Reduction of lipid contamination in MR spectroscopy imaging using signal space projection

Lipid contamination can complicate the metabolite quantification in MR spectroscopic imaging (MRSI). In addition to various experimental methods demonstrated to be feasible for lipid suppression, the postprocessing method is beneficial in the flexibility of applications. In this study, the signal space projection (SSP) algorithm is proposed to suppress the lipid signal in the MRSI.

The performance of lipid suppression using SSP and SSP combined with the Papoulis-Gerchberg (PG) algorithm (PG+SSP) is examined in 2D MRSI data and the results were compared with outer volume saturation (OVS) methods. Up to 10 lipid spatial components were extracted by SSP from lipid signals in the range of 0.8~1.5 ppm.

Our results show that most lipid signals were found in the first 4 to 5 components and that lipid signals on the spectra can be suppressed using 4 to 5 components. Metabolites concentrations were quantified using LCModel. Two regions of interest (ROIs) were manually selected on the peripheral and inner brain regions. The quantification of metabolites in terms of fitting reliability (CRLB) and spatial variations within ROIs (SpaVar) is improved using SSP. When 5 to 6 components were used in SSP and PG+SSP, the metabolite concentrations and the associated SpaVar and CRLB are at the same level as those from the OVS.

We have demonstrated that the SSP method can be used to suppress the lipid signals of MRSI and SSP with 5 to 6 components is suggested to have a similar suppression performance as the OVS method.