Multi-parametric representation of voxel-based quantitative magnetic resonance imaging

Area of Science:

• Neuroimaging
• Radiology
• Biomedical Engineering

Background:

• Quantitative Magnetic Resonance Imaging (qMRI) offers detailed tissue characterization.
• Distinguishing pathological cerebral tissue in brain disorders like Multiple Sclerosis (MS) remains a challenge.
• Voxel-wise analysis of MRI parameters can provide objective insights into tissue microstructure.

Purpose of the Study:

• To explore multi-parametric representations of voxel-wise qMRI data for objective discrimination of pathological cerebral tissue in MS patients.
• To assess the potential of quantitative MRI parameters (R(1), R(2), proton density) in identifying MS-related tissue abnormalities.
• To correlate imaging findings with clinical disability measures (Expanded Disability Status Scale - EDSS).

Main Methods:

• Recruited 19 MS patients and 19 healthy controls.
• Acquired quantitative MRI data measuring R(1), R(2), and proton density.
• Normalized parameter images and performed voxel-wise statistical comparisons.
• Visualized results using conventional and multi-parametric representations.

Main Results:

• MS patients exhibited lower R(1) and R(2) values and higher proton density in white matter regions.
• Posterior white matter showed significant MS-related abnormalities.
• Correlation with EDSS was observed primarily in the frontal cortex.
• Multi-parametric representations effectively highlighted disease-specific features.

Conclusions:

• Multi-parametric qMRI has potential for visualizing focal and diffuse cerebral tissue changes in MS.
• Voxel-based statistical analysis can guide radiologists in identifying disease-specific imaging markers.
• The proposed method may enhance objective assessment and early detection of brain disorders.