Fast High-Resolution Metabolite Mapping in the rat Brain Using H-FID-MRSI at 14.1 T.

Magnetic resonance spectroscopic imaging (MRSI) enables the simultaneous noninvasive acquisition of MR spectra from multiple spatial locations inside the brain. Although H-MRSI is increasingly used in the human brain, it is not yet widely applied in the preclinical setting, mostly because of difficulties specifically related to very small nominal voxel size in the rat brain and low concentration of brain metabolites, resulting in low signal-to-noise ratio (SNR). In this context, we implemented a free induction decay H-MRSI sequence (H-FID-MRSI) in the rat brain at 14.1 T. We combined the advantages of H-FID-MRSI with the ultra-high magnetic field to achieve higher SNR, coverage, and spatial resolution in the rat brain and developed a custom dedicated processing pipeline with a graphical user interface for Bruker H-FID-MRSI: MRS4Brain toolbox. LCModel fit, using the simulated metabolite basis set and in vivo measured MM, provided reliable fits for the data at acquisition delays of 1.30 ms. The resulting Cramér-Rao lower bounds were sufficiently low (< 30%) for eight metabolites of interest (total creatine, N-acetylaspartate, N-acetylaspartate + N-acetylaspartylglutamate, total choline, glutamine, glutamate, myo-inositol, and taurine), leading to highly reproducible metabolic maps. Similar spectral quality and metabolic maps were obtained with one and two averages, with slightly better contrast and brain coverage due to increased SNR in the latter case. Furthermore, the obtained metabolic maps were accurate enough to confirm the previously known brain regional distribution of some metabolites. The acquisitions proved high reproducibility over time. We demonstrated that the increased SNR and spectral resolution at 14.1 T can be translated into high spatial resolution in H-FID-MRSI of the rat brain in 13 min using the sequence and processing pipeline described herein. High-resolution H-FID-MRSI at 14.1 T provided robust, reproducible, and high-quality metabolic mapping of brain metabolites with minimal technical limitations.