Diffusion Anisotropy and Tensor-valued Encoding
(2020) In New Developments in NMR 2020-January(24). p.68-102- Abstract
The study of diffusion anisotropy via diffusion NMR was pioneered 40 years ago in systems pertaining to the field of porous media. Since then, its combination with MRI has attracted overwhelming interest within the neuroscience community as a way to perform non-invasive in vivo assessment of tissue microstructure. However, most conventional diffusion MRI techniques measured diffusion anisotropy metrics that are not free from the confounding effects of intra-voxel orientational ordering. In this chapter, we introduce and link two major concepts that enable the extraction of metrics, teasing apart diffusion anisotropy and orientational order: diffusion tensor distributions and tensor-valued diffusion encoding. In particular, we explain... (More)
The study of diffusion anisotropy via diffusion NMR was pioneered 40 years ago in systems pertaining to the field of porous media. Since then, its combination with MRI has attracted overwhelming interest within the neuroscience community as a way to perform non-invasive in vivo assessment of tissue microstructure. However, most conventional diffusion MRI techniques measured diffusion anisotropy metrics that are not free from the confounding effects of intra-voxel orientational ordering. In this chapter, we introduce and link two major concepts that enable the extraction of metrics, teasing apart diffusion anisotropy and orientational order: diffusion tensor distributions and tensor-valued diffusion encoding. In particular, we explain how the statistical descriptors of diffusion tensor distributions quantify relevant physical characteristics of the voxel content, such as the mean diffusivity, the variance in isotropic diffusivities, the mean anisotropy, and the orientational order. We also discuss how tensor-valued diffusion encoding enables isolating and combining different pieces of diffusion information, ultimately allowing for robust estimations of the aforementioned statistical descriptors. We then review signal inversion methods drawing from tensor-valued encoding and compare them in silico. We finally conclude by suggesting future research directions.
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- author
- Reymbaut, Alexis LU
- organization
- publishing date
- 2020
- type
- Chapter in Book/Report/Conference proceeding
- publication status
- published
- subject
- host publication
- Advanced Diffusion Encoding Methods in MRI
- series title
- New Developments in NMR
- editor
- Topgaard, Daniel
- volume
- 2020-January
- issue
- 24
- pages
- 35 pages
- publisher
- Royal Society of Chemistry
- external identifiers
-
- scopus:85095678244
- ISSN
- 2044-2548
- 2044-253X
- ISBN
- 978-1-78801-991-0
- 978-1-78801-726-8
- DOI
- 10.1039/9781788019910-00068
- language
- English
- LU publication?
- yes
- id
- 2256c187-cf7a-4f62-ba9f-6ed6a409616f
- date added to LUP
- 2020-11-25 11:49:42
- date last changed
- 2024-06-27 02:13:48
@inbook{2256c187-cf7a-4f62-ba9f-6ed6a409616f,
abstract = {{<p>The study of diffusion anisotropy via diffusion NMR was pioneered 40 years ago in systems pertaining to the field of porous media. Since then, its combination with MRI has attracted overwhelming interest within the neuroscience community as a way to perform non-invasive in vivo assessment of tissue microstructure. However, most conventional diffusion MRI techniques measured diffusion anisotropy metrics that are not free from the confounding effects of intra-voxel orientational ordering. In this chapter, we introduce and link two major concepts that enable the extraction of metrics, teasing apart diffusion anisotropy and orientational order: diffusion tensor distributions and tensor-valued diffusion encoding. In particular, we explain how the statistical descriptors of diffusion tensor distributions quantify relevant physical characteristics of the voxel content, such as the mean diffusivity, the variance in isotropic diffusivities, the mean anisotropy, and the orientational order. We also discuss how tensor-valued diffusion encoding enables isolating and combining different pieces of diffusion information, ultimately allowing for robust estimations of the aforementioned statistical descriptors. We then review signal inversion methods drawing from tensor-valued encoding and compare them in silico. We finally conclude by suggesting future research directions. </p>}},
author = {{Reymbaut, Alexis}},
booktitle = {{Advanced Diffusion Encoding Methods in MRI}},
editor = {{Topgaard, Daniel}},
isbn = {{978-1-78801-991-0}},
issn = {{2044-2548}},
language = {{eng}},
number = {{24}},
pages = {{68--102}},
publisher = {{Royal Society of Chemistry}},
series = {{New Developments in NMR}},
title = {{Diffusion Anisotropy and Tensor-valued Encoding}},
url = {{http://dx.doi.org/10.1039/9781788019910-00068}},
doi = {{10.1039/9781788019910-00068}},
volume = {{2020-January}},
year = {{2020}},
}