https://scholar.dgist.ac.kr/handle/20.500.11750/189 2026-10-07T11:14:20Z https://scholar.dgist.ac.kr/handle/20.500.11750/60848 Title: Lissajous multi-modal endomicroscopy with optical coherence tomography and confocal fluorescence microscopy Author(s): Lee, Myung Ho; Im, Jintaek; Cho, Gichan; Chang, Yeonhee; Song, Cheol Abstract: We present a multi-modal endomicroscopic system integrating confocal fluorescence microscopy (CFM) and op tical coherence tomography (OCT) into a compact, resonant fiber scanner. The probe employs an asymmetric double D-shaped fiber cantilever to achieve axis-dependent frequency separation, enabling Lissajous scanning. An analytical model was formulated to predict tip displacement by combining geometric amplification, quality factor enhancement, and frequency-dependent transfer functions. This model captured axis-specific dynamics and guided cantilever-holder optimization via simulation. The probe achieved lateral and axial resolutions of 2.19 & micro;m and 23.66 & micro;m in CFM mode, and 24.77 & micro;m and 6.98 & micro;m in OCT mode. Ex vivo fluorescein-stained porcine stomach imaging confirmed simultaneous acquisition of en-face fluorescence and depth-resolved OCT images. These results validate the system's ability to deliver complementary contrast and spatially correlated structural information in a compact platform. This multi-modal approach promises real-time surgical guidance and label-assisted diagnostics in clinical settings. 2026-03-31T15:00:00Z https://scholar.dgist.ac.kr/handle/20.500.11750/60471 Title: Precision enhancement of epidural force-sensing needle with machine learning Author(s): Cho, Gichan; Na, Jongyeol; Lee, Myung Ho; Kwon, Hyun-Jung; Song, Cheol Abstract: Epidural injection is used in pain intervention, requiring precise needle placement within the epidural space. Traditional techniques, such as loss of resistance and fluoroscopy-guided procedures, have limitations, including reliance on subjective assessment and radiation exposure. We proposed an optical force-sensing probe with an offset criterion of the needle tip-distal end to enhance the precision of puncture detection. The offset between the needle tip and the force-sensing probe is adjusted using a piezoelectric motor-based system with feedback position control. A Long Short-Term Memory model is also trained to detect the puncture. Insertion test on silicone phantom and ex-vivo specimens demonstrates that the system's offset range for enhancing precision of puncture detection is between 0.6 mm and 1 mm. Compared to the offset in the previous study, the AUC score of puncture detection increased from 0.61 to 0.86. This approach secures the improvement of puncture detection reliability in robot-assisted epidural injection. 2026-11-30T15:00:00Z https://scholar.dgist.ac.kr/handle/20.500.11750/60086 Title: 손 떨림 보정 및 광학 거리 제어를 이용한 비접촉 휴대형 공초점 망막 내시현미경 시스템 Author(s): 이명호; 조기찬; 임진택; 나종열; 송철 Abstract: We present a novel handheld confocal endomicroscopy system designed for non-contact, high-resolution retinal imaging, integrating motorized stabilization and tremor prediction. The system combines a custom PZT-driven Lissajous scanning probe, and a common-path swept-source optical coherence tomography (CPSS-OCT) sensor for depth tracking for real-time tremor compensation. This multimodal platform allows continuous imaging at the cellular level without requiring probe-to-tissue contact, addressing critical limitations of conventional probe-based confocal laser endomicroscopy(pCLE) systems. Experimental evaluations using ex-vivo bovine retina samples demonstrated significant enhancements in image clarity, with the CR score increasing by 48.43% under active tremor control. The proposed architecture offers improved image stability, precise axial focusing, and handheld portability, making it a strong candidate for clinical applications in retinal diagnostics and intraoperative imaging. 2025-06-26T15:00:00Z https://scholar.dgist.ac.kr/handle/20.500.11750/60072 Title: 비전 기반 3차원 자세 추정을 이용한 인간 로봇 충돌 방지 시스템 Author(s): 나종열; 송철 Abstract: As human-robot collaboration becomes increasingly prevalent in medical and industrial settings, the risk of collisions is rising due to shared workspaces. Traditional approaches rely on stopping mechanisms or external sensors, which may limit system adaptability. This study proposes a camera-based method utilizing a convolutional neural network (CNN) to estimate the 3D joint coordinates of robots and humans within a unified camera coordinate frame. It estimates the 3D positions of the robot's seven joints with an average error below 5.5 mm, enabling real-time collision risk assessment through joint distance computation. This vision-based approach provides precise spatial awareness without requiring additional external sensors, enhancing safety and operational efficiency. 2025-06-25T15:00:00Z