The dot-compartment revealed? Diffusion MRI with ultra-strong gradients and spherical tensor encoding in the living human brain

Abstract

The so-called "dot-compartment" is conjectured in diffusion MRI to represent small spherical spaces, such as cell bodies, in which the diffusion is restricted in all directions. Previous investigations inferred its existence from data acquired with directional diffusion encoding which does not permit a straightforward separation of signals from 'sticks' (axons) and signals from 'dots'. Here we combine isotropic diffusion encoding with ultra-strong diffusion gradients (240 mT/m) to achieve high diffusionweightings with high signal to noise ratio, while suppressing signal arising from anisotropic water compartments with significant mobility along at least one axis (e.g., axons). A dot-compartment, defined to have apparent diffusion coefficient equal to zero and no exchange, would result in a nondecaying signal at very high b-values (b ≳ 7000 s/mm 2 ). With this unique experimental setup, a residual yet slowly decaying, signal above the noise floor for b-values as high as 15000 s/mm 2 was seen clearly in the cerebellar grey matter (GM), and in several white matter (WM) regions to some extent. Upper limits of the dot-signal-fraction were estimated to be ~2% in cerebellar GM and ~0.2% in WM. By relaxing the assumption of zero diffusivity, the signal at high b-values in cerebellar GM could be represented more accurately by an isotropic water pool with a low apparent diffusivity of 0.11 µm $ /ms and a substantial signal fraction of ~7-16%. This remaining signal at high b-values has potential to serve as a novel and simple marker for isotropically-restricted water compartments in cerebellar GM.

Searching for the neurite density with diffusion MRI: Challenges for biophysical modeling

“…Obtaining indices of the neurite (or axonal) density may be possible, although their usefulness is limited to conditions where confounding factors are small or can be reliably constrained. One such condition could be healthy white matter, where the effects of T2 relaxation (Clark & Le Bihan, ; Tax et al, ) and isotropic heterogeneity (Dhital, Kellner, Reisert, & Kiselev, ; Szczepankiewicz et al, ) appear to be small, and where excellent (ex‐vivo) correlations have been demonstrated between the “stick” fraction and histological metrics of axonal density (Jespersen et al, ). Thus, in healthy white matter, existing methods such as WMTI (Fieremans, Jensen, & Helpern, ), NODDI or SMT may be able to capture the relative variation of axonal density.…”

“…Third, due to the SNR penalty from studying deep gray matter while using small voxels, we acquired multi‐echo data for a maximum b ‐value of 0.5 ms/μm 2 . Accordingly, we could only rely on a TE dependence of the mean diffusivity to separate compartment T2 values (Equation ), an effect that has previously been demonstrated to be small in the healthy brain (Clark & Le Bihan, ; Qin et al, ; Tax et al, ; Veraart, Novikov, & Fieremans, ). Acquiring multi‐echo data for higher b‐values may be a promising way forward (Veraart, Novikov, & Fieremans, ).…”

“…We refer to this as the “density assumption.” In white matter, such T2 differences may indeed be small, seeing that intra‐ and extra‐axonal water pools are not clearly distinguishable based on multi‐exponential T2 relaxation (Mackay et al, ; Whittall et al, ). Although components with different relaxation and diffusion properties have been demonstrated in peripheral nerve (Peled, Cory, Raymond, & Kirschner, ), near negligible effects on diffusion have been observed from varying the TE in human white matter (Clark & Le Bihan, ; Tax, Rudrapatna, Witzel, & Jones, ), as well as in rat brain (Does & Gore, ) and in cranial nerves of the garfish (Beaulieu, Fenrich, & Allen, ). However, more recent white matter studies indicate that the T2 value of intra‐axonal water may be longer than that of extra‐axonal water (De Santis, Assaf, & Jones, ; Veraart, Novikov, & Fieremans, ).…”

Maxwell‐compensated design of asymmetric gradient waveforms for tensor‐valued diffusion encoding

“…Tensor‐valued encoding can probe features of the tissue microstructure that cannot be probed by conventional encoding alone (e.g., microscopic anisotropy, orientation coherence, and isotropic heterogeneity of tissue). It can also inform biophysical models . Waveforms that yield tensor‐valued encoding have been proposed in both symmetric and asymmetric variants .…”