A Sparse Reconstruction Algorithm for NMR Spectroscopy Based on Approximate l0 Norm Minimization

Abstract

Abstract:

Long acquisition time often hinders the routine application of multidimensional NMR spectroscopy. A common approach to reduce the acquisition time is to replace the commonly used Nyquist grid sampling scheme with a random non-uniform sampling (NUS) scheme. However, NUS is inherently associated with degradation of spectrum quality. It has been demonstrated recently compressed sensing (CS) algorithms can be used to reconstruct highquality spectra from sparse NUS data. In this paper, a CS reconstruction algorithm called “Smoothed l₀ Norm Minimization” was introduced. The typical version of the algorithm was then-modified to improve its robustness under high noise condition. The improved algorithm was applied to reconstruct 1D realvalued signal and 2D NMR spectroscopy, and the results were compared with those obtained by other methods. The results showed that the algorithm proposed had better robustness to noise, and could be used to reconstruct high-quality spectra with fewer sampling data.
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Key words: NMR spectroscopy, compressed sensing, approximate l₀norm, re-weighted, signal-to-noise ratio

CLC Number:

O482.53

ZHANG Zheng-yang，QU Xiao-bo，LIN Yan-qin*，CHEN Zhong. A Sparse Reconstruction Algorithm for NMR Spectroscopy Based on Approximate l₀ Norm Minimization[J]. Chinese Journal of Magnetic Resonance.

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A Sparse Reconstruction Algorithm for NMR Spectroscopy Based on Approximate l₀ Norm Minimization

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