https://scholar.dgist.ac.kr/handle/20.500.11750/11752 2026-04-04T09:44:25Z 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. 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. https://scholar.dgist.ac.kr/handle/20.500.11750/59899 Title: Waste polyethylene-coated fabrics for dual-mode interfaces triboelectrification for self-powered sensors Author(s): Kaja, Kushal Ruthvik; Hajra, Sugato; Panda, Swati; Belal, Mohamed Ahmed; Nam, Sangwoo; Pakawanit, Phakkhananan; Panigrahi, Basanta Kumar; Khanbareh, Hamideh; Bowen, Chris Rhys; Yu, Jaesok Abstract: The reuse of waste cotton textiles and coating them with recycled polyethylene provides a route to improve environmental sustainability, reducing our landfill burden and supporting the circular economy through effective implementation of the 3Rs: Reduce, Reuse, and Recycle. This approach extends material life and minimizes resource consumption. This study presents a sustainable strategy for energy harvesting and sensor applications by repurposing worn-out cotton textiles that are coated with recycled polyethylene via a simple immersion method. The modified textiles are integrated into two triboelectric nanogenerator (TENG) configurations: a solid–solid TENG (S–S TENG) and a liquid–solid TENG (L–S TENG). The S–S TENG, paired with a polydimethylsiloxane (PDMS) elastomer, achieves a peak output of 250 V, 1.01 µA, and a power output of 83.7 µW at 2 Hz when subject to a 5 N compression. The device exhibits long-term stability and charges a 10 µF capacitor to 3.3 V, with sufficient energy to power light-emitting diodes (LEDs). For the L–S TENG, deionised water droplets interacting with the polyethylene-coated surface generate up to 45 nW for a 50 MΩ load, with a saturated charge of 1.3 nC. When used as a sensor, the device is employed in real-time motion tracking, integrated with an artificial neural network, and in milk adulteration detection. These results demonstrate a low-cost, flexible, and eco-friendly platform for multifunctional energy harvesting and self-powered sensing, advancing circular economy principles and enabling new applications in healthcare, food safety, and wearable electronics. 2025-11-30T15:00:00Z