scholarly journals Receive array magnetic resonance spectroscopy: Whitened singular value decomposition (WSVD) gives optimal Bayesian solution

2010 ◽  
Vol 63 (4) ◽  
pp. 881-891 ◽  
Author(s):  
Christopher T. Rodgers ◽  
Matthew D. Robson
Keyword(s):  
Magnetic Resonance ◽  
Singular Value Decomposition ◽  
Magnetic Resonance Spectroscopy ◽  
Singular Value ◽  
Resonance Spectroscopy ◽  
Value Decomposition ◽  
Receive Array

scholarly journals Coil Combination of Multichannel Single Voxel Magnetic Resonance Spectroscopy with Repeatedly Sampled In Vivo Data

Molecules ◽  
2021 ◽  
Vol 26 (13) ◽  
pp. 3896
Author(s):  
Wanqi Hu ◽  
Huiting Liu ◽  
Dicheng Chen ◽  
Tianyu Qiu ◽  
Hongwei Sun ◽  
...  
Keyword(s):  
Magnetic Resonance ◽  
Singular Value Decomposition ◽  
Magnetic Resonance Spectroscopy ◽  
Generalized Least Squares ◽  
Singular Value ◽  
Combination Method ◽  
Resonance Spectroscopy ◽  
Sensitivity Matrix ◽  
Value Decomposition

Magnetic resonance spectroscopy (MRS), as a noninvasive method for molecular structure determination and metabolite detection, has grown into a significant tool in clinical applications. However, the relatively low signal-to-noise ratio (SNR) limits its further development. Although the multichannel coil and repeated sampling are commonly used to alleviate this problem, there is still potential room for promotion. One possible improvement way is combining these two acquisition methods so that the complementary of them can be well utilized. In this paper, a novel coil-combination method, average smoothing singular value decomposition, is proposed to further improve the SNR by introducing repeatedly sampled signals into multichannel coil combination. Specifically, the sensitivity matrix of each sampling was pretreated by whitened singular value decomposition (WSVD), then the smoothing was performed along the repeated samplings’ dimension. By comparing with three existing popular methods, Brown, WSVD, and generalized least squares, the proposed method showed better performance in one phantom and 20 in vivo spectra.

Denoising of 3D magnetic resonance images by using higher-order singular value decomposition

2015 ◽  
Vol 19 (1) ◽  
pp. 75-86 ◽  
Author(s):  
Xinyuan Zhang ◽  
Zhongbiao Xu ◽  
Nan Jia ◽  
Wei Yang ◽  
Qianjin Feng ◽  
...  
Keyword(s):  
Magnetic Resonance ◽  
Singular Value Decomposition ◽  
Magnetic Resonance Images ◽  
Higher Order ◽  
Singular Value ◽  
Value Decomposition
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