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The sensitivity of diffusion MRI to microstructural properties and experimental factors

Diffusion MRI is a non-invasive technique to study brain microstructure. Differences in the microstructural properties of tissue, including size and anisotropy, can be represented in the signal if the appropriate method of acquisition is used. However, to depict the underlying properties, special ca...

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Autores principales: Afzali, Maryam, Pieciak, Tomasz, Newman, Sharlene, Garyfallidis, Eleftherios, Özarslan, Evren, Cheng, Hu, Jones, Derek K
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier/North-Holland Biomedical Press 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7762827/
https://www.ncbi.nlm.nih.gov/pubmed/33017644
http://dx.doi.org/10.1016/j.jneumeth.2020.108951
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author Afzali, Maryam
Pieciak, Tomasz
Newman, Sharlene
Garyfallidis, Eleftherios
Özarslan, Evren
Cheng, Hu
Jones, Derek K
author_facet Afzali, Maryam
Pieciak, Tomasz
Newman, Sharlene
Garyfallidis, Eleftherios
Özarslan, Evren
Cheng, Hu
Jones, Derek K
author_sort Afzali, Maryam
collection PubMed
description Diffusion MRI is a non-invasive technique to study brain microstructure. Differences in the microstructural properties of tissue, including size and anisotropy, can be represented in the signal if the appropriate method of acquisition is used. However, to depict the underlying properties, special care must be taken when designing the acquisition protocol as any changes in the procedure might impact on quantitative measurements. This work reviews state-of-the-art methods for studying brain microstructure using diffusion MRI and their sensitivity to microstructural differences and various experimental factors. Microstructural properties of the tissue at a micrometer scale can be linked to the diffusion signal at a millimeter-scale using modeling. In this paper, we first give an introduction to diffusion MRI and different encoding schemes. Then, signal representation-based methods and multi-compartment models are explained briefly. The sensitivity of the diffusion MRI signal to the microstructural components and the effects of curvedness of axonal trajectories on the diffusion signal are reviewed. Factors that impact on the quality (accuracy and precision) of derived metrics are then reviewed, including the impact of random noise, and variations in the acquisition parameters (i.e., number of sampled signals, b-value and number of acquisition shells). Finally, yet importantly, typical approaches to deal with experimental factors are depicted, including unbiased measures and harmonization. We conclude the review with some future directions and recommendations on this topic.
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spelling pubmed-77628272021-01-01 The sensitivity of diffusion MRI to microstructural properties and experimental factors Afzali, Maryam Pieciak, Tomasz Newman, Sharlene Garyfallidis, Eleftherios Özarslan, Evren Cheng, Hu Jones, Derek K J Neurosci Methods Invited Review Diffusion MRI is a non-invasive technique to study brain microstructure. Differences in the microstructural properties of tissue, including size and anisotropy, can be represented in the signal if the appropriate method of acquisition is used. However, to depict the underlying properties, special care must be taken when designing the acquisition protocol as any changes in the procedure might impact on quantitative measurements. This work reviews state-of-the-art methods for studying brain microstructure using diffusion MRI and their sensitivity to microstructural differences and various experimental factors. Microstructural properties of the tissue at a micrometer scale can be linked to the diffusion signal at a millimeter-scale using modeling. In this paper, we first give an introduction to diffusion MRI and different encoding schemes. Then, signal representation-based methods and multi-compartment models are explained briefly. The sensitivity of the diffusion MRI signal to the microstructural components and the effects of curvedness of axonal trajectories on the diffusion signal are reviewed. Factors that impact on the quality (accuracy and precision) of derived metrics are then reviewed, including the impact of random noise, and variations in the acquisition parameters (i.e., number of sampled signals, b-value and number of acquisition shells). Finally, yet importantly, typical approaches to deal with experimental factors are depicted, including unbiased measures and harmonization. We conclude the review with some future directions and recommendations on this topic. Elsevier/North-Holland Biomedical Press 2021-01-01 /pmc/articles/PMC7762827/ /pubmed/33017644 http://dx.doi.org/10.1016/j.jneumeth.2020.108951 Text en © 2020 The Authors http://creativecommons.org/licenses/by/4.0/ This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Invited Review
Afzali, Maryam
Pieciak, Tomasz
Newman, Sharlene
Garyfallidis, Eleftherios
Özarslan, Evren
Cheng, Hu
Jones, Derek K
The sensitivity of diffusion MRI to microstructural properties and experimental factors
title The sensitivity of diffusion MRI to microstructural properties and experimental factors
title_full The sensitivity of diffusion MRI to microstructural properties and experimental factors
title_fullStr The sensitivity of diffusion MRI to microstructural properties and experimental factors
title_full_unstemmed The sensitivity of diffusion MRI to microstructural properties and experimental factors
title_short The sensitivity of diffusion MRI to microstructural properties and experimental factors
title_sort sensitivity of diffusion mri to microstructural properties and experimental factors
topic Invited Review
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7762827/
https://www.ncbi.nlm.nih.gov/pubmed/33017644
http://dx.doi.org/10.1016/j.jneumeth.2020.108951
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