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Probing tissue microstructure by diffusion skewness tensor imaging

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 mi...

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Autores principales: Ning, Lipeng, Szczepankiewicz, Filip, Nilsson, Markus, Rathi, Yogesh, Westin, Carl-Fredrik
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7794496/
https://www.ncbi.nlm.nih.gov/pubmed/33420140
http://dx.doi.org/10.1038/s41598-020-79748-3
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author Ning, Lipeng
Szczepankiewicz, Filip
Nilsson, Markus
Rathi, Yogesh
Westin, Carl-Fredrik
author_facet Ning, Lipeng
Szczepankiewicz, Filip
Nilsson, Markus
Rathi, Yogesh
Westin, Carl-Fredrik
author_sort Ning, Lipeng
collection PubMed
description 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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spelling pubmed-77944962021-01-12 Probing tissue microstructure by diffusion skewness tensor imaging Ning, Lipeng Szczepankiewicz, Filip Nilsson, Markus Rathi, Yogesh Westin, Carl-Fredrik Sci Rep Article 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. Nature Publishing Group UK 2021-01-08 /pmc/articles/PMC7794496/ /pubmed/33420140 http://dx.doi.org/10.1038/s41598-020-79748-3 Text en © The Author(s) 2021 Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.
spellingShingle Article
Ning, Lipeng
Szczepankiewicz, Filip
Nilsson, Markus
Rathi, Yogesh
Westin, Carl-Fredrik
Probing tissue microstructure by diffusion skewness tensor imaging
title Probing tissue microstructure by diffusion skewness tensor imaging
title_full Probing tissue microstructure by diffusion skewness tensor imaging
title_fullStr Probing tissue microstructure by diffusion skewness tensor imaging
title_full_unstemmed Probing tissue microstructure by diffusion skewness tensor imaging
title_short Probing tissue microstructure by diffusion skewness tensor imaging
title_sort probing tissue microstructure by diffusion skewness tensor imaging
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7794496/
https://www.ncbi.nlm.nih.gov/pubmed/33420140
http://dx.doi.org/10.1038/s41598-020-79748-3
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