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T1 relaxometry of crossing fibres in the human brain.
- Source :
-
NeuroImage [Neuroimage] 2016 Nov 01; Vol. 141, pp. 133-142. Date of Electronic Publication: 2016 Jul 19. - Publication Year :
- 2016
-
Abstract
- A comprehensive tract-based characterisation of white matter should include the ability to quantify myelin and axonal attributes irrespective of the complexity of fibre organisation within the voxel. Recently, a new experimental framework that combines inversion recovery and diffusion MRI, called inversion recovery diffusion tensor imaging (IR-DTI), was introduced and applied in an animal study. IR-DTI provides the ability to assign to each unique fibre population within a voxel a specific value of the longitudinal relaxation time, T1, which is a proxy for myelin content. Here, we apply the IR-DTI approach to the human brain in vivo on 7 healthy subjects for the first time. We demonstrate that the approach is able to measure differential tract properties in crossing fibre areas, reflecting the different myelination of tracts. We also show that tract-specific T1 has less inter-subject variability compared to conventional T1 in areas of crossing fibres, suggesting increased specificity to distinct fibre populations. Finally we show in simulations that changes in myelination selectively affecting one fibre bundle in crossing fibre areas can potentially be detected earlier using IR-DTI.<br /> (Copyright © 2016 The Authors Elsevier Inc. Published by Elsevier Inc. All rights reserved.)
- Subjects :
- Adult
Female
Humans
Image Enhancement methods
Image Interpretation, Computer-Assisted methods
Male
Reproducibility of Results
Sensitivity and Specificity
Brain anatomy & histology
Brain metabolism
Diffusion Tensor Imaging methods
Myelin Sheath metabolism
Nerve Fibers, Myelinated metabolism
White Matter diagnostic imaging
White Matter metabolism
Subjects
Details
- Language :
- English
- ISSN :
- 1095-9572
- Volume :
- 141
- Database :
- MEDLINE
- Journal :
- NeuroImage
- Publication Type :
- Academic Journal
- Accession number :
- 27444568
- Full Text :
- https://doi.org/10.1016/j.neuroimage.2016.07.037