AC-LORAKS: Autocalibrated Low-Rank Modeling of Local k-Space Neighborhoods
Justin P. Haldar 1
Electrical Engineering, University of
Southern California, Los Angeles, CA, United States
Low-rank modeling of local k-space neighborhoods
(LORAKS) is a recent framework for constrained MRI.
While LORAKS is powerful, flexible, and enables the
simultaneous use of support, phase, and parallel imaging
constraints, previous implementations depended on the
use of time-consuming low-rank matrix completion
algorithms. In this work, we show that fast LORAKS
reconstructions are possible if the sampling scheme
contains an autocalibration region. Results are shown
with real data to demonstrate the advantages of the
proposed approach relative to previous LORAKS methods.
The approach can also be used as a powerful alternative
to autocalibrated parallel imaging methods like SPIRiT
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