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.