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Strong diffusion gradients allow the separation of intra- and extra-axonal gradient-echo signals in the human brain

Authors :
Elena Kleban
Chantal M.W. Tax
Umesh S. Rudrapatna
Derek K. Jones
Richard Bowtell
Source :
NeuroImage, Vol 217, Iss , Pp 116793- (2020)
Publication Year :
2020
Publisher :
Elsevier, 2020.

Abstract

The quantification of brain white matter properties is a key area of application of Magnetic Resonance Imaging (MRI), with much effort focused on using MR techniques to quantify tissue microstructure. While diffusion MRI probes white matter (WM) microstructure by characterising the sensitivity of Brownian motion of water molecules to anisotropic structures, susceptibility-based techniques probe the tissue microstructure by observing the effect of interaction between the tissue and the magnetic field. Here, we unify these two complementary approaches by combining ultra-strong (300mT/m) gradients with a novel Diffusion-Filtered Asymmetric Spin Echo (D-FASE) technique. Using D-FASE we can separately assess the evolution of the intra- and extra-axonal signals under the action of susceptibility effects, revealing differences in the behaviour in different fibre tracts. We observed that the effective relaxation rate of the ASE signal in the corpus callosum decreases with increasing b-value in all subjects (from 17.1±0.7s−1 at b=0s/mm2 to 14.6±0.7s−1 at b=4800s/mm2), while this dependence on b in the corticospinal tract is less pronounced (from 12.0±1.1s−1 at b=0s/mm2 to 10.7±0.5s−1 at b=4800s/mm2). Voxelwise analysis of the signal evolution with respect to b-factor and acquisition delay using a microscopic model demonstrated differences in gradient echo signal evolution between the intra- and extra-axonal pools.

Details

Language :
English
ISSN :
10959572
Volume :
217
Issue :
116793-
Database :
Directory of Open Access Journals
Journal :
NeuroImage
Publication Type :
Academic Journal
Accession number :
edsdoj.65afe726fb5479194903c7b0c1cd651
Document Type :
article
Full Text :
https://doi.org/10.1016/j.neuroimage.2020.116793