Skip to main content

Lund University Publications

LUND UNIVERSITY LIBRARIES

Multi-tissue spherical deconvolution of tensor-valued diffusion MRI

Jeurissen, Ben and Szczepankiewicz, Filip LU orcid (2021) In NeuroImage 245.
Abstract

Multi-tissue constrained spherical deconvolution (MT-CSD) leverages the characteristic b-value dependency of each tissue type to estimate both the apparent tissue densities and the white matter fiber orientation distribution function from diffusion MRI data. In this work, we generalize MT-CSD to tensor-valued diffusion encoding with arbitrary b-tensor shapes. This enables the use of data encoded with mixed b-tensors, rather than being limited to the subset of linear (conventional) b-tensors. Using the complete set of data, including all b-tensor shapes, provides a categorical improvement in the estimation of apparent tissue densities, fiber ODF, and resulting tractography. Furthermore, we demonstrate that including multiple b-tensor... (More)

Multi-tissue constrained spherical deconvolution (MT-CSD) leverages the characteristic b-value dependency of each tissue type to estimate both the apparent tissue densities and the white matter fiber orientation distribution function from diffusion MRI data. In this work, we generalize MT-CSD to tensor-valued diffusion encoding with arbitrary b-tensor shapes. This enables the use of data encoded with mixed b-tensors, rather than being limited to the subset of linear (conventional) b-tensors. Using the complete set of data, including all b-tensor shapes, provides a categorical improvement in the estimation of apparent tissue densities, fiber ODF, and resulting tractography. Furthermore, we demonstrate that including multiple b-tensor shapes in the analysis provides improved contrast between tissue types, in particular between gray matter and white matter. We also show that our approach provides high-quality apparent tissue density maps and high-quality fiber tracking from data, even with sparse sampling across b-tensors that yield whole-brain coverage at 2 mm isotropic resolution in approximately 5:15 min.

(Less)
Please use this url to cite or link to this publication:
author
and
organization
publishing date
type
Contribution to journal
publication status
published
subject
keywords
B-tensors, Magnetic resonance imaging, Multi-tissue constrained spherical deconvolution, Multidimensional diffusion encoding, Tensor-valued diffusion encoding, Tractography
in
NeuroImage
volume
245
article number
118717
publisher
Elsevier
external identifiers
  • pmid:34775006
  • scopus:85119623369
ISSN
1053-8119
DOI
10.1016/j.neuroimage.2021.118717
language
English
LU publication?
yes
id
defb8de2-ef8b-45a0-980a-690d0696b446
date added to LUP
2021-12-08 14:02:31
date last changed
2024-06-15 22:16:39
@article{defb8de2-ef8b-45a0-980a-690d0696b446,
  abstract     = {{<p>Multi-tissue constrained spherical deconvolution (MT-CSD) leverages the characteristic b-value dependency of each tissue type to estimate both the apparent tissue densities and the white matter fiber orientation distribution function from diffusion MRI data. In this work, we generalize MT-CSD to tensor-valued diffusion encoding with arbitrary b-tensor shapes. This enables the use of data encoded with mixed b-tensors, rather than being limited to the subset of linear (conventional) b-tensors. Using the complete set of data, including all b-tensor shapes, provides a categorical improvement in the estimation of apparent tissue densities, fiber ODF, and resulting tractography. Furthermore, we demonstrate that including multiple b-tensor shapes in the analysis provides improved contrast between tissue types, in particular between gray matter and white matter. We also show that our approach provides high-quality apparent tissue density maps and high-quality fiber tracking from data, even with sparse sampling across b-tensors that yield whole-brain coverage at 2 mm isotropic resolution in approximately 5:15 min.</p>}},
  author       = {{Jeurissen, Ben and Szczepankiewicz, Filip}},
  issn         = {{1053-8119}},
  keywords     = {{B-tensors; Magnetic resonance imaging; Multi-tissue constrained spherical deconvolution; Multidimensional diffusion encoding; Tensor-valued diffusion encoding; Tractography}},
  language     = {{eng}},
  publisher    = {{Elsevier}},
  series       = {{NeuroImage}},
  title        = {{Multi-tissue spherical deconvolution of tensor-valued diffusion MRI}},
  url          = {{http://dx.doi.org/10.1016/j.neuroimage.2021.118717}},
  doi          = {{10.1016/j.neuroimage.2021.118717}},
  volume       = {{245}},
  year         = {{2021}},
}