Mobli Mehdi, Hoch Jeffrey C
University of Connecticut Health Center, Department of Molecular, Microbial, and Structural Biology, 263 Farmington Ave., Farmington, CT 06030-3305 USA.
Concepts Magn Reson Part A Bridg Educ Res. 2008 Nov 1;32A(6):436-448. doi: 10.1002/cmr.a.20126.
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.
完成一个多维核磁共振实验所需的时间与在间接维度中采样的演化次数直接成正比。使用传统数据采集和频谱分析方法的一个后果是,间接维度的分辨率常常受到样本限制。在非常高的磁场下,这个问题变得更加尖锐,因为化学位移分散增加需要更短的时间增量来避免混叠。长期以来,人们已经认识到,避免这种限制的一种方法是利用不需要在均匀间隔采样数据的频谱分析方法,从而允许在高分辨率所需的长演化时间采集数据,而无需在采样间隔的所有中间倍数处采集数据。已经出现了几种有前景的方法,它们看似截然不同,但当应用于类似的采样策略时,可以显示出产生相似的结果,这强调了样本选择的重要性,而不管用于计算频谱的技术如何。最大熵(MaxEnt)重建是一种用于非均匀采样数据(NUS)频谱分析的非常通用的方法,并且由于它可以与基本上任意的采样策略一起使用,并且对信号的性质不做任何假设,因此它为探索样本选择对频谱质量的影响提供了一个便利的基础。在本文中,我们利用MaxEnt重建的这种通用性来比较多维核磁共振中不同的NUS方法,并通过仔细选择采样时间提出提高频谱质量的策略。