A Compressed Sensing and Resampling Based Noise Suppression Method for NMR

doi:10.11938/cjmr20160207

Abstract

Abstract:

Sensitivity enhancement is an everlasting topic in analytical chemistry, for which the two most common approaches are signal enhancing and noise suppressing. In nuclear magnetic resonance (NMR) spectroscopy, signal enhancement can be achieved by utilizing high field spectrometers, cryogenic probes and/or using sophisticated pulse sequences. However, these approaches are often associated with dramatically increased cost. The other way to enhance sensitivity is to de-noise the data by post-processing, which is obviously more cost-effective and attractive. Based on the statistical resampling principle, we previously developed an NMR data post-processing method named NASR (An Effective Approach for Simultaneous Noise and Artifact Suppression in NMR Spectroscopy), which is effective to reduce unwanted noises and suppress artifacts in one-dimensional and multiple-dimensional NMR experiments. In practice, however, the optimal parameter setting for NASR is often difficult to achieve. In this study, compressed sensing (CS) was incorporated into the original NASR approach, resulting in a novel and robust noise suppression method for NMR experiments (CS_NASR), in which the subjective factors of the handlers are negligible.

Key words: NMR, data processing, noise suppression, compressed sensing (CS), resampling

CLC Number:

O482.53

NIE Li-sha, JIANG Bin, ZHANG Xu, LIU Mai-li. A Compressed Sensing and Resampling Based Noise Suppression Method for NMR[J]. Chinese Journal of Magnetic Resonance, 2016, 33(2): 244-256.

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