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Harmonization of Brain Diffusion MRI: Concepts and Methods
MRI diffusion data suffers from significant inter- and intra-site variability, which hinders multi-site and/or longitudinal diffusion studies. This variability may arise from a range of factors, such as hardware, reconstruction algorithms and acquisition settings. To allow a reliable comparison and...
Autores principales: | , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7218137/ https://www.ncbi.nlm.nih.gov/pubmed/32435181 http://dx.doi.org/10.3389/fnins.2020.00396 |
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author | Pinto, Maíra Siqueira Paolella, Roberto Billiet, Thibo Van Dyck, Pieter Guns, Pieter-Jan Jeurissen, Ben Ribbens, Annemie den Dekker, Arnold J. Sijbers, Jan |
author_facet | Pinto, Maíra Siqueira Paolella, Roberto Billiet, Thibo Van Dyck, Pieter Guns, Pieter-Jan Jeurissen, Ben Ribbens, Annemie den Dekker, Arnold J. Sijbers, Jan |
author_sort | Pinto, Maíra Siqueira |
collection | PubMed |
description | MRI diffusion data suffers from significant inter- and intra-site variability, which hinders multi-site and/or longitudinal diffusion studies. This variability may arise from a range of factors, such as hardware, reconstruction algorithms and acquisition settings. To allow a reliable comparison and joint analysis of diffusion data across sites and over time, there is a clear need for robust data harmonization methods. This review article provides a comprehensive overview of diffusion data harmonization concepts and methods, and their limitations. Overall, the methods for the harmonization of multi-site diffusion images can be categorized in two main groups: diffusion parametric map harmonization (DPMH) and diffusion weighted image harmonization (DWIH). Whereas DPMH harmonizes the diffusion parametric maps (e.g., FA, MD, and MK), DWIH harmonizes the diffusion-weighted images. Defining a gold standard harmonization technique for dMRI data is still an ongoing challenge. Nevertheless, in this paper we provide two classification tools, namely a feature table and a flowchart, which aim to guide the readers in selecting an appropriate harmonization method for their study. |
format | Online Article Text |
id | pubmed-7218137 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-72181372020-05-20 Harmonization of Brain Diffusion MRI: Concepts and Methods Pinto, Maíra Siqueira Paolella, Roberto Billiet, Thibo Van Dyck, Pieter Guns, Pieter-Jan Jeurissen, Ben Ribbens, Annemie den Dekker, Arnold J. Sijbers, Jan Front Neurosci Neuroscience MRI diffusion data suffers from significant inter- and intra-site variability, which hinders multi-site and/or longitudinal diffusion studies. This variability may arise from a range of factors, such as hardware, reconstruction algorithms and acquisition settings. To allow a reliable comparison and joint analysis of diffusion data across sites and over time, there is a clear need for robust data harmonization methods. This review article provides a comprehensive overview of diffusion data harmonization concepts and methods, and their limitations. Overall, the methods for the harmonization of multi-site diffusion images can be categorized in two main groups: diffusion parametric map harmonization (DPMH) and diffusion weighted image harmonization (DWIH). Whereas DPMH harmonizes the diffusion parametric maps (e.g., FA, MD, and MK), DWIH harmonizes the diffusion-weighted images. Defining a gold standard harmonization technique for dMRI data is still an ongoing challenge. Nevertheless, in this paper we provide two classification tools, namely a feature table and a flowchart, which aim to guide the readers in selecting an appropriate harmonization method for their study. Frontiers Media S.A. 2020-05-06 /pmc/articles/PMC7218137/ /pubmed/32435181 http://dx.doi.org/10.3389/fnins.2020.00396 Text en Copyright © 2020 Pinto, Paolella, Billiet, Van Dyck, Guns, Jeurissen, Ribbens, den Dekker and Sijbers. http://creativecommons.org/licenses/by/4.0/ 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. |
spellingShingle | Neuroscience Pinto, Maíra Siqueira Paolella, Roberto Billiet, Thibo Van Dyck, Pieter Guns, Pieter-Jan Jeurissen, Ben Ribbens, Annemie den Dekker, Arnold J. Sijbers, Jan Harmonization of Brain Diffusion MRI: Concepts and Methods |
title | Harmonization of Brain Diffusion MRI: Concepts and Methods |
title_full | Harmonization of Brain Diffusion MRI: Concepts and Methods |
title_fullStr | Harmonization of Brain Diffusion MRI: Concepts and Methods |
title_full_unstemmed | Harmonization of Brain Diffusion MRI: Concepts and Methods |
title_short | Harmonization of Brain Diffusion MRI: Concepts and Methods |
title_sort | harmonization of brain diffusion mri: concepts and methods |
topic | Neuroscience |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7218137/ https://www.ncbi.nlm.nih.gov/pubmed/32435181 http://dx.doi.org/10.3389/fnins.2020.00396 |
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