TOPIC:

AI based Medical Image Reconstruction

ABSTRACT:

This talk will introduce various deep learning methods we developed for fast MR acquisition, low-dose CT reconstruction, and low-cost and low-dose PET acquisition. The implementation of these techniques in scanners for real clinical applications will be demonstrated. Also, comparisons with state-of-the-art acquisition methods will be discussed.

BIO:

Dinggang Shen is Professor and Founding Dean of School of Biomedical Engineering, ShanghaiTech University, and also Co-CEO of United Imaging Intelligence (UII). He is Fellow of IEEE, Fellow of The American Institute for Medical and Biological Engineering (AIMBE), Fellow of The International Association for Pattern Recognition (IAPR), and also Fellow of The Medical Image Computing and Computer Assisted Intervention (MICCAI) Society. He was Jeffrey Houpt Distinguished Investigator, and (Tenured) Full Professor in the University of North Carolina at Chapel Hill (UNC-CH), directing The Center of Image Analysis and Informatics, The Image Display, Enhancement, and Analysis (IDEA) Lab, and The Medical Image Analysis Core. He was also a tenure-track Assistant Professor in the University of Pennsylvanian (UPenn) and a faculty member in the Johns Hopkins University. His research interests include medical image analysis, computer vision, and pattern recognition. He has published more than 1100 peer-reviewed papers in the international journals and conference proceedings, with H-index 115 and >50K citations. He serves as Editor-in-Chief for Frontiers in Radiology, as well as editorial board member for eight international journals. Also, he has served in the Board of Directors, The Medical Image Computing and Computer Assisted Intervention (MICCAI) Society, in 2012-2015, and was General Chair for MICCAI 2019.

Dinggang SHEN

FIEEE, FAIMBE, FIAPR, FMICCAI, Dean of BME, Shanghai Tech University, Shanghai United Imaging Intelligence Co., Ltd.

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