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Improved fibre dispersion estimation using b-tensor encoding

Cottaar, Michiel ; Szczepankiewicz, Filip LU ; Bastiani, Matteo ; Hernandez-Fernandez, Moises ; Sotiropoulos, Stamatios N. ; Nilsson, Markus LU and Jbabdi, Saad (2020) In NeuroImage 215.
Abstract

Measuring fibre dispersion in white matter with diffusion magnetic resonance imaging (MRI) is limited by an inherent degeneracy between fibre dispersion and microscopic diffusion anisotropy (i.e., the diffusion anisotropy expected for a single fibre orientation). This means that estimates of fibre dispersion rely on strong assumptions, such as constant microscopic anisotropy throughout the white matter or specific biophysical models. Here we present a simple approach for resolving this degeneracy using measurements that combine linear (conventional) and spherical tensor diffusion encoding. To test the accuracy of the fibre dispersion when our microstructural model is only an approximation of the true tissue structure, we simulate... (More)

Measuring fibre dispersion in white matter with diffusion magnetic resonance imaging (MRI) is limited by an inherent degeneracy between fibre dispersion and microscopic diffusion anisotropy (i.e., the diffusion anisotropy expected for a single fibre orientation). This means that estimates of fibre dispersion rely on strong assumptions, such as constant microscopic anisotropy throughout the white matter or specific biophysical models. Here we present a simple approach for resolving this degeneracy using measurements that combine linear (conventional) and spherical tensor diffusion encoding. To test the accuracy of the fibre dispersion when our microstructural model is only an approximation of the true tissue structure, we simulate multi-compartment data and fit this with a single-compartment model. For such overly simplistic tissue assumptions, we show that the bias in fibre dispersion is greatly reduced (~5x) for single-shell linear and spherical tensor encoding data compared with single-shell or multi-shell conventional data. In in-vivo data we find a consistent estimate of fibre dispersion as we reduce the b-value from 3 to 1.5 ms/μm2, increase the repetition time, increase the echo time, or increase the diffusion time. We conclude that the addition of spherical tensor encoded data to conventional linear tensor encoding data greatly reduces the sensitivity of the estimated fibre dispersion to the model assumptions of the tissue microstructure.

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organization
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type
Contribution to journal
publication status
published
subject
in
NeuroImage
volume
215
article number
116832
publisher
Elsevier
external identifiers
  • pmid:32283273
  • scopus:85084399841
ISSN
1053-8119
DOI
10.1016/j.neuroimage.2020.116832
language
English
LU publication?
yes
id
a3333b5d-79f0-41ed-928a-942701fc73a7
date added to LUP
2020-06-01 14:34:26
date last changed
2020-10-27 03:51:34
@article{a3333b5d-79f0-41ed-928a-942701fc73a7,
  abstract     = {<p>Measuring fibre dispersion in white matter with diffusion magnetic resonance imaging (MRI) is limited by an inherent degeneracy between fibre dispersion and microscopic diffusion anisotropy (i.e., the diffusion anisotropy expected for a single fibre orientation). This means that estimates of fibre dispersion rely on strong assumptions, such as constant microscopic anisotropy throughout the white matter or specific biophysical models. Here we present a simple approach for resolving this degeneracy using measurements that combine linear (conventional) and spherical tensor diffusion encoding. To test the accuracy of the fibre dispersion when our microstructural model is only an approximation of the true tissue structure, we simulate multi-compartment data and fit this with a single-compartment model. For such overly simplistic tissue assumptions, we show that the bias in fibre dispersion is greatly reduced (~5x) for single-shell linear and spherical tensor encoding data compared with single-shell or multi-shell conventional data. In in-vivo data we find a consistent estimate of fibre dispersion as we reduce the b-value from 3 to 1.5 ms/μm<sup>2</sup>, increase the repetition time, increase the echo time, or increase the diffusion time. We conclude that the addition of spherical tensor encoded data to conventional linear tensor encoding data greatly reduces the sensitivity of the estimated fibre dispersion to the model assumptions of the tissue microstructure.</p>},
  author       = {Cottaar, Michiel and Szczepankiewicz, Filip and Bastiani, Matteo and Hernandez-Fernandez, Moises and Sotiropoulos, Stamatios N. and Nilsson, Markus and Jbabdi, Saad},
  issn         = {1053-8119},
  language     = {eng},
  publisher    = {Elsevier},
  series       = {NeuroImage},
  title        = {Improved fibre dispersion estimation using b-tensor encoding},
  url          = {http://dx.doi.org/10.1016/j.neuroimage.2020.116832},
  doi          = {10.1016/j.neuroimage.2020.116832},
  volume       = {215},
  year         = {2020},
}