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

Sensors

Xiaobo Qu²  Di Guo²  Xue Cao³  Shuhui Cai¹ 

[1] Department of Electronic Science, Fujian Key Laboratory of Plasma and Magnetic Resonance, Xiamen 361005, China; E-Mail:;Department of Communication Engineering, Fujian Key Laboratory of Plasma and Magnetic Resonance, Xiamen University, Xiamen 361005, China; E-Mails:;School of Software, Shanghai Jiao Tong University, Shanghai 200240, China; E-Mail:

关键词: NMR;    spectral reconstruction;    sparsity;    undersampling;    compressed sensing;   

DOI  :  10.3390/s110908888

来源: mdpi

【 摘 要 】

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 ℓ_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 ℓ₁ norm optimization.

【 授权许可】

CC BY   
© 2011 by the authors; licensee MDPI, Basel, Switzerland.