Automated ROI-Based Labeling for Multi-Voxel Magnetic Resonance Spectroscopy Data Using FreeSurfer - Spurny, Benjamin; Heckova, Eva; Seiger, Rene; Moser, Philipp; Klöbl, Manfred; Vanicek, Thomas; Spies, Marie; Bogner, Wolfgang; Lanzenberger, Rupert (Frontiers in Molecular Neuroscience, 2019)
Purpose: Advanced analysis methods for multi-voxel MRS are crucial for neurotransmitter quantification, especially for neurotransmitter showing different distributions across tissue types. So far, only a handful of studies have used region of interested (ROI)-based labeling approaches for multi-voxel MRS data. Hence, this study aims to provide an automated ROI-based labeling tool for 3D-multi-voxel MRS data. Methods: MRS data, for automated ROI-based labeling, was acquired using a spiral‐encoded, 3D‐MRS sequence with MEGA‐LASER editing. Corresponding structural T1-weighted images were segmented using FreeSurfer. Masks for individual subcortical brain regions were extracted to calculate mean metabolite distribution within each ROI. To test the reliability of automated labeling a comparison to manual labeling and single voxel selection approaches was performed for six different subcortical regions. Results: Automated ROI-based labeling showed high consistency (intra-class correlation coefficient (ICC)>0.9) for all regions compared to manual labeling. Higher variation could be shown when selected voxels, chosen from a multi-voxel grid, uncorrected for voxel composition, where compared to labeling methods using spatial averaging based on anatomical features within gray matter volumes. Conclusion: We provide an automated ROI-based analysis approach for various types of 3D-multi-voxel MRS data, which dramatically reduces hands-on time compared to manual labeling without any possible inter-rater bias.