  Rapid development of sparse sampling methodology offers dramatic increase in
power and efficiency of magnetic resonance techniques in medicine, chemistry,
molecular structural biology, and other fields. We suggest to use available yet
usually unexploited prior knowledge about the phase and the causality of the
sparsely detected NMR signal as a general approach for a major improvement of
the spectra quality. The work gives a theoretical framework of the method and
demonstrates notable improvement of the protein spectra reconstructed with two
commonly used state-of-the-art signal processing algorithms, compressed sensing
and SIFT.
