Stoica Petre, Sandgren Niclas, Selén Yngve, Vanhamme Leentje, Van Huffel Sabine
Systems and Control Division, Department of Information Technology, Uppsala University, P.O. Box 337, SE-751 05, Uppsala, Sweden.
J Magn Reson. 2003 Nov;165(1):80-8. doi: 10.1016/s1090-7807(03)00188-5.
In several applications of NMR spectroscopy the user is interested only in the components lying in a small frequency band of the spectrum. A frequency selective analysis deals precisely with this kind of NMR spectroscopy: parameter estimation of only those spectroscopic components that lie in a preselected frequency band of the NMR data spectrum, with as little interference as possible from the out-of-band components and in a computationally efficient way. In this paper we introduce a frequency-domain singular value decomposition (SVD)-based method for frequency selective spectroscopy that is computationally simple, statistically accurate, and which has a firm theoretical basis. To illustrate the good performance of the proposed method we present a number of numerical examples for both simulated and in vitro NMR data.
在核磁共振波谱学的若干应用中,用户仅对光谱中位于小频率带内的成分感兴趣。频率选择性分析恰好处理这类核磁共振波谱学:仅对位于核磁共振数据光谱预选频带内的那些光谱成分进行参数估计,同时尽可能减少带外成分的干扰,并且计算效率高。在本文中,我们介绍一种基于频域奇异值分解(SVD)的频率选择性光谱学方法,该方法计算简单、统计准确且有坚实的理论基础。为说明所提方法的良好性能,我们给出了一些针对模拟和体外核磁共振数据的数值示例。