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Abstract #0741

Calibration for Parallel MRI Using Robust Low-Rank Matrix Completion

Dan Zhu 1 , Martin Uecker 2 , Joseph Y Cheng 3 , Zhongyuan Bi 1 , Kui Ying 4 , and Michael Lustig 2

1 Biomedical Engineering, Tsinghua University, Beijing, China, 2 Electrical Engineering and Computer Sciences, University of California, Berkeley, California, United States, 3 Electrical Engineering, Stanford University, California, United States, 4 Department of Engineering Physics, Tsinghua University, Beijing, China

The goal of this work is to develop a practical calibration method for parallel MRI which is robust against both under-sampling and corruption of the calibration data. Previously, it has been demonstrated that robust low-rank matrix completion can reconstruct corrupted and under-sampled k-space data without specific auto-calibration data (ACS). Here, we show a generalized formulation for motion-robust auto-calibration and reconstruction from under-sampled data that is incorporated into ESPIRiT. The method is general and can incorporate navigation information when available. The feasibility of the method was demonstrated in simulation and in-vivo experiments.

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