https://scholar.dgist.ac.kr/handle/20.500.11750/11752 2026-08-05T17:44:36Z https://scholar.dgist.ac.kr/handle/20.500.11750/60564 Title: 민감도 및 동적 영역이 조절 가능한 광기계 초음파 센서 및 그 제어 방법 Author(s): 남상우; 유재석; 한상윤; 최동주 Abstract: 본 개시는 초음파 센서 및 그 동작 방법에 관한 것으로서, 더욱 상세하게는 캔틸레버 구조의 멤브레인의 초기 위치를 조절하여 민감도 및 동적 영역이 조절 가능한 광기계 초음파 센서 및 그 제어 방법에 관한 것이다. https://scholar.dgist.ac.kr/handle/20.500.11750/60227 Title: Toward virtual bladder: real-time bladder volume monitoring with flexible AuCNT strain sensors Author(s): Cho, Youngjun; Jo, Yujin; Kang, Minseok; Shin, Heejae; Cho Jeongmok; Jeong Hyunghwa; Suh Hyunsuk Peter; Pak Changsik John; Park, Jeonhyeong; Kwon Soonchul; Choi Hongsoo; Yu, Jaesok; Kim, Hoe Joon; Lee, Sanghoon Abstract: Digital twin technology holds considerable potential for personalized diagnostics and treatment of bladder dysfunction, particularly neurogenic conditions such as underactive bladder (UAB). In this study, to address the need for precise monitoring, we introduce a flexible, stretchable strain sensor composed of gold-coated carbon nanotubes (AuCNTs) embedded in Ecoflex. We specifically designed a three-channel configuration to capture anisotropic expansion and evaluated the sensor's performance using both two-dimensional balloon models and ex-vivo three-dimensional porcine bladder models. As a result, the AuCNT sensor demonstrated high sensitivity, and the three-channel design significantly enhanced spatial accuracy compared to single-channel approaches. Based on these measurements, we created a preliminary "Virtual Bladder" model that provides dynamic, real-time visualization of bladder volume changes. While our current model requires further development to incorporate multimodal data and anatomical variability, it serves as a foundational step towards developing advanced digital twin frameworks and closed-loop neuromodulation systems for bladder dysfunction. 2025-12-31T15:00:00Z https://scholar.dgist.ac.kr/handle/20.500.11750/59961 Title: Spatio-Temporal Oriented Gradient (STOG) Filtering for Ultrasound Localization Microscopy: Preserving Slow and Fast Flow Components Author(s): Seo, Youngho; Hosseini, Zahra; Kim, Kang; Park, Jaebum; Song, Tai Kyong; Yu, Jaesok Abstract: Ultrasound Localization Microscopy (ULM) enables super-resolution vascular imaging but depends heavily on clutter filtering. Conventional Singular Value Decomposition (SVD) struggles to distinguish slow microvascular flows from static tissue, often suppressing diagnostically important signals. We propose a Spatio-Temporal Oriented Gradient (STOG) filter that exploits pixelwise gradient features in space and time. By combining co-occurrence and temporal elevation analysis, STOG separates both fast and slow flow components. Experiments with flow phantom and in vivo rabbit data demonstrated that STOG preserves microvascular structures missed by SVD, while maintaining overall vascular patterns. Despite minor tissue artifacts, STOG suggests a promising direction for gradient-based clutter filtering in angiogenesis imaging relevant to ischemic stroke prognosis. © 2025 IEEE. 2025-09-16T15:00:00Z https://scholar.dgist.ac.kr/handle/20.500.11750/59948 Title: RFDNet: Robust Frequency-Based Denoising Network for 3D Ultrasound Vascular Imaging Using a Row-Column Addressed Array Author(s): Jung, Dongkyu; Guezzi, Nizar; Lee, Sangheon; Noman, Muhammad; Bae, Sua; Yu, Jaesok Abstract: Three-dimensional (3D) ultrasound vascular imaging (UVI) is essential for visualizing complex vascular structures. Row-column addressed (RCA) arrays, widely used for 3D UVI due to their hardware efficiency, suffer from point spread function (PSF) anisotropy, resulting in ramp-shaped noise that degrades image quality. Although existing denoising methods, including deep learning-based approaches, have shown promise, they are often limited by domain shift bias and the need for condition-specific data collection. Moreover, as full-volume 3D training is often impractical, many studies rely on 2D slice-wise training with 3D reconstruction, which can yield inter-slice intensity inconsistencies when slices are normalized independently. To overcome these limitations, we propose Robust Frequency-based Denoising Network (RFDNet), which integrates a Deep Frequency Filtering (DFF) module into a standard denoising model. The DFF module adaptively filters frequency components within the encoder, suppressing ramp-shaped noise while dynamically balancing spectral content to reduce sensitivity to domain shifts and inter-slice intensity inconsistencies. This adaptive filtering preserves vascular details and improves overall imaging consistency. Experiments on Doppler phantom, carotid artery, and abdominal datasets show that RFDNet significantly outperforms conventional methods in peak signal-to-noise ratio (PSNR), structural similarity (SSIM) and root mean squared error (RMSE). Further validation through 2D frequency spectrum analysis confirmed that the DFF module dynamically adjusts frequency components to maintain spectral balance. In addition, spectral KL divergence analysis demonstrated its robustness against inter-slice intensity inconsistencies introduced by slice-wise normalization. This approach improves domain generalization, reduces noise artifacts, and enhances clinical applicability by improving imaging reliability. Future work will explore 3D training and architectural refinements for better computational efficiency.