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.