个人简历：
Dr. Kaicong Sun is a Tenure-track Assistant Professor, Researcher, Doctoral Director, and Principal Investigator (PI) in the School of Biomedical Engineering. Dr. Sun received his B.S. and M.S. degrees from Tongji University and Technische Universität Berlin, Germany, respectively, and his Ph.D. degree from University of Stuttgart, Germany.
Dr. Kaicong Sun focuses on AI-based medical image reconstruction and computing. His research interests include deep learning-based fast MRI reconstruction, reliable and robust medical image reconstruction and computing. As first author, Dr. Kaicong Sun has published more than 30 high-quality papers in related fields, including a series of articles in IEEE Transactions on Medical Imaging, IEEE Transactions on Signal Processing, IEEE Transactions on Circuits and Systems for Video Technology, Communications Engineering, Medical Physics, etc. He has been selected in the Shanghai High-level Overseas Talent Program and Shanghai Pujiang Talent Program, and has hosted the Young Scientists Fund of the National Natural Science Foundation of China (NSFC), and has participated in several Major and Key Programs of the Ministry of Science and Technology and the China Foundation for Science and Technology (MOST) as a key member.
研究领域🏃🏻♂️：
Fast Magnetic Resonance Imaging Reconstruction
Trustworthy and Robust Medical Image Reconstruction and Generation
AI-based Medical Image Computing
教学与课程🙍🏻♂️：
学术任职🛌🏼🫓：
Invited Reviewer
IEEE Transactions on Medical Imaging，IEEE Transactions on Neural Networks and Learning Systems💁🏻♀️，Medical Physics，Signal Processing: Image Communication, et al.
代表性论文：
K. Sun, Q. Wang, D. Shen, Joint Cross-Attention Network with Deep Modality Prior for Fast MRI Reconstruction, IEEE Transactions on Medical Imaging, 2024, 43(1):558-569.
K. Sun, Y., Zhang, J., Liu, L. Yu, Y. Zhou, F. Xie, Q. Guo, H., Zhang, Q., Wang, and D. Shen, Achieving Multi-modal Brain Disease Diagnosis Performance Using Only Single-Modality Images Through Generative AI, Communications Engineering, 2024。
K. Sun, S. Simon, Bilateral Spectrum Weighted Total Variation for Noisy-Image Super-Resolution and Image Denoising, IEEE Transactions on Signal Processing, 2021, 69(11): 6329-6341.
K. Sun, M. Koch, Z. Wang, S. Jovanovic, H. Rabah, S. Simon, An FPGA-Based Recurrent Neural Network for Video Super-Resolution, IEEE Transactions on Circuits and Systems for Video Technology, 2021, 32(4): 1739-1750.
K. Sun, S. Simon, FDRN: A Fast Deformable Registration Network for Medical Images, Medical Physics, 48(10), 6453-6463, 2021.
实验室介绍🧘♀️👴🏼：
Lab Introduction: The lab focuses on the reconstruction of medical images, including such as magnetic resonance (MR) images and computed tomography (CT) images. Advanced AI technology is utilized to achieve fast, accurate, reliable and robust medical image computation. Research results are translated into clinical and engineering applications.
The main research in the laboratory includes:
Fast MRI Reconstruction: Design AI-based fast MRI reconstruction algorithms for different contrasts (scan sequences), organs, field strengths, and acceleration factors of MR imaging;
Accurate, Reliable and Robust Medical Image Reconstruction and Computing: Design advanced and trustworthy deep learning algorithms for different medical imaging modalities, such as MRI, PET, CT, by combining the prior knowledge of physical models, statistical models, and physical constraints for medical image reconstruction, image generation, and downstream tasks.
