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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