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A ranking of diffusion MRI compartment models with in vivo human brain data
PURPOSE: Diffusion magnetic resonance imaging (MRI) microstructure imaging provides a unique noninvasive probe into tissue microstructure. The technique relies on biophysically motivated mathematical models, relating microscopic tissue features to the magnetic resonance (MR) signal. This work aims t...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BlackWell Publishing Ltd
2014
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4278549/ https://www.ncbi.nlm.nih.gov/pubmed/24347370 http://dx.doi.org/10.1002/mrm.25080 |
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author | Ferizi, Uran Schneider, Torben Panagiotaki, Eleftheria Nedjati-Gilani, Gemma Zhang, Hui Wheeler-Kingshott, Claudia A M Alexander, Daniel C |
author_facet | Ferizi, Uran Schneider, Torben Panagiotaki, Eleftheria Nedjati-Gilani, Gemma Zhang, Hui Wheeler-Kingshott, Claudia A M Alexander, Daniel C |
author_sort | Ferizi, Uran |
collection | PubMed |
description | PURPOSE: Diffusion magnetic resonance imaging (MRI) microstructure imaging provides a unique noninvasive probe into tissue microstructure. The technique relies on biophysically motivated mathematical models, relating microscopic tissue features to the magnetic resonance (MR) signal. This work aims to determine which compartment models of diffusion MRI are best at describing measurements from in vivo human brain white matter. METHODS: Recent work shows that three compartment models, designed to capture intra-axonal, extracellular, and isotropically restricted diffusion, best explain multi-b-value data sets from fixed rat corpus callosum. We extend this investigation to in vivo by using a live human subject on a clinical scanner. The analysis compares models of one, two, and three compartments and ranks their ability to explain the measured data. We enhance the original methodology to further evaluate the stability of the ranking. RESULTS: As with fixed tissue, three compartment models explain the data best. However, a clearer hierarchical structure and simpler models emerge. We also find that splitting the scanning into shorter sessions has little effect on the ranking of models, and that the results are broadly reproducible across sessions. CONCLUSION: Three compartments are required to explain diffusion MR measurements from in vivo corpus callosum, which informs the choice of model for microstructure imaging applications in the brain. Magn Reson Med 72:1785–1792, 2014. © 2013 The authors. Magnetic Resonance in Medicine Published by Wiley Periodicals, Inc. on behalf of International Society of Medicine in Resonance. |
format | Online Article Text |
id | pubmed-4278549 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2014 |
publisher | BlackWell Publishing Ltd |
record_format | MEDLINE/PubMed |
spelling | pubmed-42785492014-12-31 A ranking of diffusion MRI compartment models with in vivo human brain data Ferizi, Uran Schneider, Torben Panagiotaki, Eleftheria Nedjati-Gilani, Gemma Zhang, Hui Wheeler-Kingshott, Claudia A M Alexander, Daniel C Magn Reson Med Computer Processing and Modeling—Note PURPOSE: Diffusion magnetic resonance imaging (MRI) microstructure imaging provides a unique noninvasive probe into tissue microstructure. The technique relies on biophysically motivated mathematical models, relating microscopic tissue features to the magnetic resonance (MR) signal. This work aims to determine which compartment models of diffusion MRI are best at describing measurements from in vivo human brain white matter. METHODS: Recent work shows that three compartment models, designed to capture intra-axonal, extracellular, and isotropically restricted diffusion, best explain multi-b-value data sets from fixed rat corpus callosum. We extend this investigation to in vivo by using a live human subject on a clinical scanner. The analysis compares models of one, two, and three compartments and ranks their ability to explain the measured data. We enhance the original methodology to further evaluate the stability of the ranking. RESULTS: As with fixed tissue, three compartment models explain the data best. However, a clearer hierarchical structure and simpler models emerge. We also find that splitting the scanning into shorter sessions has little effect on the ranking of models, and that the results are broadly reproducible across sessions. CONCLUSION: Three compartments are required to explain diffusion MR measurements from in vivo corpus callosum, which informs the choice of model for microstructure imaging applications in the brain. Magn Reson Med 72:1785–1792, 2014. © 2013 The authors. Magnetic Resonance in Medicine Published by Wiley Periodicals, Inc. on behalf of International Society of Medicine in Resonance. BlackWell Publishing Ltd 2014-12 2013-12-17 /pmc/articles/PMC4278549/ /pubmed/24347370 http://dx.doi.org/10.1002/mrm.25080 Text en © 2013 The Authors. Magnetic Resonance in Medicine published by Wiley Periodicals, Inc. on behalf of International Society of Medicine in Resonance. http://creativecommons.org/licenses/by/3.0/ This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Computer Processing and Modeling—Note Ferizi, Uran Schneider, Torben Panagiotaki, Eleftheria Nedjati-Gilani, Gemma Zhang, Hui Wheeler-Kingshott, Claudia A M Alexander, Daniel C A ranking of diffusion MRI compartment models with in vivo human brain data |
title | A ranking of diffusion MRI compartment models with in vivo human brain data |
title_full | A ranking of diffusion MRI compartment models with in vivo human brain data |
title_fullStr | A ranking of diffusion MRI compartment models with in vivo human brain data |
title_full_unstemmed | A ranking of diffusion MRI compartment models with in vivo human brain data |
title_short | A ranking of diffusion MRI compartment models with in vivo human brain data |
title_sort | ranking of diffusion mri compartment models with in vivo human brain data |
topic | Computer Processing and Modeling—Note |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4278549/ https://www.ncbi.nlm.nih.gov/pubmed/24347370 http://dx.doi.org/10.1002/mrm.25080 |
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