Reduction of variance in measurements of average metabolite concentration in anatomically-defined brain regions

• Published in: Journal of magnetic resonance (1997) Vol. 272; pp. 73 - 81
• Main Authors: Larsen, Ryan J., Newman, Michael, Nikolaidis, Aki
• Format: Journal Article
• Language: English
• Published: United States Elsevier Inc 01.11.2016
• Subjects: 1H-Magnetic Resonance Spectroscopy; Absolute quantification; Brain; Brain - diagnostic imaging; Humans; Magnetic Resonance Imaging; Magnetic Resonance Spectroscopy; Partial volume; Tissue segmentation; Brain; 1H-Magnetic Resonance Spectroscopy; Tissue segmentation; Partial volume; Absolute quantification; H-Magnetic Resonance Spectroscopy
• ISSN: 1090-7807; 1096-0856
• DOI: 10.1016/j.jmr.2016.09.005

[Display omitted] •Post-processing strategies for MRSI are compared in a data set with 72 subjects.•Inter-subject variance is lower when more corrections are used.•Use of water scaling and correction for tissue type are important.•Integration of interpolated data over anatomical regions also reduces variance. Multiple methods have been proposed for using Magnetic Resonance Spectroscopy Imaging (MRSI) to measure representative metabolite concentrations of anatomically-defined brain regions. Generally these methods require spectral analysis, quantitation of the signal, and reconciliation with anatomical brain regions. However, to simplify processing pipelines, it is practical to only include those corrections that significantly improve data quality. Of particular importance for cross-sectional studies is knowledge about how much each correction lowers the inter-subject variance of the measurement, thereby increasing statistical power. Here we use a data set of 72 subjects to calculate the reduction in inter-subject variance produced by several corrections that are commonly used to process MRSI data. Our results demonstrate that significant reductions of variance can be achieved by performing water scaling, accounting for tissue type, and integrating MRSI data over anatomical regions rather than simply assigning MRSI voxels with anatomical region labels.