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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