Does Myelin Water Influence DWI?
Kevin D Harkins1 and Mark D Does1,2,3,4

1Institute of Image Science, Vanderbilt University, Nashville, TN, United States, 2Biomedical Engineering, Vanderbilt University, Nashville, TN, United States, 3Radiology and Radiological Sciences, Vanderbilt University, Nashville, TN, United States, 4Electrical Engineering, Vanderbilt University, Nashville, TN, United States

Synopsis

The presence and movement of myelin water is often neglected from models of DWI signal. This study presents a Monte Carlo simulation illustrating that myelin water diffusion can have a subtle but important impact on measured Dapp and Kapp values, and that incorporating myelin water diffusion can influence myelin-content dependent changes in Dapp and Kapp.

Purpose

Myelin represents a significant barrier to water diffusion in white matter, and this phenomenon has been used in many models to describe diffusion-weighted MRI (DWI) signal in white matter in terms of its microstructural compartments1,2. As models of DWI signal become more complex, an open question is whether the presence of water within myelin and the movement of water between compartments can influence DWI measurements.

In this abstract, we use a Monte Carlo simulation to assess the role of myelin water diffusion and myelin content on the apparent diffusion coefficient (Dapp) and kurtosis (Kapp) measured by clinical DWI.

Methods

A Monte Carlo simulation of water diffusion was created that accounts for myelin, while allowing water to diffuse within myelin and exchange with the surrounding compartments. Simulations were performed in a geometry of 200 myelinated axons, represented as two concentric circles. An example geometry is given in Fig 1 with a myelin volume fraction (vm) = 0.36, and an intra-axonal volume fraction (vi) = 0.35. The biophysical properties of water in white matter were characterized by 5 parameters—the unhindered diffusion coefficient of water in intra- and extra-axonal spaces (D0 = 3.0 μm2/ms), the apparent diffusion coefficient of water in myelin (Dm), the transverse relaxation time-constants of water in the intra- and extra-axonal spaces (T2ie = 80 ms) and in myelin (T2m = 15 ms), and the relative density of water in myelin compared to the intra- and extra-axonal spaces (pm = 0.5). Simulations were implemented in CUDA, wrapped into MATLAB, and run on a Linux computer with a GeForce GTX TITAN GPU.

A pulsed gradient spin echo diffusion experiment (∆=40ms, δ=20 ms, te = 100 ms, Gmax = 0-60 mT/m) was simulated. To test the sensitivity of Dapp and Kapp to myelin water diffusion, simulations were run over a range in Dm = 0, 10-5, 10-4, 10-3, 10-2, 10-1 µm2/ms. The mean axon radius was also varied, R = 0.25, 1, and 4 µm.

Another simulation was used to test if the set of Dapp and Kapp values are uniquely sensitive to the amount of myelin present in a geometry. Using the same diffusion experiment at Dm = 10-3 µm2/ms and R = 1 µm, simulations were run with vm = 0.36 and 0.10. Since a change in vm requires a corresponding change in intra- & extra-axonal volume, geometries were generated with 6 different values in the relevant range of vi between 0.50 and 0.60.

Results and Discussion

Dapp and Kapp are plotted vs Dm in Figure 2. As Dm increased from 0, there was an initial slight decrease in Dapp followed by an increase. Conversely, Kapp increased slightly with an increase in Dm, followed by a rapid decrease. The initial decrease in Dapp and increase in Kapp observed here is due to an increased amount of signal that has resided in the slowly diffusing myelin compartment. Some studies3 have suggested that the physiologic value of Dm is around 10-3 µm2/ms. The subtle changes observed in Dapp and Kapp in this range of Dm could have an important impact on more complex models of DWI signal.

To test the influence of vm, relative differences in Dapp (blue) and Kapp (red) calculated as

$$\Delta D_{app}=\frac{D_{app}\left(v_m=0.1\right)-D_{app}\left(v_m=0.36,v_i=0.35\right)}{D_{app}\left(v_m=0.36,v_i=0.35\right)}\cdot100\%$$

$$\Delta K_{app}=\frac{K_{app}\left(v_m=0.1\right)-K_{app}\left(v_m=0.36,v_i=0.35\right)}{K_{app}\left(v_m=0.36,v_i=0.35\right)}\cdot100\%$$

are plotted in Figure 3 in the cases of Dm=0 and 10-3 µm2/ms. When myelin water diffusion was neglected, this simulation indicates that ∆Dapp and ∆Kapp were uniquely affected by the change in myelin content, as a change in vm resulted in either a change in Dapp, a change in Kapp, or a change in both parameters. However, this sensitivity to myelin content is diminished when myelin water diffusion is considered, as both ∆Dapp and ∆Kapp crossed zero near vi ≈ 0.57.

Conclusion

This simulation study suggests that myelin water and exchange of water between microstructural compartments has the potential to subtly influence DWI results. As models of DWI signal become more complex, there is the potential for models that neglect these mechanisms to be biased.

Acknowledgements

No acknowledgement found.

References

1. Basser PJ, Mattiello J, LeBihan D. MR diffusion tensor spectroscopy and imaging. Biophy J. 1994;66(1):259-267

2. Basser PJ, Jones DK. Diffusion-tensor MRI: theory, experimental design and data analysis - a technical review. NMR in Biomed 2002;15(7-8):456-467

3. Harkins KD, Dula AN, Does MD. Effect of intercompartmental water exchange on the apparent myelin water fraction in multiexponential T2 measurements of rat spinal cord. Magn Res Med 2012;67(3):793-800

Figures

Figure 1: An example geometry showing a distribution of axon sizes with R = 1 µm, vi = 0.35, and vm = 0.36.

Figure 2: The influence of the myelin water diffusion coefficient (Dm) on Dapp and Kapp at R = 0.25, 1 & 4 µm, vm = 0.36 and vi = 0.35.

Figure 3: Change in Dapp (blue) and Kapp (red) caused by a decrease in vm from 0.36 to 0.10, calculated over a range in relevant vi in the absence (solid lines) or presence (dashed line) of myelin water diffusion.



Proc. Intl. Soc. Mag. Reson. Med. 24 (2016)
2006