Bayesian Approach for Recovering Piecewise Constant Viscoelasticity from MRE Data

Jiang, Yu; Qian, Shan

doi:10.1007/s10255-020-0922-7

Cited by 2 publications

(2 citation statements)

References 21 publications

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“…The Bayesian interpretation of probability brings with it many tools, some of which have started to be used by the MRE community [71,72]. The fundamental idea behind it is simple and intuitive -observation carries with it information, and information leads to a reduction in uncertainty [73][74][75][76][77].…”

Section: Inferencementioning

confidence: 99%

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

et al. 2021

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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.

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Section: Inferencementioning

confidence: 99%

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

et al. 2021

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“…Although they use the Bayesian setting to derive the approach, they do not characterise the full posterior distribution which, as stated before, is required to quantify uncertainty of all possible positions. On the other hand, a Markov Chain Monte Carlo approach was recently used in Jiang and Qian (2020) to fully characterise the posterior for inversion in a MRE setting. This approach, however, was used in a very simplified case in 2D, where the stiffness properties were piecewise constant and the interface between healthy and diseased tissue was assumed known.…”

mentioning

confidence: 99%

Ensemble Kalman inversion for magnetic resonance elastography

et al. 2022

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Magnetic Resonance Elastography (MRE) is an MRI-based diagnostic method for measuring mechanical properties of biological tissues. MRE measurements are processed by an inversion algorithm to produce a map of the biomechanical properties. In this paper a new and powerful method (Ensemble Kalman Inversion with Level Sets (EKI)) of MRE inversion is proposed and tested. The method has critical advantages: material property variation at disease boundaries can be accurately identified, and uncertainty of the reconstructed material properties can be evaluated by consequence of the probabilistic nature of the method. EKI is tested in 2D and 3D experiments with synthetic MRE data of the human kidney. It is demonstrated that the proposed inversion method is accurate and fast.

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Bayesian Approach for Recovering Piecewise Constant Viscoelasticity from MRE Data

Cited by 2 publications

References 21 publications

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

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

Ensemble Kalman inversion for magnetic resonance elastography

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