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Probing tissue microstructure by diffusion skewness tensor imaging

Ning, Lipeng ; Szczepankiewicz, Filip LU orcid ; Nilsson, Markus LU ; Rathi, Yogesh and Westin, Carl Fredrik (2021) In Scientific Reports 11(1).
Abstract

Probing the cellular structure of in vivo biological tissue is a fundamental problem in biomedical imaging and medical science. This work introduces an approach for analyzing diffusion magnetic resonance imaging data acquired by the novel tensor-valued encoding technique for characterizing tissue microstructure. Our approach first uses a signal model to estimate the variance and skewness of the distribution of apparent diffusion tensors modeling the underlying tissue. Then several novel imaging indices, such as weighted microscopic anisotropy and microscopic skewness, are derived to characterize different ensembles of diffusion processes that are indistinguishable by existing techniques. The contributions of this work also include a... (More)

Probing the cellular structure of in vivo biological tissue is a fundamental problem in biomedical imaging and medical science. This work introduces an approach for analyzing diffusion magnetic resonance imaging data acquired by the novel tensor-valued encoding technique for characterizing tissue microstructure. Our approach first uses a signal model to estimate the variance and skewness of the distribution of apparent diffusion tensors modeling the underlying tissue. Then several novel imaging indices, such as weighted microscopic anisotropy and microscopic skewness, are derived to characterize different ensembles of diffusion processes that are indistinguishable by existing techniques. The contributions of this work also include a theoretical proof that shows that, to estimate the skewness of a diffusion tensor distribution, the encoding protocol needs to include full-rank tensor diffusion encoding. This proof provides a guideline for the application of this technique. The properties of the proposed indices are illustrated using both synthetic data and in vivo data acquired from a human brain.

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author
; ; ; and
organization
publishing date
type
Contribution to journal
publication status
published
subject
in
Scientific Reports
volume
11
issue
1
article number
135
publisher
Nature Publishing Group
external identifiers
  • scopus:85098995469
  • pmid:33420140
ISSN
2045-2322
DOI
10.1038/s41598-020-79748-3
language
English
LU publication?
yes
id
98d10aa2-af97-4724-8423-b4906d42844d
date added to LUP
2021-01-19 08:28:36
date last changed
2024-06-13 05:38:14
@article{98d10aa2-af97-4724-8423-b4906d42844d,
  abstract     = {{<p>Probing the cellular structure of in vivo biological tissue is a fundamental problem in biomedical imaging and medical science. This work introduces an approach for analyzing diffusion magnetic resonance imaging data acquired by the novel tensor-valued encoding technique for characterizing tissue microstructure. Our approach first uses a signal model to estimate the variance and skewness of the distribution of apparent diffusion tensors modeling the underlying tissue. Then several novel imaging indices, such as weighted microscopic anisotropy and microscopic skewness, are derived to characterize different ensembles of diffusion processes that are indistinguishable by existing techniques. The contributions of this work also include a theoretical proof that shows that, to estimate the skewness of a diffusion tensor distribution, the encoding protocol needs to include full-rank tensor diffusion encoding. This proof provides a guideline for the application of this technique. The properties of the proposed indices are illustrated using both synthetic data and in vivo data acquired from a human brain.</p>}},
  author       = {{Ning, Lipeng and Szczepankiewicz, Filip and Nilsson, Markus and Rathi, Yogesh and Westin, Carl Fredrik}},
  issn         = {{2045-2322}},
  language     = {{eng}},
  number       = {{1}},
  publisher    = {{Nature Publishing Group}},
  series       = {{Scientific Reports}},
  title        = {{Probing tissue microstructure by diffusion skewness tensor imaging}},
  url          = {{http://dx.doi.org/10.1038/s41598-020-79748-3}},
  doi          = {{10.1038/s41598-020-79748-3}},
  volume       = {{11}},
  year         = {{2021}},
}