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 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.
| Medienart: |
E-Artikel |
|---|
| Erscheinungsjahr: |
2021 |
|---|---|
| Erschienen: |
2021 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:8 |
|---|---|
| Enthalten in: |
Frontiers in physics - 8(2021) vom: 01. Jan. |
| Sprache: |
Englisch |
|---|
| Beteiligte Personen: |
Sowinski, Damian R [VerfasserIn] |
|---|
| Links: |
|---|
| Themen: |
Bayesian Inference |
|---|
| Anmerkungen: |
Date Revised 08.11.2022 published: Print-Electronic Citation Status PubMed-not-MEDLINE |
|---|
| doi: |
10.3389/fphy.2020.617582 |
|---|
| funding: |
|
|---|---|
| Förderinstitution / Projekttitel: |
|
| PPN (Katalog-ID): |
NLM348548133 |
|---|
| LEADER | 01000naa a22002652 4500 | ||
|---|---|---|---|
| 001 | NLM348548133 | ||
| 003 | DE-627 | ||
| 005 | 20231226040516.0 | ||
| 007 | cr uuu---uuuuu | ||
| 008 | 231226s2021 xx |||||o 00| ||eng c | ||
| 024 | 7 | |a 10.3389/fphy.2020.617582 |2 doi | |
| 028 | 5 | 2 | |a pubmed24n1161.xml |
| 035 | |a (DE-627)NLM348548133 | ||
| 035 | |a (NLM)36340954 | ||
| 035 | |a (PII)617582 | ||
| 040 | |a DE-627 |b ger |c DE-627 |e rakwb | ||
| 041 | |a eng | ||
| 100 | 1 | |a Sowinski, Damian R |e verfasserin |4 aut | |
| 245 | 1 | 0 | |a Poroelasticity as a Model of Soft Tissue Structure |b Hydraulic Permeability Reconstruction for Magnetic Resonance Elastography in Silico |
| 264 | 1 | |c 2021 | |
| 336 | |a Text |b txt |2 rdacontent | ||
| 337 | |a ƒaComputermedien |b c |2 rdamedia | ||
| 338 | |a ƒa Online-Ressource |b cr |2 rdacarrier | ||
| 500 | |a Date Revised 08.11.2022 | ||
| 500 | |a published: Print-Electronic | ||
| 500 | |a Citation Status PubMed-not-MEDLINE | ||
| 520 | |a 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 | ||
| 650 | 4 | |a Journal Article | |
| 650 | 4 | |a Bayesian Inference | |
| 650 | 4 | |a Biomechanics | |
| 650 | 4 | |a Biophysics | |
| 650 | 4 | |a Continuum Mechanics | |
| 650 | 4 | |a Effective Field Theory | |
| 650 | 4 | |a Elastography | |
| 650 | 4 | |a MRI | |
| 650 | 4 | |a Porous Materials | |
| 700 | 1 | |a McGarry, Matthew D J |e verfasserin |4 aut | |
| 700 | 1 | |a Van Houten, Elijah E W |e verfasserin |4 aut | |
| 700 | 1 | |a Gordon-Wylie, Scott |e verfasserin |4 aut | |
| 700 | 1 | |a Weaver, John B |e verfasserin |4 aut | |
| 700 | 1 | |a Paulsen, Keith D |e verfasserin |4 aut | |
| 773 | 0 | 8 | |i Enthalten in |t Frontiers in physics |d 2014 |g 8(2021) vom: 01. Jan. |w (DE-627)NLM244586748 |x 2296-424X |7 nnns |
| 773 | 1 | 8 | |g volume:8 |g year:2021 |g day:01 |g month:01 |
| 856 | 4 | 0 | |u http://dx.doi.org/10.3389/fphy.2020.617582 |3 Volltext |
| 912 | |a GBV_USEFLAG_A | ||
| 912 | |a GBV_NLM | ||
| 951 | |a AR | ||
| 952 | |d 8 |j 2021 |b 01 |c 01 | ||