Afzali, M, Mueller, L, Szczepankiewicz, F et al. (2 more authors) (2022) Quantification of Tissue Microstructure Using Tensor-Valued Diffusion Encoding: Brain and Body. Frontiers in Physics, 10. 809133. ISSN 2296-424X
Abstract
Diffusion-weighted magnetic resonance imaging (DW-MRI) is a non-invasive technique to probe tissue microstructure. Conventional Stejskal–Tanner diffusion encoding (i.e., encoding along a single axis), is unable to disentangle different microstructural features within a voxel; If a voxel contains microcompartments that vary in more than one attribute (e.g., size, shape, orientation), it can be difficult to quantify one of those attributes in isolation using Stejskal–Tanner diffusion encoding. Multidimensional diffusion encoding, in which the water diffusion is encoded along multiple directions in q-space (characterized by the so-called “b-tensor”) has been proposed previously to solve this problem. The shape of the b-tensor can be used as an additional encoding dimension and provides sensitivity to microscopic anisotropy. This has been applied in multiple organs, including brain, heart, breast, kidney and prostate. In this work, we discuss the advantages of using b-tensor encoding in different organs.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2022 Afzali, Mueller, Szczepankiewicz, Jones and Schneider. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
Keywords: | diffusion weighted imaging, b-tensor encoding, microstructure, brain, heart, body, microscopic anisotropy |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Medicine and Health (Leeds) > School of Medicine (Leeds) > Leeds Institute of Cardiovascular and Metabolic Medicine (LICAMM) > Biomedical Imaging Science Dept (Leeds) |
Depositing User: | Symplectic Publications |
Date Deposited: | 17 Feb 2022 14:04 |
Last Modified: | 25 Jun 2023 22:54 |
Status: | Published |
Publisher: | Frontiers Media |
Identification Number: | 10.3389/fphy.2022.809133 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:183657 |