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 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.

journal_name

Sci Rep

journal_title

Scientific reports

authors

Ning L,Szczepankiewicz F,Nilsson M,Rathi Y,Westin CF

doi

10.1038/s41598-020-79748-3

subject

Has Abstract

pub_date

2021-01-08 00:00:00

pages

135

issue

1

issn

2045-2322

pii

10.1038/s41598-020-79748-3

journal_volume

11

pub_type

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