Lipid‐suppressed and tissue‐fraction corrected metabolic distributions in human central brain structures using 2D (1)H magnetic resonance spectroscopic imaging at 7 T

INTRODUCTION: Magnetic resonance spectroscopic imaging (MRSI) has the potential to add a layer of understanding of the neurobiological mechanisms underlying brain diseases, disease progression, and treatment efficacy. Limitations related to metabolite fitting of low signal‐to‐noise ratios data, signal variations due to partial‐volume effects, acquisition and extracranial lipid artifacts, along with clinically relevant aspects such as scan time constraints, are among the challenges associated with in vivo MRSI. METHODS: The aim of this work was to address some of these factors and to develop an acquisition, reconstruction, and postprocessing pipeline to derive lipid‐suppressed metabolite values of central brain structures based on free‐induction decay measurements made using a 7 T MR scanner. Anatomical images were used to perform high‐resolution (1 mm(3)) partial‐volume correction to account for gray matter, white matter (WM), and cerebral‐spinal fluid signal contributions. Implementation of automatic quality control thresholds and normalization of metabolic maps from 23 subjects to the Montreal Neurological Institute (MNI) standard atlas facilitated the creation of high‐resolution average metabolite maps of several clinically relevant metabolites in central brain regions, while accounting for macromolecular distributions. Partial‐volume correction improved the delineation of deep brain nuclei. We report average metabolite values including glutamate + glutamine (Glx), glycerophosphocholine, choline and phosphocholine (tCho), (phospo)creatine, myo‐inositol and glycine (mI‐Gly), glutathione, N‐acetyl‐aspartyl glutamate(and glutamine), and N‐acetyl‐aspartate in the basal ganglia, central WM (thalamic radiation, corpus callosum) as well as insular cortex and intracalcarine sulcus. CONCLUSION: MNI‐registered average metabolite maps facilitate group‐based analysis, thus offering the possibility to mitigate uncertainty in variable MRSI data.