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A simple and effective method, SIFT (spectroscopy by integration of frequency and time domain information), is introduced for processing nonuniformly sampled multidimensional NMR data. Applying the computationally efficient Gerchberg-Papoulis (G-P) algorithm, used previously in picture processing and medical imaging, SIFT supplements data at nonuniform points in the time domain with the information carried by known "dark" points (i.e., empty regions) in the frequency domain. We demonstrate that this rapid integration not only removes the severe pseudonoise characteristic of the Fourier transforms of nonuniformly sampled data, but also provides a robust procedure for using frequency information to replace time measurements. The latter can be used to avoid unnecessary sampling in sampling-limited experiments, and the former can be used to take advantage of the ability of nonuniformly sampled data to minimize trade-offs between the signal-to-noise ratio and the resolution in sensitivity-limited experiments. Processing 2D and 3D data sets takes about 0.1 and 2 min, respectively, on a personal computer. With these several attractive features, SIFT offers a novel, model-independent, flexible, and user-friendly tool for efficient and accurate processing of multidimensional NMR data.

Spectroscopy by Integration of Frequency and Time Domain Information for Fast Acquisition of High-Resolution Dark Spectra