Iterative algorithm of discrete Fourier transform for processing randomly sampled NMR
Abstract Spectra obtained by application of multidimensional Fourier Transformation (MFT) to sparsely sampled nD NMR signals are usually corrupted due to missing data. In the present paper this phenomenon is investigated on simulations and experiments. An effective iterative algorithm for artifact suppression for sparse on-grid NMR data sets is discussed in detail. It includes automated peak recognition based on statistical methods. The results enable one to study NMR spectra of high dynamic range of peak intensities preserving benefits of random sampling, namely the superior resolution in indirectly measured dimensions. Experimental examples include 3D 15N- and 13C-edited NOESY-HSQC spectra of human ubiquitin.
Content Type Journal Article
DOI 10.1007/s10858-010-9411-2
Authors
Jan Stanek, University of Warsaw Faculty of Chemistry Pasteura 1 02-093 Warsaw Poland
Wiktor KoźmiÅ?ski, University of Warsaw Faculty of Chemistry Pasteura 1 02-093 Warsaw Poland
Application of iterative soft thresholding for fast reconstruction of NMR data non-uniformly sampled with multidimensional Poisson Gap scheduling
Application of iterative soft thresholding for fast reconstruction of NMR data non-uniformly sampled with multidimensional Poisson Gap scheduling
Abstract The fast Fourier transformation has been the gold standard for transforming data from time to frequency domain in many spectroscopic methods, including NMR. While reliable, it has as a drawback that it requires a grid of uniformly sampled data points. This needs very long measuring times for sampling in multidimensional experiments in all indirect dimensions uniformly and even does not allow reaching optimal evolution times that would...
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Iterative algorithm of discrete Fourier transform for processing randomly sampled NMR
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