Berkay
Kanberoglu1, Lina J. Karam1, Josef P. Debbins2
1Electrical Engineering, Arizona State
University, Tempe, AZ, United States; 2Keller Center for Imaging
Innovation, Barrow Neurological Institute, Phoenix, AZ, United States
For
GRAPPA reconstruction, large kernel sizes can be disadvantageous in some
cases due to the large number of GRAPPA coefficients. A system like this
needs a large number of equations to construct an over-determined system.
Small kernel sizes can be advantageous when there is a small number of
equations. Proposed algorithm employs a small kernel size and a clustering
method to produce more than one set of GRAPPA weights within a slice.