GROG is an attractive alternative to convolutional gridding and non-uniform DFT methods because of comparatively low cost and no density correction. However, for large multicoil datasets, many fractional matrix powers must be performed which scale with the cube of the number of channels. For SC-GROG and real-time SC-GROG, time and memory requirements can be significantly lowered for precomputation and updates of fractional powers by decomposing required powers into smaller, composable pieces. This is an NP-hard combinatorial change-making problem. We propose a simple solution based on prime factorization which leads to significant computational and memory savings with little performance degradation.
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