• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2012.05a
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• pp.876-879
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• 2012

In this paper we studied the radiation detection technology which described the radiation level distribution in high radiation area with remotely and safely. The designed radiation mapping system was composed of radiation nodes and radiation station. The radiation nodes could sense the radiation dose values with pMOSFET radiation sensors and transmit them to the radiation station. At the radiation station the received radiation values were merged with a geometric information and visualized at the virtual graphic location. For the functional verification of the above system, we attached the radiation nodes to each corner in our laboratory, executed the mapping tests, and confirmed the designed functions finally.

• 제어로봇시스템학회:학술대회논문집
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• 1997.10a
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• pp.1884-1887
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• 1997

The increasing concern over radiation exposure in the nuclear industry has fostered agrressive efforts to reduce the levels of radiation exposure. One area of the effot to reduce the radiation exposure is the development of a remote radiation monitoring system. Remote radiation monitoring can serve many benificaial functions reduce exposure to radiation by plant personnel, impruve the quality of the data that is collected and recognize the radiation environment easily. Radiation mapping system gives a good information that represents radiation level distribution. The system we have developed consists of a data acquistion parts, mobile robot and remote control parts. Data acquisition parts consist of radiation detection module and vision acquistion module which collect radiation data, visiion data and distance information. In remote control parts, the acquision data are processed and displayed. We have constructed radiation mapping image by overlaying the vision and radiation data. The radiation mapping techniques for displaying the results of the survey in an easily comprehendable form will facilitate a better understanding of the radiation environment in the facility. This system can reduce workers radiation exposure and aid to help work plan, so it has significant benifits in cost and safety.

• Journal of the Korean Society of Radiology
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• v.14 no.2
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• pp.157-168
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• 2020

Advancements in segmentation methodology has made automatic segmentation of brain structures using structural images accurate and consistent. One method of automatic segmentation, which involves registering atlas information from template space to subject space, requires a high quality atlas with accurate boundaries for consistent segmentation. The Allen Mouse Brain Atlas, which has been widely accepted as a high quality reference of the mouse brain, has been used in various segmentations and can provide accurate coordinates and boundaries of mouse brain structures for tractography. Through probabilistic tractography, diffusion tensor images can be used to map comprehensive neuronal network of white matter pathways of the brain. Comparisons between neural networks of mouse and human brains showed that various clinical tests on mouse models were able to simulate disease pathology of human brains, increasing the importance of clinical mouse brain studies. However, differences between brain size of human and mouse brain has made it difficult to achieve the necessary image quality for analysis and the conditions for sufficient image quality such as a long scan time makes using live samples unrealistic. In order to secure a mouse brain image with a sufficient scan time, an Ex-vivo experiment of a mouse brain was conducted for this study. Using FSL, a tool for analyzing tensor images, we proposed a semi-automated segmentation and tractography analysis pipeline of the mouse brain and applied it to various mouse models. Also, in order to determine the useful signal-to-noise ratio of the diffusion tensor image acquired for the tractography analysis, images with various excitation numbers were compared.

• Journal of the Korean Society of Radiology
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• v.18 no.3
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• pp.187-201
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• 2024

Osteoporosis is a major health issue globally, often remaining undetected until a fracture occurs. To facilitate early detection, deep learning (DL) models were developed to classify osteoporosis using abdominal computed tomography (CT) scans. This study was conducted using retrospectively collected data from 3,012 contrast-enhanced abdominal CT scans. The DL models developed in this study were constructed for using image data, demographic/clinical information, and multi-modality data, respectively. Patients were categorized into the normal, osteopenia, and osteoporosis groups based on their T-scores, obtained from dual-energy X-ray absorptiometry, into normal, osteopenia, and osteoporosis groups. The models showed high accuracy and effectiveness, with the combined data model performing the best, achieving an area under the receiver operating characteristic curve of 0.94 and an accuracy of 0.80. The image-based model also performed well, while the demographic data model had lower accuracy and effectiveness. In addition, the DL model was interpreted by gradient-weighted class activation mapping (Grad-CAM) to highlight clinically relevant features in the images, revealing the femoral neck as a common site for fractures. The study shows that DL can accurately identify osteoporosis stages from clinical data, indicating the potential of abdominal CT scans in early osteoporosis detection and reducing fracture risks with prompt treatment.