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Abstract #3498

Image Reconstruction with Low-rankness and Self-consistency of k-space Data in Parallel MRI

Xinlin Zhang1, Di Guo2, Yiman Huang1, Ying Chen1, Liansheng Wang3, Feng Huang4, and Xiaobo Qu1
1Department of Electronic Science, Fujian Provincial Key Laboratory of Plasma and Magnetic Resonance, Xiamen University, Xiamen, China, 2School of Computer and Information Engineering, Fujian Provincial University Key Laboratory of Internet of Things Application Technology, Xiamen University of Technology, Xiamen, China, 3Department of Computer Science, School of Information Science and Engineering, Xiamen University, Xiamen, China, 4Neusoft Medical System, Shanghai, China

Recent low-rank reconstruction methods offer encouraging image reconstruction results enabling promising acceleration of parallel magnetic resonance imaging, however, they were not originally designed to exploit the routinely acquired calibration data for performance improvement in parallel magnetic resonance imaging. In this work, we proposed an image reconstruction approach to simultaneously explore the low-rankness of the k-space data and mine the data correlation among multiple receiver coils with the use of the calibration data. The proposed method outperforms the state-of-the-art methods in terms of suppressing artifacts and achieving lowest error, and exhibits robust reconstructions even with limited auto-calibration signals.

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