Skip to main content

Lund University Publications

LUND UNIVERSITY LIBRARIES

Probing brain tissue microstructure with MRI : principles, challenges, and the role of multidimensional diffusion-relaxation encoding

Lampinen, Björn LU ; Szczepankiewicz, Filip LU orcid ; Lätt, Jimmy LU ; Knutsson, Linda LU orcid ; Mårtensson, Johan LU ; Björkman-Burtscher, Isabella M LU ; van Westen, Danielle LU orcid ; Sundgren, Pia C LU orcid ; Ståhlberg, Freddy LU and Nilsson, Markus LU (2023) In NeuroImage 282.
Abstract

Diffusion MRI uses the random displacement of water molecules to sensitize the signal to brain microstructure and to properties such as the density and shape of cells. Microstructure modeling techniques aim to estimate these properties from acquired data by separating the signal between virtual tissue 'compartments' such as the intra-neurite and the extra-cellular space. A key challenge is that the diffusion MRI signal is relatively featureless compared with the complexity of brain tissue. Another challenge is that the tissue microstructure is wildly different within the gray and white matter of the brain. In this review, we use results from multidimensional diffusion encoding techniques to discuss these challenges and their tentative... (More)

Diffusion MRI uses the random displacement of water molecules to sensitize the signal to brain microstructure and to properties such as the density and shape of cells. Microstructure modeling techniques aim to estimate these properties from acquired data by separating the signal between virtual tissue 'compartments' such as the intra-neurite and the extra-cellular space. A key challenge is that the diffusion MRI signal is relatively featureless compared with the complexity of brain tissue. Another challenge is that the tissue microstructure is wildly different within the gray and white matter of the brain. In this review, we use results from multidimensional diffusion encoding techniques to discuss these challenges and their tentative solutions. Multidimensional encoding increases the information content of the data by varying not only the b-value and the encoding direction but also additional experimental parameters such as the shape of the b-tensor and the echo time. Three main insights have emerged from such encoding. First, multidimensional data contradict common model assumptions on diffusion and T
2 relaxation and illustrates how the use of these assumptions cause erroneous interpretations in both healthy brain and pathology. Second, many model assumptions can be dispensed with if data are acquired with multidimensional encoding. The necessary data can be easily acquired in vivo using protocols optimized to minimize Cramér-Rao lower bounds. Third, microscopic diffusion anisotropy reflects the presence of axons but not dendrites. This insight stands in contrast to current 'neurite models' of brain tissue, which assume that axons in white matter and dendrites in gray matter feature highly similar diffusion. Nevertheless, as an axon-based contrast, microscopic anisotropy can differentiate gray and white matter when myelin alterations confound conventional MRI contrasts.

(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
in
NeuroImage
volume
282
article number
120338
publisher
Elsevier
external identifiers
  • scopus:85174178876
  • pmid:37598814
ISSN
1095-9572
DOI
10.1016/j.neuroimage.2023.120338
language
English
LU publication?
yes
id
d38421d0-ffe5-4a00-aa69-0dd0a2c7a70b
date added to LUP
2023-08-27 15:20:04
date last changed
2024-04-20 06:27:08
@article{d38421d0-ffe5-4a00-aa69-0dd0a2c7a70b,
  abstract     = {{<p>Diffusion MRI uses the random displacement of water molecules to sensitize the signal to brain microstructure and to properties such as the density and shape of cells. Microstructure modeling techniques aim to estimate these properties from acquired data by separating the signal between virtual tissue 'compartments' such as the intra-neurite and the extra-cellular space. A key challenge is that the diffusion MRI signal is relatively featureless compared with the complexity of brain tissue. Another challenge is that the tissue microstructure is wildly different within the gray and white matter of the brain. In this review, we use results from multidimensional diffusion encoding techniques to discuss these challenges and their tentative solutions. Multidimensional encoding increases the information content of the data by varying not only the b-value and the encoding direction but also additional experimental parameters such as the shape of the b-tensor and the echo time. Three main insights have emerged from such encoding. First, multidimensional data contradict common model assumptions on diffusion and T<br>
 2 relaxation and illustrates how the use of these assumptions cause erroneous interpretations in both healthy brain and pathology. Second, many model assumptions can be dispensed with if data are acquired with multidimensional encoding. The necessary data can be easily acquired in vivo using protocols optimized to minimize Cramér-Rao lower bounds. Third, microscopic diffusion anisotropy reflects the presence of axons but not dendrites. This insight stands in contrast to current 'neurite models' of brain tissue, which assume that axons in white matter and dendrites in gray matter feature highly similar diffusion. Nevertheless, as an axon-based contrast, microscopic anisotropy can differentiate gray and white matter when myelin alterations confound conventional MRI contrasts.<br>
 </p>}},
  author       = {{Lampinen, Björn and Szczepankiewicz, Filip and Lätt, Jimmy and Knutsson, Linda and Mårtensson, Johan and Björkman-Burtscher, Isabella M and van Westen, Danielle and Sundgren, Pia C and Ståhlberg, Freddy and Nilsson, Markus}},
  issn         = {{1095-9572}},
  language     = {{eng}},
  month        = {{08}},
  publisher    = {{Elsevier}},
  series       = {{NeuroImage}},
  title        = {{Probing brain tissue microstructure with MRI : principles, challenges, and the role of multidimensional diffusion-relaxation encoding}},
  url          = {{http://dx.doi.org/10.1016/j.neuroimage.2023.120338}},
  doi          = {{10.1016/j.neuroimage.2023.120338}},
  volume       = {{282}},
  year         = {{2023}},
}