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A numerical framework for interstitial fluid pressure imaging in poroelastic MRE

A numerical framework for interstitial fluid pressure imaging (IFPI) in biphasic materials is investigated based on three-dimensional nonlinear finite element poroelastic inversion. The objective is to reconstruct the time-harmonic pore-pressure field from tissue excitation in addition to the elasti...

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Autores principales: Tan, Likun, McGarry, Matthew D. J., Van Houten, Elijah E. W., Ji, Ming, Solamen, Ligin, Zeng, Wei, Weaver, John B., Paulsen, Keith D.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2017
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5460821/
https://www.ncbi.nlm.nih.gov/pubmed/28586393
http://dx.doi.org/10.1371/journal.pone.0178521
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author Tan, Likun
McGarry, Matthew D. J.
Van Houten, Elijah E. W.
Ji, Ming
Solamen, Ligin
Zeng, Wei
Weaver, John B.
Paulsen, Keith D.
author_facet Tan, Likun
McGarry, Matthew D. J.
Van Houten, Elijah E. W.
Ji, Ming
Solamen, Ligin
Zeng, Wei
Weaver, John B.
Paulsen, Keith D.
author_sort Tan, Likun
collection PubMed
description A numerical framework for interstitial fluid pressure imaging (IFPI) in biphasic materials is investigated based on three-dimensional nonlinear finite element poroelastic inversion. The objective is to reconstruct the time-harmonic pore-pressure field from tissue excitation in addition to the elastic parameters commonly associated with magnetic resonance elastography (MRE). The unknown pressure boundary conditions (PBCs) are estimated using the available full-volume displacement data from MRE. A subzone-based nonlinear inversion (NLI) technique is then used to update mechanical and hydrodynamical properties, given the appropriate subzone PBCs, by solving a pressure forward problem (PFP). The algorithm was evaluated on a single-inclusion phantom in which the elastic property and hydraulic conductivity images were recovered. Pressure field and material property estimates had spatial distributions reflecting their true counterparts in the phantom geometry with RMS errors around 20% for cases with 5% noise, but degraded significantly in both spatial distribution and property values for noise levels > 10%. When both shear moduli and hydraulic conductivity were estimated along with the pressure field, property value error rates were as high as 58%, 85% and 32% for the three quantities, respectively, and their spatial distributions were more distorted. Opportunities for improving the algorithm are discussed.
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spelling pubmed-54608212017-06-15 A numerical framework for interstitial fluid pressure imaging in poroelastic MRE Tan, Likun McGarry, Matthew D. J. Van Houten, Elijah E. W. Ji, Ming Solamen, Ligin Zeng, Wei Weaver, John B. Paulsen, Keith D. PLoS One Research Article A numerical framework for interstitial fluid pressure imaging (IFPI) in biphasic materials is investigated based on three-dimensional nonlinear finite element poroelastic inversion. The objective is to reconstruct the time-harmonic pore-pressure field from tissue excitation in addition to the elastic parameters commonly associated with magnetic resonance elastography (MRE). The unknown pressure boundary conditions (PBCs) are estimated using the available full-volume displacement data from MRE. A subzone-based nonlinear inversion (NLI) technique is then used to update mechanical and hydrodynamical properties, given the appropriate subzone PBCs, by solving a pressure forward problem (PFP). The algorithm was evaluated on a single-inclusion phantom in which the elastic property and hydraulic conductivity images were recovered. Pressure field and material property estimates had spatial distributions reflecting their true counterparts in the phantom geometry with RMS errors around 20% for cases with 5% noise, but degraded significantly in both spatial distribution and property values for noise levels > 10%. When both shear moduli and hydraulic conductivity were estimated along with the pressure field, property value error rates were as high as 58%, 85% and 32% for the three quantities, respectively, and their spatial distributions were more distorted. Opportunities for improving the algorithm are discussed. Public Library of Science 2017-06-06 /pmc/articles/PMC5460821/ /pubmed/28586393 http://dx.doi.org/10.1371/journal.pone.0178521 Text en © 2017 Tan et al http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Article
Tan, Likun
McGarry, Matthew D. J.
Van Houten, Elijah E. W.
Ji, Ming
Solamen, Ligin
Zeng, Wei
Weaver, John B.
Paulsen, Keith D.
A numerical framework for interstitial fluid pressure imaging in poroelastic MRE
title A numerical framework for interstitial fluid pressure imaging in poroelastic MRE
title_full A numerical framework for interstitial fluid pressure imaging in poroelastic MRE
title_fullStr A numerical framework for interstitial fluid pressure imaging in poroelastic MRE
title_full_unstemmed A numerical framework for interstitial fluid pressure imaging in poroelastic MRE
title_short A numerical framework for interstitial fluid pressure imaging in poroelastic MRE
title_sort numerical framework for interstitial fluid pressure imaging in poroelastic mre
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5460821/
https://www.ncbi.nlm.nih.gov/pubmed/28586393
http://dx.doi.org/10.1371/journal.pone.0178521
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