Introduction

Recently, reports of myelin visualization using magnetic resonance imaging (MRI) have been increasing. The interest in Myelin water fraction (MWF) imaging for detection of normal appearing white matter (NAWM) water content has been growing. Some derivation methods for MWF have been reported; e.g., multi-gradient-echo ¹, gradient and spin-echo ², T₂ preparation imaging ³, multicomponent driven-equilibrium single-pulse observation of T₁ and T₂ (mcDESPOT) ⁴ and as well as others. However, there are often possibilities of numerical problems raising from curve fitting and T₂ cut-off range settings. On the other hand, we developed novel Myelin imaging using physical parameters derived from quantitative parameter mapping (QPM) ⁵. The purpose of our study is to evaluate Myelin imaging derived from QPM-relaxation parameters compared with the MWF derived from the same QPM gradient-echo dataset.

Materials & Methods

Figure 1 shows the preprocessing procedure for two-type myelin images, which was used to calculate the R₁·R₂^* product and MWF from QPM dataset in this study. After On a 3 T MR system (Hitachi, Ltd.), QPM datasets of the brain (nine healthy volunteers; mean age, 41 years) were acquired with a three-dimensional radio frequency-spoiled steady state gradient-echo (3D-RSSG) sequence; the imaging parameters were echo times (TEs), 4.6-36.8 ms (∆TEs, 4.6 ms); repetition times (TRs), 10, 20, 32.1, and 41.3 ms; flip angles (FAs), 10 and 40 degrees; RF phases, 160, 162, 172, and 173. After acquiring QPM datasets, myelin maps using relaxation rate R₁ and R₂^* based on the T1w/T2w ratio method as follows ⁵;
$$\frac{T_{1}w}{T_{2}w}\approx\frac{\frac{1}{T_{1}}PD}{T_{2}^{*}PD}=\frac{R_{1}}{\frac{1}{R_{2}^{*}}}=R_{1}\cdot{R_{1}^{*}}$$
where T₁, T₂, and T₂^* are relaxation times and is proton density of a tissue. Signals of T₁w and T₂w images are derived from each relation as $$$T_1w\approx\frac{1}{T_1}PD,T_2w\approx T_2PD\approx T_2^*PD$$$. Using parameter map derived from QPM can be quantitatively visualized without signal bias. On the other hand, the MWF was derived using multi-component analysis based on T₂^* decay relaxometry of multi-gradient-echo sequence. The following bi-exponential fitting model (i.e., two-pool model) was used as follows;
$$S\left(t\right)=A_{short}\exp{\left(-\frac{TE}{T_{2,short}^*}\right)}+A_{long}\exp{\left(-\frac{TE}{T_{2,long}^*}\right)}$$
where S is the signal of each TE, A and T₂^* are amplitude and T₂^*relaxation time for short and long water pools, respectively. After fitting the measured decay signals, MWF is estimated as;
$$MWF=\frac{A_{short}}{A_{short}+A_{long}}$$
MWF was defined as the ratio of myelin water to total water was defined. MWF was drived from the same as QPM dataset, e.g., TEs = 4.6-36.8 ms; TR = 41.3 ms; FA = 40 degree. After setting regions of interest (ROIs) on bilateral WMs (e.g., frontal and occipital WM) and bilateral grey matter (GM) structures (e.g., caudate nucleus, putamen) in a basal ganglia slice for each subject, we measured the mean values and its standard deviation (SD). Measurements of R₁·R₂^* products and MWF were compared for each region.

Results & Discussions

Figure 2 shows mean MWF and R₁·R₂^* values of each region. Figure 3 shows representative histograms and maps of R₁·R₂^* product and MWF obtained from QPM dataset. Figure 4 shows each myelin map of a representative subject. Figure 5 shows the relationship between R₁·R₂^* products values and MWF values derived from same QPM dataset across all nine bilateral structures. Linear regression analysis shows a correlation between R₁·R₂^* products values and MWF values derived from same QPM dataset (R = 0.53, P < 0.0001). The R₁·R₂^* products map was observed with less field inhomogeneous area than the MWF map. The R₁·R₂^* products map derived from QPM offers as follow advantage; quantitative analysis; artifact free; and post-processing without fitting algorithm after QPM estimation.

Conclusion

Myelin map derived from QPM can be applied for the quantitative assessment of white matter structure as substitute for MWF.

Acknowledgements

This study was partly supported by JSPS KAKENHI [grant number 20K07997].