Optimized truncation to integrate multi-channel MRS data using rank-R singular value decomposition.
Optimized truncation to integrate multi-channel MRS data using rank-R singular value decomposition.
复制标题
使用 R 阶奇异值分解优化截断以集成多通道 MRS 数据。
DOI：
10.1002/nbm.4297
复制
发表时间：
2020
影响因子：
2.9
通讯作者：
Fleischer,CandaceC
中科院分区：
文献类型：
--
作者：
Sung,Dongsuk;Risk,BenjaminB;Owusu-Ansah,Maame;Zhong,Xiaodong;Mao,Hui;Fleischer,CandaceC
多通道相控接收阵列已广泛应用于磁共振成像（MRI）和光谱学（MRS）。使用 MRS 接收阵列的一个重要步骤是组合从各个线圈通道收集的光谱。这项工作的目标是实施一种名为 OpTIMUS 的改进策略（即使用秩-R奇异值分解优化截断来整合多通道 MRS 数据），以组合来自各个通道的数据。 OpTIMUS 依靠频谱加窗和 R 分解来计算最佳线圈通道权重。首先使用白化变换对从脑光谱模型和 11 名健康志愿者获取的 MRS 数据进行处理，以消除相关噪声。然后对白化光谱进行迭代加窗或截断，然后进行秩-R奇异值分解（SVD）以凭经验确定线圈通道权重。使用供应商提供的方法、信号/噪声加权、先前报告的白化 SVD（等级 1）和 OpTIMUS 组合光谱，并使用信噪比 (SNR) 进行评估。与其他三种组合算法相比，结合 OpTIMUS 的大脑 MRS 数据的 SNR 显着增加，范围为 6% 至 33%（P≤0.05）。 Rank-1SVD 最大化 SNR 的假设经过实证检验，更高的Rank-R 分解与 SVD 之前的频谱加窗相结合，导致 SNR 增加。
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.,optimizedtruncation tointegratemulti‐channel MRS datausing rank‐Rsingular value decomposition) for combining data from individual channels. OpTIMUS relies on spectral windowing coupled with a rank‐Rdecomposition 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‐Rsingular value decomposition (SVD) to empirically determine the coil channel weights. Spectra combined using the vendor‐supplied method, signal/noise2weighting, 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‐1SVD maximizes SNR was tested empirically, and a higher rank‐Rdecomposition, combined with spectral windowing prior to SVD, resulted in increased SNR.