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Open AccessArticle

Reconstruction of Self-Sparse 2D NMR Spectra from Undersampled Data in the Indirect Dimension

by Xiaobo Qu 1,2, Di Guo 1, Xue Cao 3, Shuhui Cai 2 and Zhong Chen 2,*
1
Department of Communication Engineering, Fujian Key Laboratory of Plasma and Magnetic Resonance, Xiamen University, Xiamen 361005, China
2
Department of Electronic Science, Fujian Key Laboratory of Plasma and Magnetic Resonance, Xiamen 361005, China
3
School of Software, Shanghai Jiao Tong University, Shanghai 200240, China
*
Author to whom correspondence should be addressed.
A one page abstract of this work was presented at the 18th Scientific Meeting of the International Society for Magnetic Resonance in Medicine, Stockholm, Sweden, 1–7 May 2010; p. 3371.
Sensors 2011, 11(9), 8888-8909; https://doi.org/10.3390/s110908888
Received: 31 July 2011 / Revised: 31 August 2011 / Accepted: 5 September 2011 / Published: 15 September 2011
(This article belongs to the Section Chemical Sensors)
Reducing the acquisition time for two-dimensional nuclear magnetic resonance (2D NMR) spectra is important. One way to achieve this goal is reducing the acquired data. In this paper, within the framework of compressed sensing, we proposed to undersample the data in the indirect dimension for a type of self-sparse 2D NMR spectra, that is, only a few meaningful spectral peaks occupy partial locations, while the rest of locations have very small or even no peaks. The spectrum is reconstructed by enforcing its sparsity in an identity matrix domain with p (p = 0.5) norm optimization algorithm. Both theoretical analysis and simulation results show that the proposed method can reduce the reconstruction errors compared with the wavelet-based 1 norm optimization. View Full-Text
Keywords: NMR; spectral reconstruction; sparsity; undersampling; compressed sensing NMR; spectral reconstruction; sparsity; undersampling; compressed sensing
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MDPI and ACS Style

Qu, X.; Guo, D.; Cao, X.; Cai, S.; Chen, Z. Reconstruction of Self-Sparse 2D NMR Spectra from Undersampled Data in the Indirect Dimension. Sensors 2011, 11, 8888-8909.

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