Probing tissue microstructure by diffusion skewness tensor imaging

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...

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Autores principales:	Lipeng Ning, Filip Szczepankiewicz, Markus Nilsson, Yogesh Rathi, Carl-Fredrik Westin
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/388cb1de5fce460c9aab873308dc254b
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spelling	oai:doaj.org-article:388cb1de5fce460c9aab873308dc254b2021-12-02T11:46:06ZProbing tissue microstructure by diffusion skewness tensor imaging10.1038/s41598-020-79748-32045-2322https://doaj.org/article/388cb1de5fce460c9aab873308dc254b2021-01-01T00:00:00Zhttps://doi.org/10.1038/s41598-020-79748-3https://doaj.org/toc/2045-2322Abstract 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.Lipeng NingFilip SzczepankiewiczMarkus NilssonYogesh RathiCarl-Fredrik WestinNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-10 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Lipeng Ning Filip Szczepankiewicz Markus Nilsson Yogesh Rathi Carl-Fredrik Westin Probing tissue microstructure by diffusion skewness tensor imaging
description	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 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.
format	article
author	Lipeng Ning Filip Szczepankiewicz Markus Nilsson Yogesh Rathi Carl-Fredrik Westin
author_facet	Lipeng Ning Filip Szczepankiewicz Markus Nilsson Yogesh Rathi Carl-Fredrik Westin
author_sort	Lipeng Ning
title	Probing tissue microstructure by diffusion skewness tensor imaging
title_short	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_sort	probing tissue microstructure by diffusion skewness tensor imaging
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/388cb1de5fce460c9aab873308dc254b
work_keys_str_mv	AT lipengning probingtissuemicrostructurebydiffusionskewnesstensorimaging AT filipszczepankiewicz probingtissuemicrostructurebydiffusionskewnesstensorimaging AT markusnilsson probingtissuemicrostructurebydiffusionskewnesstensorimaging AT yogeshrathi probingtissuemicrostructurebydiffusionskewnesstensorimaging AT carlfredrikwestin probingtissuemicrostructurebydiffusionskewnesstensorimaging
_version_	1718395221044101120

Probing tissue microstructure by diffusion skewness tensor imaging

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