Improved myelin water imaging using B ₁ ⁺ correction and data-driven global feature extraction: Application on people...

Area of Science:

• Neuroimaging
• Biomarker Discovery
• Quantitative MRI

Background:

• Multicompartment T2 (mcT2) analysis is standard for quantifying white matter myelin.
• Current mcT2 analysis is sensitive to noise and B1+ field inhomogeneities, limiting accuracy.
• Developing robust myelin quantification methods is crucial for understanding neurological diseases.

Purpose of the Study:

• To develop a more robust and accurate mcT2 analysis for myelin quantification.
• To address limitations of noise and B1+ inhomogeneities in current methods.
• To validate the new technique and assess its potential as a biomarker for multiple sclerosis (MS).

Main Methods:

• Implemented a data-driven preprocessing step to identify tissue-specific mcT2 motifs.
• Utilized a novel algorithm for correcting B1+ inhomogeneities.
• Validated the method with numerical and physical phantoms, followed by in vivo application in healthy subjects and MS patients.

Main Results:

• The data-driven approach significantly improved fitting accuracy and precision compared to conventional methods.
• Successful validation against ground truth in phantoms was demonstrated.
• In vivo studies revealed reduced myelin content in the normal-appearing white matter of MS patients.

Conclusions:

• The developed data-driven mcT2 analysis offers improved robustness and reproducibility for myelin quantification.
• The technique successfully identified myelin reduction in MS patients outside of visible lesions.
• Data-driven myelin values show promise as a radiological biomarker for MS detection and monitoring.