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Abstract #2871

Context Based GRAPPA Reconstruction Using a Small Kernel

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.