Cargando…

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...

Descripción completa

Detalles Bibliográficos
Autores principales: Ferizi, Uran, Schneider, Torben, Panagiotaki, Eleftheria, Nedjati-Gilani, Gemma, Zhang, Hui, Wheeler-Kingshott, Claudia A M, Alexander, Daniel C
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BlackWell Publishing Ltd 2014
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
_version_ 1782350545096278016
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
work_keys_str_mv AT feriziuran arankingofdiffusionmricompartmentmodelswithinvivohumanbraindata
AT schneidertorben arankingofdiffusionmricompartmentmodelswithinvivohumanbraindata
AT panagiotakieleftheria arankingofdiffusionmricompartmentmodelswithinvivohumanbraindata
AT nedjatigilanigemma arankingofdiffusionmricompartmentmodelswithinvivohumanbraindata
AT zhanghui arankingofdiffusionmricompartmentmodelswithinvivohumanbraindata
AT wheelerkingshottclaudiaam arankingofdiffusionmricompartmentmodelswithinvivohumanbraindata
AT alexanderdanielc arankingofdiffusionmricompartmentmodelswithinvivohumanbraindata
AT feriziuran rankingofdiffusionmricompartmentmodelswithinvivohumanbraindata
AT schneidertorben rankingofdiffusionmricompartmentmodelswithinvivohumanbraindata
AT panagiotakieleftheria rankingofdiffusionmricompartmentmodelswithinvivohumanbraindata
AT nedjatigilanigemma rankingofdiffusionmricompartmentmodelswithinvivohumanbraindata
AT zhanghui rankingofdiffusionmricompartmentmodelswithinvivohumanbraindata
AT wheelerkingshottclaudiaam rankingofdiffusionmricompartmentmodelswithinvivohumanbraindata
AT alexanderdanielc rankingofdiffusionmricompartmentmodelswithinvivohumanbraindata