Probing tissue microstructure by diffusion skewness tensor imaging
(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
- Ning, Lipeng ; Szczepankiewicz, Filip LU ; Nilsson, Markus LU ; Rathi, Yogesh and Westin, Carl Fredrik
- organization
- publishing date
- 2021
- 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
- 2024-09-05 13:45:40
@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}}, }