The
increased sensitivity under weighted non-uniform sampling (NUS)
is demonstrated and quantified using Monte Carlo simulations of nuclear
magnetic resonance (NMR) time- and frequency-domain signals. The concept
of spectral knowledge is introduced and shown to be superior to the
frequency-domain signal-to-noise ratio for assessing the quality of
NMR data. Two methods for rigorously preserving spectral knowledge
and the time-domain NUS knowledge enhancement upon transformation
to the frequency domain are demonstrated, both theoretically and numerically.
The first, non-uniform weighted sampling using consistent root-mean-square
noise, is applicable to data sampled on the Nyquist grid, whereas
the second, the block Fourier transform using consistent root-mean-square
noise, can be used to transform time-domain data acquired with arbitrary,
off-grid NUS.