https://scholar.dgist.ac.kr/handle/20.500.11750/11753 2026-10-27T07:58:02Z https://scholar.dgist.ac.kr/handle/20.500.11750/60980 Title: Silicon photonic MEMS platform-based air-coupled optomechanical ultrasound receiver at 2.8 MHz with an ultra-compact active acoustic sensing area Author(s): Nam, Sangwoo; Kim, Jae Hyeon; Park, Young Jae; Choi, Dongju; Kim, Dong Uk; Guezzi, Nizar; Lee, Sangheon; Seo, Youngho; Choi, Suyoung; Kim, Myeongchan; Seong, Hyojin; Lee, Changho; Ahn, Changmin; Kim, Jungwon; Yu, Jaesok; Han, Sangyoon Abstract: High-resolution non-contact non-destructive evaluation (NDE) utilizing MHz-range ultrasound in air-coupled environments faces two significant challenges: severe signal attenuation and reduced sensitivity due to transducer miniaturization. Optical ultrasound sensors have emerged as promising candidates to overcome these limitations; however, widespread adoption remains challenging. Conventional discrete optical sensors are limited by complex fabrication and rigid form factors. Furthermore, the efforts to implement these on photonic integrated circuits (PICs)-while addressing scalability-are typically constrained by limited sensitivity. To address these requirements, we report a chip-scale optomechanical ultrasound receiver implemented, for the first time, on a silicon photonics MEMS platform. This platform facilitates CMOS-compatible scalability and design freedom to independently optimize mechanical components and optical sensitivity. Consequently, the receiver demonstrates a compact active acoustic sensing area of 4 & times; 10-4 mm2. Despite this size reduction, in the noise-equivalent pressure (NEP)-active-area comparison, the proposed receiver's measured and estimated NEP values are positioned within a range comparable to or lower than those for previously reported air-coupled optical and CMUT sensors, when interpreted relative to the ideal area-scaling trend. Designed for a 2.8 MHz mechanical resonance, the receiver was benchmarked against a commercial 1 MHz air-coupled PZT transducer to provide a practical, controlled experimental baseline. In this controlled experimental comparison, the receiver showed a more than 25,000-fold higher received response per unit active acoustic area than the PZT baseline, serving as a complementary metric for unit-area received response and structural scaling potential. These findings suggest that the silicon photonic MEMS platform can provide a route toward compact, dense-array-compatible optical ultrasound receivers for localized air-coupled sensing. (c) 2026 Chinese Laser Press 2026-08-31T15:00:00Z 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/20.500.11750/59948 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. https://scholar.dgist.ac.kr/handle/20.500.11750/59949 Title: DAUS-Net: Toward Ultrasound Scanner-Agnostic Domain Generalized Robust and Accurate Segmentation Author(s): Lee, Sangheon; Jung, Dongkyu; Guezzi Nizar; Nam, Sangwoo; Yu, Jaesok Abstract: In medical imaging, segmentation is a critical task for analysis and diagnosis. Deep learning-based segmentation has been actively studied and has shown remarkable performance. Building high-accuracy segmentation models requires a large amount of high-quality labeled data, but the cost of collecting such data is extremely high in medical imaging. In ultrasound imaging, the differences in image features depending on the equipment are significantly greater compared to other medical imaging modalities. Consequently, models need to be trained for each specific device, which entails substantial costs and time, leading to various practical challenges. To address these challenges, we propose a robust and accurate segmentation network that can operate independently of the ultrasound equipment. We integrated the Deep Frequency Filtering (DFF) module into a U-Net-based model. The proposed model retains the U-Net's encoder-decoder structure but applies frequency filtering within the latent space of each encoder layer, enabling adaptive selection of frequency components for breast tumor detection. Moreover, batch normalization was replaced with instance normalization to remove stylish features. We evaluated the model using three public datasets acquired from different scanners, achieving superior performance on unseen testing datasets compared to existing models. Notably, when tested on the unseen BUS-BRA dataset, DAUS-Net achieved a Dice score of 0.76, compared to 0.61 by the conventional U-Net. This improvement is attributed to the synergy between the DFF module and instance normalization. Our results demonstrate that the proposed model consistently detects and segments breast tumors, highlighting its potential for generalized clinical segmentation task. The source code for implementing DAUS-Net is publicly available at https://github.com/shlee8638/DAUS-Net. 2026-04-30T15:00:00Z