Literature DB >> 22452461

Influences of brain tissue poroelastic constants on intracranial pressure (ICP) during constant-rate infusion.

Xiaogai Li1, Hans von Holst, Svein Kleiven.   

Abstract

A 3D finite element (FE) model has been developed to study the mean intracranial pressure (ICP) response during constant-rate infusion using linear poroelasticity. Due to the uncertainties in the poroelastic constants for brain tissue, the influence of each of the main parameters on the transient ICP infusion curve was studied. As a prerequisite for transient analysis, steady-state simulations were performed first. The simulated steady-state pressure distribution in the brain tissue for a normal cerebrospinal fluid (CSF) circulation system showed good correlation with experiments from the literature. Furthermore, steady-state ICP closely followed the infusion experiments at different infusion rates. The verified steady-state models then served as a baseline for the subsequent transient models. For transient analysis, the simulated ICP shows a similar tendency to that found in the experiments, however, different values of the poroelastic constants have a significant effect on the infusion curve. The influence of the main poroelastic parameters including the Biot coefficient α, Skempton coefficient B, drained Young's modulus E, Poisson's ratio ν, permeability κ, CSF absorption conductance C(b) and external venous pressure p(b) was studied to investigate the influence on the pressure response. It was found that the value of the specific storage term S(ε) is the dominant factor that influences the infusion curve, and the drained Young's modulus E was identified as the dominant parameter second to S(ε). Based on the simulated infusion curves from the FE model, artificial neural network (ANN) was used to find an optimised parameter set that best fit the experimental curve. The infusion curves from both the FE simulation and using ANN confirmed the limitation of linear poroelasticity in modelling the transient constant-rate infusion.

Entities:  

Mesh:

Year:  2012        PMID: 22452461     DOI: 10.1080/10255842.2012.670853

Source DB:  PubMed          Journal:  Comput Methods Biomech Biomed Engin        ISSN: 1025-5842            Impact factor:   1.763


  3 in total

1.  Parameter-robust multiphysics algorithms for Biot model with application in brain edema simulation.

Authors:  Guoliang Ju; Mingchao Cai; Jingzhi Li; Jing Tian
Journal:  Math Comput Simul       Date:  2020-05-04       Impact factor: 2.463

2.  Biphasic modeling of brain tumor biomechanics and response to radiation treatment.

Authors:  Stelios Angeli; Triantafyllos Stylianopoulos
Journal:  J Biomech       Date:  2016-03-30       Impact factor: 2.712

3.  Subject-specific multi-poroelastic model for exploring the risk factors associated with the early stages of Alzheimer's disease.

Authors:  Liwei Guo; John C Vardakis; Toni Lassila; Micaela Mitolo; Nishant Ravikumar; Dean Chou; Matthias Lange; Ali Sarrami-Foroushani; Brett J Tully; Zeike A Taylor; Susheel Varma; Annalena Venneri; Alejandro F Frangi; Yiannis Ventikos
Journal:  Interface Focus       Date:  2017-12-15       Impact factor: 3.906

  3 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.