Analysis of Gradient Echo Myelin Water Imaging (GRE-MWI) for water exchange and scan parameters
Hyeong-Geol Shin1, Se-Hong Oh2, Joon Yul Choi1, Kyeongseon Min1, Hyunsung Eun1, and Jongho Lee1

1Department of Electrical and Computer Engineering, Seoul National University, Seoul, Korea, Republic of, 2Biomedical Engineering, Hankuk University of Foreign Studies, Yongin, Korea, Republic of


In this study, we investigate the effects of the compartmental water exchange on gradient echo myelin water imaging (GRE-MWI). We simulate MWF variation from different scan parameters (flip angle and TR) using a four pool white matter model and compare the simulation results with the in-vivo measurements. The results demonstrate that 1) the simulation with the water exchange better explains the in-vivo results and 2) GRE-MWI with a long TR can provide robust myelin water quantification regardless of changes in flip angle. Therefore, our results suggest GRE-MWI with a long TR as a robust option for myelin water imaging.


Gradient echo myelin water imaging (GRE-MWI) is an emerging tool for imaging myelin concentration in the brain.1,2 The method may have particular importance at ultra-high field MRI where conventional MWI suffers from the strict specific absorption rate limit.1,2 Another potential advantage of GRE-MWI is that the method may have limited influence from the compartmental water exchange because of the short acquisition window (~30 ms), which is shorter than the residence time of myelin water (50 to 500 ms).3-5 However, no study has explored the effect systematically. In this study, we investigated the effects of the water exchange in GRE-MWI by using a four pool white matter model.6,7 The model simulation was performed for various scan parameters, and the results were compared with in-vivo measurements.


[Simulation] A computer simulation was implemented for a four pool model described in (6,7) (Fig. 1a). For the model parameters, the values measured in (8) were used as the default. Additional T1 values9 and myelin water residence time10 including infinite myelin water residence time (i.e. no exchange) were also tested to accommodate the diverse values reported in the literature. The simulation parameters are summarized in Fig. 1b. To explore the effects of the data acquisition parameters on MWF, the flip angle (5° to 90°) and TR (50 ms to 2000 ms) were varied in the simulation.

[Experiment] To demonstrate the effects of TR on MWF, MWI datasets from a previous study11 were utilized. These datasets were acquired using a wide range of TRs (56, 100, 150, 300, 500, 1000, and 1630 ms) and the following parameters: resolution = 2×2×2 mm3, TE = 2.4:2.2:33 (or 2.1:2.2:34.8) ms, and bandwidth = 500 Hz/pixel. The flip angle (FA) was set as the Ernst angle of each TR. The effects of FA at different TRs were tested in four subjects at a 3T Siemens scanner. Two long TR (= 2000 ms) MWI with two different FAs (FA = 45° and 85° (Ernst angle for 840 ms)) and two short TR (= 70ms) MWI with FAs of 20° (Ernst angle for 840 ms) and 40° were acquired using the following scan parameters: resolution = 2×2×2 mm3, TE = 2.4:2.2:44.2 ms, and bandwidth = 500 Hz/pixel.

[Data analysis] MWF was calculated by fitting a three pool complex model2,12 using nonlinear least square fitting with the same initial values in (2). Region of interest (ROI) analyses were performed on six ROIs to test the TR effects and one ROI to test the FA effects (red circles in Fig. 4).


Figure 2 shows simulated MWF at different FAs and TRs using the parameter sets in Fig. 1b ({PSET1} to {PSET6}). Overall, MWF was overestimated for short TR and/or large FA. The myelin water residence time also affects MWF, revealing less TR/FA dependency for shorter residence time. When the simulation results are compared with the experiments (Fig. 3), the models with water exchange show better correspondence, revealing smaller MWF variations for different TRs.

When the FA effects are analyzed, both simulation and experiment confirm that a large FA can substantially increase MWF in the short TR (Figs. 4 and 5). On the other hand, little or no variation is observed for the long TR, particularly for the model with exchange (Fig. 4).

Conclusion and Discussion

In this study, we simulated the effects of the scan parameters (FA and TR) on GRE-MWI using six different multi-compartment exchange scenarios and compared the simulation results with the in-vivo measurements. The results demonstrate that the model considering water exchange better explains the in-vivo MWF. In addition, both simulation and experimental results indicate that GRE-MWI using a long TR (~2000 ms) can provide consistent myelin quantification regardless of changes in flip angle, suggesting 2D GRE-MWI with a long TR is a robust option for MWI. Our simulation results do not fully explain the in-vivo results. The discrepancy may be explained by different water exchange rates across the white matter fibers3,10; uncertainty of myelin water residence time (13 ms13 to 520 ms9); limitation in the simulation parameters measured from the in-vitro bovine NMR study.


This research was supported by the National Research Foundation of Korea (NRF) funded by the MSIT (NRF-2018R1A4A1025891).


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Figure 1. (a) Illustration of the four pool model in white matter. (b) Relevant physical parameter sets for the model (8-10). T1,i and M0,i denotes T1 relaxation time and pool size of compartment i (i∈{m, mw, ie, nm}). TCRi indicates the cross relaxation time between compartment i and the adjacent water pool. To test the condition of no exchange, infinite TCRD is used.

Figure 2. Simulated MWF at different TRs and FAs using multiple parameter scenarios. Assigned MWF is 13%. Magenta lines in the figure denote the Ernst angle for each TR. MWF is overestimated for short TR and/or large flip angle. MWF with faster water exchange (i.e., smaller TCRD value) demonstrates less dependency to TR and FA.

Figure 3. (a) Simulated MWF according to TR (50 ms to 2000 ms) at different parameter scenarios when the Ernst angle is used. (b) ROI-averaged MWF measurements at different TRs (56, 100, 150, 300, 500, 1000, and 1630 ms) reported in (11). The results of simulation assuming water exchange show better correspondence with the in-vivo measurements, revealing the water exchange effects in vivo. In the case of short TR, simulation result displays substantial MWF changes when the different exchange rates are tested, while small changes are observed in a long TR.

Figure 4. FA dependency of MWF for a short (= 70 ms) and long TR (= 2000 ms). Each panel shows the simulation results from different parameter set. Longer TR provides more consistent MWF values at every parameter sets. The TR/FA dependency of MWF is also related to the exchange rate and becomes smaller when water exchange gets faster.

Figure 5. (a,b,d,e) MWF maps of the different FAs at the different TRs. (c,f) Comparison between simulation results ({PSET6}) and MWF measurements in SLF (red circles). (c) In simulation, the short TR MWF using large FA demonstrates larger MWF than small FA, and the experiment results confirm it (p<0.001). On the other hand, when the long TR is used, the simulation results of the different FAs show little MWF change (+0.4%), and measurements also do not reveal statistically significant difference (p=0.391). The simulation results are scaled, assuming both MWF values are same at FA=45° and TR=2000 ms.

Proc. Intl. Soc. Mag. Reson. Med. 27 (2019)