Randomization improves sparse sampling in multidimensional NMR.
While a number of strategies have been developed to reduce data collection requirements for multidimensional NMR based on non-Fourier methods of spectrum analysis, there is an increasing awareness that the principal differences in the performance of these methods is attributable to the sampling strategies employed, and not the method of spectrum analysis per se. The ability of maximum entropy reconstruction to utilize essentially arbitrary sampling schemes makes it a useful platform for comparative analysis of sampling strategies. Here we use maximum entropy reconstruction to demonstrate that artifacts characteristic of sparse sampling result from regularity in the sampling pattern, and that they can be substantially reduced by introducing a degree of randomness to an otherwise regular sampling scheme, without requiring additional sampling.
Department of Molecular, Microbial, and Structural Biology, University of Connecticut Health Center, 263 Farmington Avenue, Farmington, CT 06030-3305, USA. hoch@uchc.edu
Jeffrey C
JC
Hoch
Mark W
MW
Maciejewski
Blagoje
B
Filipovic
D000465:Algorithms; D003198:Computer Simulation; D003627:Data Interpretation, Statistical; D005583:Fourier Analysis; D009682:Magnetic Resonance Spectroscopy / Q000379:methods*; D008956:Models, Chemical; D015233:Models, Statistical; D015203:Reproducibility of Results; D018401:Sample Size; D012680:Sensitivity and Specificity