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Accurate scoring of non-uniform sampling schemes for quantitative NMR

机译:定量NMR的非均匀采样方案的准确评分

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摘要

Non-uniform sampling (NUS) in NMR spectroscopy is a recognized and powerful tool to minimize acquisition time. Recent advances in reconstruction methodologies are paving the way for the use of NUS in quantitative applications, where accurate measurement of peak intensities is crucial. The presence or absence of NUS artifacts in reconstructed spectra ultimately determines the success of NUS in quantitative NMR. The quality of reconstructed spectra from NUS acquired data is dependent upon the quality of the sampling scheme. Here we demonstrate that the best performing sampling schemes make up a very small percentage of the total randomly generated schemes. A scoring method is found to accurately predict the quantitative similarity between reconstructed NUS spectra and those of fully sampled spectra. We present an easy-to-use protocol to batch generate and rank optimal Poisson-gap NUS schedules for use with 2D NMR with minimized noise and accurate signal reproduction, without the need for the creation of synthetic spectra.
机译:NMR光谱中的非均匀采样(NUS)是一种公认​​的强大工具,可最大程度地减少采集时间。重建方法学的最新进展为在定量应用中使用NUS铺平了道路,在定量应用中,准确测量峰强度至关重要。重建光谱中NUS伪影的存在或不存在最终决定了NUS在定量NMR中的成功。从NUS采集的数据中重建光谱的质量取决于采样方案的质量。在这里,我们证明了性能最佳的采样方案仅占随机生成方案总数的很小一部分。发现一种计分方法可准确预测重建的NUS光谱与完全采样的光谱之间的定量相似性。我们提出了一种易于使用的协议,用于批量生成和排列最佳Poisson-gap NUS计划,以便与2D NMR一起使用,并具有最小的噪声和准确的信号再现,而无需创建合成光谱。

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