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Quantification of Tissue Microstructure Using Tensor-Valued Diffusion Encoding: Brain and Body

Afzali, Maryam ; Mueller, Lars ; Szczepankiewicz, Filip LU orcid ; Jones, Derek K. and Schneider, Jürgen E. (2022) In Frontiers in Physics 10.
Abstract
Diffusion-weighted magnetic resonance imaging (DW-MRI) is a non-invasive technique to probe tissue microstructure. Conventional Stejskal–Tanner diffusion encoding (i.e., encoding along a single axis), is unable to disentangle different microstructural features within a voxel; If a voxel contains microcompartments that vary in more than one attribute (e.g., size, shape, orientation), it can be difficult to quantify one of those attributes in isolation using Stejskal–Tanner diffusion encoding. Multidimensional diffusion encoding, in which the water diffusion is encoded along multiple directions in q-space (characterized by the so-called “b-tensor”) has been proposed previously to solve this problem. The shape of the b-tensor can be used as... (More)
Diffusion-weighted magnetic resonance imaging (DW-MRI) is a non-invasive technique to probe tissue microstructure. Conventional Stejskal–Tanner diffusion encoding (i.e., encoding along a single axis), is unable to disentangle different microstructural features within a voxel; If a voxel contains microcompartments that vary in more than one attribute (e.g., size, shape, orientation), it can be difficult to quantify one of those attributes in isolation using Stejskal–Tanner diffusion encoding. Multidimensional diffusion encoding, in which the water diffusion is encoded along multiple directions in q-space (characterized by the so-called “b-tensor”) has been proposed previously to solve this problem. The shape of the b-tensor can be used as an additional encoding dimension and provides sensitivity to microscopic anisotropy. This has been applied in multiple organs, including brain, heart, breast, kidney and prostate. In this work, we discuss the advantages of using b-tensor encoding in different organs. (Less)
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organization
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type
Contribution to journal
publication status
published
subject
in
Frontiers in Physics
volume
10
article number
809133
publisher
Frontiers Media S. A.
external identifiers
  • scopus:85125219384
ISSN
2296-424X
DOI
10.3389/fphy.2022.809133
language
English
LU publication?
yes
id
b0bdebc7-838c-4ce6-b811-bcf0fbfa7fd7
alternative location
https://www.frontiersin.org/articles/10.3389/fphy.2022.809133/full
date added to LUP
2022-04-04 12:47:56
date last changed
2023-09-18 11:14:06
@article{b0bdebc7-838c-4ce6-b811-bcf0fbfa7fd7,
  abstract     = {{Diffusion-weighted magnetic resonance imaging (DW-MRI) is a non-invasive technique to probe tissue microstructure. Conventional Stejskal–Tanner diffusion encoding (i.e., encoding along a single axis), is unable to disentangle different microstructural features within a voxel; If a voxel contains microcompartments that vary in more than one attribute (e.g., size, shape, orientation), it can be difficult to quantify one of those attributes in isolation using Stejskal–Tanner diffusion encoding. Multidimensional diffusion encoding, in which the water diffusion is encoded along multiple directions in q-space (characterized by the so-called “b-tensor”) has been proposed previously to solve this problem. The shape of the b-tensor can be used as an additional encoding dimension and provides sensitivity to microscopic anisotropy. This has been applied in multiple organs, including brain, heart, breast, kidney and prostate. In this work, we discuss the advantages of using b-tensor encoding in different organs.}},
  author       = {{Afzali, Maryam and Mueller, Lars and Szczepankiewicz, Filip and Jones, Derek K. and Schneider, Jürgen E.}},
  issn         = {{2296-424X}},
  language     = {{eng}},
  publisher    = {{Frontiers Media S. A.}},
  series       = {{Frontiers in Physics}},
  title        = {{Quantification of Tissue Microstructure Using Tensor-Valued Diffusion Encoding: Brain and Body}},
  url          = {{http://dx.doi.org/10.3389/fphy.2022.809133}},
  doi          = {{10.3389/fphy.2022.809133}},
  volume       = {{10}},
  year         = {{2022}},
}