Comparison of Different Sampling Schemes in Compressed Sensing Reconstruction for DQ-SQ experiments

doi:10.11938/cjmr20140408

Abstract

Abstract:

To increase the speed of acquisition of two-dimensional solid-state DQ-SQ spectrum, a compressed sensing algorithm which makes use of the self-sparsity of the spectrum to construct under-sampled data. The energy function used in optimization is l₁ norm together with the finite difference term. In the finite different term, we used different weights for the horizontal and vertical finite differences. Different sampling schemes were compared and pseudo-random sampling combined with compressed sensing reconstruction was found to yield the best results. Furthermore, we found that the extreme case of
pseudo-random sampling, that is, t1-cutoff sampling may be the best choice.the best results. Furthermore, we found that the extreme case of pseudo-random sampling, that is, t₁-cutoff sampling may be the best choice.

Key words: solid-state NMR, compressed sensing, sampling scheme, DQ-SQ, pseudorandom

CLC Number:

O482.53

ZHENG Hui，HAN Ming-yue，HU Bing-wen*，YANG Guang*. Comparison of Different Sampling Schemes in Compressed Sensing Reconstruction for DQ-SQ experiments[J]. Chinese Journal of Magnetic Resonance, 2014, 31(4): 535-547.

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