Heuristic optimization algorithms applied to the quantification of spectroscopic data.
The quantification of in vivo MR spectra imposes severe problems because of low spectral resolution and poor signal-to-noise ratio. Maximum likelihood methods are often applied. However, with conventional spectrum analysis procedures, the search for a global minimum in a multidimensional space often terminates in only a local minimum. Heuristic optimization procedures are able to circumvent this difficulty. Two approaches, the genetic algorithm and the simulated annealing, have been adapted to the quantification of MR spectra. For evaluation purposes, the procedures have been applied to synthetic and in vivo spectra with different noise levels. They both allowed a reliable spectrum quantification. The areas of most peaks were quantified reproducibly, although in some cases, the discrimination between spectroscopically almost identical metabolites (e.g., glutamate and glutamine) was not completely satisfactory. The two algorithms are found to be valuable alternative methods in the quantification of in vivo MR spectra.