SEGMENTATION OF MRS SIGNALS USING ASPECT (ANALYSIS OF SPECTRA USING EIGENVECTOR DECOMPOSITION OF TARGETS)

Efforts to minimize the effects of partial volume contamination (PVC) in in vivo magnetic resonance spectroscopy (MRs) have focused upon imp roving the sensitivity and efficiency of spatially localized MRS measu rements. Such improvements may improve spatial resolution and reduce t he time required to acquire multiple spectra, however, PVC can affect in vivo spectra at any resolution. In this paper, a model for segmenti ng in vivo MRS signals compromised by PVC in selected applications is introduced. The segmentation algorithm used is linear and is based on filters originally developed for image processing applications. The mo del is developed from first principles and evaluated using computer si mulations. It is suited for segmenting multivoxel or chemical shift im aging data, and can be used with Spectra acquired at any spatial resol ution. It is used to estimate the size of the partial volumes contribu ting to. a voxel compromised by PVC and the spatially selective signal components that would be expected to arise from these partial volumes if they could be measured directly. Several spectral perturbants pres ent in in vivo MRS measurements violate the linearity assumptions unde rlying the model and produce systematic errors that must be accounted for. A number of perturbants are discussed, and the potential in vivo applications of the model are illustrated using solvent-suppressed H-1 -CSI spectra from the normal human brain.