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Look-up tables (LUTs) are a common method for increasing the speed of many algorithms. Their use can be extended to the reconstruction of nonuniformly sampled k-space data using either a discrete Fourier transform (DFT) algorithm or a convolution-based gridding algorithm. A table for the DFT would be precalculated arrays of weights describing how each data point affects all of image space. A table for a convolution-based gridding operation would be a precalculated table of weights describing how each data point affects a small k-space neighborhood. These LUT methods were implemented in C++ on a modest personal computer system; they allowed a radial k-space acquisition sequence, consisting of 180 view's of 256 points each, to be gridded in 36.2 ms, or, in approximately 800 ns/point. By comparison, a similar implementation of the gridding operation, without LUTs, required 45 times longer (1639.2 ms) to grid the same data. This was possible even while using a 4×4 Kaiser-Bessel convolution kernel, which is larger than typically used. These table-based computations will allow real time reconstruction in the future and can currently be run concurrently with the acquisition allowing for completely real-time gridding.