報 告 人：Professor Dinggang Shen，University of North Carolina at Chapel Hill
邀 請 人：應時輝 、岳曉冬
報告摘要：This talk will introduce our recent papers on image synthesis with deep learning. Specifically, for reducing scanning time or potential risk, we proposed three single-modality based image synthesis methods. For example, to address the issue of missing modality such as PET in brain disease diagnosis, we developed 3D-CycleGAN for learning the bidirectional mappings between MRI and PET and thus imputing the missing PET for helping multimodality based disease diagnosis. Also, to enhance the quality of 3T MRI, we proposed a dual-domain cascaded regression for interactive learning at both spatial and frequency domains, with guidance from corresponding 7T MRI during the training. On the other hand, by using the relationship between different sequences of images acquired in MR scanners as well as the nature of simultaneous acquisitions of PET and MR in PET/MR scanners, we proposed two multi-modality based image synthesis methods. For example, for fast T2 MRI acquisition, we developed a novel deep learning framework, based on dense Unet, to reconstruct T2 MRI from T1 MRI and under-sampled T2 MRI. To reduce dose for PET acquisition in PET/MR scanners, we developed locality-adaptive multi-modality GANs for estimating standard-dose PET from low-dose PET and MRI. The details of all these five methods will be provided in this talk, by also introducing both clinical significance and motivation for each developed method.
報告人簡介：Dinggang Shen is Jeffrey Houpt Distinguished Investigator, and a Professor of Radiology, Biomedical Research Imaging Center (BRIC), Computer Science, and Biomedical Engineering in the University of North Carolina at Chapel Hill (UNC-CH). He is currently directing the Center for Image Analysis and Informatics, the Image Display, Enhancement, and Analysis (IDEA) Lab in the Department of Radiology, and also the medical image analysis core in the BRIC. He was a tenure-track assistant professor in the University of Pennsylvanian (UPenn), and a faculty member in the Johns Hopkins University. Dr. Shen’s research interests include medical image analysis, computer vision, and pattern recognition. He has published more than 1000 papers in the international journals and conference proceedings, with H-index 90. He serves as an editorial board member for eight international journals. He has also served in the Board of Directors, The Medical Image Computing and Computer Assisted Intervention (MICCAI) Society, in 2012-2015. He is General Chair for MICCAI 2019 in Shenzhen, China. He is Fellow of IEEE, Fellow of The American Institute for Medical and Biological Engineering (AIMBE), and Fellow of The International Association for Pattern Recognition (IAPR).