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Abstract #2430

AC-LORAKS: Autocalibrated Low-Rank Modeling of Local k-Space Neighborhoods

Justin P. Haldar 1

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 and PRUNO.

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