Fluorescence Molecular Imaging
- Inflammation, tumor, myocardial infarction
Molecular imaging is defined as visualization and measurement of biological processes at the cellular or molecular level inside the living body. Recently, fluorescence molecular imaging has received particular attention to provide highly sensitive and versatile tool for biological research and clinical diagnosis. Our group is interested in development of novel fluorescent imaging technologies for intraoperative guidance, diagnosis and prognostication of cardiovascular disease and cancer. Also, we focus on multimodal imaging strategy by combining with PET and SPECT molecular imaging to achieve whole body noninvasive diagnosis and accurate surgical guidance with same functional information.
Collaborators: Prof. Won Woo Lee, Prof. Byung-Chul Lee (Department of Nuclear Medicine, Seoul National University Bundang Hospital, Seoul National University College of Medicine)
Fluorescence imaging of lymph node metastasis. Up) metastasized lymph node in tumor model. Down) normal lymph node
Label-free In Vivo Imaging
- optical property based tissue imaging
Nanomedicine with Intravital Imaging
Optical imaging has the potential to aid surgeons to perform surgery faster, better and less expensively than current standards. We aim to develop spectral reflectance imaging system for label-free intraoperative imaging for specific tissue identification. Our study includes 1) Investigation of novel optical characteristics of tissue 2) Design and construction of spectral reflectance imaging system 3) Development of image processing technique to display anatomical and functional images simultaneously in real-time 4) Intraoperative imaging of specific tissue in disease models.
Collaborator: Prof. Changsoon Kim (Graduate School of Convergence Science and Technology, Seoul National University)
Grant: Mid-Career Researcher Program, National Research Foundation of Korea (NRF, MSIP)
Nanomaterials can advance personalized clinical care by providing diagnostic and prognostic information, quantifying therapy efficacy and planning better treatment strategy. Imaging and treating inflammation and tumor have particular benefits when using nanomaterials. However, there is an urgent need to develop more specific nanoprobe/nanodrug and exploit their in vivo distribution and dynamics accurately. We utilize high-resolution intravital imaging as well as whole-body fluorescence imaging to speculate characteristics of nanomaterials inside living body and develop specific nanoprobe/nanodrug by cancer or macrophage targeting.
Collaborator: Prof. Yuanzhe Piao (Graduate School of Convergence Science and Technology, Seoul National University), Dr. Tae-Rin Lee (Advanced Institutes of Convergence Technology), Prof. Pilhan Kim (KAIST)
Artificial Intelligence in Biomedical Imaging
Application of artificial intelligence in biomedical imaging has attracted great interest and is now the subject of interdisciplinary researches. A variety of applications have shown initial promise of artificial intelligence technique to solve problems of tedious image analysis in bioimaging as well as to improve diagnosis accuracy and throughput in medical imaging. Our focus was given to application of cutting-edge technologies of artificial intelligence to develop novel optical imaging technique and to improve clinical practice in medical imaging. Multiple projects are ongoing including deep learning based developments for real-time peripheral nerve segmentation technique, label-free characterization of different immune cells, and low-dose and fast myocardical single-photon emission computerized tomography (MPI-SPECT).
Collaborator: Dr. Xiaoming Wu (Department of Computing, The Hong Kong Polytechnic University), Dr. Minsik Lee (Division of Electrical Engineering, Hanyang University - ERICA Campus), Dr. Boom Ting Kung and Dr. Ting Kung Au Yong (Nuclear medicine physicians, Queen Elizabeth Hospital), Prof. Jing Cai (Department of Health Technology and Informatics, The Hong Kong Polytechnic University)
Reconstruction of MPI-SPECT images using deep learning
Immunotherapy Response Monitoring Technique
Immunotherapy has revolutionized current cancer treatment with huge clinical success by using patients' own immune system to recognize and attack cancer. However, there is a significant unmet need for a robust tool to identify responders to specific immunotherapy. Our group has focused on in vivo identification of different immune components and environment to accurately assess treatment outcome of various immunotherapies using optical and molecular imaging technologies as well as applying deep learning technologies.