论文标题
作为软组织结构模型的孔隙弹性：在计算机中对磁共振弹性的液压渗透性推断
Poroelasticity as a Model of Soft Tissue Structure: Hydraulic Permeability Inference for Magnetic Resonance Elastography in Silico
论文作者
论文摘要
磁共振弹性图可以通过测量施加的应力引起的位移并拟合机械模型来对组织机械性能进行非侵入性可视化。孔隙弹性自然可以描述组织 - 一种由固体和流体成分组成的双相培养基。本文回顾了孔隙弹性的理论，并表明液压渗透性的空间分布（固体基质允许在压力梯度下允许流体流动的易于），可以忠实地重建在模拟环境中的无空间先验的情况下。本文描述了一个内部MRE计算平台 - 一个多网状的，有限元的Poro弹性求解器，该求解器与人工认知药物结合，能够运行贝叶斯推断，从测量的位移字段中重建贝叶斯的推断。在先前工作的基础上，探索了推理的收敛域，表明在几个数量级上的液压渗透率几乎没有事先了解真实空间分布，因此可以重建几个数量级。
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.
