Xiaobo Qu1,
Xue Cao2, Di Guo3, Zhong Chen4
1Department of Communication
Engineering,, Xiamen University, Xiamen, Fujian, China; 2School Of
Software, Shanghai Jiao Tong University, Shanghai, China; 3Department
of Communication Engineering, Xiamen University, Xiamen, Fujian, China; 4Department
of Physics, Xiamen University, Xiamen, Fujian, China
Multidimensional
magnetic resonance spectroscopy (MRS) can provide additional information at
the expense of longer acquisition time than 1D MRS. Assuming 2D MRS is sparse
in wavelet domain, Iddo[1] first introduced compressed sensing (CS) [2][3] to
reconstruct multidimensional MRS from partial and random free induction decay
(FID) data. However, the darkness in 1D NMR spectra derives from the discrete
nature of chemical groups [4]. Significant peaks in these MRS takes up
partial location of the full MRS while the rest locations own very small or
even no peaks. This type of MRS can be considered to be sparse itself, named
sparse MRS. In the concept of sparsity and coherence for CS[5], we will
demonstrate that wavelet is not necessary to sparsify sparse MRS and even
makes the reconstructed MRS worse than without wavelet. Furthermore, a lp
quasi-norm compressed sensing reconstruction is employed to improve the
quality of reconstruction.
Keywords