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MR imaging of human brain mechanics in vivo: New measurements to facilitate the development of computational models of brain injury

Computational models of the brain and its biomechanical response to skull accelerations are important tools for understanding and predicting traumatic brain injuries (TBIs). However, most models have been developed using experimental data collected on animal models and cadaveric specimens, both of w...

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Autores principales: Bayly, PV, Alshareef, A, Knutsen, AK, Upadhyay, K, Okamoto, RJ, Carass, A, Butman, JA, Pham, DL, Prince, JL, Ramesh, KT, Johnson, CL
Formato: Online Artículo Texto
Lenguaje:English
Publicado: 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8516723/
https://www.ncbi.nlm.nih.gov/pubmed/34212235
http://dx.doi.org/10.1007/s10439-021-02820-0
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author Bayly, PV
Alshareef, A
Knutsen, AK
Upadhyay, K
Okamoto, RJ
Carass, A
Butman, JA
Pham, DL
Prince, JL
Ramesh, KT
Johnson, CL
author_facet Bayly, PV
Alshareef, A
Knutsen, AK
Upadhyay, K
Okamoto, RJ
Carass, A
Butman, JA
Pham, DL
Prince, JL
Ramesh, KT
Johnson, CL
author_sort Bayly, PV
collection PubMed
description Computational models of the brain and its biomechanical response to skull accelerations are important tools for understanding and predicting traumatic brain injuries (TBIs). However, most models have been developed using experimental data collected on animal models and cadaveric specimens, both of which differ from the living human brain. Here we describe efforts to noninvasively measure the biomechanical response of the human brain with MRI—at non-injurious strain levels—and generate data that can be used to develop, calibrate, and evaluate computational brain biomechanics models. Specifically, this paper reports on a project supported by the National Institute of Neurological Disorders and Stroke to comprehensively image brain anatomy and geometry, mechanical properties, and brain deformations that arise from impulsive and harmonic skull loadings. The outcome of this work will be a publicly available dataset (http://www.nitrc.org/projects/bbir) that includes measurements on both males and females across an age range from adolescence to older adulthood. This article describes the rationale and approach for this study, the data available, and how these data may be used to develop new computational models and augment existing approaches; it will serve as a reference to researchers interested in using these data.
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spelling pubmed-85167232022-10-01 MR imaging of human brain mechanics in vivo: New measurements to facilitate the development of computational models of brain injury Bayly, PV Alshareef, A Knutsen, AK Upadhyay, K Okamoto, RJ Carass, A Butman, JA Pham, DL Prince, JL Ramesh, KT Johnson, CL Ann Biomed Eng Article Computational models of the brain and its biomechanical response to skull accelerations are important tools for understanding and predicting traumatic brain injuries (TBIs). However, most models have been developed using experimental data collected on animal models and cadaveric specimens, both of which differ from the living human brain. Here we describe efforts to noninvasively measure the biomechanical response of the human brain with MRI—at non-injurious strain levels—and generate data that can be used to develop, calibrate, and evaluate computational brain biomechanics models. Specifically, this paper reports on a project supported by the National Institute of Neurological Disorders and Stroke to comprehensively image brain anatomy and geometry, mechanical properties, and brain deformations that arise from impulsive and harmonic skull loadings. The outcome of this work will be a publicly available dataset (http://www.nitrc.org/projects/bbir) that includes measurements on both males and females across an age range from adolescence to older adulthood. This article describes the rationale and approach for this study, the data available, and how these data may be used to develop new computational models and augment existing approaches; it will serve as a reference to researchers interested in using these data. 2021-07-01 2021-10 /pmc/articles/PMC8516723/ /pubmed/34212235 http://dx.doi.org/10.1007/s10439-021-02820-0 Text en https://creativecommons.org/licenses/by/4.0/This AM is a PDF file of the manuscript accepted for publication after peer review, when applicable, but does not reflect post-acceptance improvements, or any corrections. Use of this AM is subject to the publisher’s embargo period and AM terms of use. Under no circumstances may this AM be shared or distributed under a Creative Commons or other form of open access license, nor may it be reformatted or enhanced, whether by the Author or third parties. See here for Springer Nature’s terms of use for AM versions of subscription articles: https://www.springernature.com/gp/open-research/policies/accepted-manuscript-terms
spellingShingle Article
Bayly, PV
Alshareef, A
Knutsen, AK
Upadhyay, K
Okamoto, RJ
Carass, A
Butman, JA
Pham, DL
Prince, JL
Ramesh, KT
Johnson, CL
MR imaging of human brain mechanics in vivo: New measurements to facilitate the development of computational models of brain injury
title MR imaging of human brain mechanics in vivo: New measurements to facilitate the development of computational models of brain injury
title_full MR imaging of human brain mechanics in vivo: New measurements to facilitate the development of computational models of brain injury
title_fullStr MR imaging of human brain mechanics in vivo: New measurements to facilitate the development of computational models of brain injury
title_full_unstemmed MR imaging of human brain mechanics in vivo: New measurements to facilitate the development of computational models of brain injury
title_short MR imaging of human brain mechanics in vivo: New measurements to facilitate the development of computational models of brain injury
title_sort mr imaging of human brain mechanics in vivo: new measurements to facilitate the development of computational models of brain injury
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8516723/
https://www.ncbi.nlm.nih.gov/pubmed/34212235
http://dx.doi.org/10.1007/s10439-021-02820-0
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