https://scholar.dgist.ac.kr/handle/20.500.11750/12143 2026-04-17T07:38:27Z 2026-04-17T07:38:27Z Kim, Jinwoo Park, Hyeongyu Kim, Haemin Chang, Jin Ho https://scholar.dgist.ac.kr/handle/20.500.11750/57815 2025-07-25T02:43:21Z 2024-04-06T15:00:00Z

Title: Control of optical imaging depth using ultrasound-induced microbubbles for deep optical microscopy Author(s): Kim, Jinwoo; Park, Hyeongyu; Kim, Haemin; Chang, Jin Ho Abstract: Control of the ultrasound-induced gas bubble for deep optical microscopy can enable selective deep optical imaging. Changing the ultrasound operating frequency may be a solution for controlling the thickness of the bubble cloud. © 2024 The Author(s)

2024-04-06T15:00:00Z Park, Hyeongyu Kim, Jinwoo Chang, Jin Ho https://scholar.dgist.ac.kr/handle/20.500.11750/57814 2025-07-25T02:43:21Z 2024-04-06T15:00:00Z

Title: Design of ultrasound transducers for localized neuromodulation Author(s): Park, Hyeongyu; Kim, Jinwoo; Chang, Jin Ho Abstract: Conventional ultrasound transducers have a broad focus that hinders efficient research using small animal models for localized neuromodulation. Acoustic hologram lenses and an increase in operating frequency can be a solution for precise neural targeting. © 2024 The Author(s)

2024-04-06T15:00:00Z Lee, Kyungsu Yang, Jaeseung Lee, Moon Hwan Chang, Jin Ho Kim, Jun-Young Hwang, Jae Youn https://scholar.dgist.ac.kr/handle/20.500.11750/46816 2025-07-25T04:07:14Z 2022-09-18T15:00:00Z

Title: USG-Net: Deep Learning-based Ultrasound Scanning-Guide for an Orthopedic Sonographer Author(s): Lee, Kyungsu; Yang, Jaeseung; Lee, Moon Hwan; Chang, Jin Ho; Kim, Jun-Young; Hwang, Jae Youn Abstract: Ultrasound (US) imaging is widely used in the field of medicine. US images containing pathological information are essential for better diagnosis. However, it is challenging to obtain informative US images because of their anatomical complexity, which is significantly dependent on the expertise of the sonographer. Therefore, in this study, we propose a fully automatic scanning-guide algorithm that assists unskilled sonographers in acquiring informative US images by providing accurate directions of probe movement to search for target disease regions. The main contributions of this study are: (1) proposing a new scanning-guide task that searches for a rotator cuff tear (RCT) region using a deep learning-based algorithm, i.e., ultrasound scanning-guide network (USG-Net); (2) constructing a dataset to optimize the corresponding deep learning algorithm. Multidimensional US images collected from 80 patients with RCT were processed to optimize the scanning-guide algorithm which classified the existence of RCT. Furthermore, the algorithm provides accurate directions for the RCT, if it is not in the current frame. The experimental results demonstrate that the fully optimized scanning-guide algorithm offers accurate directions to localize a probe within target regions and helps to acquire informative US images. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

2022-09-18T15:00:00Z