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.