Myelin Water Imaging
Shannon Kolind1

1University of British Columbia, Vancouver, BC, Canada

Synopsis

Myelin water imaging (MWI) provides quantitative measurements specific to myelin by separating the MRI signal into contributions from the various water pools present within a voxel. In central nervous system tissue these water pools generally correspond to intra- and extra-cellular water, which relaxes slowly, and water trapped between the myelin bilayers, which relaxes quickly. The fraction of water corresponding to the water trapped within the myelin sheath, the myelin water fraction (MWF), provides a quantitative measure related to myelin content. This course will discuss acquisition and analysis techniques as well as common artefacts and pitfalls.

Background

At present, myelin water imaging (MWI) is considered the most direct means of assessing alterations in myelin non-invasively1. MWI provides quantitative measurements specific to myelin2-4 by separating the MRI signal into contributions from the various water pools present within a voxel. In central nervous system tissue, these water pools generally correspond to intra- and extra-cellular water, which relaxes slowly, and water trapped between the myelin bilayers, which relaxes quickly. The fraction of the MR signal corresponding to the water trapped within the myelin sheath, the myelin water fraction (MWF), has been shown to strongly correlate with gold-standard histopathological staining for myelin content5-7. Thus, MWF has been validated as corresponding to the amount of myelin present.

Traditionally, multi-echo T2 relaxation MWI techniques have been limited in terms of volumetric coverage and spatial resolution, taking over 20 minutes to acquire a single slice of data4,8. More recently, several improvements to MWI have been developed, resulting in increased speed of acquisition and brain coverage. MWF values have been compared across centres9, as well as across MRI vendors10. MWI has been employed in a variety of settings ranging from ex-vivo work to in-vivo preclinical models, and from healthy human subjects to disease and injury cases11.


Purpose

This course will cover the concept of myelin water imaging, and introduce several acquisition and analysis methods that have been employed to measure the myelin water fraction. Typical image artefacts and other considerations such as correction for stimulated echoes will be discussed.

Methods

MWI acquisition techniques employed have included1:

  • Multi-spin echo T2 relaxation3 – often considered the “gold standard” MWI technique, consists of a modified Carr-Purcell-Meiboom-Gill (CPMG) sequence, typically including 32 echoes with 10ms echo spacing.
  • Combined GRAdient and Spin Echo (GRASE) sequence12 – allowing acceleration through inclusion of gradient echoes.
  • T2 Preparation imaging methods13,14 – The signal is “prepared” with a certain amount of T2-weighting before full-volume sampling, allowing for efficient data acquisition at optimized T2-weightings.
  • Multi-gradient echo (MGRE) techniques15,16 – T2* instead of T2 decays are characterised.
  • mcDESPOT17 – steady state imaging is used with a variety of flip angles to characterise T1 and T2 for various water pools.

Common causes of image artifact or contamination of MWF calculations include:

  • Stimulated echoes – When refocusing pulses are not 180 degrees, whether by design or due to imperfect B1, some magnetization becomes trapped along the longitudinal axis until a later pulse may transfer it back into the transverse plane; this effect leads to secondary and stimulated echoes which cause the exponential signal decay measurement to be corrupted. These stimulated echoes can be minimized, or modeled8,19.
  • T1 weighting – insufficient delay between excitation pulses results in T1 weighting, which may differ for myelin water compared to intra- and extra-cellular water.
  • Magnetization transfer effects – multi-slice acquisition techniques may result in magnetization transfer effects due to off-resonance pulses from other slices, complicating signal decay.
  • Model limitations – assumptions about factors such as the number of water pools, expected relaxation time ranges, effects of exchange between water pools, and Rician/Gaussian noise, all have an effect on estimated MWF values.

Acknowledgements

MS Society of Canada, NSERC, VCHRI, Michael Smith Foundation for Health Research, Milan & Maureen Ilich Foundation

References

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