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

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 permi...

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Autores principales: Tax, Chantal M.W., Szczepankiewicz, Filip, Nilsson, Markus, Jones, Derek K.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Academic Press 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7429990/
https://www.ncbi.nlm.nih.gov/pubmed/31931157
http://dx.doi.org/10.1016/j.neuroimage.2020.116534
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author Tax, Chantal M.W.
Szczepankiewicz, Filip
Nilsson, Markus
Jones, Derek K.
author_facet Tax, Chantal M.W.
Szczepankiewicz, Filip
Nilsson, Markus
Jones, Derek K.
author_sort Tax, Chantal M.W.
collection PubMed
description 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 diffusion-weightings 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 non-decaying signal at very high b-values ([Formula: see text]). With this unique experimental setup, a residual yet slowly decaying signal above the noise floor for b-values as high as [Formula: see text] 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 1.8% in cerebellar GM and 0.5% 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 0.12 [Formula: see text] and a substantial signal fraction of 9.7%. The T2 of this component was estimated to be around [Formula: see text]. 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.
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spelling pubmed-74299902020-08-19 The dot-compartment revealed? Diffusion MRI with ultra-strong gradients and spherical tensor encoding in the living human brain Tax, Chantal M.W. Szczepankiewicz, Filip Nilsson, Markus Jones, Derek K. Neuroimage Article 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 diffusion-weightings 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 non-decaying signal at very high b-values ([Formula: see text]). With this unique experimental setup, a residual yet slowly decaying signal above the noise floor for b-values as high as [Formula: see text] 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 1.8% in cerebellar GM and 0.5% 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 0.12 [Formula: see text] and a substantial signal fraction of 9.7%. The T2 of this component was estimated to be around [Formula: see text]. 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. Academic Press 2020-04-15 /pmc/articles/PMC7429990/ /pubmed/31931157 http://dx.doi.org/10.1016/j.neuroimage.2020.116534 Text en © 2020 The Authors http://creativecommons.org/licenses/by/4.0/ This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Tax, Chantal M.W.
Szczepankiewicz, Filip
Nilsson, Markus
Jones, Derek K.
The dot-compartment revealed? Diffusion MRI with ultra-strong gradients and spherical tensor encoding in the living human brain
title The dot-compartment revealed? Diffusion MRI with ultra-strong gradients and spherical tensor encoding in the living human brain
title_full The dot-compartment revealed? Diffusion MRI with ultra-strong gradients and spherical tensor encoding in the living human brain
title_fullStr The dot-compartment revealed? Diffusion MRI with ultra-strong gradients and spherical tensor encoding in the living human brain
title_full_unstemmed The dot-compartment revealed? Diffusion MRI with ultra-strong gradients and spherical tensor encoding in the living human brain
title_short The dot-compartment revealed? Diffusion MRI with ultra-strong gradients and spherical tensor encoding in the living human brain
title_sort dot-compartment revealed? diffusion mri with ultra-strong gradients and spherical tensor encoding in the living human brain
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7429990/
https://www.ncbi.nlm.nih.gov/pubmed/31931157
http://dx.doi.org/10.1016/j.neuroimage.2020.116534
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