Meeting Banner
Abstract #2644

Improved Compressed Sensing and Parallel MRI Through the Generalized Series Modeling

Xi Peng1, 2, Leslie Ying3, Xin Liu1, 4, Dong Liang1, 2

1Paul C. Lauterbur Research Centre for Biomedical Imaging, Shenzhen Institutes of Advanced Technology, Shenzhen, Guangdong, China; 2Key Laboratory of Health Informatics, Chinese Academy of Sciences, Shenzhen, Guangdong, China; 3Department of Biomedical Engineering, Department of Electrical Engineering, The State University of New York at Buffalo, Buffalo, NY, United States; 4Key Laboratory of Health Informatics , Chinese Academy of Sciences, Shenzhen, Guangdong, China

The problem of reconstructing a high-spatial-temporal-resolution MR image sequence occurs in various MR applications, such as interventional imaging, dynamic contrast enhanced imaging, cardiac imaging, where a static reference image can be obtained with relative ease before the whole dynamic process. This work addresses the problem by integrating the generalized series (GS) model, in which the reference prior is incorporated, with standard compressed sensing (CS) and parallel imaging (PI) techniques. The proposed method is validated in a Monte-Carlo study and is shown to provide superior imaging quality with decreased g-factor to existing CS and PI based reconstruction methods.