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用于多孔弹性磁共振弹性成像中间质流体压力成像的数值框架。

A numerical framework for interstitial fluid pressure imaging in poroelastic MRE.

作者信息

Tan Likun, McGarry Matthew D J, Van Houten Elijah E W, Ji Ming, Solamen Ligin, Zeng Wei, Weaver John B, Paulsen Keith D

机构信息

Thayer School of Engineering, Dartmouth College, Hanover, NH 03755, United States of America.

Department of Biomedical Engineering, Columbia University, New York, NY 10027, United States of America.

出版信息

PLoS One. 2017 Jun 6;12(6):e0178521. doi: 10.1371/journal.pone.0178521. eCollection 2017.

DOI:10.1371/journal.pone.0178521
PMID:28586393
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC5460821/
Abstract

A numerical framework for interstitial fluid pressure imaging (IFPI) in biphasic materials is investigated based on three-dimensional nonlinear finite element poroelastic inversion. The objective is to reconstruct the time-harmonic pore-pressure field from tissue excitation in addition to the elastic parameters commonly associated with magnetic resonance elastography (MRE). The unknown pressure boundary conditions (PBCs) are estimated using the available full-volume displacement data from MRE. A subzone-based nonlinear inversion (NLI) technique is then used to update mechanical and hydrodynamical properties, given the appropriate subzone PBCs, by solving a pressure forward problem (PFP). The algorithm was evaluated on a single-inclusion phantom in which the elastic property and hydraulic conductivity images were recovered. Pressure field and material property estimates had spatial distributions reflecting their true counterparts in the phantom geometry with RMS errors around 20% for cases with 5% noise, but degraded significantly in both spatial distribution and property values for noise levels > 10%. When both shear moduli and hydraulic conductivity were estimated along with the pressure field, property value error rates were as high as 58%, 85% and 32% for the three quantities, respectively, and their spatial distributions were more distorted. Opportunities for improving the algorithm are discussed.

摘要

基于三维非线性有限元多孔弹性反演,研究了双相材料中间质流体压力成像(IFPI)的数值框架。目的是除了重建与磁共振弹性成像(MRE)通常相关的弹性参数外,还从组织激发中重建时谐孔隙压力场。利用来自MRE的可用全容积位移数据估计未知压力边界条件(PBC)。然后,在给定适当子区域PBC的情况下,使用基于子区域的非线性反演(NLI)技术通过求解压力正问题(PFP)来更新力学和流体动力学特性。该算法在单包含体模型上进行了评估,其中恢复了弹性特性和水力传导率图像。压力场和材料特性估计值的空间分布反映了它们在模型几何形状中的真实对应物,对于5%噪声的情况,均方根误差约为20%,但对于噪声水平>10%的情况,在空间分布和特性值方面均显著退化。当同时估计剪切模量和水力传导率以及压力场时,这三个量的特性值错误率分别高达58%、85%和32%,并且它们的空间分布更加扭曲。讨论了改进该算法的机会。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c3f9/5460821/570b633add27/pone.0178521.g010.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c3f9/5460821/5716898886dc/pone.0178521.g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c3f9/5460821/e33c0dcbcedf/pone.0178521.g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c3f9/5460821/9f99e323f9df/pone.0178521.g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c3f9/5460821/b22b15174baf/pone.0178521.g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c3f9/5460821/482b5f9f43fd/pone.0178521.g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c3f9/5460821/e33b2e680366/pone.0178521.g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c3f9/5460821/587be147acdf/pone.0178521.g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c3f9/5460821/fde60e211062/pone.0178521.g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c3f9/5460821/a7e6d74eea35/pone.0178521.g009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c3f9/5460821/570b633add27/pone.0178521.g010.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c3f9/5460821/5716898886dc/pone.0178521.g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c3f9/5460821/e33c0dcbcedf/pone.0178521.g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c3f9/5460821/9f99e323f9df/pone.0178521.g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c3f9/5460821/b22b15174baf/pone.0178521.g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c3f9/5460821/482b5f9f43fd/pone.0178521.g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c3f9/5460821/e33b2e680366/pone.0178521.g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c3f9/5460821/587be147acdf/pone.0178521.g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c3f9/5460821/fde60e211062/pone.0178521.g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c3f9/5460821/a7e6d74eea35/pone.0178521.g009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c3f9/5460821/570b633add27/pone.0178521.g010.jpg

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