Interpretation from a spectroscopists perspective. Reconstruction guarantees get to the heart of the question most spectrocopists are interested in, what is the minimal number of samples needed to accurately recover a spectrum? Though the CS theorems provide precise answers, their application to real-world NMR spectroscopy is subject to a number of caveats. First, CS theorems assume random sampling, which is rarely employed due to the ensuing low sensitivity when applied to experiements with exponentially decaying signals. A second caveat is that they assume \(\ell_{1}\) minimization. Not all spectral reconstruction methods for sparse recovery in NMR use \(\ell_{1}\) minimization, and those that do are not always guaranteed to converge to minimum \(\ell_{1}\) . Additional considerations are that spectroscopists frequently don't know a priori how many signals are in the spectrum, especially for experiments with high dynamic range. The notion of spectral sparsity for the latter, where the number of recoverable signals depends on the noise level, requires generalization to approximate sparsity, where the most of the spectrum is smaller than some value\(\)rather than equal to zero\(\)
Phase transition
[Hatef - description of phase transition here]
Interpretation from a spectroscopists perspective. The existence of a phase transition separating successful recovery of a spectrum from failure holds profound implications for experiment design, including all aspects that affect spectral sparsity (field strength, type of experiment, dimensionality, etc.), the sampling sparsity, and the spectral reconstruction method. In Figure Q, depicting the phase space of the likelihood of accurate spectral recovery as a function of sampling coverage and spectral sparsity ratio (define these), a vertical column corresponds to fixed experiment time, or a fixed number of FIDs acquired. Performing the same experiment at higher magnetic field decreases the sparsity ratio (down), but also decreases the sampling coverage (left) because the increase in dispersion with field requires a concomitant decrease in the sampling dwell time (more samples are required to obtain the same maximum evolution time).
Hidden/residual Coherence - generalized coherence measure
[Hatef - description of generalized coherence here]
Interpretation from a spectroscopists perspective. Sampling matrices become block-diagonal in contexts where uniform sampling is employed along one or more dimensions. If non-Fourier spectral reconstruction includes the uniformly-sampled acquisition time domain, then the sampling matrix is block-diagonal. The additional coherence of block-diagonal sampling is especially manifest in experiments in which NUS is applied along a single indirect time dimension, and leads to particularly large sampling artifacts. These were observed by Stern et al. in comparisons of NUS and linear prediction extrapolation (figure X of Y). Schuyler et al. demonstrated that decoherence can be "borrowed" from orthogonal dimensions (ref), so that the impact of block-diagonal coherence diminishes when additional time dimensions are sampled uniformly. The residual coherence resulting from block-diagonal sampling is not fully captured by simple measures of coherence such as the peak-to-sidelobe ratio (PSR), necessitating the generalized measure of coherence. This generalized measure is required for CS theorems to apply quantitatively to NMR when block-diagonal sampling is employed.