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April 12, 2026Magnetic Resonance in Medicine0 citationsOpen Access

The Impact and Reliability of Tissue Segmentation on In Vivo Magnetic Resonance Spectroscopy Metabolite Quantification

JAJessica ArchibaldKIKay Chioma IgweAKAntonia Kaiser

Key Points

  • The research aims to assess the effect of different tissue segmentation tools on the reliability of metabolite quantification via MRS.
  • Evaluated three segmentation tools: ANTs, FSL, and SPM.
  • Applied voxelwise gray matter, white matter, and cerebrospinal fluid fractions to compute total creatine concentrations.
  • Used linear mixed-effects modeling and intraclass correlation coefficients to analyze variability.
  • Segmentation tools showed systematically different tissue fractions, affecting total creatine concentration estimates.
  • 56.8% to 51.3% of total creatine variability was linked to the segmentation tool used.
  • Test-retest reliability was high when segmentation variability was constant but dropped to approximately 0.5 with additional variability.

Abstract

ABSTRACT Purpose Quantification 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. Methods Three 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. Results Segmentation 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. Conclusion Tissue 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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Cite This Study

Archibald et al. (2026) studied this question.

synapsesocial.com/papers/69db383b4fe01fead37c6764https://doi.org/10.1002/mrm.70380
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