blood flow reactivity test revealed that blood flow signals can be quickly measured for diabetes classification. Finally, we evaluated the influence of each blood flow oscillation data on diabetes classification through feature importance and Pearson correlation analysis. The results of this study should provide a basis for the future development of hemodynamic-based disease diagnostic methods. © 2024 The Author(s). Published by IOP Publishing Ltd. Sat, 30 Nov 2024 15:00:00 GMT https://scholar.dgist.ac.kr/handle/20.500.11750/57317 2024-11-30T15:00:00Z https://scholar.dgist.ac.kr/handle/20.500.11750/57313 Title: High-speed tissue metabolism measurement using a combination of diffuse speckle contrast analysis and near-infrared spectroscopy Author(s): Yeo, Chaebeom; Chang, Yeonhee; Song, Cheol Abstract: This study presents and validates a multimodal optical system combining diffuse speckle contrast analysis (DSCA) and near-infrared spectroscopy (NIRS) with a unique system design for high-speed tMRO2 monitoring. The optical system for simultaneous dual-wavelength illumination and detection by a single camera is constructed using dichroic mirrors without an external trigger device. Phantom experiments and in-vivo arterial occlusion tests are conducted by varying the camera exposure time from 0.5 ms to 10 ms to validate system performance. We analyze and compare the results according to the exposure time to acquire the optimal relative tMRO2 (rtMRO2) signal by dual-exposure time control. The in-vivo experiment confirmed that the relative blood flow (rBF) and tissue oxygenation index (TOI) signals from the two modalities had a trade-off with the camera exposure time. We obtained the optimal rtMRO2 using dual-exposure time control. We conclude that the proposed DSCA/NIRS provides real-time rtMRO2 assessment, which can provide biomarkers for diagnosing vascular diseases. © 2024 The Author(s). Published with license by Taylor & Francis Group, LLC. Sat, 30 Nov 2024 15:00:00 GMT https://scholar.dgist.ac.kr/handle/20.500.11750/57313 2024-11-30T15:00:00Z