Changheun Hyun Oh – Medical Image Reconstruction- Best Researcher Award

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Changheun Hyun Oh – Medical Image Reconstruction

Changheun Hyun oh distinguished academic and researcher in the field Deep Learning based Medical Image Reconstruction. In terms of professional experience, he contributed to LG Electronics from 2017 to 2018. Subsequently, he joined the Neuroscience Research Institute of Gachon University in Fall 2018, where he has been actively involved in research endeavors. Throughout his educational and professional journey, he has demonstrated a commitment to advancing the field of Electrical Engineering and contributing to both academic and industrial domains.

 

🌐 Professional Profiles

Educations: 📚🎓

He earned his Ph.D. degree in Electrical Engineering from the Korea Advanced Institute of Science and Technology (KAIST) during the period of Fall 2011 to Fall 2020, under the guidance of Professor HyunWook Park in the Image Computing System Lab. Prior to that, he completed his M.S. degree in Electrical Engineering at KAIST from Fall 2009 to Spring 2011, continuing his research with Professor Park. His academic journey at KAIST began with a B.S. degree in Electrical Engineering, spanning from Spring 2005 to Spring 2009.

conferences

He has actively contributed to numerous international conferences, showcasing his research in the field of Electrical Engineering and Medical Imaging. In 2011, as the first author, he presented a paper on “A Switching Circuit for the Rx Signal in the MRI System” at the International Technical Conference on Circuits/Systems, Computers, and Communications (ITC-CSCC). Subsequently, he continued to make significant contributions to conferences such as the International Forum on Medical Imaging in Asia and the Annual Meeting of the International Society for Magnetic Resonance in Medicine (ISMRM).

His research work encompasses diverse topics, including the optimization of RF birdcage coils, the development of an optimum RF shield for simultaneous MRI-PET systems, and innovations in MRI techniques like water-fat separation and diffusion-weighted imaging. He also explored applications beyond medical imaging, such as his involvement in the International Symposium on Electronic Art and the International Society for Music Information Retrieval Conference.

In 2018, he presented a paper on “ETER-net: End to End MR Image Reconstruction Using Recurrent Neural Network” at the First International Workshop on Machine Learning in Medical Image Reconstruction, held in conjunction with the International Conference on Medical Image Computing & Computer Assisted Intervention (MICCAI) 2018. His research continued to advance in 2020, where he discussed “A direct MR image reconstruction from k-space via End-To-End reconstruction network using recurrent neural network (ETER-net)” at the Annual Meeting of the ISMRM.

In 2022, as the first author, he presented another breakthrough with “A deep learning based direct mapping method for EPI image reconstruction” at the Annual Meeting of the ISMRM. His active participation in these conferences reflects his dedication to pushing the boundaries of knowledge in his field and applying cutting-edge technologies to advance medical imaging techniques.

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