Evaluation of signal processing methods for the quantification of a multi–exponential signal: the glycogen13C-1 NMR signal

Abstract

The 13C-1 NMR peak in proton-decoupled spectra of liver glycogen solution was quantitatively analyzed by three types of model-function fitting algorithms: iterative line-fitting in the frequency domain (MDCON); iterative least-squares fitting (VARPRO) in the time domain; and noniterative singular value decomposition-based analysis (HTLS), also in the time domain. Quantification results were compared with manual integration values. Performance of the algorithms was tested at different signal-to-noise ratios (S/N) of the glycogen C-1 peak. This was achieved by varying the number of scans summed prior to analysis. Since T2 relaxation in glycogen has been shown to be multiexponential [Overloop, K. et al. Magn. Reson. Med. 36, 45-51 (1996], the exact quantification of the C-1 glycogen signal requires a model function comprising a sum of Lorentzian components, each with a different broadening at the glycogen frequency. This paper focuses on the performances of the above methods to fit such a multicomponent resonance line. In the frequency domain, line fitting with two Lorentz lines gives good results at sufficiently high S/N. In the time domain, VARPRO performs better than HTLS because fixed values can be imposed to the linewidth of the components at the common C-1 frequency, thereby reducing convergence problems at low S/N.