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Updated: May 21, 2026
Automated Segmentation of Cortical Grey Matter from T1-Weighted MRI Images
Published on: January 7, 2019
The role of gray and white matter segmentation in quantitative proton MR spectroscopic imaging
Assaf Tal1, Ivan I Kirov, Robert I Grossman
1Department of Radiology, New York University School of Medicine, New York, 660 First Avenue, 4th Floor, New York, New York 10016, USA.
Abstract:
Since the brain's gray matter (GM) and white matter (WM) metabolite concentrations differ, their partial volumes can vary the voxel's ¹H MR spectroscopy (¹H-MRS) signal, reducing sensitivity to changes. While single-voxel ¹H-MRS cannot differentiate between WM and GM signals, partial volume correction is feasible by MR spectroscopic imaging (MRSI) using segmentation of the MRI acquired for VOI placement. To determine the magnitude of this effect on metabolic quantification, we segmented a 1-mm³ resolution MRI into GM, WM and CSF masks that were co-registered with the MRSI grid to yield their partial volumes in approximately every 1 cm³ spectroscopic voxel. Each voxel then provided one equation with two unknowns: its i- metabolite's GM and WM concentrations C(i) (GM) , C(i) (WM) . With the voxels' GM and WM volumes as independent coefficients, the over-determined system of equations was solved for the global averaged C(i) (GM) and C(i) (WM) . Trading off local concentration differences offers three advantages: (i) higher sensitivity due to combined data from many voxels; (ii) improved specificity to WM versus GM changes; and (iii) reduced susceptibility to partial volume effects. These improvements made no additional demands on the protocol, measurement time or hardware. Applying this approach to 18 volunteered 3D MRSI sets of 480 voxels each yielded N-acetylaspartate, creatine, choline and myo-inositol C(i) (GM) concentrations of 8.5 ± 0.7, 6.9 ± 0.6, 1.2 ± 0.2, 5.3 ± 0.6 mM, respectively, and C(i) (WM) concentrations of 7.7 ± 0.6, 4.9 ± 0.5, 1.4 ± 0.1 and 4.4 ± 0.6mM, respectively. We showed that unaccounted voxel WM or GM partial volume can vary absolute quantification by 5-10% (more for ratios), which can often double the sample size required to establish statistical significance.
Insights
This study developed a method using MR spectroscopic imaging (MRSI) to correct for gray matter (GM) and white matter (WM) partial volume effects in brain metabolite measurements. The new approach enhances sensitivity and specificity for detecting metabolic changes in the brain.
Area of Science:
- Neuroimaging
- Biophysics
- Medical Physics
Background:
- Proton magnetic resonance spectroscopy (¹H-MRS) is sensitive to variations in gray matter (GM) and white matter (WM) metabolite concentrations.
- Partial volume effects from differing GM and WM metabolite concentrations can reduce the sensitivity of single-voxel ¹H-MRS to changes.
- MR spectroscopic imaging (MRSI) with MRI segmentation offers a method for partial volume correction in ¹H-MRS.
Purpose of the Study:
- To determine the impact of partial volume effects on metabolic quantification in the brain.
- To develop and validate a method for correcting partial volume effects in ¹H-MRS data using MRSI.
- To improve the sensitivity and specificity of brain metabolite measurements by accounting for GM and WM contributions.
Main Methods:
- Segmentation of 1-mm³ resolution MRI into GM, WM, and CSF masks.
- Co-registration of segmented masks with the MRSI grid to determine partial volumes within each spectroscopic voxel.
- Solving an over-determined system of equations to calculate global averaged GM and WM metabolite concentrations (C(i)(GM), C(i)(WM)).
Main Results:
- The method improved sensitivity and specificity by combining data from multiple voxels and differentiating WM versus GM changes.
- Unaccounted partial volume effects can alter absolute quantification by 5-10% (or more for ratios).
- Application to 18 datasets yielded specific concentrations for N-acetylaspartate, creatine, choline, and myo-inositol in GM and WM.
Conclusions:
- The developed MRSI-based partial volume correction method enhances brain metabolite quantification without additional hardware or time.
- Accurate partial volume correction is crucial for reliable metabolic analysis and can reduce the required sample size for statistical significance.
- This approach offers improved specificity to WM versus GM changes, crucial for understanding neurological conditions.
