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Maximum Entropy Spectral Reconstruction of Non-Uniformly Sampled Data

机译:非均匀采样数据的最大熵谱重构

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摘要

The time required to complete a multidimensional NMR experiment is directly proportional to the number of evolution times sampled in the indirect dimensions. A consequence when utilizing conventional methods of data acquisition and spectrum analysis is that resolution in the indirect dimensions is frequently sample-limited. The problem becomes more acute at very high magnetic fields, where increased chemical shift dispersion requires shorter time increments to avoid aliasing. It has long been recognized that a way to avoid this limitation is to utilize methods of spectrum analysis that do not require data to be sampled at uniform intervals, permitting the collection of data at long evolution times requisite for high resolution without requiring collection of data at all intervening multiples of the sampling interval. Several promising methods have evolved that are seemingly quite different, yet can be shown to yield similar results when applied to similar sampling strategies, emphasizing the importance of the choice of samples, regardless of the technique used to compute the spectrum. Maximum entropy (MaxEnt) reconstruction is a very general method for spectrum analysis of non-uniformly sampled data (NUS), and because it can be used with essentially arbitrary sampling strategies and makes no assumptions about the nature of the signal, it provides a convenient basis for exploring the influence of the choice of samples on spectral quality. In this article we use this versatility of MaxEnt reconstruction to compare different approaches to NUS in multidimensional NMR and suggest strategies for improving spectral quality by careful choice of sample times.
机译:完成多维NMR实验所需的时间与间接维度中采样的演化次数成正比。当使用常规的数据采集和频谱分析方法时,其结果是,间接尺寸的分辨率通常受到样本的限制。在非常高的磁场下,问题变得更加严重,在此情况下,化学位移分散度的增加需要更短的时间增量以避免混叠。早已认识到,避免这种局限性的方法是利用频谱分析方法,该方法不需要以均匀的间隔对数据进行采样,从而可以在高分辨率下以较长的演化时间收集数据,而无需在采样间隔的所有中间倍数。已经开发出了几种看似完全不同的有前途的方法,但是当应用于相似的采样策略时,它们可以显示出相似的结果,从而强调了选择样本的重要性,而与计算频谱的技术无关。最大熵(MaxEnt)重构是用于非均匀采样数据(NUS)频谱分析的一种非常通用的方法,并且由于它可以与基本上任意的采样策略一起使用并且无需对信号的性质进行任何假设,因此它提供了一种方便的方法探索样本选择对光谱质量影响的基础。在本文中,我们将利用MaxEnt重构的多功能性来比较多维NMR中NUS的不同方法,并提出通过谨慎选择采样时间来提高光谱质量的策略。

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