Poroelasticity as a Model of Soft Tissue Structure: Hydraulic Permeability Inference for Magnetic Resonance Elastography in Silico

TL;DR

This paper demonstrates that hydraulic permeability in poroelastic tissue models can be accurately inferred from magnetic resonance elastography data using a novel computational platform, without requiring spatial priors.

Contribution

It introduces a Bayesian inference framework with a finite element poroelastic solver for reconstructing hydraulic permeability in simulated tissues, expanding the domain of convergence.

Findings

Hydraulic permeability can be reconstructed over several orders of magnitude.

The method works with minimal prior knowledge.

The in-house computational platform effectively couples MRE data with poroelastic modeling.

Abstract

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.