XCloud-VIP：虚拟峰实现高度加速的核磁共振波谱与忠实的定量测量
XCloud-VIP: Virtual Peak Enables Highly Accelerated NMR Spectroscopy and Faithful Quantitative Measures
AI总结：
针对多维核磁共振波谱非均匀采样在高加速因子下产生的谱图失真和定量不忠实问题，提出虚拟峰（VIP）方法并结合云计算重建高保真谱图，显著降低了核间距和代谢物浓度的定量误差。
摘要：
核磁共振（NMR）波谱是确定代谢物浓度和分子结构等的重要生物工程工具。然而，多维NMR的数据采集时间非常长。非均匀采样是加速数据采集的有效方法，但在高加速因子下可能会遇到严重的谱图失真和不忠实的定量测量。通过将采集的信号建模为叠加指数，我们提出了一种虚拟峰（VIP）方法来自适应先验谱图信息（如共振频率和峰线型），然后将这些信息输入到重建中。所提出的方法进一步通过云计算实现，以促进在线、开放和便捷的访问。在模拟和实验数据上的结果表明，与低秩Hankel矩阵方法相比，新方法从高度欠采样数据中重建出高保真NMR谱图，并实现了更准确的定量。核间距和混合物中代谢物浓度的最大定量误差分别降低了61.1%和57.7%。
英文摘要：
Nuclear Magnetic Resonance (NMR) spectroscopy is an important bio-engineering tool to determine the metabolic concentrations, molecule structures and so on. The data acquisition time, however, is very long in multi-dimensional NMR. To accelerate data acquisition, non-uniformly sampling is an effective way but may encounter severe spectral distortions and unfaithful quantitative measures when the acceleration factor is high. By modelling the acquired signal as the superimposed exponentials, we proposed a virtual peak (VIP) approach to selfadapt the prior spectral information, such as the resonance frequency and peak lineshape, and then feed these information into the reconstruction. The proposed method is further implemented with cloud computing to facilitate online, open, and easy access. Results on simulated and experimental data demonstrate that, compared with the low-rank Hankel matrix method, the new approach reconstructs high-fidelity NMR spectra from highly undersampled data and achieves more accurate quantification. The maximum quantitative errors of distances between nuclear pairs and concentrations of metabolites in mixtures have been reduced by 61.1% and 57.7%, respectively.
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