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Abstract #4880

Combining Nonconvex Compressed Sensing and GRAPPA Using the Nullspace Method

Daniel Stuart Weller1, Jonathan R. Polimeni2,3, Leo J. Grady4, Lawrence L. Wald2,3, Elfar Adalsteinsson1, Vivek K. Goyal1

1Research Laboratory of Electronics, Massachusetts Institute of Technology, Cambridge, MA, United States; 2A.A. Martinos Center, Department of Radiology, Massachusetts General Hospital, Charlestown, MA, United States; 3Harvard Medical School, Boston, MA, United States; 4Imaging and Visualization, Siemens Corporate Research, Princeton, NJ, United States

This work combines GRAPPA, a parallel image reconstruction method, with compressed sensing in a joint optimization framework. To enforce consistency with the acquired data, the optimization problem operates in the nullspace of the sampling pattern, which more accurately preserves the acquired data than a data feasibility penalty in the objective. The L0 penalty was approximated using a continuation procedure with a differentiable nonconvex regularizer. The proposed method was implemented using an iterative reweighted least squares routine. The combined method was applied to highly under-sampled MPRAGE data. This approach reconstructed images at higher quality than GRAPPA and CS alone.