Webinar #14: Deep Learning-based Medical Image Reconstruction - Prof. Dinggang Shen

Title: Deep Learning-based Medical Image Reconstruction

Abstract:
This talk will introduce our developed deep learning methods 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 other state-of-the-art acquisition methods will be discussed.

Bio:
Dinggang Shen is a Professor and a Founding Dean with School of Biomedical Engineering, ShanghaiTech University, Shanghai, China, and also a Co-CEO of United Imaging Intelligence (UII), Shanghai. He is a Fellow of IEEE, The American Institute for Medical and Biological Engineering (AIMBE), The International Association for Pattern Recognition (IAPR), and The Medical Image Computing and Computer Assisted Intervention (MICCAI) Society. He was Jeffrey Houpt Distinguished Investigator and a Full Professor (Tenured) with The University of North Carolina at Chapel Hill (UNC-CH), Chapel Hill, NC, USA, directing The Center of Image Analysis and Informatics, The Image Display, Enhancement, and Analysis (IDEA) Lab, and The Medical Image Analysis Core. His research interests include medical image analysis, machine learning, deep learning, and computer vision. He has published more than 1500 peer-reviewed papers in the international journals and conference proceedings, with H-index 122 and over 60K citations. He serves as an Editor-in-Chief for Frontiers in Radiology, as well as an associate editor (or 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.