One component? Two components? Three? The effect of including a nonexchanging "free" water component in multicomponent driven equilibrium single pulse observation of T1 and T2

Abstract

Quantitative myelin content imaging provides novel and pertinent information related to underlying pathogenetic mechanisms of myelin-related disease or disorders arising from aberrant connectivity. Multicomponent driven equilibrium single pulse observation of T1 and T2 is a time-efficient multicomponent relaxation analysis technique that provides estimates of the myelin water fraction, a surrogate measure of myelin volume. Unfortunately, multicomponent driven equilibrium single pulse observation of T1 and T2 relies on a two water-pool model (myelin-associated water and intra/extracellular water), which is inadequate within partial volume voxels, i.e., containing brain tissue and ventricle or meninges, resulting in myelin water fraction underestimation. To address this, a third, nonexchanging "free-water" component was introduced to the multicomponent driven equilibrium single pulse observation of T1 and T2 model. Numerical simulations and experimental in vivo data show that the model to perform advantageously within partial volume regions while providing robust and reproducible results. It is concluded that this model is preferable for future studies and analysis.

Figure 1

Results from the numerical simulation comparing the 2 and 3-pool model myelin water fraction value accuracy over and range of myelin and free water fractions.

Figure 2

Two and three-pool model derived myelin water fraction maps for the 9 month old infant, 24 year old adult, and MS and AD patients. Shown for each are corresponding reference anatomical, the calculated free water volume fraction map, and a myelin water fraction difference map calculated by subtracting the three-pool MWF map from the two-pool result.

Figure 3

Example raw data and reconstructed two and three-pool signal curves for two different brain regions. The two-pool model is shown by the grey line, the three-pool model by the dark line. The BIC metrics calculated for both cases show the three-pool model to be preferred.

Figure 4

Results from the (a) intra-scan; (b) inter-subject variability; and (c) algorithm reproducibility studies. Shown for each are maps of the coefficient of variation calculated from repeated measures. Outline boxes in (a) denote regions of interest used to determine the mean CoV (Table 1). For the inter-scan reproducibility, a mean CoV of 0.059 was calculated. Inter-subject variability was found to be 0.1 1; and for the algorithm reproducibility, a mean CoV of 0.09 was found.