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Abstract #3079

Fast T2 Mapping with Improved Accuracy Using Memory-Efficient Low-Rank Hankel Matrix Reconstruction

Xinlin Zhang1, Hengfa Lu2, Zi Wang1, Xi Peng3, Feng Huang4, Qin Xu4, Di Guo5, and Xiaobo Qu1
1Department of Electronic Science, School of Electronic Science and Engineering (National Model Microelectronics College), National Institute for Data Science in Health and Medicine, Xiamen University, Xiamen, China, 2College of Optical Science and Engineering, Zhejiang University, Hangzhou, China, 3Department of Radiology, Mayo Clinic, Rochester, MN, United States, 4Neusoft Medical System, Shanghai, China, 5School of Computer and Information Engineering, Fujian Provincial University Key Laboratory of Internet of Things Application Technology, Xiamen University of Technology, Xiamen, China

Being the state-of-the-art parallel magnetic resonance imaging methods other than the deep learning approaches, the low-rank Hankel approaches embrace the advantage of holding low reconstruction errors. However, they demand intensive computations and high memory consumptions, thereby result in long reconstruction time. We proposed a new strategy for exploiting the low rankness and applied it to accelerate 2D imaging and T2 mapping. It is shown that the proposed method outperforms the state-of-the-art approaches in terms of lower reconstruction errors and more accurate mapping estimations. Besides, the proposed method required much less computation and memory consumption.

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