Optimized truncation to integrate multi-channel MRS data using rank-Rsingular value decomposition

• Type of Material: Article
• Authors: Dongsuk Sung, Georgia Institute of Technology; Benjamin Risk, Emory University; Maame Owusu-Ansah, Emory University; Xiaodong Zhong, Emory University; Hui Mao, Emory University; Candace Fleischer, Emory University
• Language: English
• Date: 2020-04-06
• Publisher: Wiley
• Publication Version: Final Published Version
• Copyright Statement: © 2020 The Authors. NMR in Biomedicine published by John Wiley & Sons Ltd.
• License: Creative Commons Attribution-NonCommercial 4.0 International
• Final Published Version (URL): http://dx.doi.org/10.1002/nbm.4297
• Title of Journal or Parent Work: NMR in Biomedicine
• Volume: 33
• Issue: 7
• Start Page: e4297
• End Page: e4297
• Grant/Funding Information: This work was supported in part by the National Institutes of Health (NIH R01CA203388) and the Emory University Department of Radiology & Imaging Sciences.
• Keywords: Life Sciences & Biomedicine; Signal to noise; singular value decomposition; phased array combination; signal-to-noise; Technology; Spectroscopy data; Spectroscopy; Phased array combination; Radiology, Nuclear Medicine & Medical Imaging; Science & Technology; Biophysics; magnetic resonance spectroscopy
• Research Categories: Engineering, Biomedical; Biology, Bioinformatics; Biophysics, Medical; Health Sciences, Radiology

Abstract

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., optimized truncation to integrate multi-channel MRS data using rank-R singular 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/noise2 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.