Improved MRSI in a murine glioma model using semiLASER: refining the metabolomics data obtained from murine models

Background: Magnetic resonance spectroscopic imaging (MRSI) offers valuable metabolic information for assessing brain tumor progression and therapeutic response, but its performance in rodent models is often hindered by the low signal-to-noise ratio (SNR) and spatially heterogeneous spectral quality, particularly in peripheral voxels. These issues reduce the number of usable spectra available for quantitative and classifier-based analyses. To address this, we implemented a multi-voxel MRSI-semiLASER sequence—widely recommended in clinical practice—on a 7T Bruker Biospec system running ParaVision 5.1 to improve spectral homogeneity in mouse brain tumor studies.

Results: Compared with the vendor CSI-PRESS sequence, MRSI-semiLASER produced more uniform spectra across the grid and achieved up to a 1.2-fold SNR increase in murine glioma, enabling a 20% reduction in slice thickness without compromising spectral quality. Importantly, the sequence produced a substantial gain in the proportion of spectra amenable to analysis, particularly in outer grid voxels that frequently fail with CSI-PRESS. The implemented MRSI-semiLASER sequence and instructions are openly available to the community.

Conclusions: MRSI-semiLASER improves spectral homogeneity, increases the proportion of usable spectra, and supports higher spatial detail. These technical improvements may enhance data yield per subject and may facilitate future applications such as more robust pattern recognition workflows and greater data efficiency in longitudinal studies, although such aspects were not evaluated here.