The ability to study the biochemical composition of the brain is becoming important to better understand
neurodegenerative and neurodevelopmental disorders. Magnetic Resonance Spectroscopy (MRS) can non-invasively
provide quantification of brain metabolites in localized regions. The reliability of MRS is limited in part due to partial
volume artifacts. This results from the relatively large voxels that are required to acquire sufficient signal-to-noise ratios
for the studies. Partial volume artifacts result when a MRS voxel contains a mixture of tissue types. Concentrations of
metabolites vary from tissue to tissue. When a voxel contains a heterogeneous tissue composition, the spectroscopic
signal acquired from this voxel will consist of the signal from different tissues making reliable measurements difficult.
We have developed a novel tool for the estimation of partial volume tissue composition within MRS voxels thus
allowing for the correction of partial volume artifacts. In addition, the tool can localize MR spectra to anatomical regions
of interest. The tool uses tissue classification information acquired as part of a structural MR scan for the same subject.
The tissue classification information is co-registered with the spectroscopic data. The user can quantify the partial
volume composition of each voxel and use this information as covariates for metabolite concentrations.