Filter diagonalization using a "sensitivity-enhanced basis": improved performance for noisy NMR spectra.

The Filter Diagonalization Method (FDM) has been used to process NMR data in liquids and can be advantageous when the spectrum is sparse enough, the lines are sharp and Lorentzian, raw sensitivity is adequate, and the measured time-domain data is short, so that the Fourier Transform spectrum exhibits distorted line shapes. Noise can adversely impact resolution and/or frequency accuracy in FDM spectral estimates. Paradoxically, more complete data can lead to worse FDM spectra if there is appreciable noise. However, by modifying the numerical method, the FDM noise performance improves significantly, without apparently losing any of the existing advantages. The two key modifications are to adjust the FDM basis functions so that matrix elements computed from them have less noise contribution on average, and to regularize each dimension of a multidimensional spectrum independently. The modifications can be recommended for general-purpose use in the case of somewhat noisy, incomplete data.