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Multimodal Ultrasound and Optical Imaging Systems Towards Intelligent Cancer Diagnosis
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Title
Multimodal Ultrasound and Optical Imaging Systems Towards Intelligent Cancer Diagnosis
Issued Date
2022-10-27
Citation
Hwang, Jae Youn. (2022-10-27). Multimodal Ultrasound and Optical Imaging Systems Towards Intelligent Cancer Diagnosis. 24th International Congress on Acoustics, ICA 2022.
Type
Conference Paper
ISSN
2226-7808
Abstract
To achieve the bench-to-bedside goal of translational research in the biomedical imaging field, the development of new approaches, allowing deployment into a clinical setting, will be needed. We have been developing novel multimodal imaging techniques by combining various high-frequency ultrasound and optical imaging modalities for the achievement of better outcomes in the detection and characterization of diseased regions within the human body, especially quantitatively without contrast agents. Particularly, for the early diagnosis of malignant diseases, new tools are needed, with likely new outcomes. For the last decades, we have proposed and implemented multimodal approaches to biomedical imaging at all levels, optimized for earlier, more quantitative, and reproducible detection of abnormalities. We could improve disease management through addressing major areas of unmet need in the clinical realm with these new approaches. In this talk, our work on cancer detection and treatment will be described with emphasis on the new technologies needed to achieve the desired imaging performance. Particularly, novel multimodal imaging techniques by combining high-frequency ultrasound and optical imaging will be demonstrated for quantitative examinations of human colorectal tumor biopsies in situ. After that, multimodal endoscopic imaging and analysis with high-frequency ultrasound and light will be highlighted for precise tumor detection. © ICA 2022.All rights reserved
URI
http://hdl.handle.net/20.500.11750/46787
Publisher
International Commission for Acoustics (ICA)
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황재윤
Hwang, Jae Youn황재윤

Department of Electrical Engineering and Computer Science

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