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Abstract #3795

Improving Compressed Sensing Initialization and Convergence Using an Efficient Auto-Calibrating Parallel Imaging Method

Peng Lai1, Shreyas S. Vasanawala2, Michael Lustig3, Kang Wang4, Anja C.S Brau5

1MR Applications & Workflow , GE Healthcare, Menlo Park, CA, United States; 2Radiology, Stanford University, Stanford, CA, United States; 3Electrical Engineering & Computer Science, University of California, Berkeley, CA, United States; 4MR Applications & Workflow, GE Healthcare, Madison, WI, United States; 5MR Applications & Workflow, GE Healthcare, Garching, Munchen, Germany

Compressed sensing reconstruction requires many iterations to converge. This work developed an auto-calibrating parallel imaging method that can efficiently reconstruct coil-combined k-space data from random k-space sampling. Our preliminary results show that the proposed method can provide similar reconstruction accuracy with much faster computation compared to conventional auto-calibrating parallel imaging and can significantly improve the initial condition and convergence of compressed sensing reconstruction.