  Fast NMR spectroscopy is strongly desired in modern biology and chemistry. To
accelerate data acquisition, non-uniformly sampling is an effective way for
multidimensional NMR. However, severe spectral distortions and unfaithful
quantitative measures may be encountered when the acceleration factor is high.
To address this issue, a virtual peak approach is introduced to learn prior
knowledge on the peak frequency and lineshape. This prior information is then
fed into a reconstruction model to reduce the degree of freedom to restore the
spectrum. To better serve the NMR community, the proposed method is implemented
using cloud computing to provide online access for anyone at any time. Results
on synthetic and experimental data demonstrate that, compared with the
state-of-the-art methods, the new approach provides much better spectra and
quantitative measures, including peak intensity regression, distances between
nuclear pairs, and metabolite concentrations in mixtures. This work can be
viewed as a proof-of-concept on the virtual peak reconstruction for reliable
and highly accelerated NMR.
