ArticleMagnetic resonance in medicine2026
The Impact and Reliability of Tissue Segmentation on In Vivo Magnetic Resonance Spectroscopy Metabolite Quantification.
Article in Magnetic resonance in medicine, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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Abstract
purposeQuantification of metabolite concentrations using MRS requires tissue-dependent signal corrections. Accurate estimation of voxel tissue composition is therefore essential. Commonly used brain tissue segmentation tools differ in their algorithms and implementation, potentially introducing variability in MRS-derived concentration estimates. This study investigates the impact and reliability of tissue segmentation on metabolite quantification.
methodsThree segmentation tools (ANTs, FSL, SPM) were evaluated using an in vivo test-retest MRI/MRS dataset. Voxelwise GM/WM/CSF fractions were applied to compute tissue-corrected total creatine (tCr) concentrations. Linear mixed-effects modeling, variance-component partitioning, and intraclass correlation coefficients (ICCs) quantified tool-, session-, and participant-related variance under permutation scenarios that isolated segmentation- and MRS-related effects. As a benchmark for segmentation performance, comparisons with manually segmented data were conducted across three brain regions.
resultsSegmentation tools produced systematically different tissue fractions that propagated into differences in tCr concentration estimates. Variance partitioning attributed 56.8%, 50.0%, and 51.3% of total tCr concentration variability to segmentation tool across the three permutations, with participant-specific factors accounting for 34.7%, 36.2%, and 28.5%, respectively. When segmentation variability was held constant, test-retest reliability was high (ICC > 0.8) but dropped to ∼0.5 when both segmentation and MRS variability varied. Agreement with manual segmentation was region- and tool-dependent, with the lowest agreement in the thalamus.
conclusionTissue segmentation contributes substantially to the variability in MRS-derived metabolite concentration estimates. These results underscore the need for transparent segmentation reporting and data sharing to ensure reproducibility and cross-study comparability in MRS research.
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