XCloud-VIP: Virtual Peak Enables Highly Accelerated NMR Spectroscopy and Faithful Quantitative Measures

Di Guo, Zhangren Tu, Yi Guo, Yirong Zhou, Jian Wang, Zi Wang, Tianyu Qiu, Min Xiao, Yinran Chen, Liubin Feng, Yuqing Huang, Donghai Lin, Qing Hong, Amir Goldbourt, Meijin Lin, Xiaobo Qu

Research output: Contribution to journalArticlepeer-review


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 self-adapt 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.

Original languageEnglish
Pages (from-to)1043-1057
Number of pages15
StatePublished - 2023


  • Hankel matrix
  • Machine learning
  • fast sampling
  • nuclear magnetic resonance spectroscopy

All Science Journal Classification (ASJC) codes

  • Signal Processing
  • Computer Science Applications
  • Computational Mathematics


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