Many of the ubiquitous experiments of biomolecular NMR, including [Formula: see text], [Formula: see text], and CEST, involve acquiring repeated 2D spectra under slightly different conditions. Such experiments are amenable to acceleration using non-uniform sampling spectral reconstruction methods that take advantage of prior information. We previously developed one such technique, an iterated maps method (DiffMap) that we successfully applied to 2D NMR spectra, including [Formula: see text] relaxation dispersion data. In that prior work, we took a top-down approach to reconstructing the 2D spectrum with a minimal number of sparse samples, reaching an undersampling fraction that appeared to leave some room for improvement. In this study, we develop an in-depth understanding of the action of the DiffMap algorithm, identifying the factors that cause reconstruction errors for different undersampling fractions. This improved understanding allows us to formulate a bottom-up approach to finding the lowest number of sparse samples required to accurately reconstruct individual spectral features with DiffMap. We also discuss the difficulty of extending this method to reconstructing many peaks at once, and suggest a way forward.

中文翻译：

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达到稀疏采样极限，以使用迭代图在2D NMR谱图中重建单个峰。

许多普遍存在的生物分子NMR实验，包括[公式：参见文字]，[公式：参见文字]和CEST，涉及在略有不同的条件下获取重复的2D光谱。此类实验适合使用利用先验信息的非均匀采样频谱重建方法进行加速。我们以前开发了一种这样的技术，即迭代图方法（DiffMap），我们已成功地将其应用于2D NMR光谱，包括[公式：参见文本]弛豫色散数据。在该先前的工作中，我们采用了自上而下的方法来以最少的稀疏样本数量重建2D频谱，达到欠采样率，这似乎还有一些改进的余地。在这项研究中，我们对DiffMap算法的作用有了更深入的了解，确定导致不同欠采样分数重构误差的因素。这种更好的理解使我们能够制定一种自下而上的方法，以找到使用DiffMap准确重建各个光谱特征所需的最少数量的稀疏样本。我们还讨论了将该方法扩展为一次重建多个峰的困难，并提出了前进的道路。