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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