Premium
Optimized truncation to integrate multi‐channel MRS data using rank‐ R singular value decomposition
Author(s) -
Sung Dongsuk,
Risk Benjamin B.,
OwusuAnsah Maame,
Zhong Xiaodong,
Mao Hui,
Fleischer Candace C.
Publication year - 2020
Publication title -
nmr in biomedicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.278
H-Index - 114
eISSN - 1099-1492
pISSN - 0952-3480
DOI - 10.1002/nbm.4297
Subject(s) - singular value decomposition , rank (graph theory) , channel (broadcasting) , signal to noise ratio (imaging) , algorithm , noise (video) , weighting , truncation (statistics) , imaging phantom , signal (programming language) , computer science , mathematics , physics , statistics , acoustics , artificial intelligence , optics , combinatorics , telecommunications , image (mathematics) , programming language
Multi‐channel phased receive arrays have been widely adopted for magnetic resonance imaging (MRI) and spectroscopy (MRS). An important step in the use of receive arrays for MRS is the combination of spectra collected from individual coil channels. The goal of this work was to implement an improved strategy termed OpTIMUS (i.e., op timized t runcation to i ntegrate m ulti‐channel MRS data u sing rank‐ R s ingular value decomposition) for combining data from individual channels. OpTIMUS relies on spectral windowing coupled with a rank‐ R decomposition to calculate the optimal coil channel weights. MRS data acquired from a brain spectroscopy phantom and 11 healthy volunteers were first processed using a whitening transformation to remove correlated noise. Whitened spectra were then iteratively windowed or truncated, followed by a rank‐ R singular value decomposition (SVD) to empirically determine the coil channel weights. Spectra combined using the vendor‐supplied method, signal/noise 2 weighting, previously reported whitened SVD (rank‐ 1 ), and OpTIMUS were evaluated using the signal‐to‐noise ratio (SNR). Significant increases in SNR ranging from 6% to 33% ( P ≤ 0.05) were observed for brain MRS data combined with OpTIMUS compared with the three other combination algorithms. The assumption that a rank‐ 1 SVD maximizes SNR was tested empirically, and a higher rank‐ R decomposition, combined with spectral windowing prior to SVD, resulted in increased SNR.