Poroelasticity as a Model of Soft Tissue Structure: Hydraulic Permeability Reconstruction for Magnetic Resonance Elastography in Silico
Magnetic Resonance Elastography allows noninvasive visualization of tissue mechanical properties by measuring the displacements resulting from applied stresses, and fitting a mechanical model. Poroelasticity naturally lends itself to describing tissue - a biphasic medium, consisting of both solid an...
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doaj-35aefe535be945479aa7080894238ae72021-01-21T04:23:46ZengFrontiers Media S.A.Frontiers in Physics2296-424X2021-01-01810.3389/fphy.2020.617582617582Poroelasticity as a Model of Soft Tissue Structure: Hydraulic Permeability Reconstruction for Magnetic Resonance Elastography in SilicoDamian R. Sowinski0Matthew D. J. McGarry1Elijah E. W. Van Houten2Scott Gordon-Wylie3John B Weaver4John B Weaver5John B Weaver6Keith D. Paulsen7Keith D. Paulsen8Keith D. Paulsen9Thayer School of Engineering, Dartmouth College, Hanover, NH, United StatesThayer School of Engineering, Dartmouth College, Hanover, NH, United StatesDépartement de génie mécanique, Universite de Sherbrooke, Sherbrooke, QC, CanadaThayer School of Engineering, Dartmouth College, Hanover, NH, United StatesThayer School of Engineering, Dartmouth College, Hanover, NH, United StatesGeisel School of Medicine, Dartmouth College, Hanover, NH, United StatesDartmouth-Hitchcock Medical Center, Department of Radiology, Lebanon, NH, United StatesThayer School of Engineering, Dartmouth College, Hanover, NH, United StatesGeisel School of Medicine, Dartmouth College, Hanover, NH, United StatesDartmouth-Hitchcock Medical Center, Center for Surgical Innovation, Lebanon, NH, United StatesMagnetic Resonance Elastography allows noninvasive visualization of tissue mechanical properties by measuring the displacements resulting from applied stresses, and fitting a mechanical model. Poroelasticity naturally lends itself to describing tissue - a biphasic medium, consisting of both solid and fluid components. This article reviews the theory of poroelasticity, and shows that the spatial distribution of hydraulic permeability, the ease with which the solid matrix permits the flow of fluid under a pressure gradient, can be faithfully reconstructed without spatial priors in simulated environments. The paper describes an in-house MRE computational platform - a multi-mesh, finite element poroelastic solver coupled to an artificial epistemic agent capable of running Bayesian inference to reconstruct inhomogenous model mechanical property images from measured displacement fields. Building on prior work, the domain of convergence for inference is explored, showing that hydraulic permeabilities over several orders of magnitude can be reconstructed given very little prior knowledge of the true spatial distribution.https://www.frontiersin.org/articles/10.3389/fphy.2020.617582/fullElastographyMRIBiophysicsBiomechanicsBayesian InferenceEffective Field Theory |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Damian R. Sowinski Matthew D. J. McGarry Elijah E. W. Van Houten Scott Gordon-Wylie John B Weaver John B Weaver John B Weaver Keith D. Paulsen Keith D. Paulsen Keith D. Paulsen |
spellingShingle |
Damian R. Sowinski Matthew D. J. McGarry Elijah E. W. Van Houten Scott Gordon-Wylie John B Weaver John B Weaver John B Weaver Keith D. Paulsen Keith D. Paulsen Keith D. Paulsen Poroelasticity as a Model of Soft Tissue Structure: Hydraulic Permeability Reconstruction for Magnetic Resonance Elastography in Silico Frontiers in Physics Elastography MRI Biophysics Biomechanics Bayesian Inference Effective Field Theory |
author_facet |
Damian R. Sowinski Matthew D. J. McGarry Elijah E. W. Van Houten Scott Gordon-Wylie John B Weaver John B Weaver John B Weaver Keith D. Paulsen Keith D. Paulsen Keith D. Paulsen |
author_sort |
Damian R. Sowinski |
title |
Poroelasticity as a Model of Soft Tissue Structure: Hydraulic Permeability Reconstruction for Magnetic Resonance Elastography in Silico |
title_short |
Poroelasticity as a Model of Soft Tissue Structure: Hydraulic Permeability Reconstruction for Magnetic Resonance Elastography in Silico |
title_full |
Poroelasticity as a Model of Soft Tissue Structure: Hydraulic Permeability Reconstruction for Magnetic Resonance Elastography in Silico |
title_fullStr |
Poroelasticity as a Model of Soft Tissue Structure: Hydraulic Permeability Reconstruction for Magnetic Resonance Elastography in Silico |
title_full_unstemmed |
Poroelasticity as a Model of Soft Tissue Structure: Hydraulic Permeability Reconstruction for Magnetic Resonance Elastography in Silico |
title_sort |
poroelasticity as a model of soft tissue structure: hydraulic permeability reconstruction for magnetic resonance elastography in silico |
publisher |
Frontiers Media S.A. |
series |
Frontiers in Physics |
issn |
2296-424X |
publishDate |
2021-01-01 |
description |
Magnetic Resonance Elastography allows noninvasive visualization of tissue mechanical properties by measuring the displacements resulting from applied stresses, and fitting a mechanical model. Poroelasticity naturally lends itself to describing tissue - a biphasic medium, consisting of both solid and fluid components. This article reviews the theory of poroelasticity, and shows that the spatial distribution of hydraulic permeability, the ease with which the solid matrix permits the flow of fluid under a pressure gradient, can be faithfully reconstructed without spatial priors in simulated environments. The paper describes an in-house MRE computational platform - a multi-mesh, finite element poroelastic solver coupled to an artificial epistemic agent capable of running Bayesian inference to reconstruct inhomogenous model mechanical property images from measured displacement fields. Building on prior work, the domain of convergence for inference is explored, showing that hydraulic permeabilities over several orders of magnitude can be reconstructed given very little prior knowledge of the true spatial distribution. |
topic |
Elastography MRI Biophysics Biomechanics Bayesian Inference Effective Field Theory |
url |
https://www.frontiersin.org/articles/10.3389/fphy.2020.617582/full |
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