Compressed sensing: Reconstruction of non-uniformly sampled multidimensional NMR data
Compressed sensing: Reconstruction of non-uniformly sampled multidimensional NMR data
复制标题
DOI：
10.1002/cmr.a.21438
复制
发表时间：
2017-03-01
影响因子：
0.6
通讯作者：
Nietlispach, Daniel
中科院分区：
文献类型：
--
作者：
Bostock, Mark;Nietlispach, Daniel
核磁共振（NMR）光谱广泛应用于物理、化学和生物科学。NMR研究的核心组成部分是多维实验，它使一个或多个NMR活性核的性质相关。在高分辨率生物分子NMR中，常见的核是H-1，N-15和C-13，使用这三种核的三重共振实验形成NMR结构研究的支柱。在其他领域，可以使用一系列其他核。多维核磁共振实验提供了无与伦比的信息内容，但这是以实现必要的分辨率和灵敏度所需的长实验时间为代价的。非均匀采样（NUS）技术，以减少所需的数据采样已经存在了几十年。最近，由于用于从这样的NUS数据集重建光谱的压缩感测（CS）方法的发展，这样的技术已经受到高度关注。当联合应用时，这些方法提供了一种强大的方法来显着提高每时间单位的光谱分辨率，并在适当的条件下，也可以导致信噪比的改善。在这篇综述中，我们探讨了NUS方法的基础，NUS重建的基本特征，使用CS和CS方法的基础上的应用程序，包括扩展到更高的维度的生物分子NMR实验的剧目的好处。我们讨论了最近的一些算法和软件包，并提供实用的技巧，记录和处理NUS数据的CS。
Nuclear magnetic resonance (NMR) spectroscopy is widely used across the physical, chemical, and biological sciences. A core component of NMR studies is multidimensional experiments, which enable correlation of properties from one or more NMR-active nuclei. In high-resolution biomolecular NMR, common nuclei are H-1, N-15, and C-13, and triple resonance experiments using these three nuclei form the backbone of NMR structural studies. In other fields, a range of other nuclei may be used. Multidimensional NMR experiments provide unparalleled information content, but this comes at the price of long experiment times required to achieve the necessary resolution and sensitivity. Non-uniform sampling (NUS) techniques to reduce the required data sampling have existed for many decades. Recently, such techniques have received heightened interest due to the development of compressed sensing (CS) methods for reconstructing spectra from such NUS datasets. When applied jointly, these methods provide a powerful approach to dramatically improve the resolution of spectra per time unit and under suitable conditions can also lead to signal-to-noise ratio improvements. In this review, we explore the basis of NUS approaches, the fundamental features of NUS reconstruction using CS and applications based on CS approaches including the benefits of expanding the repertoire of biomolecular NMR experiments into higher dimensions. We discuss some of the recent algorithms and software packages and provide practical tips for recording and processing NUS data by CS.