Myelin Measurement Using Quantitative Magnetic Resonance Imaging: A Correlation Study Comparing Various Imaging Techniques in Patients with Multiple Sclerosis.
Laetitia SaccentiAkifumi HagiwaraChristina AndicaKazumasa YokoyamaShohei FujitaShimpei KatoTomoko MaekawaKoji KamagataAlice Le BerreMasaaki HoriAkihiko WadaUkihide TateishiNobutaka HattoriShigeki AokiPublished in: Cells (2020)
Evaluation of myelin by magnetic resonance imaging (MRI) is a difficult challenge, but holds promise in demyelinating diseases, such as multiple sclerosis (MS). Although multiple techniques have been developed, no gold standard has been established. This study aims to evaluate the correlation between synthetic MRI myelin volume fraction (SyMRIMVF) and myelin fraction estimated by other techniques, i.e., magnetization transfer saturation (MTsat), T1-weighted images divided by T2-weighted images (T1w/T2w), and radial diffusivity (RD) in patients with MS. We also compared the sensitivities of these techniques for detecting MS-related myelin damage. SyMRIMVF, MTsat, T1w/T2w, and RD were averaged on plaque, periplaque white matter, and normal-appearing white matter (NAWM). Pairwise correlation was calculated using Spearman's correlation analysis. For all segmented regions, strong correlations were found between SyMRIMVF and T1w/T2w (Rho = 0.89), MTsat (Rho = 0.82), or RD (Rho = -0.75). For each technique, the average estimated myelin differed significantly among regions, but the percentage change of NAWM from both periplaque white matter and plaque were highest in SyMRIMVF. SyMRIMVF might be suitable for myelin evaluation in MS patients, with relevant results as compared to other well-studied techniques. Moreover, it presented better sensitivity for the detection of the difference between plaque or periplaque white matter and NAWM.
Keyphrases
- white matter
- multiple sclerosis
- magnetic resonance imaging
- contrast enhanced
- mass spectrometry
- coronary artery disease
- computed tomography
- ms ms
- magnetic resonance
- high resolution
- deep learning
- diffusion weighted imaging
- oxidative stress
- convolutional neural network
- smooth muscle
- machine learning
- quantum dots
- clinical evaluation