Whole brain 3D MR fingerprinting in multiple sclerosis: a pilot study

Area of Science:

• Radiology and Imaging Science
• Neuroscience
• Biomedical Engineering

Background:

• Multiple Sclerosis (MS) diagnosis can be challenging.
• Novel imaging techniques are needed for accurate MS detection and characterization.
• MR fingerprinting (MRF) shows promise for MS assessment.

Purpose of the Study:

• To evaluate if MR fingerprinting (MRF) relaxometry can distinguish Multiple Sclerosis (MS) patients from controls.
• To assess differences in frontal normal appearing white matter (F-NAWM) and splenium between MS patients and controls.
• To characterize demyelinating plaque relaxometry in relation to the time of MS diagnosis.

Main Methods:

• Acquired 3D MRF data on a 3.0T MRI system with isotropic voxels.
• Collected data from 18 MS patients and 18 matched controls.
• Utilized T1 and T2 relaxometry features with machine learning (t-SNE, PLS-DA) to classify MS lesions, F-NAWM, and splenium.

Main Results:

• MRF achieved 90% accuracy in discriminating the splenium between MS patients and controls (p < 0.01).
• Perfect discrimination (AUC=1) was obtained between MS lesions and F-NAWM.
• Significant correlations were found between time since diagnosis and lesion T2, minimum T1, and splenium uniformity.

Conclusions:

• 3D-MRF reliably differentiates MS patients from controls based on white matter relaxometry.
• Quantitative assessment of lesions using MRF provides chronological insights into MS progression.
• MRF is a valuable tool for MS diagnosis and monitoring disease evolution.