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The in vivo quantification of magnetic resonance spectroscopy (MRS) signals is a method to estimate metabolite concentrations of living tissue. Obtaining reliable concentrations is still a challenge due to the experimental conditions affecting spectral quality. Additionally, lipids and macromolecules overlap with the metabolites of interest, affecting their reliable estimation. In this study, we propose to combine the self-deconvolution lineshape estimation method, which accounts for spectral shape distortions, with two different approaches for taking into account the macromolecular baseline contribution: (a) based on macromolecules and lipids measured in vivo using an inversion recovery technique, and (b) based on the simulation of macromolecular resonances using prior knowledge from a database of inversion recovery signals. The ultimate goal is to measure macromolecular and lipid data only once as described in (a) to create macromolecular and lipid profiles. These profiles then can be used as described in (b) for data measured under the same conditions. The method is evaluated on in vivo 1H MRS signals at 9.4 T from mouse hippocampus. Results show that better metabolite fits are obtained when lineshape and baseline estimations are simultaneously performed and that baseline estimation based on prior knowledge from macromolecular measured signals can be reliably used to replace time-consuming individual macromolecular and lipid acquisitions.

No abstract