Fast MR Imaging Using Sparse Sampling
Abstract
Magnetic Resonance Imaging (MRI) is one of the most important imaging modalities in clinic. However, relative slow imaging speed limits its wild applications. Recently, considering the proportional relationship between the number of measurements and scan time, applying compressed sensing to MRI has been extensively explored to accelerate MR imaging by sparsely sampling the measurement space. In this seminar, the speaker will introduce the background of fast MRI and his work in this topic including sparse representation, the combination of compressed sensing and parallel imaging, and the application in MR parametric imaging.
About the speaker
Prof. Liang Dong received his PhD from Shanghai Jiao Tong University in 2006. After that he went to the University of Hong Kong and the University of Wisconsin-Milwaukee successively as a postdoctoral researcher in 2006 – 2007 and in 2007 – 2011. He joined Shenzhen Institutes of Advanced Technology of the Chinese Academy of Sciences (SIAT) in 2011 and is currently the Professor in Paul C. Lauterbur Research Center for Biomedical Imaging of the Institute of Biomedical and Health Engineering at SIAT.
Prof Liang’s current research interests include compressed sensing, image reconstruction, magnetic resonance imaging, and machine learning. He has published over 100 scientific papers and abstracts in international journals and conference proceedings. He is a senior member of the Institute of Electrical and Electronics Engineers (IEEE) and a member of The International Society for Magnetic Resonance in Medicine (ISMRM). He is also the Associate Editor of IEEE Transactions on Medical Imaging.
About the program
For more information, please refer to the program website http://iasprogram.ust.hk/inverseproblems for details.
