Sparse multidimensional iterative lineshape-enhanced (SMILE) reconstruction of both non-uniformly sampled and conventional NMR data
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Implementation of a new algorithm, SMILE, is described for reconstruction of non-uniformly sampled two-, three- and four-dimensional NMR data, which takes advantage of the known phases of the NMR spectrum and the exponential decay of underlying time domain signals. The method is very robust with respect to the chosen sampling protocol and, in its default mode, also extends the truncated time domain signals by a modest amount of non-sampled zeros. SMILE can likewise be used to extend conventional uniformly sampled data, as an effective multidimensional alternative to linear prediction. The program is provided as a plug-in to the widely used NMRPipe software suite, and can be used with default parameters for mainstream application, or with user control over the iterative process to possibly further improve reconstruction quality and to lower the demand on computational resources. For large data sets, the method is robust and demonstrated for sparsities down to ca 1%, and final all-real spectral sizes as large as 300 Gb. Comparison between fully sampled, conventionally processed spectra and randomly selected NUS subsets of this data shows that the reconstruction quality approaches the theoretical limit in terms of peak position fidelity and intensity. SMILE essentially removes the noise-like appearance associated with the point-spread function of signals that are a default of five-fold above the noise level, but impacts the actual thermal noise in the NMR spectra only minimally. Therefore, the appearance and interpretation of SMILE-reconstructed spectra is very similar to that of fully sampled spectra generated by Fourier transformation.
KeywordsLinear prediction Non-uniform sampling NUS Multi-dimensional NMR Sampling scheme Spectral reconstruction Sparse sampling 4D NMR
We thank Alex Maltsev and Yang Shen for useful discussions, James L Baber and Dan Garrett for technical assistance, and Jung Ho Lee, Venkatraman Ramanujam, and Nikolaos Sgourakis for providing experimental data sets used for testing and illustrating the performance of SMILE. We also thank Michal Górka, Szymon Żerko, and Wiktor Koźmiński for providing the reconstructed (H)N(COCO)NH data by SSA, Victor Jaravine and Vladislav Orekhov for their help with running the MddNMR program, and Brian Coggins for helpful discussions about using SCRUB. This work was supported by the Intramural Research Program of the NIDDK and by the Intramural Antiviral Target Program of the Office of the Director, NIH. We acknowledge use of the high-performance computational capabilities of the NIH Biowulf Linux cluster. Funding was provided by National Institute of Diabetes and Digestive and Kidney Diseases (Grant No. ZIA DK029046-10).
- OpenMP Architecture Review Board, OpenMP Application Program Interface, Version 3.1, July 2011, http://www.openmp.org
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