Time-domain quantification of series of biomedical magnetic resonance spectroscopy signals

Quantification of individual magnetic resonance spectroscopy (MRS) signals is possible in the time domain using interactive nonlinear least-squares fitting methods which provide maximum likelihood parameter estimates under certain assumptions or using fully automatic, but statistically suboptimal, black-box methods. In kinetic experiments time series of consecutive MRS spectra are measured in which information concerning the time evolution of some of the signal parameters is often present. The purpose of this paper is to show how AMARES, a representative example of the interactive methods, can be extended to the simultaneous processing of all spectra in the time series using the common information present in the spectra. We show that this approach yields statistically better results than processing the individual signals separately.